Repository: meta-llama/llama-models Branch: main Commit: 0e0b8c519242 Files: 105 Total size: 6.3 MB Directory structure: gitextract_941i2ph3/ ├── .github/ │ ├── CODEOWNERS │ └── workflows/ │ └── publish-to-test-pypi.yml ├── .gitignore ├── .pre-commit-config.yaml ├── .ruff.toml ├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── LICENSE ├── MANIFEST.in ├── README.md ├── SECURITY.md ├── docs/ │ └── license_header.txt ├── models/ │ ├── __init__.py │ ├── checkpoint.py │ ├── cli/ │ │ ├── __init__.py │ │ ├── describe.py │ │ ├── download.py │ │ ├── list.py │ │ ├── llama.py │ │ ├── prompt_format.py │ │ ├── remove.py │ │ ├── safety_models.py │ │ ├── subcommand.py │ │ ├── table.py │ │ ├── utils.py │ │ └── verify_download.py │ ├── datatypes.py │ ├── llama2/ │ │ ├── LICENSE │ │ ├── MODEL_CARD.md │ │ └── USE_POLICY.md │ ├── llama3/ │ │ ├── LICENSE │ │ ├── MODEL_CARD.md │ │ ├── USE_POLICY.md │ │ ├── __init__.py │ │ ├── args.py │ │ ├── chat_format.py │ │ ├── generation.py │ │ ├── model.py │ │ ├── multimodal/ │ │ │ ├── __init__.py │ │ │ ├── encoder_utils.py │ │ │ ├── image_transform.py │ │ │ ├── model.py │ │ │ └── utils.py │ │ ├── quantization/ │ │ │ └── loader.py │ │ ├── requirements.txt │ │ ├── scripts/ │ │ │ ├── __init__.py │ │ │ ├── chat_completion.py │ │ │ └── completion.py │ │ ├── tests/ │ │ │ └── api/ │ │ │ ├── test_generation.py │ │ │ ├── test_tokenizer.py │ │ │ └── test_tool_utils.py │ │ ├── tokenizer.model │ │ ├── tokenizer.py │ │ └── tool_utils.py │ ├── llama3_1/ │ │ ├── LICENSE │ │ ├── MODEL_CARD.md │ │ ├── USE_POLICY.md │ │ ├── eval_details.md │ │ └── prompt_format.md │ ├── llama3_2/ │ │ ├── LICENSE │ │ ├── MODEL_CARD.md │ │ ├── MODEL_CARD_VISION.md │ │ ├── USE_POLICY.md │ │ ├── eval_details.md │ │ ├── text_prompt_format.md │ │ └── vision_prompt_format.md │ ├── llama3_3/ │ │ ├── LICENSE │ │ ├── MODEL_CARD.md │ │ ├── USE_POLICY.md │ │ ├── eval_details.md │ │ └── prompt_format.md │ ├── llama4/ │ │ ├── LICENSE │ │ ├── MODEL_CARD.md │ │ ├── USE_POLICY.md │ │ ├── __init__.py │ │ ├── args.py │ │ ├── chat_format.py │ │ ├── datatypes.py │ │ ├── ffn.py │ │ ├── generation.py │ │ ├── model.py │ │ ├── moe.py │ │ ├── preprocess.py │ │ ├── prompt_format.md │ │ ├── quantization/ │ │ │ ├── __init__.py │ │ │ └── loader.py │ │ ├── scripts/ │ │ │ ├── chat_completion.py │ │ │ ├── completion.py │ │ │ └── quantize.py │ │ ├── tests/ │ │ │ ├── __init__.py │ │ │ └── api/ │ │ │ ├── __init__.py │ │ │ └── test_chat_format.py │ │ ├── tokenizer.model │ │ ├── tokenizer.py │ │ └── vision/ │ │ ├── embedding.py │ │ └── encoder.py │ ├── quantize_impls.py │ ├── sku_list.py │ ├── sku_types.py │ ├── tokenizer_utils.py │ └── utils/ │ ├── __init__.py │ ├── config.py │ └── model_utils.py ├── pyproject.toml └── requirements.txt ================================================ FILE CONTENTS ================================================ ================================================ FILE: .github/CODEOWNERS ================================================ # Each line is a file pattern followed by one or more owners. # These owners will be the default owners for everything in # the repo. Unless a later match takes precedence, * @ashwinb @yanxi0830 @hardikjshah @dltn @raghotham @ehhuang ================================================ FILE: .github/workflows/publish-to-test-pypi.yml ================================================ name: Publish Python 🐍 distribution 📦 to TestPyPI on: repository_dispatch: # on trigger from llama-stack types: [build-models-package] workflow_dispatch: # Keep manual trigger inputs: version: description: 'Version number (e.g. 0.0.63.dev20250111)' required: true type: string jobs: build: name: Build distribution 📦 runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: persist-credentials: false - name: Get date id: date run: echo "date=$(date +'%Y%m%d')" >> $GITHUB_OUTPUT - name: Update version for repository_dispatch if: github.event_name == 'repository_dispatch' && github.event.client_payload.source == 'llama-stack-nightly' run: | sed -i 's/version="\([^"]*\)"/version="${{ github.event.client_payload.version }}"/' setup.py - name: Update version for manual RC if: github.event_name == 'workflow_dispatch' run: | sed -i 's/version="\([^"]*\)"/version="${{ inputs.version }}"/' setup.py - name: Set up Python uses: actions/setup-python@v5 with: python-version: "3.11" - name: Install pypa/build run: >- python3 -m pip install build --user - name: Build a binary wheel and a source tarball run: python3 -m build - name: Store the distribution packages uses: actions/upload-artifact@v4 with: name: python-package-distributions path: dist/ publish-to-testpypi: name: Publish Python 🐍 distribution 📦 to TestPyPI needs: - build runs-on: ubuntu-latest environment: name: testrelease url: https://test.pypi.org/p/llama-models permissions: id-token: write # IMPORTANT: mandatory for trusted publishing steps: - name: Download all the dists uses: actions/download-artifact@v4 with: name: python-package-distributions path: dist/ - name: Publish distribution 📦 to TestPyPI uses: pypa/gh-action-pypi-publish@release/v1 with: repository-url: https://test.pypi.org/legacy/ ================================================ FILE: .gitignore ================================================ __pycache__ dist *.egg-info build .DS_Store .vscode/ ================================================ FILE: .pre-commit-config.yaml ================================================ exclude: 'build/' default_language_version: python: python3 repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v5.0.0 # Latest stable version hooks: - id: check-merge-conflict - id: check-added-large-files args: ['--maxkb=1000'] - id: end-of-file-fixer exclude: '^(.*\.svg)$' # Temporarily disabling this # - id: no-commit-to-branch # args: ['--branch=main'] - repo: https://github.com/Lucas-C/pre-commit-hooks rev: v1.5.4 hooks: - id: insert-license files: \.py$|\.sh$ args: - --license-filepath - docs/license_header.txt - repo: https://github.com/astral-sh/ruff-pre-commit rev: v0.9.4 hooks: - id: ruff args: [ --fix, --exit-non-zero-on-fix ] - id: ruff-format - repo: https://github.com/adamchainz/blacken-docs rev: 1.19.0 hooks: - id: blacken-docs additional_dependencies: - black==24.3.0 - repo: https://github.com/astral-sh/uv-pre-commit rev: 0.5.26 hooks: - id: uv-export args: ["--frozen", "--no-hashes", "--no-emit-project"] # - repo: https://github.com/pre-commit/mirrors-mypy # rev: v1.14.0 # hooks: # - id: mypy # additional_dependencies: # - types-requests # - types-setuptools # - pydantic # args: [--ignore-missing-imports] # - repo: https://github.com/jsh9/pydoclint # rev: d88180a8632bb1602a4d81344085cf320f288c5a # hooks: # - id: pydoclint # args: [--config=pyproject.toml] # - repo: https://github.com/tcort/markdown-link-check # rev: v3.11.2 # hooks: # - id: markdown-link-check # args: ['--quiet'] # - repo: local # hooks: # - id: distro-codegen # name: Distribution Template Codegen # additional_dependencies: # - rich # - pydantic # entry: python -m llama_stack.scripts.distro_codegen # language: python # pass_filenames: false # require_serial: true # files: ^llama_stack/templates/.*$ # stages: [manual] ci: autofix_commit_msg: 🎨 [pre-commit.ci] Auto format from pre-commit.com hooks autoupdate_commit_msg: ⬆ [pre-commit.ci] pre-commit autoupdate ================================================ FILE: .ruff.toml ================================================ # Suggested config from pytorch that we can adapt lint.select = ["B", "C", "E" , "F" , "N", "W", "B9"] line-length = 120 # C408 ignored because we like the dict keyword argument syntax # E501 is not flexible enough, we're using B950 instead # N812 ignored because import torch.nn.functional as F is PyTorch convention # N817 ignored because importing using acronyms is convention (DistributedDataParallel as DDP) # E731 allow usage of assigning lambda expressions # E701 let black auto-format statements on one line # E704 let black auto-format statements on one line lint.ignore = [ "E203", "E305", "E402", "E501", "E721", "E741", "F405", "F821", "F841", "C408", "E302", "W291", "E303", "N812", "N817", "E731", "E701", # These are the additional ones we started ignoring after moving to ruff. We should look into each one of them later. "C901", "C405", "C414", "N803", "N999", "C403", "C416", "B028", "C419", "C401", "B023", # shebang has extra meaning in fbcode lints, so I think it's not worth trying # to line this up with executable bit "EXE001", # random naming hints don't need "N802", # these ignores are from flake8-bugbear; please fix! "B007", "B008" ] exclude = [ "./.git", "./docs/*", "./build", "./scripts", "./venv", "*.pyi", ".pre-commit-config.yaml", "*.md", ".flake8" ] ================================================ FILE: CODE_OF_CONDUCT.md ================================================ # Code of Conduct ## Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. ## Our Standards Examples of behavior that contributes to creating a positive environment include: * Using welcoming and inclusive language * Being respectful of differing viewpoints and experiences * Gracefully accepting constructive criticism * Focusing on what is best for the community * Showing empathy towards other community members Examples of unacceptable behavior by participants include: * The use of sexualized language or imagery and unwelcome sexual attention or advances * Trolling, insulting/derogatory comments, and personal or political attacks * Public or private harassment * Publishing others' private information, such as a physical or electronic address, without explicit permission * Other conduct which could reasonably be considered inappropriate in a professional setting ## Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. ## Scope This Code of Conduct applies within all project spaces, and it also applies when an individual is representing the project or its community in public spaces. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. This Code of Conduct also applies outside the project spaces when there is a reasonable belief that an individual's behavior may have a negative impact on the project or its community. ## Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at . All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq ================================================ FILE: CONTRIBUTING.md ================================================ # Contributing to Llama-Models We want to make contributing to this project as easy and transparent as possible. ## Pull Requests We actively welcome your pull requests. 1. Fork the repo and create your branch from `main`. 2. If you've added code that should be tested, add tests. 3. If you've changed APIs, update the documentation. 4. Ensure the test suite passes. 5. Make sure your code lints. 6. If you haven't already, complete the Contributor License Agreement ("CLA"). ## Contributor License Agreement ("CLA") In order to accept your pull request, we need you to submit a CLA. You only need to do this once to work on any of Meta's open source projects. Complete your CLA here: ## Issues We use GitHub issues to track public bugs. Please ensure your description is clear and has sufficient instructions to be able to reproduce the issue. Meta has a [bounty program](http://facebook.com/whitehat/info) for the safe disclosure of security bugs. In those cases, please go through the process outlined on that page and do not file a public issue. ## Coding Style * 2 spaces for indentation rather than tabs * 80 character line length * ... ## License By contributing to Llama, you agree that your contributions will be licensed under the LICENSE file in the root directory of this source tree. ================================================ FILE: LICENSE ================================================ https://github.com/meta-llama/llama-models/blob/main/README.md#llama-models-1 ================================================ FILE: MANIFEST.in ================================================ include pyproject.toml include README.md include models/llama3/tokenizer.model include models/llama4/tokenizer.model include models/resources/dog.jpg include models/resources/pasta.jpeg include models/llama3_1/prompt_format.md include models/llama3_2/text_prompt_format.md include models/llama3_2/vision_prompt_format.md include llama_models/llama3/tokenizer.model include llama_models/llama4/tokenizer.model include llama_models/resources/dog.jpg include llama_models/resources/pasta.jpeg include llama_models/llama3_1/prompt_format.md include llama_models/llama3_2/text_prompt_format.md include llama_models/llama3_2/vision_prompt_format.md ================================================ FILE: README.md ================================================

🤗 Models on Hugging Face  | Blog  | Website  | Get Started  | Llama Cookbook 
--- # Llama Models Llama is an accessible, open large language model (LLM) designed for developers, researchers, and businesses to build, experiment, and responsibly scale their generative AI ideas. Part of a foundational system, it serves as a bedrock for innovation in the global community. A few key aspects: 1. **Open access**: Easy accessibility to cutting-edge large language models, fostering collaboration and advancements among developers, researchers, and organizations 2. **Broad ecosystem**: Llama models have been downloaded hundreds of millions of times, there are thousands of community projects built on Llama and platform support is broad from cloud providers to startups - the world is building with Llama! 3. **Trust & safety**: Llama models are part of a comprehensive approach to trust and safety, releasing models and tools that are designed to enable community collaboration and encourage the standardization of the development and usage of trust and safety tools for generative AI Our mission is to empower individuals and industry through this opportunity while fostering an environment of discovery and ethical AI advancements. The model weights are licensed for researchers and commercial entities, upholding the principles of openness. ## Llama Models [![PyPI - Downloads](https://img.shields.io/pypi/dm/llama-models)](https://pypi.org/project/llama-models/) [![Discord](https://img.shields.io/discord/1257833999603335178)](https://discord.gg/TZAAYNVtrU) | **Model** | **Launch date** | **Model sizes** | **Context Length** | **Tokenizer** | **Acceptable use policy** | **License** | **Model Card** | | :----: | :----: | :----: | :----:|:----:|:----:|:----:|:----:| | Llama 2 | 7/18/2023 | 7B, 13B, 70B | 4K | Sentencepiece | [Use Policy](models/llama2/USE_POLICY.md) | [License](models/llama2/LICENSE) | [Model Card](models/llama2/MODEL_CARD.md) | | Llama 3 | 4/18/2024 | 8B, 70B | 8K | TikToken-based | [Use Policy](models/llama3/USE_POLICY.md) | [License](models/llama3/LICENSE) | [Model Card](models/llama3/MODEL_CARD.md) | | Llama 3.1 | 7/23/2024 | 8B, 70B, 405B | 128K | TikToken-based | [Use Policy](models/llama3_1/USE_POLICY.md) | [License](models/llama3_1/LICENSE) | [Model Card](models/llama3_1/MODEL_CARD.md) | | Llama 3.2 | 9/25/2024 | 1B, 3B | 128K | TikToken-based | [Use Policy](models/llama3_2/USE_POLICY.md) | [License](models/llama3_2/LICENSE) | [Model Card](models/llama3_2/MODEL_CARD.md) | | Llama 3.2-Vision | 9/25/2024 | 11B, 90B | 128K | TikToken-based | [Use Policy](models/llama3_2/USE_POLICY.md) | [License](models/llama3_2/LICENSE) | [Model Card](models/llama3_2/MODEL_CARD_VISION.md) | | Llama 3.3 | 12/04/2024 | 70B | 128K | TikToken-based | [Use Policy](models/llama3_3/USE_POLICY.md) | [License](models/llama3_3/LICENSE) | [Model Card](models/llama3_3/MODEL_CARD.md) | | Llama 4 | 4/5/2025 | Scout-17B-16E, Maverick-17B-128E | 10M, 1M | TikToken-based | [Use Policy](models/llama4/USE_POLICY.md) | [License](models/llama4/LICENSE) | [Model Card](models/llama4/MODEL_CARD.md) | ## Download To download the model weights and tokenizer: 1. Visit the [Meta Llama website](https://llama.meta.com/llama-downloads/). 2. Read and accept the license. 3. Once your request is approved you will receive a signed URL via email. 4. Install the Llama Models CLI: `pip install llama-models`. (**<-- Start Here if you have received an email already.**) 5. Run `llama-model list` to show the latest available models and determine the model ID you wish to download. **NOTE**: If you want older versions of models, run `llama-model list --show-all` to show all the available Llama models. 6. Run: `llama-model download --source meta --model-id CHOSEN_MODEL_ID` 7. Pass the URL provided when prompted to start the download. Remember that the links expire after 24 hours and a certain amount of downloads. You can always re-request a link if you start seeing errors such as `403: Forbidden`. ### CLI Commands Reference Once installed, the `llama-model` CLI provides the following commands: ```bash llama-model list # List available models llama-model list --show-all # List all models (including older versions) llama-model describe -m MODEL_ID # Show detailed information about a model llama-model download # Download models from Meta or Hugging Face llama-model verify-download # Verify integrity of downloaded models llama-model remove -m MODEL_ID # Remove a downloaded model llama-model prompt-format -m MODEL_ID # Show the prompt format for a model ``` For detailed help on any command, run `llama-model COMMAND --help`. ## Running the models In order to run the models, you will need to install dependencies after checking out the repository. ```bash # Run this within a suitable Python environment (uv, conda, or virtualenv) pip install .[torch] ``` Example scripts are available in `models/{ llama3, llama4 }/scripts/` sub-directory. Note that the Llama4 series of models require at least 4 GPUs to run inference at full (bf16) precision. ```bash #!/bin/bash NGPUS=4 CHECKPOINT_DIR=~/.llama/checkpoints/Llama-4-Scout-17B-16E-Instruct PYTHONPATH=$(git rev-parse --show-toplevel) \ torchrun --nproc_per_node=$NGPUS \ -m models.llama4.scripts.chat_completion $CHECKPOINT_DIR \ --world_size $NGPUS ``` The above script should be used with an Instruct (Chat) model. For a Base model, update the `CHECKPOINT_DIR` path and use the script `models.llama4.scripts.completion`. ## Running inference with FP8 and Int4 Quantization You can reduce the memory footprint of the models at the cost of minimal loss in accuracy by running inference with FP8 or Int4 quantization. Use the `--quantization-mode` flag to specify the quantization mode. There are two modes: - `fp8_mixed`: Mixed precision inference with FP8 for some weights and bfloat16 for activations. - `int4_mixed`: Mixed precision inference with Int4 for some weights and bfloat16 for activations. Using FP8, running Llama-4-Scout-17B-16E-Instruct requires 2 GPUs with 80GB of memory. Using Int4, you need a single GPU with 80GB of memory. ```bash MODE=fp8_mixed # or int4_mixed if [ $MODE == "fp8_mixed" ]; then NGPUS=2 else NGPUS=1 fi CHECKPOINT_DIR=~/.llama/checkpoints/Llama-4-Scout-17B-16E-Instruct PYTHONPATH=$(git rev-parse --show-toplevel) \ torchrun --nproc_per_node=$NGPUS \ -m models.llama4.scripts.chat_completion $CHECKPOINT_DIR \ --world_size $NGPUS \ --quantization-mode $MODE ``` For more flexibility in running inference (including using other providers), please see the [`Llama Stack`](https://github.com/meta-llama/llama-stack) toolset. ## Access to Hugging Face We also provide downloads on [Hugging Face](https://huggingface.co/meta-llama), in both transformers and native `llama4` formats. To download the weights from Hugging Face, please follow these steps: - Visit one of the repos, for example [meta-llama/Llama-4-Scout-17B-16E](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E). - Read and accept the license. Once your request is approved, you'll be granted access to all Llama 3.1 models as well as previous versions. Note that requests used to take up to one hour to get processed. - To download the original native weights to use with this repo, click on the "Files and versions" tab and download the contents of the `original` folder. You can also download them from the command line if you `pip install huggingface-hub`: ```bash huggingface-cli download meta-llama/Llama-4-Scout-17B-16E-Instruct-Original --local-dir meta-llama/Llama-4-Scout-17B-16E-Instruct-Original ``` - To use with transformers, the following snippet will download and cache the weights: ```python # inference.py from transformers import AutoTokenizer, Llama4ForConditionalGeneration import torch model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt", return_dict=True ) model = Llama4ForConditionalGeneration.from_pretrained( model_id, device_map="auto", torch_dtype=torch.bfloat16 ) outputs = model.generate(**inputs.to(model.device), max_new_tokens=100) outputs = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[-1] :]) print(outputs[0]) ``` ```bash torchrun --nnodes=1 --nproc_per_node=8 inference.py ``` ## Installations You can install this repository as a [package](https://pypi.org/project/llama-models/) by just doing `pip install llama-models` ## Responsible Use Llama models are a new technology that carries potential risks with use. Testing conducted to date has not — and could not — cover all scenarios. To help developers address these risks, we have created the [Responsible Use Guide](https://ai.meta.com/static-resource/responsible-use-guide/). ## Issues Please report any software “bug” or other problems with the models through one of the following means: - Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues) - Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Questions For common questions, the FAQ can be found [here](https://llama.meta.com/faq), which will be updated over time as new questions arise. ================================================ FILE: SECURITY.md ================================================ # Security Policy ## Reporting a Vulnerability Please report vulnerabilities to our bug bounty program at https://bugbounty.meta.com/ ================================================ FILE: docs/license_header.txt ================================================ Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved. This source code is licensed under the terms described in the LICENSE file in top-level folder for each specific model found within the models/ directory at the top-level of this source tree. ================================================ FILE: models/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. ================================================ FILE: models/checkpoint.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import concurrent.futures import re from pathlib import Path from typing import Any, Dict, List, Optional, Union import numpy as np import torch from fairscale.nn.model_parallel.initialize import get_model_parallel_rank, get_model_parallel_world_size def map_mp_rank(old_mp_size: int, new_mp_size: int, new_mp_rank: int) -> List[int]: """Map a new MP rank to a list of old MP ranks given a change in MP size.""" if new_mp_size % old_mp_size == 0: # Read old MP shard and split it into smaller ones return [new_mp_rank * old_mp_size // new_mp_size] elif old_mp_size % new_mp_size == 0: # Merge old MP shards into a single one mp_factor = old_mp_size // new_mp_size return list(range(new_mp_rank * mp_factor, (new_mp_rank + 1) * mp_factor)) else: raise ValueError( f"Either old MP size or new MP size should be a multiple of the other: " f"{old_mp_size} % {new_mp_size} != 0 and {new_mp_size} % {old_mp_size} != 0" ) def maybe_reshard_state_dict( ckpt_paths: List[Path], n_kv_heads: int, moe_num_experts: Optional[int] = None, map_location: Union[str, torch.device] = "cpu", mmap: bool = True, ) -> Dict[str, torch.Tensor]: if str(map_location) == "cpu": torch.set_default_tensor_type(torch.BFloat16Tensor) else: torch.set_default_tensor_type(torch.cuda.BFloat16Tensor) ckpt_paths = np.array(sorted(ckpt_paths)) new_mp_size, new_mp_rank = get_model_parallel_world_size(), get_model_parallel_rank() old_mp_size = len(ckpt_paths) old_mp_ranks = map_mp_rank(old_mp_size, new_mp_size, new_mp_rank) print(f"Loading checkpoint shards:\n{str(ckpt_paths[old_mp_ranks])}") # type: ignore paths = ckpt_paths[old_mp_ranks] # type: ignore state_dicts = [torch.load(str(p), map_location=map_location, mmap=mmap) for p in paths] if new_mp_size == old_mp_size: return state_dicts[0] # type: ignore if moe_num_experts is not None: state_dicts = [convert_moe_weights(d, moe_num_experts) for d in state_dicts] print(f"Resharding {len(state_dicts)} state dicts from MP size {old_mp_size} to MP size {new_mp_size}") return reshard_mp( state_dicts, size=max(new_mp_size // old_mp_size, 1), rank=new_mp_rank % max(new_mp_size // old_mp_size, 1), repeat_qk_qv=max(new_mp_size // n_kv_heads, 1), ) _WEIGHT_ROW_KEY = { "feed_forward.w2", "feed_forward.mlp.fc2", "attention.wo", "feed_forward.mlp.fc2_weight", "feed_forward.w_out_shared_DF.weight", "attn.wo.weight", "mlp.c_proj.weight", } _MOE_WEIGHT_ROW_KEY = {"feed_forward.experts.(moe_w_in_eD_F|moe_w_swiglu_eD_F)"} _WEIGHT_COLUMN_KEY = { "output", "feed_forward.(w1|w3)", "feed_forward.mlp.(fc1|fc3)", "feed_forward.mlp.fc1_weight", "attention.(wk|wq|wv|wqkv).weight", "feed_forward.(w_in_shared_FD|w_swiglu_FD)", "attn.(wk|wq|wv).weight", "attn.(wk|wq|wv).bias", "mlp.c_fc.weight", "mlp.c_fc.bias", "conv1._linear.weight", "tok_embeddings.weight", "vision_projection.weight", } _MOE_WEIGHT_COLUMN_KEY = {"feed_forward.experts.moe_w_out_eF_D"} def reshard_mp( state_dicts: List[Dict[str, torch.Tensor]], size: int, rank: int, repeat_qk_qv: int = 1, ) -> Dict[str, torch.Tensor]: """ Reshard a list of state dicts into a single state dict given a change in MP size. If the list has more than one state dict, we concatenate the values of the same key across all state dicts. Otherwise, we just slice it for the current MP rank. """ def concat_or_chunk(tensors: List[torch.Tensor], dim: int) -> torch.Tensor: if len(tensors) > 1: return torch.cat(tensors, dim=dim) return tensors[0].chunk(size, dim=dim)[rank].clone() def process_key(key: str) -> torch.Tensor: if row_regex.search(key): return concat_or_chunk([s[key] for s in state_dicts], dim=-1) elif column_regex.search(key): if "w13" in key or "fc1_weight" in key: dims = state_dicts[0][key].size() values = [s[key].view(2, dims[0] // 2, *dims[1:]) for s in state_dicts] return concat_or_chunk(values, dim=1).flatten(0, 1) elif "qkv" in key: q_dim = state_dicts[0][key.replace("qkv", "o")].size(1) kv_dim = (state_dicts[0][key].size(0) - q_dim) // 2 values = [s[key].split((q_dim, kv_dim, kv_dim)) for s in state_dicts] return torch.cat([concat_or_chunk(x, dim=0) for x in zip(*values)]) # type: ignore elif "wk.weight" in key or "wv.weight" in key: # Support MP > #kv_head return concat_or_chunk([s[key].repeat(repeat_qk_qv, 1) for s in state_dicts], dim=0) elif key == "output.bias" or key == "fc.weight": return concat_or_chunk([s[key] for s in state_dicts], dim=0) elif "w_" in key: return concat_or_chunk([s[key] for s in state_dicts], dim=-2) else: return concat_or_chunk([s[key] for s in state_dicts], dim=0) else: return state_dicts[0][key].clone() row_keys = _WEIGHT_ROW_KEY | _MOE_WEIGHT_ROW_KEY column_keys = _WEIGHT_COLUMN_KEY | _MOE_WEIGHT_COLUMN_KEY column_regex = re.compile("|".join(column_keys)) row_regex = re.compile("|".join(row_keys)) output: Dict[str, torch.Tensor] = {} with concurrent.futures.ThreadPoolExecutor() as executor: # Note: only processes keys in the first state dict. # Assumes keys are the same across all state dicts. mappings = {executor.submit(process_key, key): key for key in state_dicts[0]} for future in concurrent.futures.as_completed(mappings): output[mappings[future]] = future.result() return output def convert_moe_weights(state_dict: Dict[str, Any], num_experts: int) -> Dict[str, Any]: routed_keys = _MOE_WEIGHT_ROW_KEY | _MOE_WEIGHT_COLUMN_KEY routed_regex = re.compile("|".join(routed_keys)) keys = list(state_dict.keys()) for key in keys: if routed_regex.search(key): state_dict[key] = state_dict.pop(key).unflatten(0, (num_experts, -1)).squeeze(dim=0) return state_dict ================================================ FILE: models/cli/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. ================================================ FILE: models/cli/describe.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import argparse import json from llama_models.cli.subcommand import Subcommand from llama_models.cli.table import print_table from llama_models.sku_list import resolve_model class Describe(Subcommand): """Show details about a model""" def __init__(self, subparsers: argparse._SubParsersAction): super().__init__() self.parser = subparsers.add_parser( "describe", prog="llama-model describe", description="Show details about a llama model", formatter_class=argparse.RawTextHelpFormatter, ) self._add_arguments() self.parser.set_defaults(func=self._run_model_describe_cmd) def _add_arguments(self): self.parser.add_argument( "-m", "--model-id", type=str, required=True, help="See `llama-model list` or `llama-model list --show-all` for the list of available models", ) def _run_model_describe_cmd(self, args: argparse.Namespace) -> None: from llama_models.cli.safety_models import prompt_guard_model_sku_map prompt_guard_model_map = prompt_guard_model_sku_map() if args.model_id in prompt_guard_model_map.keys(): model = prompt_guard_model_map[args.model_id] else: model = resolve_model(args.model_id) if model is None: self.parser.error( f"Model {args.model_id} not found; try 'llama-model list' for a list of available models." ) return headers = [ "Model", model.descriptor(), ] rows = [ ("Hugging Face ID", model.huggingface_repo or ""), ("Description", model.description), ("Context Length", f"{model.max_seq_length // 1024}K tokens"), ("Weights format", model.quantization_format.value), ("Model params.json", json.dumps(model.arch_args, indent=4)), ] print_table( rows, headers, separate_rows=True, ) ================================================ FILE: models/cli/download.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import argparse import asyncio import json import os import shutil import sys from dataclasses import dataclass from datetime import UTC, datetime from functools import partial from pathlib import Path import httpx from pydantic import BaseModel, ConfigDict from rich.console import Console from rich.progress import ( BarColumn, DownloadColumn, Progress, TextColumn, TimeRemainingColumn, TransferSpeedColumn, ) from termcolor import cprint from llama_models.cli.subcommand import Subcommand from llama_models.sku_list import LlamaDownloadInfo from llama_models.sku_types import Model class Download(Subcommand): """Llama cli for downloading llama toolchain assets""" def __init__(self, subparsers: argparse._SubParsersAction): super().__init__() self.parser = subparsers.add_parser( "download", prog="llama download", description="Download a model from llama.meta.com or Hugging Face Hub", formatter_class=argparse.RawTextHelpFormatter, ) setup_download_parser(self.parser) def setup_download_parser(parser: argparse.ArgumentParser) -> None: parser.add_argument( "--source", choices=["meta", "huggingface"], default="meta", ) parser.add_argument( "--model-id", required=False, help="See `llama model list` or `llama model list --show-all` for the list of available models. Specify multiple model IDs with commas, e.g. --model-id Llama3.2-1B,Llama3.2-3B", ) parser.add_argument( "--hf-token", type=str, required=False, default=None, help="Hugging Face API token. Needed for gated models like llama2/3. Will also try to read environment variable `HF_TOKEN` as default.", ) parser.add_argument( "--meta-url", type=str, required=False, help="For source=meta, URL obtained from llama.meta.com after accepting license terms", ) parser.add_argument( "--max-parallel", type=int, required=False, default=3, help="Maximum number of concurrent downloads", ) parser.add_argument( "--ignore-patterns", type=str, required=False, default="*.safetensors", help="""For source=huggingface, files matching any of the patterns are not downloaded. Defaults to ignoring safetensors files to avoid downloading duplicate weights. """, ) parser.add_argument( "--manifest-file", type=str, help="For source=meta, you can download models from a manifest file containing a file => URL mapping", required=False, ) parser.set_defaults(func=partial(run_download_cmd, parser=parser)) @dataclass class DownloadTask: url: str output_file: str total_size: int = 0 downloaded_size: int = 0 task_id: int | None = None retries: int = 0 max_retries: int = 3 class DownloadError(Exception): pass class CustomTransferSpeedColumn(TransferSpeedColumn): def render(self, task): if task.finished: return "-" return super().render(task) class ParallelDownloader: def __init__( self, max_concurrent_downloads: int = 3, buffer_size: int = 1024 * 1024, timeout: int = 30, ): self.max_concurrent_downloads = max_concurrent_downloads self.buffer_size = buffer_size self.timeout = timeout self.console = Console() self.progress = Progress( TextColumn("[bold blue]{task.description}"), BarColumn(bar_width=40), "[progress.percentage]{task.percentage:>3.1f}%", DownloadColumn(), CustomTransferSpeedColumn(), TimeRemainingColumn(), console=self.console, expand=True, ) self.client_options = { "timeout": httpx.Timeout(timeout), "follow_redirects": True, } async def retry_with_exponential_backoff(self, task: DownloadTask, func, *args, **kwargs): last_exception = None for attempt in range(task.max_retries): try: return await func(*args, **kwargs) except Exception as e: last_exception = e if attempt < task.max_retries - 1: wait_time = min(30, 2**attempt) # Cap at 30 seconds self.console.print( f"[yellow]Attempt {attempt + 1}/{task.max_retries} failed, " f"retrying in {wait_time} seconds: {str(e)}[/yellow]" ) await asyncio.sleep(wait_time) continue raise last_exception async def get_file_info(self, client: httpx.AsyncClient, task: DownloadTask) -> None: if task.total_size > 0: self.progress.update(task.task_id, total=task.total_size) return async def _get_info(): response = await client.head(task.url, headers={"Accept-Encoding": "identity"}, **self.client_options) response.raise_for_status() return response try: response = await self.retry_with_exponential_backoff(task, _get_info) task.url = str(response.url) task.total_size = int(response.headers.get("Content-Length", 0)) if task.total_size == 0: raise DownloadError( f"Unable to determine file size for {task.output_file}. " "The server might not support range requests." ) # Update the progress bar's total size once we know it if task.task_id is not None: self.progress.update(task.task_id, total=task.total_size) except httpx.HTTPError as e: self.console.print(f"[red]Error getting file info: {str(e)}[/red]") raise def verify_file_integrity(self, task: DownloadTask) -> bool: if not os.path.exists(task.output_file): return False return os.path.getsize(task.output_file) == task.total_size async def download_chunk(self, client: httpx.AsyncClient, task: DownloadTask, start: int, end: int) -> None: async def _download_chunk(): headers = {"Range": f"bytes={start}-{end}"} async with client.stream("GET", task.url, headers=headers, **self.client_options) as response: response.raise_for_status() with open(task.output_file, "ab") as file: file.seek(start) async for chunk in response.aiter_bytes(self.buffer_size): file.write(chunk) task.downloaded_size += len(chunk) self.progress.update( task.task_id, completed=task.downloaded_size, ) try: await self.retry_with_exponential_backoff(task, _download_chunk) except Exception as e: raise DownloadError( f"Failed to download chunk {start}-{end} after {task.max_retries} attempts: {str(e)}" ) from e async def prepare_download(self, task: DownloadTask) -> None: output_dir = os.path.dirname(task.output_file) os.makedirs(output_dir, exist_ok=True) if os.path.exists(task.output_file): task.downloaded_size = os.path.getsize(task.output_file) async def download_file(self, task: DownloadTask) -> None: try: async with httpx.AsyncClient(**self.client_options) as client: await self.get_file_info(client, task) # Check if file is already downloaded if os.path.exists(task.output_file): if self.verify_file_integrity(task): self.console.print(f"[green]Already downloaded {task.output_file}[/green]") self.progress.update(task.task_id, completed=task.total_size) return await self.prepare_download(task) try: # Split the remaining download into chunks chunk_size = 27_000_000_000 # Cloudfront max chunk size chunks = [] current_pos = task.downloaded_size while current_pos < task.total_size: chunk_end = min(current_pos + chunk_size - 1, task.total_size - 1) chunks.append((current_pos, chunk_end)) current_pos = chunk_end + 1 # Download chunks in sequence for chunk_start, chunk_end in chunks: await self.download_chunk(client, task, chunk_start, chunk_end) except Exception as e: raise DownloadError(f"Download failed: {str(e)}") from e except Exception as e: self.progress.update(task.task_id, description=f"[red]Failed: {task.output_file}[/red]") raise DownloadError(f"Download failed for {task.output_file}: {str(e)}") from e def has_disk_space(self, tasks: list[DownloadTask]) -> bool: try: total_remaining_size = sum(task.total_size - task.downloaded_size for task in tasks) dir_path = os.path.dirname(os.path.abspath(tasks[0].output_file)) free_space = shutil.disk_usage(dir_path).free # Add 10% buffer for safety required_space = int(total_remaining_size * 1.1) if free_space < required_space: self.console.print( f"[red]Not enough disk space. Required: {required_space // (1024 * 1024)} MB, " f"Available: {free_space // (1024 * 1024)} MB[/red]" ) return False return True except Exception as e: raise DownloadError(f"Failed to check disk space: {str(e)}") from e async def download_all(self, tasks: list[DownloadTask]) -> None: if not tasks: raise ValueError("No download tasks provided") if not os.environ.get("LLAMA_DOWNLOAD_NO_SPACE_CHECK") and not self.has_disk_space(tasks): raise DownloadError("Insufficient disk space for downloads") failed_tasks = [] with self.progress: for task in tasks: desc = f"Downloading {Path(task.output_file).name}" task.task_id = self.progress.add_task(desc, total=task.total_size, completed=task.downloaded_size) semaphore = asyncio.Semaphore(self.max_concurrent_downloads) async def download_with_semaphore(task: DownloadTask): async with semaphore: try: await self.download_file(task) except Exception as e: failed_tasks.append((task, str(e))) await asyncio.gather(*(download_with_semaphore(task) for task in tasks)) if failed_tasks: self.console.print("\n[red]Some downloads failed:[/red]") for task, error in failed_tasks: self.console.print(f"[red]- {Path(task.output_file).name}: {error}[/red]") raise DownloadError(f"{len(failed_tasks)} downloads failed") def _hf_download( model: "Model", hf_token: str, ignore_patterns: str, parser: argparse.ArgumentParser, ): from huggingface_hub import snapshot_download from huggingface_hub.utils import GatedRepoError, RepositoryNotFoundError from llama_models.utils.model_utils import model_local_dir repo_id = model.huggingface_repo if repo_id is None: raise ValueError(f"No repo id found for model {model.descriptor()}") output_dir = model_local_dir(model.descriptor()) os.makedirs(output_dir, exist_ok=True) try: true_output_dir = snapshot_download( repo_id, local_dir=output_dir, ignore_patterns=ignore_patterns, token=hf_token, library_name="llama-stack", ) except GatedRepoError: parser.error( "It looks like you are trying to access a gated repository. Please ensure you " "have access to the repository and have provided the proper Hugging Face API token " "using the option `--hf-token` or by running `huggingface-cli login`." "You can find your token by visiting https://huggingface.co/settings/tokens" ) except RepositoryNotFoundError: parser.error(f"Repository '{repo_id}' not found on the Hugging Face Hub or incorrect Hugging Face token.") except Exception as e: parser.error(e) print(f"\nSuccessfully downloaded model to {true_output_dir}") def _meta_download( model: "Model", model_id: str, meta_url: str, info: "LlamaDownloadInfo", max_concurrent_downloads: int, ): from llama_models.utils.model_utils import model_local_dir output_dir = Path(model_local_dir(model.descriptor())) os.makedirs(output_dir, exist_ok=True) # Create download tasks for each file tasks = [] for f in info.files: output_file = str(output_dir / f) url = meta_url.replace("*", f"{info.folder}/{f}") total_size = info.pth_size if "consolidated" in f else 0 tasks.append(DownloadTask(url=url, output_file=output_file, total_size=total_size, max_retries=3)) # Initialize and run parallel downloader downloader = ParallelDownloader(max_concurrent_downloads=max_concurrent_downloads) asyncio.run(downloader.download_all(tasks)) cprint(f"\nSuccessfully downloaded model to {output_dir}", color="green", file=sys.stderr) cprint( f"\nView MD5 checksum files at: {output_dir / 'checklist.chk'}", file=sys.stderr, ) cprint( f"\n[Optionally] To run MD5 checksums, use the following command: llama model verify-download --model-id {model_id}", color="yellow", file=sys.stderr, ) class ModelEntry(BaseModel): model_id: str files: dict[str, str] model_config = ConfigDict(protected_namespaces=()) class Manifest(BaseModel): models: list[ModelEntry] expires_on: datetime def _download_from_manifest(manifest_file: str, max_concurrent_downloads: int): from llama_models.utils.model_utils import model_local_dir with open(manifest_file) as f: d = json.load(f) manifest = Manifest(**d) if datetime.now(UTC) > manifest.expires_on.astimezone(UTC): raise ValueError(f"Manifest URLs have expired on {manifest.expires_on}") console = Console() for entry in manifest.models: console.print(f"[blue]Downloading model {entry.model_id}...[/blue]") output_dir = Path(model_local_dir(entry.model_id)) os.makedirs(output_dir, exist_ok=True) if any(output_dir.iterdir()): console.print(f"[yellow]Output directory {output_dir} is not empty.[/yellow]") while True: resp = input("Do you want to (C)ontinue download or (R)estart completely? (continue/restart): ") if resp.lower() in ["restart", "r"]: shutil.rmtree(output_dir) os.makedirs(output_dir, exist_ok=True) break elif resp.lower() in ["continue", "c"]: console.print("[blue]Continuing download...[/blue]") break else: console.print("[red]Invalid response. Please try again.[/red]") # Create download tasks for all files in the manifest tasks = [ DownloadTask(url=url, output_file=str(output_dir / fname), max_retries=3) for fname, url in entry.files.items() ] # Initialize and run parallel downloader downloader = ParallelDownloader(max_concurrent_downloads=max_concurrent_downloads) asyncio.run(downloader.download_all(tasks)) def run_download_cmd(args: argparse.Namespace, parser: argparse.ArgumentParser): """Main download command handler""" try: if args.manifest_file: _download_from_manifest(args.manifest_file, args.max_parallel) return if args.model_id is None: parser.error("Please provide a model id") return # Handle comma-separated model IDs model_ids = [model_id.strip() for model_id in args.model_id.split(",")] from llama_models.sku_list import llama_meta_net_info, resolve_model from .model.safety_models import ( prompt_guard_download_info_map, prompt_guard_model_sku_map, ) prompt_guard_model_sku_map = prompt_guard_model_sku_map() prompt_guard_download_info_map = prompt_guard_download_info_map() for model_id in model_ids: if model_id in prompt_guard_model_sku_map.keys(): model = prompt_guard_model_sku_map[model_id] info = prompt_guard_download_info_map[model_id] else: model = resolve_model(model_id) if model is None: parser.error(f"Model {model_id} not found") continue info = llama_meta_net_info(model) if args.source == "huggingface": _hf_download(model, args.hf_token, args.ignore_patterns, parser) else: meta_url = args.meta_url or input( f"Please provide the signed URL for model {model_id} you received via email " f"after visiting https://www.llama.com/llama-downloads/ " f"(e.g., https://llama3-1.llamameta.net/*?Policy...): " ) if "llamameta.net" not in meta_url: parser.error("Invalid Meta URL provided") _meta_download(model, model_id, meta_url, info, args.max_parallel) except Exception as e: parser.error(f"Download failed: {str(e)}") ================================================ FILE: models/cli/list.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import argparse import os import time from pathlib import Path from llama_models.cli.subcommand import Subcommand from llama_models.cli.table import print_table from llama_models.utils.config import DEFAULT_CHECKPOINT_DIR from llama_models.sku_list import all_registered_models def _get_model_size(model_dir): return sum(f.stat().st_size for f in Path(model_dir).rglob("*") if f.is_file()) def _convert_to_model_descriptor(model): for m in all_registered_models(): if model == m.descriptor().replace(":", "-"): return str(m.descriptor()) return str(model) def _run_model_list_downloaded_cmd() -> None: headers = ["Model", "Size", "Modified Time"] rows = [] for model in os.listdir(DEFAULT_CHECKPOINT_DIR): abs_path = os.path.join(DEFAULT_CHECKPOINT_DIR, model) space_usage = _get_model_size(abs_path) model_size = f"{space_usage / (1024**3):.2f} GB" modified_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(os.path.getmtime(abs_path))) rows.append( [ _convert_to_model_descriptor(model), model_size, modified_time, ] ) print_table( rows, headers, separate_rows=True, ) class List(Subcommand): """List available llama models""" def __init__(self, subparsers: argparse._SubParsersAction): super().__init__() self.parser = subparsers.add_parser( "list", prog="llama-model list", description="Show available llama models", formatter_class=argparse.RawTextHelpFormatter, ) self._add_arguments() self.parser.set_defaults(func=self._run_model_list_cmd) def _add_arguments(self): self.parser.add_argument( "--show-all", action="store_true", help="Show all models (not just defaults)", ) self.parser.add_argument( "--downloaded", action="store_true", help="List the downloaded models", ) self.parser.add_argument( "-s", "--search", type=str, required=False, help="Search for the input string as a substring in the model descriptor(ID)", ) def _run_model_list_cmd(self, args: argparse.Namespace) -> None: from llama_models.cli.safety_models import prompt_guard_model_skus if args.downloaded: return _run_model_list_downloaded_cmd() headers = [ "Model Descriptor(ID)", "Hugging Face Repo", "Context Length", ] rows = [] for model in all_registered_models() + prompt_guard_model_skus(): if not args.show_all and not model.is_featured: continue descriptor = model.descriptor() if not args.search or args.search.lower() in descriptor.lower(): rows.append( [ descriptor, model.huggingface_repo, f"{model.max_seq_length // 1024}K", ] ) if len(rows) == 0: print(f"Did not find any model matching `{args.search}`.") else: print_table( rows, headers, separate_rows=True, ) ================================================ FILE: models/cli/llama.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import argparse from .describe import Describe from .download import Download from .list import List from .prompt_format import PromptFormat from .remove import Remove from .utils import print_subcommand_description from .verify_download import VerifyDownload class LlamaModelsCLIParser: """Defines CLI parser for Llama Models CLI""" def __init__(self): self.parser = argparse.ArgumentParser( prog="llama-model", description="Llama Model Management CLI", add_help=True, formatter_class=argparse.RawTextHelpFormatter, ) # Default command is to print help self.parser.set_defaults(func=lambda args: self.parser.print_help()) subparsers = self.parser.add_subparsers(title="subcommands") # Add sub-commands List.create(subparsers) Describe.create(subparsers) Download.create(subparsers) PromptFormat.create(subparsers) Remove.create(subparsers) VerifyDownload.create(subparsers) print_subcommand_description(self.parser, subparsers) def parse_args(self) -> argparse.Namespace: args = self.parser.parse_args() if not isinstance(args, argparse.Namespace): raise TypeError(f"Expected argparse.Namespace, got {type(args)}") return args def run(self, args: argparse.Namespace) -> None: args.func(args) def main(): parser = LlamaModelsCLIParser() args = parser.parse_args() parser.run(args) if __name__ == "__main__": main() ================================================ FILE: models/cli/prompt_format.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import argparse import textwrap from io import StringIO from pathlib import Path from llama_models.cli.subcommand import Subcommand from llama_models.cli.table import print_table from llama_models.sku_types import CoreModelId, ModelFamily, is_multimodal, model_family ROOT_DIR = Path(__file__).parent.parent.parent class PromptFormat(Subcommand): """Llama model cli for describe a model prompt format (message formats)""" def __init__(self, subparsers: argparse._SubParsersAction): super().__init__() self.parser = subparsers.add_parser( "prompt-format", prog="llama-model prompt-format", description="Show llama model message formats", epilog=textwrap.dedent( """ Example: llama model prompt-format """ ), formatter_class=argparse.RawTextHelpFormatter, ) self._add_arguments() self.parser.set_defaults(func=self._run_model_template_cmd) def _add_arguments(self): self.parser.add_argument( "-m", "--model-name", type=str, help="Example: Llama3.1-8B or Llama3.2-11B-Vision, etc\n" "(Run `llama-model list` to see a list of valid model names)", ) self.parser.add_argument( "-l", "--list", action="store_true", help="List all available models", ) def _run_model_template_cmd(self, args: argparse.Namespace) -> None: import importlib.resources # Only Llama 3.1 and 3.2 are supported supported_model_ids = [ m for m in CoreModelId if model_family(m) in {ModelFamily.llama3_1, ModelFamily.llama3_2} ] model_list = [m.value for m in supported_model_ids] if args.list: headers = ["Model(s)"] rows = [] for m in model_list: rows.append( [ m, ] ) print_table( rows, headers, separate_rows=True, ) return try: model_id = CoreModelId(args.model_name) except ValueError: self.parser.error( f"{args.model_name} is not a valid Model. Choose one from the list of valid models. " f"Run `llama-model list` to see the valid model names." ) if model_id not in supported_model_ids: self.parser.error( f"{model_id} is not a valid Model. Choose one from the list of valid models. " f"Run `llama-model list` to see the valid model names." ) llama_3_1_file = ROOT_DIR / "llama3_1" / "prompt_format.md" llama_3_2_text_file = ROOT_DIR / "llama3_2" / "text_prompt_format.md" llama_3_2_vision_file = ROOT_DIR / "llama3_2" / "vision_prompt_format.md" if model_family(model_id) == ModelFamily.llama3_1: with importlib.resources.as_file(llama_3_1_file) as f: content = f.open("r").read() elif model_family(model_id) == ModelFamily.llama3_2: if is_multimodal(model_id): with importlib.resources.as_file(llama_3_2_vision_file) as f: content = f.open("r").read() else: with importlib.resources.as_file(llama_3_2_text_file) as f: content = f.open("r").read() render_markdown_to_pager(content) def render_markdown_to_pager(markdown_content: str): from rich.console import Console from rich.markdown import Markdown from rich.style import Style from rich.text import Text class LeftAlignedHeaderMarkdown(Markdown): def parse_header(self, token): level = token.type.count("h") content = Text(token.content) header_style = Style(color="bright_blue", bold=True) header = Text(f"{'#' * level} ", style=header_style) + content self.add_text(header) # Render the Markdown md = LeftAlignedHeaderMarkdown(markdown_content) # Capture the rendered output output = StringIO() console = Console(file=output, force_terminal=True, width=100) # Set a fixed width console.print(md) rendered_content = output.getvalue() print(rendered_content) ================================================ FILE: models/cli/remove.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import argparse import os import shutil from llama_models.cli.subcommand import Subcommand from llama_models.utils.config import DEFAULT_CHECKPOINT_DIR from llama_models.sku_list import resolve_model class Remove(Subcommand): """Remove the downloaded llama model""" def __init__(self, subparsers: argparse._SubParsersAction): super().__init__() self.parser = subparsers.add_parser( "remove", prog="llama-model remove", description="Remove the downloaded llama model", formatter_class=argparse.RawTextHelpFormatter, ) self._add_arguments() self.parser.set_defaults(func=self._run_model_remove_cmd) def _add_arguments(self): self.parser.add_argument( "-m", "--model", required=True, help="Specify the llama downloaded model name, see `llama-model list --downloaded`", ) self.parser.add_argument( "-f", "--force", action="store_true", help="Used to forcefully remove the llama model from the storage without further confirmation", ) def _run_model_remove_cmd(self, args: argparse.Namespace) -> None: from llama_models.cli.safety_models import prompt_guard_model_sku_map prompt_guard_model_map = prompt_guard_model_sku_map() if args.model in prompt_guard_model_map.keys(): model = prompt_guard_model_map[args.model] else: model = resolve_model(args.model) model_path = os.path.join(DEFAULT_CHECKPOINT_DIR, args.model.replace(":", "-")) if model is None or not os.path.isdir(model_path): print(f"'{args.model}' is not a valid llama model or does not exist.") return if args.force: shutil.rmtree(model_path) print(f"{args.model} removed.") else: if input(f"Are you sure you want to remove {args.model}? (y/n): ").strip().lower() == "y": shutil.rmtree(model_path) print(f"{args.model} removed.") else: print("Removal aborted.") ================================================ FILE: models/cli/safety_models.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. from typing import Any from pydantic import BaseModel, ConfigDict, Field from llama_models.sku_list import LlamaDownloadInfo from llama_models.sku_types import CheckpointQuantizationFormat class PromptGuardModel(BaseModel): """Make a 'fake' Model-like object for Prompt Guard. Eventually this will be removed.""" model_id: str huggingface_repo: str description: str = "Prompt Guard. NOTE: this model will not be provided via `llama` CLI soon." is_featured: bool = False max_seq_length: int = 512 is_instruct_model: bool = False quantization_format: CheckpointQuantizationFormat = CheckpointQuantizationFormat.bf16 arch_args: dict[str, Any] = Field(default_factory=dict) def descriptor(self) -> str: return self.model_id model_config = ConfigDict(protected_namespaces=()) def prompt_guard_model_skus(): return [ PromptGuardModel(model_id="Prompt-Guard-86M", huggingface_repo="meta-llama/Prompt-Guard-86M"), PromptGuardModel( model_id="Llama-Prompt-Guard-2-86M", huggingface_repo="meta-llama/Llama-Prompt-Guard-2-86M", ), PromptGuardModel( model_id="Llama-Prompt-Guard-2-22M", huggingface_repo="meta-llama/Llama-Prompt-Guard-2-22M", ), ] def prompt_guard_model_sku_map() -> dict[str, Any]: return {model.model_id: model for model in prompt_guard_model_skus()} def prompt_guard_download_info_map() -> dict[str, LlamaDownloadInfo]: return { model.model_id: LlamaDownloadInfo( folder="Prompt-Guard" if model.model_id == "Prompt-Guard-86M" else model.model_id, files=[ "model.safetensors", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json", ], pth_size=1, ) for model in prompt_guard_model_skus() } ================================================ FILE: models/cli/subcommand.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. class Subcommand: """All llama cli subcommands must inherit from this class""" def __init__(self, *args, **kwargs): pass @classmethod def create(cls, *args, **kwargs): return cls(*args, **kwargs) def _add_arguments(self): pass ================================================ FILE: models/cli/table.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. from collections.abc import Iterable from rich.console import Console from rich.table import Table def print_table(rows, headers=None, separate_rows: bool = False, sort_by: Iterable[int] = tuple()): # Convert rows and handle None values rows = [[x or "" for x in row] for row in rows] # Sort rows if sort_by is specified if sort_by: rows.sort(key=lambda x: tuple(x[i] for i in sort_by)) # Create Rich table table = Table(show_lines=separate_rows) # Add headers if provided if headers: for header in headers: table.add_column(header, style="bold white") else: # Add unnamed columns based on first row for _ in range(len(rows[0]) if rows else 0): table.add_column() # Add rows for row in rows: table.add_row(*row) # Print table console = Console() console.print(table) ================================================ FILE: models/cli/utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. def print_subcommand_description(parser, subparsers): """Print descriptions of subcommands.""" description_text = "" for name, subcommand in subparsers.choices.items(): description = subcommand.description description_text += f" {name:<21} {description}\n" parser.epilog = description_text ================================================ FILE: models/cli/verify_download.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import argparse import hashlib from dataclasses import dataclass from functools import partial from pathlib import Path from rich.console import Console from rich.progress import Progress, SpinnerColumn, TextColumn from llama_models.cli.subcommand import Subcommand @dataclass class VerificationResult: filename: str expected_hash: str actual_hash: str | None exists: bool matches: bool class VerifyDownload(Subcommand): """Llama cli for verifying downloaded model files""" def __init__(self, subparsers: argparse._SubParsersAction): super().__init__() self.parser = subparsers.add_parser( "verify-download", prog="llama verify-download", description="Verify integrity of downloaded model files", formatter_class=argparse.RawTextHelpFormatter, ) setup_verify_download_parser(self.parser) def setup_verify_download_parser(parser: argparse.ArgumentParser) -> None: parser.add_argument( "--model-id", required=True, help="Model ID to verify (only for models downloaded from Meta)", ) parser.set_defaults(func=partial(run_verify_cmd, parser=parser)) def calculate_sha256(filepath: Path, chunk_size: int = 8192) -> str: sha256_hash = hashlib.sha256() with open(filepath, "rb") as f: for chunk in iter(lambda: f.read(chunk_size), b""): sha256_hash.update(chunk) return sha256_hash.hexdigest() def load_checksums(checklist_path: Path) -> dict[str, str]: checksums = {} with open(checklist_path) as f: for line in f: if line.strip(): sha256sum, filepath = line.strip().split(" ", 1) # Remove leading './' if present filepath = filepath.lstrip("./") checksums[filepath] = sha256sum return checksums def verify_files(model_dir: Path, checksums: dict[str, str], console: Console) -> list[VerificationResult]: results = [] with Progress( SpinnerColumn(), TextColumn("[progress.description]{task.description}"), console=console, ) as progress: for filepath, expected_hash in checksums.items(): full_path = model_dir / filepath task_id = progress.add_task(f"Verifying {filepath}...", total=None) exists = full_path.exists() actual_hash = None matches = False if exists: actual_hash = calculate_sha256(full_path) matches = actual_hash == expected_hash results.append( VerificationResult( filename=filepath, expected_hash=expected_hash, actual_hash=actual_hash, exists=exists, matches=matches, ) ) progress.remove_task(task_id) return results def run_verify_cmd(args: argparse.Namespace, parser: argparse.ArgumentParser): from llama_models.utils.model_utils import model_local_dir console = Console() model_dir = Path(model_local_dir(args.model_id)) checklist_path = model_dir / "checklist.chk" if not model_dir.exists(): parser.error(f"Model directory not found: {model_dir}") if not checklist_path.exists(): parser.error(f"Checklist file not found: {checklist_path}") checksums = load_checksums(checklist_path) results = verify_files(model_dir, checksums, console) # Print results console.print("\nVerification Results:") all_good = True for result in results: if not result.exists: console.print(f"[red]❌ {result.filename}: File not found[/red]") all_good = False elif not result.matches: console.print( f"[red]❌ {result.filename}: Hash mismatch[/red]\n" f" Expected: {result.expected_hash}\n" f" Got: {result.actual_hash}" ) all_good = False else: console.print(f"[green]✓ {result.filename}: Verified[/green]") if all_good: console.print("\n[green]All files verified successfully![/green]") ================================================ FILE: models/datatypes.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import base64 from enum import Enum from io import BytesIO from typing import Any, Dict, List, Literal, Optional, Union from pydantic import BaseModel, ConfigDict, Field, field_serializer, field_validator from typing_extensions import Annotated # The goal is that these set of types are relevant for all Llama models. # That isn't the current state yet -- e.g., BuiltinTool is somewhat specific to # the llama3 series of models. class Role(Enum): system = "system" user = "user" assistant = "assistant" tool = "tool" class BuiltinTool(Enum): brave_search = "brave_search" wolfram_alpha = "wolfram_alpha" photogen = "photogen" code_interpreter = "code_interpreter" Primitive = Union[str, int, float, bool, None] RecursiveType = Union[Primitive, List[Primitive], Dict[str, Primitive]] class ToolCall(BaseModel): call_id: str tool_name: Union[BuiltinTool, str] # Plan is to deprecate the Dict in favor of a JSON string # that is parsed on the client side instead of trying to manage # the recursive type here. # Making this a union so that client side can start prepping for this change. # Eventually, we will remove both the Dict and arguments_json field, # and arguments will just be a str arguments: Union[str, Dict[str, RecursiveType]] arguments_json: Optional[str] = None @field_validator("tool_name", mode="before") @classmethod def validate_field(cls, v): if isinstance(v, str): try: return BuiltinTool(v) except ValueError: return v return v class ToolPromptFormat(Enum): """Prompt format for calling custom / zero shot tools. :cvar json: JSON format for calling tools. It takes the form: { "type": "function", "function" : { "name": "function_name", "description": "function_description", "parameters": {...} } } :cvar function_tag: Function tag format, pseudo-XML. This looks like: (parameters) :cvar python_list: Python list. The output is a valid Python expression that can be evaluated to a list. Each element in the list is a function call. Example: ["function_name(param1, param2)", "function_name(param1, param2)"] """ json = "json" function_tag = "function_tag" python_list = "python_list" class StopReason(Enum): end_of_turn = "end_of_turn" end_of_message = "end_of_message" out_of_tokens = "out_of_tokens" class ToolParamDefinition(BaseModel): param_type: str description: Optional[str] = None required: Optional[bool] = True default: Optional[Any] = None class ToolDefinition(BaseModel): tool_name: Union[BuiltinTool, str] description: Optional[str] = None parameters: Optional[Dict[str, ToolParamDefinition]] = None @field_validator("tool_name", mode="before") @classmethod def validate_field(cls, v): if isinstance(v, str): try: return BuiltinTool(v) except ValueError: return v return v class RawMediaItem(BaseModel): type: Literal["image"] = "image" data: bytes | BytesIO model_config = ConfigDict(arbitrary_types_allowed=True) @field_serializer("data") def serialize_data(self, data: Optional[bytes], _info): if data is None: return None return base64.b64encode(data).decode("utf-8") @field_validator("data", mode="before") @classmethod def validate_data(cls, v): if isinstance(v, str): return base64.b64decode(v) return v class RawTextItem(BaseModel): type: Literal["text"] = "text" text: str RawContentItem = Annotated[Union[RawTextItem, RawMediaItem], Field(discriminator="type")] RawContent = str | RawContentItem | List[RawContentItem] class RawMessage(BaseModel): role: Literal["user"] | Literal["system"] | Literal["tool"] | Literal["assistant"] content: RawContent # This is for RAG but likely should be absorbed into content context: Optional[RawContent] = None # These are for the output message coming from the assistant stop_reason: Optional[StopReason] = None tool_calls: List[ToolCall] = Field(default_factory=list) class GenerationResult(BaseModel): token: int text: str logprobs: Optional[List[float]] = None source: Literal["input"] | Literal["output"] # index within the batch batch_idx: int # whether generation for this item is already finished. note that tokens can # get returned even afterwards since other items in the batch can still be generating tokens finished: bool # because a batch is parallel processed, useful decoding for one item can correspond to processing # pad tokens or tokens beyond EOS for other items. we could have decided to return None for this case # but it's more convenient to return a list of GenerationResult and filter out the ignored tokens ignore_token: bool class QuantizationMode(str, Enum): none = "none" fp8_mixed = "fp8_mixed" int4_mixed = "int4_mixed" ================================================ FILE: models/llama2/LICENSE ================================================ LLAMA 2 COMMUNITY LICENSE AGREEMENT Llama 2 Version Release Date: July 18, 2023 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at ai.meta.com/resources/models-and- libraries/llama-downloads/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Llama 2" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at ai.meta.com/resources/models-and- libraries/llama-downloads/. 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If you distribute or make the Llama Materials, or any derivative works thereof, available to a third party, you shall provide a copy of this Agreement to such third party. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a "Notice" text file distributed as a part of such copies: "Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved." iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://ai.meta.com/llama/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Llama 2 or derivative works thereof). 2. Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee's affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials. b. Subject to Meta's ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 2 outputs or results, or any portion of any of the foregoing, constitutes an infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ================================================ FILE: models/llama2/MODEL_CARD.md ================================================ # **Model Details** Meta developed and released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. ||Training Data|Params|Context Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10-4 Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10-4 Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10-4 **Llama 2 family of models.** Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. The 70B version uses Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** More information can be found in the paper "Llama-2: Open Foundation and Fine-tuned Chat Models", available at https://ai.meta.com/research/publications/llama-2-open-foundation-and-fine-tuned-chat-models/. **Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). # **Intended Use** **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 2 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 2 models for languages beyond English provided they comply with the Llama 2 Community License and the Acceptable Use Policy. # **Hardware and Software** **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO2eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO2 emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. # **Training Data** **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. # **Evaluation Results** In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks. For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at the top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. # **Ethical Considerations and Limitations** Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide/) ================================================ FILE: models/llama2/USE_POLICY.md ================================================ # Llama 2 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 2. If you access or use Llama 2, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy). ## Prohibited Uses We want everyone to use Llama 2 safely and responsibly. You agree you will not use, or allow others to use, Llama 2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Llama 2 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com) ================================================ FILE: models/llama3/LICENSE ================================================ META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Meta Llama 3” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement. 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof). 2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at https://llama.meta.com/llama3/use-policy Prohibited Uses We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others’ rights, including to: a. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: i. Violence or terrorism ii. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material iii. Human trafficking, exploitation, and sexual violence iv. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. v. Sexual solicitation vi. Any other criminal activity b. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals c. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services d. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices e. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws f. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials g. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following: a. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State b. Guns and illegal weapons (including weapon development) c. Illegal drugs and regulated/controlled substances d. Operation of critical infrastructure, transportation technologies, or heavy machinery e. Self-harm or harm to others, including suicide, cutting, and eating disorders f. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following: a. Generating, promoting, or furthering fraud or the creation or promotion of disinformation b. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content c. Generating, promoting, or further distributing spam d. Impersonating another individual without consent, authorization, or legal right e. Representing that the use of Meta Llama 3 or outputs are human-generated f. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement g. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: https://github.com/meta-llama/llama3 * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com ================================================ FILE: models/llama3/MODEL_CARD.md ================================================ ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Llama 3 family of models** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. Llama 3 uses a tokenizer with a vocabulary of 128K tokens, and was trained on on sequences of 8,192 tokens. Grouped-Query Attention (GQA) is used for all models to improve inference efficiency. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
Training Data Params Context length GQA Token count Knowledge cutoff
Llama 3 A new mix of publicly available online data. 8B 8k Yes 15T+ March, 2023
70B 8k Yes December, 2023
Note: Token counts refer to pretraining data only. **Model Release Date** April 18, 2024. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) **Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the [Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/) and [Llama 3 Community License](https://llama.meta.com/llama3/license/). Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the [Llama 3 Community License](https://llama.meta.com/llama3/license/) and the [Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/). ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
Time (GPU hours) Power Consumption (W) Carbon Emitted(tCO2eq)
Llama 3 8B 1.3M 700 390
Llama 3 70B 6.4M 700 1900
Total 7.7M 2290
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 8B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_details.md). ### Base pretrained models
Category Benchmark Llama 3 8B Llama2 7B Llama2 13B Llama 3 70B Llama2 70B
General MMLU (5-shot) 66.6 45.7 53.8 79.5 69.7
AGIEval English (3-5 shot) 45.9 28.8 38.7 63.0 54.8
CommonSenseQA (7-shot) 72.6 57.6 67.6 83.8 78.7
Winogrande (5-shot) 76.1 73.3 75.4 83.1 81.8
BIG-Bench Hard (3-shot, CoT) 61.1 38.1 47.0 81.3 65.7
ARC-Challenge (25-shot) 78.6 53.7 67.6 93.0 85.3
Knowledge reasoning TriviaQA-Wiki (5-shot) 78.5 72.1 79.6 89.7 87.5
Reading comprehension SQuAD (1-shot) 76.4 72.2 72.1 85.6 82.6
QuAC (1-shot, F1) 44.4 39.6 44.9 51.1 49.4
BoolQ (0-shot) 75.7 65.5 66.9 79.0 73.1
DROP (3-shot, F1) 58.4 37.9 49.8 79.7 70.2
### Instruction tuned models
Benchmark Llama 3 8B Llama 2 7B Llama 2 13B Llama 3 70B Llama 2 70B
MMLU (5-shot) 68.4 34.1 47.8 82.0 52.9
GPQA (0-shot) 34.2 21.7 22.3 39.5 21.0
HumanEval (0-shot) 62.2 7.9 14.0 81.7 25.6
GSM-8K (8-shot, CoT) 79.6 25.7 41.2 93.0 57.5
MATH (4-shot, CoT) 30.0 3.8 6.7 50.4 11.6
### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. Safety For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. Refusals In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks CBRNE (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### Cyber Security We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### Child Safety Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [GitHub repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions ``` @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ``` ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Amit Sangani; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Ash JJhaveri; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hamid Shojanazeri; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Puxin Xu; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos ================================================ FILE: models/llama3/USE_POLICY.md ================================================ # Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Llama 3. If you access or use Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [ai.meta.com/llama/use-policy](http://ai.meta.com/llama/use-policy). ## Prohibited Uses We want everyone to use Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Llama 3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 3 Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Llama 3 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com) ================================================ FILE: models/llama3/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. ================================================ FILE: models/llama3/args.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. from dataclasses import dataclass from enum import Enum from typing import Optional class QuantizationScheme(Enum): int4_weight_int8_dynamic_activation = "int4_weight_int8_dynamic_activation" @dataclass class QuantizationArgs: scheme: Optional[QuantizationScheme] = None group_size: Optional[int] = None spinquant: bool = False def __init__(self, **kwargs): for k, v in kwargs.items(): if k == "scheme": setattr(self, k, QuantizationScheme(v)) else: if hasattr(self, k): setattr(self, k, v) @dataclass class LoRAArgs: rank: int scale: float @dataclass class ModelArgs: dim: int = 4096 n_layers: int = 32 n_heads: int = 32 n_kv_heads: Optional[int] = None vocab_size: int = -1 multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2 ffn_dim_multiplier: Optional[float] = None norm_eps: float = 1e-5 rope_theta: float = 500000 use_scaled_rope: bool = False max_batch_size: int = 32 max_seq_len: int = 2048 # vision model params vision_chunk_size: int = -1 # image resolution for image models vision_max_num_chunks: int = 4 vision_num_cross_attention_layers: int = -1 quantization_args: Optional[QuantizationArgs] = None lora_args: Optional[LoRAArgs] = None def __init__(self, **kwargs): for k, v in kwargs.items(): if k == "lora_args": setattr(self, k, LoRAArgs(**v)) elif k == "quantization_args": setattr(self, k, QuantizationArgs(**v)) elif k == "vision_model" and "cross_attention_adapter" in v: self.vision_num_cross_attention_layers = v["cross_attention_adapter"]["num_layers"] else: if hasattr(self, k): setattr(self, k, v) if self.n_kv_heads is None: self.n_kv_heads = self.n_heads assert self.n_kv_heads <= self.n_heads assert self.n_heads % self.n_kv_heads == 0 assert self.dim % self.n_heads == 0 ================================================ FILE: models/llama3/chat_format.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import io import uuid from dataclasses import dataclass from typing import Dict, List, Optional, Tuple from PIL import Image as PIL_Image from ..datatypes import ( BuiltinTool, RawContent, RawMediaItem, RawMessage, RawTextItem, Role, StopReason, ToolCall, ToolPromptFormat, ) from .tokenizer import Tokenizer from .tool_utils import ToolUtils @dataclass class VisionInput: mask: List[List[int]] images: List[PIL_Image.Image] @dataclass class LLMInput: tokens: List[int] vision: Optional[VisionInput] = None def role_str(role: Role) -> str: role_strs = { Role.user: "user", Role.system: "system", Role.tool: "ipython", # special Role.assistant: "assistant", } return role_strs[role] class ChatFormat: possible_headers: Dict[Role, str] def __init__(self, tokenizer: Tokenizer): self.tokenizer = tokenizer self.possible_headers = {role: f"<|start_header_id|>{role_str(role)}<|end_header_id|>\n\n" for role in Role} self.vision_token = self.tokenizer.special_tokens["<|image|>"] def _encode_header(self, role: str) -> List[int]: tokens = [] tokens.append(self.tokenizer.special_tokens["<|start_header_id|>"]) tokens.extend(self.tokenizer.encode("ipython" if role == "tool" else role, bos=False, eos=False)) tokens.append(self.tokenizer.special_tokens["<|end_header_id|>"]) tokens.extend(self.tokenizer.encode("\n\n", bos=False, eos=False)) return tokens def encode_content(self, content: RawContent) -> LLMInput: tokens, images = self._encode_content(content, bos=True) return self._model_input_from_tokens_images(tokens, images) def _encode_content(self, content: RawContent, bos: bool = False) -> Tuple[List[int], List[PIL_Image.Image]]: tokens = [] images = [] added_bos = False def _process(c): nonlocal added_bos, bos if isinstance(c, str) or isinstance(c, RawTextItem): if isinstance(c, RawTextItem): c = c.text tokens.extend(self.tokenizer.encode(c, bos=False if added_bos else bos, eos=False)) added_bos = True elif isinstance(c, RawMediaItem): bos = False if added_bos else bos if bos: tokens.append(self.tokenizer.special_tokens["<|begin_of_text|>"]) added_bos = True tokens.append(self.vision_token) bytes_io = io.BytesIO(c.data) if isinstance(c.data, bytes) else c.data image = PIL_Image.open(bytes_io) image = image.convert("RGB") images.append(image) if isinstance(content, list): for c in content: _process(c) else: _process(content) return tokens, images def encode_message( self, message: RawMessage, tool_prompt_format: ToolPromptFormat ) -> Tuple[List[int], List[PIL_Image.Image]]: tokens = self._encode_header(message.role) images = [] def _process_content(c): toks, imgs = self._encode_content(c) tokens.extend(toks) images.extend(imgs) if ( message.role == "assistant" and len(message.tool_calls) > 0 and message.tool_calls[0].tool_name == BuiltinTool.code_interpreter ): tokens.append(self.tokenizer.special_tokens["<|python_tag|>"]) _process_content(message.content) if message.role == "user" and message.context is not None: # This is RAG context; why is it here in the chat format? I don't think # this is needed and can be moved upwards _process_content("\n\n") _process_content(message.context) if message.role == "assistant": for t in message.tool_calls: content = ToolUtils.encode_tool_call(t, tool_prompt_format) _process_content(content) eom = False if message.role == "assistant": eom = message.stop_reason == StopReason.end_of_message tokens.append(self.tokenizer.special_tokens["<|eom_id|>" if eom else "<|eot_id|>"]) return tokens, images def encode_dialog_prompt( self, messages: List[RawMessage], tool_prompt_format: Optional[ToolPromptFormat] = None, ) -> LLMInput: tool_prompt_format = tool_prompt_format or ToolPromptFormat.json tokens = [] images = [] tokens.append(self.tokenizer.special_tokens["<|begin_of_text|>"]) for message in messages: toks, imgs = self.encode_message(message, tool_prompt_format) tokens.extend(toks) images.extend(imgs) # Add the start of an assistant message for the model to complete. tokens.extend(self._encode_header("assistant")) return self._model_input_from_tokens_images(tokens, images) # TODO(this should be generic, not only for assistant messages) def decode_assistant_message(self, tokens: List[int], stop_reason: StopReason) -> RawMessage: content = self.tokenizer.decode(tokens) return self.decode_assistant_message_from_content(content, stop_reason) def decode_assistant_message_from_content(self, content: str, stop_reason: StopReason) -> RawMessage: content = content.strip(" ") header_str = self.possible_headers[Role.assistant] if content.startswith(header_str): content = content[len(header_str) :] ipython = content.startswith("<|python_tag|>") if ipython: content = content[len("<|python_tag|>") :] if content.endswith("<|eot_id|>"): content = content[: -len("<|eot_id|>")] stop_reason = StopReason.end_of_turn elif content.endswith("<|eom_id|>"): content = content[: -len("<|eom_id|>")] stop_reason = StopReason.end_of_message tool_name = None tool_arguments = {} custom_tool_info = ToolUtils.maybe_extract_custom_tool_call(content) if custom_tool_info is not None: tool_name, tool_arguments = custom_tool_info # Sometimes when agent has custom tools alongside builin tools # Agent responds for builtin tool calls in the format of the custom tools # This code tries to handle that case if tool_name in BuiltinTool.__members__: tool_name = BuiltinTool[tool_name] tool_arguments = { "query": list(tool_arguments.values())[0], } else: builtin_tool_info = ToolUtils.maybe_extract_builtin_tool_call(content) if builtin_tool_info is not None: tool_name, query = builtin_tool_info tool_arguments = { "query": query, } if tool_name in BuiltinTool.__members__: tool_name = BuiltinTool[tool_name] elif ipython: tool_name = BuiltinTool.code_interpreter tool_arguments = { "code": content, } tool_calls = [] if tool_name is not None and tool_arguments is not None: call_id = str(uuid.uuid4()) tool_calls.append( ToolCall( call_id=call_id, tool_name=tool_name, arguments=tool_arguments, ) ) content = "" return RawMessage( role="assistant", content=content, stop_reason=stop_reason, tool_calls=tool_calls, ) def _model_input_from_tokens_images(self, tokens: List[int], images: List[PIL_Image.Image]) -> LLMInput: vision_input = None if len(images) > 0: vision_input = VisionInput( mask=create_vision_mask(tokens, self.vision_token), images=images, ) return LLMInput( tokens=[128256 if token == self.vision_token else token for token in tokens], vision=vision_input, ) def create_vision_mask( tokens: List[int], vision_token: int, ) -> List[List[int]]: vision_token_locations = [i for i, token in enumerate(tokens) if token == vision_token] if len(vision_token_locations) == 0: return [] if len(vision_token_locations) == 1: # only one image present, unmask until end of sequence return [[vision_token_locations[0], -1]] vision_masks = [[loc1, loc2] for loc1, loc2 in zip(vision_token_locations[:-1], vision_token_locations[1:])] # last image will attend to all subsequent text vision_masks.append([vision_token_locations[-1], len(tokens)]) # if there are two or more consecutive vision tokens, # they should all attend to all subsequent # text present last_mask_end = vision_masks[-1][1] for vision_mask in vision_masks[::-1]: if vision_mask[0] == vision_mask[1] - 1: vision_mask[1] = last_mask_end last_mask_end = vision_mask[1] return vision_masks ================================================ FILE: models/llama3/generation.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import json import os import sys import time from pathlib import Path from packaging import version from typing import Callable, Generator, List, Optional import torch import torch.nn.functional as F from fairscale.nn.model_parallel.initialize import ( initialize_model_parallel, model_parallel_is_initialized, ) from termcolor import cprint from ..checkpoint import maybe_reshard_state_dict from ..datatypes import GenerationResult, QuantizationMode, RawContent, RawMessage, ToolPromptFormat from .args import ModelArgs from .chat_format import ChatFormat, LLMInput from .model import Transformer from .multimodal.model import CrossAttentionTransformer from .tokenizer import Tokenizer def is_xccl_available(): if version.parse(torch.__version__).release >= version.parse("2.7").release: return torch.distributed.distributed_c10d.is_xccl_available() return False class Llama3: @staticmethod def build( ckpt_dir: str, max_seq_len: int, max_batch_size: int, world_size: Optional[int] = None, quantization_mode: Optional[QuantizationMode] = None, seed: int = 1, device: str = "cuda", ): device = torch.device(device) if ( device.type == "cuda" and not torch.cuda.is_available() or device.type == "xpu" and not torch.xpu.is_available() ): raise RuntimeError(f"PyTorch backend for {device.type} device type is not available") if not torch.distributed.is_initialized(): if device.type == "cuda": torch.distributed.init_process_group("nccl") elif device.type == "xpu" and is_xccl_available(): torch.distributed.init_process_group("xccl") else: torch.distributed.init_process_group("gloo") if not model_parallel_is_initialized(): if world_size is None: world_size = int(os.environ.get("WORLD_SIZE", 1)) initialize_model_parallel(world_size) local_rank = int(os.environ.get("LOCAL_RANK", 0)) if device.type == "cuda": torch.cuda.set_device(local_rank) elif device.type == "xpu": torch.xpu.set_device(local_rank) torch.manual_seed(seed) if local_rank > 0: sys.stdout = open(os.devnull, "w") start_time = time.time() ckpt_paths = sorted(Path(ckpt_dir).glob("*.pth")) assert len(ckpt_paths) > 0, f"no checkpoint files found in {ckpt_dir}" print(f"Loading a checkpoint (shards={len(ckpt_paths)}, current-mp-size={world_size})") with open(Path(ckpt_dir) / "params.json", "r") as f: params = json.loads(f.read()) model_args: ModelArgs = ModelArgs( max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params, ) tokenizer = Tokenizer.get_instance() state_dict = maybe_reshard_state_dict( ckpt_paths, n_kv_heads=model_args.n_kv_heads if model_args.n_kv_heads else model_args.n_heads, ) assert model_args.vocab_size == tokenizer.n_words def build_model(): if model_args.vision_chunk_size > 0: model = CrossAttentionTransformer(model_args) model.setup_cache(model_args.max_batch_size, device=device, dtype=torch.get_default_dtype()) else: model = Transformer(model_args) return model if quantization_mode == QuantizationMode.fp8_mixed or quantization_mode == QuantizationMode.int4_mixed: from .quantization.loader import convert_to_quantized_model torch.set_default_tensor_type(torch.BFloat16Tensor) model = build_model() print("Loading state dict...") model.load_state_dict(state_dict, strict=False) print("Done...") model = convert_to_quantized_model(model, ckpt_dir, quantization_mode, device=device) torch.set_default_device(device) else: print(f"Setting default device to {device}") torch.set_default_device(device) if device.type == "cuda": if torch.cuda.is_bf16_supported(): torch.set_default_dtype(torch.bfloat16) else: torch.set_default_dtype(torch.half) elif device.type == "xpu": if torch.xpu.is_bf16_supported(): torch.set_default_dtype(torch.bfloat16) else: torch.set_default_dtype(torch.half) model = build_model() print("Loading state dict...") model.load_state_dict(state_dict, strict=True) model.to(device) print("Done...") print(f"Loaded in {time.time() - start_time:.2f} seconds") return Llama3(model, tokenizer, model_args) def __init__(self, model: Transformer | CrossAttentionTransformer, tokenizer: Tokenizer, args: ModelArgs): self.args = args self.model = model self.tokenizer = tokenizer self.formatter = ChatFormat(tokenizer) @torch.inference_mode() def generate( self, model_inputs: List[LLMInput], temperature: float = 0.6, top_p: float = 0.9, max_gen_len: Optional[int] = None, logprobs: bool = False, echo: bool = False, print_model_input: bool = False, logits_processor: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, ) -> Generator[List[GenerationResult], None, None]: if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.args.max_seq_len: max_gen_len = self.args.max_seq_len - 1 params = self.model.params print_model_input = print_model_input or os.environ.get("LLAMA_MODELS_DEBUG", "0") == "1" if print_model_input: for inp in model_inputs: tokens_to_print = [self.formatter.vision_token if t == 128256 else t for t in inp.tokens] cprint( "Input to model:\n" + self.tokenizer.decode(tokens_to_print) + "\n", "red", ) prompt_tokens = [inp.tokens for inp in model_inputs] bsz = len(model_inputs) assert bsz <= params.max_batch_size, (bsz, params.max_batch_size) min_prompt_len = min(len(t) for t in prompt_tokens) max_prompt_len = max(len(t) for t in prompt_tokens) if max_prompt_len >= params.max_seq_len: cprint(f"Out of token budget {max_prompt_len} vs {params.max_seq_len}", "red") return total_len = min(max_gen_len + max_prompt_len, params.max_seq_len) pad_id = self.tokenizer.pad_id tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long) for k, t in enumerate(prompt_tokens): tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long) if logprobs: token_logprobs = torch.zeros_like(tokens, dtype=torch.float) is_vision = not isinstance(self.model, Transformer) if is_vision: images = [inp.vision.images if inp.vision is not None else [] for inp in model_inputs] mask = [inp.vision.mask if inp.vision is not None else [] for inp in model_inputs] xattn_caches, cross_attention_masks, full_text_row_masked_out_mask = self.model.compute_vision_tokens_masks( batch_images=images, batch_masks=mask, total_len=total_len, device=tokens.device, ) eos_reached = torch.tensor([False] * bsz) input_text_mask = tokens != pad_id if echo: for i in range(max_prompt_len): results = [] for j, t in enumerate(tokens[:, i]): results.append( GenerationResult( token=t.item(), text=self.tokenizer.decode([t.item()]), source="input", logprobs=(token_logprobs[j, i : i + 1].tolist() if logprobs else None), batch_idx=j, finished=False, ignore_token=t.item() == pad_id, ) ) yield results stop_tokens = torch.tensor(self.tokenizer.stop_tokens) prev_pos = 0 for cur_pos in range(min_prompt_len, total_len): if is_vision: position_ids = torch.arange(prev_pos, cur_pos, dtype=torch.long) text_only_inference = all(inp.vision is None for inp in model_inputs) logits = self.model.forward( position_ids, tokens, cross_attention_masks, full_text_row_masked_out_mask, xattn_caches, text_only_inference, ) else: logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos) if logits_processor is not None: logits = logits_processor(tokens[:, :cur_pos], logits) if temperature > 0: probs = torch.softmax(logits[:, -1] / temperature, dim=-1) next_token = sample_top_p(probs, top_p) else: next_token = torch.argmax(logits[:, -1], dim=-1) next_token = next_token.reshape(-1) # only replace token if prompt has already been generated next_token = torch.where(input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token) tokens[:, cur_pos] = next_token target = tokens[:, prev_pos + 1 : cur_pos + 1] if is_vision: # the logits space (num_classes) is designed to never contain a media_token # however our input token stream does contain them. we need to nuke them here # or else the CUDA kernels will crash with an illegal memory access vision_tokens = [self.tokenizer.special_tokens["<|image|>"], 128256] masks = [target.eq(t) for t in vision_tokens] if len(masks) > 1: mask = torch.logical_or(*masks) else: mask = masks[0] target[mask] = 0 if logprobs: token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy( input=logits.transpose(1, 2), target=target, reduction="none", ignore_index=pad_id, ) eos_reached |= (~input_text_mask[:, cur_pos]) & (torch.isin(next_token, stop_tokens)) results = [] for idx, t in enumerate(next_token): results.append( GenerationResult( token=t.item(), text=self.tokenizer.decode([t.item()]), source="output", logprobs=(token_logprobs[idx, cur_pos : cur_pos + 1].tolist() if logprobs else None), batch_idx=idx, finished=eos_reached[idx], ignore_token=cur_pos < len(prompt_tokens[idx]), ) ) yield results prev_pos = cur_pos if all(eos_reached): break def completion( self, contents: List[RawContent], temperature: float = 0.6, top_p: float = 0.9, max_gen_len: Optional[int] = None, logprobs: bool = False, echo: bool = False, ) -> Generator[List[GenerationResult], None, None]: model_inputs = [self.formatter.encode_content(c) for c in contents] for result in self.generate( model_inputs=model_inputs, temperature=temperature, top_p=top_p, max_gen_len=max_gen_len, logprobs=logprobs, echo=echo, ): yield result if all(r.finished for r in result): break def chat_completion( self, messages_batch: List[List[RawMessage]], temperature: float = 0.6, top_p: float = 0.9, max_gen_len: Optional[int] = None, logprobs: bool = False, tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json, echo: bool = False, ) -> Generator[List[GenerationResult], None, None]: model_inputs = [self.formatter.encode_dialog_prompt(messages) for messages in messages_batch] for result in self.generate( model_inputs=model_inputs, temperature=temperature, top_p=top_p, max_gen_len=max_gen_len, logprobs=logprobs, echo=echo, ): yield result if all(r.finished for r in result): break def sample_top_p(probs, p): """ Perform top-p (nucleus) sampling on a probability distribution. Args: probs (torch.Tensor): Probability distribution tensor. p (float): Probability threshold for top-p sampling. Returns: torch.Tensor: Sampled token indices. Note: Top-p sampling selects the smallest set of tokens whose cumulative probability mass exceeds the threshold p. The distribution is renormalized based on the selected tokens. """ probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True) probs_sum = torch.cumsum(probs_sort, dim=-1) mask = probs_sum - probs_sort > p probs_sort[mask] = 0.0 probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True)) next_token = torch.multinomial(probs_sort, num_samples=1) next_token = torch.gather(probs_idx, -1, next_token) return next_token ================================================ FILE: models/llama3/model.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # Copyright (c) Meta Platforms, Inc. and affiliates. # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement. import math from typing import Optional, Tuple import fairscale.nn.model_parallel.initialize as fs_init import torch import torch.nn.functional as F from fairscale.nn.model_parallel.layers import ( ColumnParallelLinear, RowParallelLinear, VocabParallelEmbedding, ) from torch import nn from .args import ModelArgs # **NOTE**: This code is not runnable without installing `torch` and `fairscale` # dependencies. These dependencies are not part of the default dependencies # (requirements.txt) of the `llama-models` package. class RMSNorm(torch.nn.Module): def __init__(self, dim: int, eps: float = 1e-6): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x): output = self._norm(x.float()).type_as(x) return output * self.weight def apply_scaling(freqs: torch.Tensor) -> torch.Tensor: # Values obtained from grid search scale_factor = 8 low_freq_factor = 1 high_freq_factor = 4 old_context_len = 8192 # original llama3 length low_freq_wavelen = old_context_len / low_freq_factor high_freq_wavelen = old_context_len / high_freq_factor wavelen = 2 * torch.pi / freqs new_freqs = torch.where(wavelen > low_freq_wavelen, freqs / scale_factor, freqs) smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) return torch.where( (wavelen >= high_freq_wavelen) & (wavelen <= low_freq_wavelen), (1 - smooth) * new_freqs / scale_factor + smooth * new_freqs, new_freqs, ) def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, use_scaled: bool = False): freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) t = torch.arange(end, device=freqs.device, dtype=torch.float32) if use_scaled: freqs = apply_scaling(freqs) freqs = torch.outer(t, freqs) freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 return freqs_cis def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): ndim = x.ndim assert 0 <= 1 < ndim assert freqs_cis.shape == (x.shape[1], x.shape[-1]) shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] return freqs_cis.view(*shape) def apply_rotary_emb( xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor]: xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) freqs_cis = reshape_for_broadcast(freqs_cis, xq_) xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) return xq_out.type_as(xq), xk_out.type_as(xk) def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor: """torch.repeat_interleave(x, dim=2, repeats=n_rep)""" bs, slen, n_kv_heads, head_dim = x.shape if n_rep == 1: return x return ( x[:, :, :, None, :] .expand(bs, slen, n_kv_heads, n_rep, head_dim) .reshape(bs, slen, n_kv_heads * n_rep, head_dim) ) class Attention(nn.Module): def __init__(self, args: ModelArgs): super().__init__() self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads world_size = fs_init.get_model_parallel_world_size() self.n_local_heads = args.n_heads // world_size self.n_local_kv_heads = self.n_kv_heads // world_size self.n_rep = self.n_local_heads // self.n_local_kv_heads self.head_dim = args.dim // args.n_heads self.wq = ColumnParallelLinear( args.dim, args.n_heads * self.head_dim, bias=False, gather_output=False, init_method=lambda x: x, ) self.wk = ColumnParallelLinear( args.dim, self.n_kv_heads * self.head_dim, bias=False, gather_output=False, init_method=lambda x: x, ) self.wv = ColumnParallelLinear( args.dim, self.n_kv_heads * self.head_dim, bias=False, gather_output=False, init_method=lambda x: x, ) self.wo = RowParallelLinear( args.n_heads * self.head_dim, args.dim, bias=False, input_is_parallel=True, init_method=lambda x: x, ) self.cache_k = torch.zeros( ( args.max_batch_size, args.max_seq_len, self.n_local_kv_heads, self.head_dim, ) ) self.cache_v = torch.zeros( ( args.max_batch_size, args.max_seq_len, self.n_local_kv_heads, self.head_dim, ) ) def forward( self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor], ): bsz, seqlen, _ = x.shape xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis) self.cache_k = self.cache_k.to(xq) self.cache_v = self.cache_v.to(xq) self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv keys = self.cache_k[:bsz, : start_pos + seqlen] values = self.cache_v[:bsz, : start_pos + seqlen] # repeat k/v heads if n_kv_heads < n_heads keys = repeat_kv(keys, self.n_rep) # (bs, cache_len + seqlen, n_local_heads, head_dim) values = repeat_kv(values, self.n_rep) # (bs, cache_len + seqlen, n_local_heads, head_dim) xq = xq.transpose(1, 2) # (bs, n_local_heads, seqlen, head_dim) keys = keys.transpose(1, 2) # (bs, n_local_heads, cache_len + seqlen, head_dim) values = values.transpose(1, 2) # (bs, n_local_heads, cache_len + seqlen, head_dim) scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(self.head_dim) if mask is not None: scores = scores + mask # (bs, n_local_heads, seqlen, cache_len + seqlen) scores = F.softmax(scores.float(), dim=-1).type_as(xq) output = torch.matmul(scores, values) # (bs, n_local_heads, seqlen, head_dim) output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1) return self.wo(output) class FeedForward(nn.Module): def __init__( self, dim: int, hidden_dim: int, multiple_of: int, ffn_dim_multiplier: Optional[float], ): super().__init__() hidden_dim = int(2 * hidden_dim / 3) # custom dim factor multiplier if ffn_dim_multiplier is not None: hidden_dim = int(ffn_dim_multiplier * hidden_dim) hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) self.w1 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x) self.w2 = RowParallelLinear(hidden_dim, dim, bias=False, input_is_parallel=True, init_method=lambda x: x) self.w3 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x) def forward(self, x): return self.w2(F.silu(self.w1(x)) * self.w3(x)) class TransformerBlock(nn.Module): def __init__(self, layer_id: int, args: ModelArgs): super().__init__() self.n_heads = args.n_heads self.dim = args.dim self.head_dim = args.dim // args.n_heads self.attention = Attention(args) self.feed_forward = FeedForward( dim=args.dim, hidden_dim=4 * args.dim, multiple_of=args.multiple_of, ffn_dim_multiplier=args.ffn_dim_multiplier, ) self.layer_id = layer_id self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps) self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps) def forward( self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor], ): h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask) out = h + self.feed_forward(self.ffn_norm(h)) return out class Transformer(nn.Module): def __init__(self, params: ModelArgs): super().__init__() self.params = params self.vocab_size = params.vocab_size self.n_layers = params.n_layers self.tok_embeddings = VocabParallelEmbedding(params.vocab_size, params.dim, init_method=lambda x: x) self.layers = torch.nn.ModuleList() for layer_id in range(params.n_layers): self.layers.append(TransformerBlock(layer_id, params)) self.norm = RMSNorm(params.dim, eps=params.norm_eps) self.output = ColumnParallelLinear(params.dim, params.vocab_size, bias=False, init_method=lambda x: x) self.freqs_cis = precompute_freqs_cis( params.dim // params.n_heads, params.max_seq_len * 2, params.rope_theta, params.use_scaled_rope, ) @torch.inference_mode() def forward(self, tokens: torch.Tensor, start_pos: int): _bsz, seqlen = tokens.shape h = self.tok_embeddings(tokens) self.freqs_cis = self.freqs_cis.to(h.device) freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen] mask = None if seqlen > 1: mask = torch.full((seqlen, seqlen), float("-inf"), device=tokens.device) mask = torch.triu(mask, diagonal=1) # https://github.com/pytorch/pytorch/issues/100005 # torch.triu is buggy when the device is mps: filled values are # nan instead of 0. if mask.device.type == torch.device("mps").type: mask = torch.nan_to_num(mask, nan=0.0) # When performing key-value caching, we compute the attention scores # only for the new sequence. Thus, the matrix of scores is of size # (seqlen, cache_len + seqlen), and the only masked entries are (i, j) for # j > cache_len + i, since row i corresponds to token cache_len + i. mask = torch.hstack([torch.zeros((seqlen, start_pos), device=tokens.device), mask]).type_as(h) for layer in self.layers: h = layer(h, start_pos, freqs_cis, mask) h = self.norm(h) output = self.output(h).float() return output ================================================ FILE: models/llama3/multimodal/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. ================================================ FILE: models/llama3/multimodal/encoder_utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # Copyright (c) Meta Platforms, Inc. and its affiliates. import math from logging import getLogger import torch import torch.nn.functional as F from .utils import get_negative_inf_value, to_2tuple logger = getLogger() def resize_local_position_embedding(orig_pos_embed, grid_size): """ Resize position embedding for vision encoder. Original position embedding is [n_tiles * n_tiles + 1, dim] New position embedding will be [grid_size[0] * grid_size[1] + 1, dim] """ new_grid_size = to_2tuple(grid_size) orig_grid_size = to_2tuple(int(math.sqrt(len(orig_pos_embed) - 1))) new_seq_len = new_grid_size[0] * new_grid_size[1] + 1 new_pos_emb_tok, new_pos_emb_img = ( orig_pos_embed[:1], orig_pos_embed[1:], ) logger.info(f"resizing position embedding grid-size from {orig_grid_size} to {new_grid_size}") new_pos_emb_img = new_pos_emb_img.reshape(1, orig_grid_size[0], orig_grid_size[1], -1).permute(0, 3, 1, 2) new_pos_emb_img = F.interpolate( new_pos_emb_img, size=new_grid_size, mode="bilinear", align_corners=True, ) new_pos_emb_img = new_pos_emb_img.permute(0, 2, 3, 1).reshape(1, new_grid_size[0] * new_grid_size[1], -1)[0] new_pos_embed = torch.cat([new_pos_emb_tok, new_pos_emb_img], dim=0) return new_pos_embed def initialize_global_position_embedding_from_local(pos_and_cls_embed, grid_size, x_scale, y_scale): """ Takes a local position embedding for vision encoder and uses it to initialize the global position embedding. Input: local position embedding of shape [grid_size[0] * grid_size[1] + 1, dim] Returns: global position embedding of shape [x_scale, y_scale, grid_size[0] * grid_size[1] + 1, dim] Here x_scale and y_scale are the number of tiles along x-axis and y-axis respectively. """ pos_embed = pos_and_cls_embed[1:] cls_embed = pos_and_cls_embed[0].view(1, 1, 1, -1) grid_size = to_2tuple(grid_size) new_pos_emb_img = pos_embed.reshape(1, grid_size[0], grid_size[1], -1).permute(0, 3, 1, 2) new_grid_size = (x_scale * grid_size[0], y_scale * grid_size[1]) new_pos_emb_img = F.interpolate( new_pos_emb_img, size=new_grid_size, mode="bilinear", align_corners=True, ) new_pos_emb_img = new_pos_emb_img.permute(0, 2, 3, 1) new_pos_emb_img = new_pos_emb_img.view(x_scale, grid_size[0], y_scale, grid_size[1], -1) new_pos_emb_img = new_pos_emb_img.permute(0, 2, 1, 3, 4).contiguous() new_pos_emb_img = new_pos_emb_img.reshape(x_scale, y_scale, grid_size[0] * grid_size[1], -1) cls_embed = cls_embed.expand(x_scale, y_scale, -1, -1) pos_and_cls_embed = torch.cat([cls_embed, new_pos_emb_img], dim=2) return pos_and_cls_embed def resize_global_position_embedding(pos_and_cls_embed, grid_size, x_scale, y_scale): """ Takes a global position embedding for vision encoder and resizes it to new size. Input: global position embedding of shape [x_old, y_old, old_grid_size[0] * old_grid_size[1] + 1, dim] Returns: global position embedding of shape [x_scale, y_scale, grid_size[0] * grid_size[1] + 1, dim] Here x_scale and y_scale are the number of tiles along x-axis and y-axis respectively. """ # first remove cls token pos_embed = pos_and_cls_embed[:, :, 1:] cls_embed = pos_and_cls_embed[:, :, 0].unsqueeze(2) xs_old, ys_old, ntok, dim = pos_embed.shape old_grid_size = int(math.sqrt(ntok)) # move to correct form for interpolation pos_embed = pos_embed.view(xs_old, ys_old, old_grid_size, old_grid_size, dim) pos_embed = pos_embed.permute(0, 2, 1, 3, 4).contiguous() pos_embed = pos_embed.view(xs_old * old_grid_size, ys_old * old_grid_size, dim) pos_embed = pos_embed.unsqueeze(0) # interpolate new_size = (grid_size[0] * x_scale, grid_size[1] * y_scale) pos_embed = pos_embed.permute(0, 3, 1, 2) pos_embed_resized = F.interpolate( pos_embed, size=new_size, mode="bilinear", align_corners=True, ) pos_embed = pos_embed_resized.permute(0, 2, 3, 1)[0] # move it back in place pos_embed = pos_embed.view(x_scale, grid_size[0], y_scale, grid_size[1], dim) pos_embed = pos_embed.permute(0, 2, 1, 3, 4).contiguous() pos_embed = pos_embed.view(x_scale, y_scale, grid_size[0] * grid_size[1], dim) # interpolate cls token cls_embed = cls_embed.permute(2, 3, 0, 1) cls_embed_resized = F.interpolate( cls_embed, size=(x_scale, y_scale), mode="bilinear", align_corners=True, ) cls_embed = cls_embed_resized.permute(2, 3, 0, 1) # add cls token back in pos_and_cls_embed = torch.cat([cls_embed, pos_embed], dim=2) return pos_and_cls_embed def build_encoder_attention_mask( x: torch.Tensor, ar: torch.Tensor, ntok: int, num_chunks: int, n_heads: int, ): """ Build vision encoder attention mask that omits padding tokens. """ masks = [] for arx in ar: mask_i = torch.ones((num_chunks, x.shape[2], 1), dtype=x.dtype) mask_i[: arx[0] * arx[1], :ntok] = 0 mask_i = mask_i.view(num_chunks * x.shape[2], -1) mask_i = mask_i @ mask_i.T * get_negative_inf_value(x.dtype) mask_i = mask_i.unsqueeze(0) masks.append(mask_i) masks = torch.stack(masks).to(x.device).expand(-1, n_heads, -1, -1) return masks def expand_num_tokens_to_mult8(x): num_pad_tokens = 8 - (x.shape[-2] % 8) if num_pad_tokens == 0: return x, 0 else: return ( torch.cat( [ x, torch.zeros( (x.shape[0], x.shape[1], num_pad_tokens, x.shape[-1]), dtype=x.dtype, device=x.device, ), ], dim=-2, ), num_pad_tokens, ) def contract_num_tokens_from_mult8(x, num_pad_tokens): if num_pad_tokens == 0: return x return x[:, :, :-num_pad_tokens] ================================================ FILE: models/llama3/multimodal/image_transform.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import math from collections import defaultdict from logging import getLogger from typing import Any, Optional, Set, Tuple import torch import torchvision.transforms as tv from PIL import Image from torchvision.transforms import functional as F IMAGE_RES = 224 logger = getLogger() class VariableSizeImageTransform(object): """ This class accepts images of any size and dynamically resize, pads and chunks it based on the image aspect ratio and the number of image chunks we allow. The algorithm will NOT distort the image fit a certain aspect ratio, because that leads to a significant degradation in image quality. It can be summarized in 6 steps: 1. Find all possible canvas combinations of max_num_chunks; 2. Find the best canvas to fit the image; 3. Resize without distortion 4. Pad 5. Normalize 6. Chunk For example, if an input image is of size 300x800, patch_size of 224, and max_num_chunks = 8, it will find the closest aspect ratio that is allowed within 8 image chunks, with some restrictions. In this case, 2:4 = 2 horizontal patches and 4 vertical patches, giving a total of 8 chunks. If resize_to_max_canvas, the image will be resized (without distortion), to the largest possible resolution. In this case, 388:896, and padded to 448:896, where we maintain the original aspect ratio and pad with zeros value for the rest. This approach minimizes the amount of padding required for any arbitrary resolution. However, if limit_upscaling_to_patch_size is set to True, the upscaling will be limited to the patch size. In the example above, the image would remain 300x800 (no upscaling), and then padded to 448:896. The final output will therefore be of shape (8, 3, 224, 224), where 2x4 patches are coming from the resizing and chunking. """ def __init__(self, size: int = IMAGE_RES) -> None: self.size = size logger.info(f"VariableSizeImageTransform size: {self.size}") self.to_tensor = tv.ToTensor() self._mean = (0.48145466, 0.4578275, 0.40821073) self._std = (0.26862954, 0.26130258, 0.27577711) self.normalize = tv.Normalize( mean=self._mean, std=self._std, inplace=True, ) self.resample = tv.InterpolationMode.BILINEAR @staticmethod def get_factors(n: int) -> Set[int]: """ Calculate all factors of a given number, i.e. a dividor that leaves no remainder. For example, if n=12, it will return {1, 2, 3, 4, 6, 12}. Args: n (int): The number to find factors for. Returns: set: A set containing all factors of the number. """ factors_set = set() for i in range(1, int(n**0.5) + 1): if n % i == 0: factors_set.add(i) factors_set.add(n // i) return factors_set def find_supported_resolutions(self, max_num_chunks: int, patch_size: int) -> torch.Tensor: """ Computes all of the allowed resoltuions for a fixed number of chunks and patch_size. Useful for when dividing an image into chunks. Args: max_num_chunks (int): Maximum number of chunks for processing. patch_size (int): Size of the side of the patch. Returns: torch.Tensor: List of possible resolutions as tuples (height, width). Example: >>> max_num_chunks = 5 >>> patch_size = 224 >>> find_supported_resolutions(max_num_chunks, patch_size) tensor([(224, 896), (448, 448), (224, 224), (896, 224), (224, 672), (672, 224), (224, 448), (448, 224)]) Given max_num_chunks=4, patch_size=224, it will create a dictionary: { 0.25: [(1, 4)], 1.0: [(2, 2), (1, 1)], 4.0: [(4, 1)], 0.33: [(1, 3)], 3.0: [(3, 1)], 0.5: [(1, 2)], 2.0: [(2, 1)] } and return the resolutions multiplied by the patch_size: [(1*224, 4*224), (2*224, 2*224), ..., (2*224, 1*224)] """ asp_dict = defaultdict(list) for chunk_size in range(max_num_chunks, 0, -1): _factors = sorted(self.get_factors(chunk_size)) _asp_ratios = [(factor, chunk_size // factor) for factor in _factors] for height, width in _asp_ratios: ratio_float = height / width asp_dict[ratio_float].append((height, width)) # get the resolutions multiplied by the patch_size possible_resolutions = [] for key, value in asp_dict.items(): for height, depth in value: possible_resolutions.append((height * patch_size, depth * patch_size)) return possible_resolutions @staticmethod def get_max_res_without_distortion( image_size: Tuple[int, int], target_size: Tuple[int, int], ) -> Tuple[int, int]: """ Determines the maximum resolution to which an image can be resized to without distorting its aspect ratio, based on the target resolution. Args: image_size (Tuple[int, int]): The original resolution of the image (height, width). target_resolution (Tuple[int, int]): The desired resolution to fit the image into (height, width). Returns: Tuple[int, int]: The optimal dimensions (height, width) to which the image should be resized. Example: >>> _get_max_res_without_distortion([200, 300], target_size = [450, 200]) (134, 200) >>> _get_max_res_without_distortion([800, 600], target_size = [450, 1300]) (450, 338) """ original_width, original_height = image_size target_width, target_height = target_size scale_w = target_width / original_width scale_h = target_height / original_height if scale_w < scale_h: new_width = target_width new_height = min(math.floor(original_height * scale_w), target_height) else: new_height = target_height new_width = min(math.floor(original_width * scale_h), target_width) return new_width, new_height def _pad(self, image: Image.Image, target_size) -> Image.Image: new_width, new_height = target_size new_im = Image.new(mode="RGB", size=(new_width, new_height), color=(0, 0, 0)) # type: ignore new_im.paste(image) return new_im def _split(self, image: torch.Tensor, ncw: int, nch: int) -> torch.Tensor: # Split image into number of required tiles (width x height) num_channels, height, width = image.size() image = image.view(num_channels, nch, height // nch, ncw, width // ncw) # Permute dimensions to reorder the axes image = image.permute(1, 3, 0, 2, 4).contiguous() # Reshape into the desired output shape (batch_size * 4, num_channels, width/2, height/2) image = image.view(ncw * nch, num_channels, height // nch, width // ncw) return image def resize_without_distortion( self, image: torch.Tensor, target_size: Tuple[int, int], max_upscaling_size: Optional[int], ) -> torch.Tensor: """ Used to resize an image to target_resolution, without distortion. If target_size requires upscaling the image, the user can set max_upscaling_size to limit the upscaling to a maximum size. In this case, since we rescale without distortion, modifying target_size works as a boundary for the image's largest side. Args: resample (str): Resampling method used when resizing images. Supports "nearest", "nearest_exact", "bilinear", "bicubic". max_upscaling_size (int): The maximum size to upscale the image to. If None, there is no limit. Examples: >>> target_size = (1000, 1200) >>> max_upscaling_size = 600 >>> image_size = (400, 200) >>> resize_without_distortion(image_size, target_size, max_upscaling_size) (600, 300) # new_size_without_distortion >>> target_size = (1000, 1200) >>> max_upscaling_size = 600 >>> image_size = (2000, 200) >>> resize_without_distortion(image_size, target_size, max_upscaling_size) (1000, 100) # new_size_without_distortion >>> target_size = (1000, 1200) >>> max_upscaling_size = 2000 >>> image_size = (400, 200) >>> resize_without_distortion(image_size, target_size, max_upscaling_size) (1000, 500) # new_size_without_distortion >>> target_size = (1000, 1200) >>> max_upscaling_size = None >>> image_size = (400, 200) >>> resize_without_distortion(image_size, target_size, max_upscaling_size) (1000, 500) # new_size_without_distortion """ image_width, image_height = image.size image_size = (image_width, image_height) # If target_size requires upscaling, we might want to limit the upscaling to max_upscaling_size if max_upscaling_size is not None: new_target_width = min(max(image_width, max_upscaling_size), target_size[0]) new_target_height = min(max(image_height, max_upscaling_size), target_size[1]) target_size = (new_target_width, new_target_height) # resize to target_size while preserving aspect ratio new_size_without_distortion = self.get_max_res_without_distortion(image_size, target_size) image = F.resize( image, (new_size_without_distortion[1], new_size_without_distortion[0]), interpolation=self.resample, ) return image def get_best_fit( self, image_size: Tuple[int, int], possible_resolutions: torch.Tensor, resize_to_max_canvas: bool = False, ) -> Tuple[int, int]: """ Determines the best canvas possible from a list of possible resolutions to, without distortion, resize an image to. For each possible resolution, calculates the scaling factors for width and height, and selects the smallest one, which is the limiting side. E.g. to match the canvas you can upscale height by 2x, and width by 1.5x, therefore, the maximum upscaling you can do is min(2, 1.5) = 1.5. If upscaling is possible (any of the scaling factors is greater than 1), then picks the smallest upscaling factor > 1, unless resize_to_max_canvas is True. If upscaling is not possible, then picks the largest scaling factor <= 1, i.e. reduce downscaling as much as possible. If there are multiple resolutions with the same max scale, we pick the one with the lowest area, to minimize padding. E.g., the same image can be upscaled to 224x224 and 224x448, but the latter has more padding. Args: image_size (Tuple[int, int]): A tuple containing the height and width of the image. possible_resolutions (torch.Tensor): A tensor of shape (N, 2) where each row represents a possible resolution (height, width). use_max_upscaling (bool): If True, will return the largest upscaling resolution. Returns: List[int]: The best resolution [height, width] for the given image. Example: >>> image_size = (200, 300) >>> possible_resolutions = torch.tensor([[224, 672], ... [672, 224], ... [224, 448], ... [448, 224], ... [224, 224]]) >>> _get_smallest_upscaling_possibility(image_size, possible_resolutions) [224, 448] We have: scale_w = tensor([2.2400, 0.7467, 1.4933, 0.7467, 0.7467]) scale_h = tensor([1.1200, 3.3600, 1.1200, 2.2400, 1.1200]) scales = tensor([1.1200, 0.7467, 1.1200, 0.7467, 0.7467]) Only one of the scales > 1: upscaling_possible = tensor([1.1200, 1.1200]) smallest_rescale = tensor(1.1200) So we pick the resolution with the smallest smallest area: areas = tensor([150528, 100352]) # [672, 224], [224, 448] optimal_canvas = tensor([224, 448]) """ original_width, original_height = image_size # get all possible resolutions heights/widths target_widths, target_heights = ( possible_resolutions[:, 0], possible_resolutions[:, 1], ) # get scaling factors to resize the image without distortion scale_w = target_widths / original_width scale_h = target_heights / original_height # get the min scale between width and height (limiting side -> no distortion) scales = torch.where(scale_w > scale_h, scale_h, scale_w) # filter only scales that allow upscaling upscaling_options = scales[scales >= 1] if len(upscaling_options) > 0: if resize_to_max_canvas: selected_scale = torch.max(upscaling_options) else: selected_scale = torch.min(upscaling_options) else: # no upscaling possible, # get the minimum downscaling (max scale for scales<1) downscaling_options = scales[scales < 1] selected_scale = torch.max(downscaling_options) # get all resolutions that support this scaling factor, # e.g. you can upscale to 224x224, 224x448, 224x672 without distortion chosen_canvas = possible_resolutions[scales == selected_scale] # if there are multiple resolutions, # get the one with minimum area to reduce padding if len(chosen_canvas) > 1: areas = chosen_canvas[:, 0] * chosen_canvas[:, 1] optimal_idx = torch.argmin(areas) optimal_canvas = chosen_canvas[optimal_idx] else: optimal_canvas = chosen_canvas[0] return tuple(optimal_canvas.tolist()) def __call__( self, image: Image.Image, max_num_chunks: int, normalize_img: bool = True, resize_to_max_canvas: bool = False, ) -> Tuple[Any, Any]: """ Args: image (PIL.Image): Image to be resized. max_num_chunks (int): Maximum number of chunks to split the image into. normalize_img (bool): Whether to normalize the image. resize_to_max_canvas (bool): Whether to resize the image to the maximum canvas size. If True, picks the canvas the allows the largest resizing without distortion. If False, downsample as little as possible, including no resizing at all, but never upsample, unless the image is smaller than the patch size. """ assert max_num_chunks > 0 assert isinstance(image, Image.Image), type(image) w, h = image.size possible_resolutions = self.find_supported_resolutions(max_num_chunks=max_num_chunks, patch_size=self.size) possible_resolutions = torch.tensor(possible_resolutions) best_resolution = self.get_best_fit( image_size=(w, h), possible_resolutions=possible_resolutions, resize_to_max_canvas=resize_to_max_canvas, ) max_upscaling_size = None if resize_to_max_canvas else self.size image = self.resize_without_distortion(image, best_resolution, max_upscaling_size) image = self._pad(image, best_resolution) image = self.to_tensor(image) if normalize_img: image = self.normalize(image) ratio_w, ratio_h = ( best_resolution[0] // self.size, best_resolution[1] // self.size, ) image = self._split(image, ratio_w, ratio_h) # type: ignore ar = (ratio_h, ratio_w) return image, ar ================================================ FILE: models/llama3/multimodal/model.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import logging import math from functools import partial from typing import Any, Callable, Dict, List, Optional, Tuple, Union import fairscale.nn.model_parallel.initialize as fs_init import torch import torch.nn.functional as F from fairscale.nn.model_parallel.layers import ( ColumnParallelLinear, RowParallelLinear, VocabParallelEmbedding, ) from PIL import Image as PIL_Image from torch import Tensor, nn from torch.distributed import _functional_collectives as funcol from ..model import ModelArgs, RMSNorm, apply_rotary_emb, precompute_freqs_cis from .encoder_utils import ( build_encoder_attention_mask, contract_num_tokens_from_mult8, expand_num_tokens_to_mult8, initialize_global_position_embedding_from_local, resize_global_position_embedding, resize_local_position_embedding, ) from .image_transform import VariableSizeImageTransform from .utils import get_negative_inf_value, to_2tuple logger = logging.getLogger(__name__) MP_SCALE = 8 def reduce_from_tensor_model_parallel_region(input_): """All-reduce the input tensor across model parallel group.""" output = funcol.all_reduce(input_, "sum", group=fs_init.get_model_parallel_group()) output = funcol.wait_tensor(output) return output def gather_from_tensor_model_parallel_region(input_): """Gather tensors and concatenate along the last dimension.""" world_size = fs_init.get_model_parallel_world_size() # Size and dimension. last_dim = input_.dim() - 1 rank = fs_init.get_model_parallel_rank() tensor_list = [torch.empty_like(input_) for _ in range(world_size)] tensor_list[rank] = input_ output = funcol.all_gather_tensor( input_, gather_dim=last_dim, group=fs_init.get_model_parallel_group(), ) output = funcol.wait_tensor(output) return output def _get_full_row_masked_out_mask( attn_bias, negative_inf_value, ): """ attn_bias should be a 4D tensor of shape [B, H, S1, S2] where B is the batch size, H is the number of heads, and S1/S2 are the sequence lengths. This returns a 4D tensor of shape [B, H, S1, 1] which stores boolean values which are 0 if the a full row in the last dimension contains negative infinity values, otherwise it's 1. """ return (attn_bias != negative_inf_value).any(dim=-1).type_as(attn_bias)[..., None] # Image encoder for inference class LayerNorm(nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16.""" def forward(self, x: torch.Tensor): x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) return x class ColumnParallelConv2dPatch(torch.nn.Module): """Conv2D Patching layer with model parallelism. Column parallel over unfolded input. Arguments: in_channels: Input channels. out_channels: Output channels. kernel_size: Size of convolution kernel. stride (default 1): Stride for convolution. bias (default False): Use bias in Conv2d. Input: (bsz, in_channels, width, height) Output: (bsz, num_tokens, out_channels) """ def __init__( self, in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, int]], stride: Union[int, Tuple[int, int]], bias: Optional[bool] = False, ) -> None: super().__init__() if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size) self._unfold = torch.nn.Unfold(kernel_size=kernel_size, stride=stride) self._linear = ColumnParallelLinear( in_channels * kernel_size[0] * kernel_size[1], out_channels, bias=bias, ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self._unfold(x) x = x.permute(0, 2, 1) x = F.linear(x, self._linear.weight) x = gather_from_tensor_model_parallel_region(x) return x class ImageFeedForward(torch.nn.Module): def __init__( self, dim: int, hidden_dim: int, dropout: float, act_layer: Callable = nn.GELU, ): super().__init__() # layers self.c_fc = ColumnParallelLinear( dim, hidden_dim, bias=True, gather_output=False, init_method=lambda x: x, ) self.c_proj = RowParallelLinear( hidden_dim, dim, bias=True, input_is_parallel=True, init_method=lambda x: x, ) self.non_linearity = act_layer() self.dropout = dropout def forward(self, x): hidden = F.linear(x, self.c_fc.weight, self.c_fc.bias) hidden = self.non_linearity(hidden) hidden = F.linear(hidden, self.c_proj.weight) hidden = reduce_from_tensor_model_parallel_region(hidden) hidden += self.c_proj.bias return hidden class ImageAttention(nn.Module): def __init__( self, dim, head_dim, n_heads, ): super().__init__() world_size = fs_init.get_model_parallel_world_size() qkvo_replication = 1 if world_size > 16: qkvo_replication = world_size // 8 self.n_kv_heads = n_heads self.n_local_heads = n_heads * qkvo_replication // world_size self.n_local_kv_heads = self.n_kv_heads * qkvo_replication // world_size self.n_rep = self.n_local_heads // self.n_local_kv_heads self.head_dim = dim // n_heads self.wq = ColumnParallelLinear( dim, qkvo_replication * n_heads * self.head_dim, bias=False, gather_output=False, init_method=lambda x: x, ) self.wk = ColumnParallelLinear( dim, qkvo_replication * self.n_kv_heads * self.head_dim, bias=False, gather_output=False, init_method=lambda x: x, ) self.wv = ColumnParallelLinear( dim, qkvo_replication * self.n_kv_heads * self.head_dim, bias=False, gather_output=False, init_method=lambda x: x, ) self.wo = RowParallelLinear( qkvo_replication * n_heads * self.head_dim, dim, bias=False, input_is_parallel=True, init_method=lambda x: x, ) self.qkvo_replication = qkvo_replication def forward( self, x: torch.Tensor, mask: torch.Tensor = None, ): xq, xk, xv = [F.linear(x, w) for w in [self.wq.weight, self.wk.weight, self.wv.weight]] bs, slen, _ = xq.shape xq = xq.view(bs, slen, self.n_local_heads, self.head_dim) xk = xk.view(bs, xk.shape[1], self.n_local_kv_heads, self.head_dim) xv = xv.view(bs, xv.shape[1], self.n_local_kv_heads, self.head_dim) xq, xk, xv = [tensor.transpose(1, 2) for tensor in (xq, xk, xv)] xk = xk.repeat_interleave(self.n_rep, dim=1) xv = xv.repeat_interleave(self.n_rep, dim=1) attn_output = F.scaled_dot_product_attention(xq, xk, xv, attn_mask=mask, dropout_p=0.0) attn_output = attn_output.transpose(1, 2).contiguous().reshape(bs, slen, -1) out = F.linear(attn_output, self.wo.weight) out = reduce_from_tensor_model_parallel_region(out) out = out / self.qkvo_replication return out class ImageTransformerBlock(nn.Module): def __init__( self, d_model: int, n_head: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU, gated: bool = False, ): super().__init__() assert d_model % n_head == 0 self.n_heads = n_head self.head_dim = d_model // self.n_heads self.attn = ImageAttention( dim=d_model, head_dim=self.head_dim, n_heads=self.n_heads, ) self.ln_1 = LayerNorm(d_model) self.mlp = ImageFeedForward( dim=d_model, hidden_dim=int(mlp_ratio * d_model), dropout=0.0, act_layer=act_layer, ) self.ln_2 = LayerNorm(d_model) self.gated = gated if gated: self.gate_attn = nn.Parameter(torch.zeros(1)) self.gate_ffn = nn.Parameter(torch.zeros(1)) def forward( self, x: torch.Tensor, mask: torch.Tensor = None, ): _gate_attn = 1 if not self.gated else self.gate_attn.tanh() _gate_ffn = 1 if not self.gated else self.gate_ffn.tanh() x = x + _gate_attn * self.attn(self.ln_1(x), mask=mask) x = x + _gate_ffn * self.mlp(self.ln_2(x)) return x class ImageTransformer(nn.Module): def __init__( self, width: int, layers: int, heads: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU, gated: bool = False, ): super().__init__() self.width = width self.layers = layers self.resblocks = nn.ModuleList( [ ImageTransformerBlock( d_model=width, n_head=heads, mlp_ratio=mlp_ratio, act_layer=act_layer, gated=gated, ) for _ in range(self.layers) ] ) def forward(self, x: torch.Tensor, return_intermediate=None, mask=None): out = [] for idx, r in enumerate(self.resblocks): if return_intermediate is not None and idx in return_intermediate: out.append(x) x = r(x, mask=mask) if return_intermediate is not None: return x, torch.stack(out, dim=-1) return x class VisionEncoder(nn.Module): def __init__( self, max_num_tiles: int, ckpt_path: str = None, image_size: int = 224, patch_size: int = 14, width: int = 1280, layers: int = 32, heads: int = 16, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU, in_channels: int = 3, load_ckpt: bool = False, n_global_layers: int = 2, global_model: bool = False, return_intermediate=None, ): super().__init__() self.global_model = global_model self.return_intermediate = return_intermediate self.max_num_tiles = max_num_tiles self.image_size = to_2tuple(image_size) self.patch_size = to_2tuple(patch_size) self.grid_size = ( self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1], ) self.conv1 = ColumnParallelConv2dPatch( in_channels=in_channels, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False, ) scale = width**-0.5 self.class_embedding = nn.Parameter(scale * torch.randn(width)) self.positional_embedding = nn.Parameter(scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, width)) self.ln_post = LayerNorm(width) self.ln_pre = LayerNorm(width) self.transformer = ImageTransformer(width, layers, heads, mlp_ratio, act_layer=act_layer) # pre and post tile position embedding self.global_transformer = ImageTransformer( width, n_global_layers, heads, mlp_ratio, act_layer=act_layer, gated=True ) # pre and post tile position embedding self.pre_tile_pos_embed = TilePositionEmbedding( num_tiles=max_num_tiles, width=width, gated=True, ) self.post_tile_pos_embed = TilePositionEmbedding( num_tiles=max_num_tiles, width=width, gated=True, ) self.gated_positional_embedding = nn.Parameter( scale * torch.randn( max_num_tiles, max_num_tiles, self.grid_size[0] * self.grid_size[1] + 1, width, ) ) self.gated_positional_embedding_gate = nn.Parameter(torch.zeros(1)) self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool = True, missing_keys: List[str] = None, unexpected_keys: List[str] = None, error_msgs: List[str] = None, return_state_dict: bool = False, ) -> None: orig_pos_embed = state_dict.get(prefix + "positional_embedding") if orig_pos_embed is not None: new_pos_embed = resize_local_position_embedding(orig_pos_embed, self.grid_size) state_dict[prefix + "positional_embedding"] = new_pos_embed if hasattr(self, "gated_positional_embedding"): if prefix + "gated_positional_embedding" not in state_dict: # resize positional_embedding to fit the new grid size global_pos_embed = initialize_global_position_embedding_from_local( new_pos_embed, self.grid_size, self.max_num_tiles, self.max_num_tiles, ) state_dict[prefix + "gated_positional_embedding"] = global_pos_embed state_dict[prefix + "gated_positional_embedding_gate"] = torch.zeros(1, dtype=global_pos_embed.dtype) logger.info(f"Initialized global positional embedding with size {global_pos_embed.size()}") else: global_pos_embed = resize_global_position_embedding( state_dict[prefix + "gated_positional_embedding"], self.grid_size, self.max_num_tiles, self.max_num_tiles, ) logger.info( f"Resized global positional embedding from {state_dict[prefix + 'gated_positional_embedding'].size()} to {global_pos_embed.size()}" ) state_dict[prefix + "gated_positional_embedding"] = global_pos_embed if return_state_dict: return state_dict def apply_positional_embedding(self, x, ar): # apply regular position embedding bsz, num_chunks, num_tokens, dim = x.shape x = x.view(bsz * num_chunks, num_tokens, dim) x = x + self.positional_embedding * (1 - self.gated_positional_embedding_gate.tanh()) x = x.view(bsz, num_chunks, num_tokens, dim) for idx, arx in enumerate(ar): _pos_embed = self.gated_positional_embedding[: arx[0], : arx[1]] _pos_embed = _pos_embed.reshape(arx[0] * arx[1], *_pos_embed.shape[2:]) x[idx, : arx[0] * arx[1]] += _pos_embed * self.gated_positional_embedding_gate.tanh() return x def apply_class_embedding(self, x): x = torch.cat( [ self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x, ], dim=1, ) # shape = [*, grid ** 2 + 1, width] return x def forward(self, images: torch.Tensor, ar: torch.Tensor) -> torch.Tensor: if images.ndim == 5: num_concurrent_media = 1 bsz, num_chunks, nch, w, h = images.shape else: bsz, num_concurrent_media, num_chunks, nch, w, h = images.shape images = images.reshape(bsz * num_concurrent_media * num_chunks, nch, w, h) ar = ar.reshape(bsz * num_concurrent_media, 2) # patch embedding x = images.reshape(bsz * num_concurrent_media * num_chunks, nch, w, h) x = self.conv1(x) # shape = [*, width, grid ** 2] _, ntok, dim = x.shape x = x.reshape(bsz * num_concurrent_media, num_chunks, ntok, dim) # tile embeddings x = self.pre_tile_pos_embed(x, ar) x = x.reshape(bsz * num_concurrent_media * num_chunks, ntok, dim) # apply cls token x = self.apply_class_embedding(x) ntok += 1 # apply position embeddings x = x.reshape(bsz * num_concurrent_media, num_chunks, ntok, dim) x = self.apply_positional_embedding(x, ar) x = self.ln_pre(x) npad, attn_mask = 0, None x, npad = expand_num_tokens_to_mult8(x) attn_mask = build_encoder_attention_mask(x, ar, ntok, num_chunks, 1) x = x.view(bsz * num_concurrent_media, -1, dim) x, int_x = self.transformer(x, return_intermediate=self.return_intermediate, mask=attn_mask) x = self.ln_post(x) x = x.reshape(bsz * num_concurrent_media, num_chunks, ntok + npad, dim) x = self.post_tile_pos_embed(x, ar) x = x.reshape(bsz * num_concurrent_media, num_chunks * (ntok + npad), dim) x = self.global_transformer(x, mask=attn_mask) x = x.reshape(bsz * num_concurrent_media, num_chunks, ntok + npad, dim) x = contract_num_tokens_from_mult8(x, npad) # adding back intermediate layer outputs x = x.reshape(bsz, num_concurrent_media, num_chunks, ntok, dim) int_x = int_x.reshape(bsz * num_concurrent_media, num_chunks, ntok + npad, -1) int_x = contract_num_tokens_from_mult8(int_x, npad) int_x = int_x.reshape(bsz, num_concurrent_media, num_chunks, ntok, -1) x = torch.cat([x, int_x], dim=-1) return x class Attention(nn.Module): """Multi-head attention module.""" def __init__(self, args: ModelArgs): """ Initialize the Attention module. Args: args (ModelArgs): Model configuration parameters. Attributes: n_kv_heads (int): Number of key and value heads. n_local_heads (int): Number of local query heads. n_local_kv_heads (int): Number of local key and value heads. n_rep (int): Number of repetitions for local heads. head_dim (int): Dimension size of each attention head. wq (ColumnParallelLinear): Linear transformation for queries. wk (ColumnParallelLinear): Linear transformation for keys. wv (ColumnParallelLinear): Linear transformation for values. wo (RowParallelLinear): Linear transformation for output. cache_k (torch.Tensor): Cached keys for attention. cache_v (torch.Tensor): Cached values for attention. """ super().__init__() world_size = fs_init.get_model_parallel_world_size() replication_factor = 1 if world_size > 8: replication_factor = world_size // MP_SCALE self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads self.n_kv_heads *= replication_factor self.n_local_heads = args.n_heads // world_size self.n_local_kv_heads = self.n_kv_heads // world_size self.n_rep = self.n_local_heads // self.n_local_kv_heads self.head_dim = args.dim // args.n_heads self.max_seq_len = args.max_seq_len self.wq = ColumnParallelLinear( args.dim, args.n_heads * self.head_dim, bias=False, gather_output=False, init_method=lambda x: x, ) self.wk = ColumnParallelLinear( args.dim, self.n_kv_heads * self.head_dim, bias=False, gather_output=False, init_method=lambda x: x, ) self.wv = ColumnParallelLinear( args.dim, self.n_kv_heads * self.head_dim, bias=False, gather_output=False, init_method=lambda x: x, ) self.wo = RowParallelLinear( args.n_heads * self.head_dim, args.dim, bias=False, input_is_parallel=True, init_method=lambda x: x, ) self.n_heads = args.n_heads def setup_cache(self, max_batch_size: int, dtype: torch.dtype): cache_shape = ( max_batch_size, self.max_seq_len, self.n_local_kv_heads, self.head_dim, ) self.register_buffer( "key_cache", torch.zeros( cache_shape, dtype=dtype, ), persistent=False, ) self.register_buffer( "value_cache", torch.zeros( cache_shape, dtype=dtype, ), persistent=False, ) def forward( self, x: torch.Tensor, mask: torch.Tensor, freqs_cis: torch.Tensor, position_ids: torch.LongTensor, ): self.key_cache = self.key_cache.to(x.device) self.value_cache = self.value_cache.to(x.device) xq, xk, xv = [F.linear(x, w) for w in [self.wq.weight, self.wk.weight, self.wv.weight]] bs, slen, _ = xq.shape xq = xq.view(bs, slen, self.n_local_heads, self.head_dim) xk = xk.view(bs, xk.shape[1], self.n_local_kv_heads, self.head_dim) xv = xv.view(bs, xv.shape[1], self.n_local_kv_heads, self.head_dim) xq, xk = apply_rotary_emb(xq, xk, freqs_cis) self.key_cache[:bs, position_ids, ...] = xk self.value_cache[:bs, position_ids, ...] = xv # TODO: we can avoid slicing on first dimension by always padding to max_batch_size() xk = self.key_cache[:bs, ...] xv = self.value_cache[:bs, ...] xq, xk, xv = [tensor.transpose(1, 2) for tensor in (xq, xk, xv)] xk = xk.repeat_interleave(self.n_rep, dim=1) xv = xv.repeat_interleave(self.n_rep, dim=1) attn_output = F.scaled_dot_product_attention(xq, xk, xv, attn_mask=mask, dropout_p=0.0) attn_output = attn_output.transpose(1, 2).contiguous().reshape(bs, slen, -1) out = F.linear(attn_output, self.wo.weight) out = reduce_from_tensor_model_parallel_region(out) return out class FeedForward(nn.Module): def __init__( self, dim: int, hidden_dim: int, multiple_of: int, ffn_dim_multiplier: Optional[float], ): """ Initialize the FeedForward module. Args: dim (int): Input dimension. hidden_dim (int): Hidden dimension of the feedforward layer. multiple_of (int): Value to ensure hidden dimension is a multiple of this value. ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None. Attributes: w1 (ColumnParallelLinear): Linear transformation for the first layer. w2 (RowParallelLinear): Linear transformation for the second layer. w3 (ColumnParallelLinear): Linear transformation for the third layer. """ super().__init__() hidden_dim = int(2 * hidden_dim / 3) # custom dim factor multiplier if ffn_dim_multiplier is not None: hidden_dim = int(ffn_dim_multiplier * hidden_dim) hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) self.w1 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x) self.w2 = RowParallelLinear(hidden_dim, dim, bias=False, input_is_parallel=True, init_method=lambda x: x) self.w3 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x) def forward(self, x): x1, x3 = [F.linear(x, w) for w in [self.w1.weight, self.w3.weight]] x1 = F.silu(x1) x_in = x1 * x3 out = F.linear(x_in, self.w2.weight) out = reduce_from_tensor_model_parallel_region(out) return out class TransformerBlock(nn.Module): def __init__(self, layer_id: int, args: ModelArgs): """ Initialize a TransformerBlock. Args: layer_id (int): Identifier for the layer. args (ModelArgs): Model configuration parameters. Attributes: n_heads (int): Number of attention heads. dim (int): Dimension size of the model. head_dim (int): Dimension size of each attention head. attention (Attention): Attention module. feed_forward (FeedForward): FeedForward module. layer_id (int): Identifier for the layer. attention_norm (RMSNorm): Layer normalization for attention output. ffn_norm (RMSNorm): Layer normalization for feedforward output. """ super().__init__() self.n_heads = args.n_heads self.dim = args.dim self.head_dim = args.dim // args.n_heads self.attention = Attention(args) self.feed_forward = FeedForward( dim=args.dim, hidden_dim=4 * args.dim, multiple_of=args.multiple_of, ffn_dim_multiplier=args.ffn_dim_multiplier, ) self.layer_id = layer_id self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps) self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps) def setup_cache(self, max_batch_size: int, dtype: torch.dtype): self.attention.setup_cache(max_batch_size, dtype) def forward( self, x: torch.Tensor, freqs_cis: torch.Tensor, mask: torch.Tensor, position_ids: torch.LongTensor, ) -> torch.Tensor: """ Perform a forward pass through the TransformerBlock. Args: x (torch.Tensor): Input tensor. start_pos (int): Starting position for attention caching. freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies. mask (torch.Tensor, optional): Masking tensor for attention. Defaults to None. Returns: torch.Tensor: Output tensor after applying attention and feedforward layers. """ h = self.attention.forward( x=self.attention_norm(x), freqs_cis=freqs_cis, mask=mask, position_ids=position_ids, ) h = h + x out = h + self.feed_forward.forward(self.ffn_norm(h)) return out class TilePositionEmbedding(nn.Module): def __init__( self, num_tiles: int, width: int, gated: bool = False, ): super().__init__() self.num_tiles = num_tiles self.width = width self.embedding = nn.Parameter(torch.randn(num_tiles, num_tiles, 1, width) / math.sqrt(width)) self.gated = gated if gated: self.gate = nn.Parameter(torch.zeros(1)) self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs, ): # load the weights from the checkpoint embed = state_dict.get(prefix + "embedding") if embed is not None: # reshape the weights to the correct shape nt_old, nt_old, _, w = embed.shape logging.info(f"Resizing tile embedding from {nt_old}x{nt_old} to {self.num_tiles}x{self.num_tiles}") embed_new = TilePositionEmbedding._dynamic_resize(embed, self.num_tiles) # assign the weights to the module state_dict[prefix + "embedding"] = embed_new @staticmethod def _dynamic_resize(embed: torch.Tensor, num_tiles: int): nt_old, nt_old, _, w = embed.shape embed = embed.permute(2, 3, 0, 1) embed_new = F.interpolate( embed, size=(num_tiles, num_tiles), mode="bilinear", align_corners=True, ) # reshape the weights to the correct shape embed_new = embed_new.permute(2, 3, 0, 1) return embed_new def forward(self, x: torch.Tensor, ar: torch.Tensor, num_tiles: int = None): embed = self.embedding if num_tiles is None: num_tiles = self.num_tiles elif num_tiles > self.num_tiles: embed = TilePositionEmbedding._dynamic_resize(self.embedding, num_tiles) out_pos_embed = torch.zeros(x.shape[0], num_tiles, 1, self.width, device=x.device, dtype=x.dtype) for idx, arx in enumerate(ar): h, w = arx out_pos_embed[idx, : w * h] = embed[:h, :w].reshape(w * h, 1, self.width) if self.gated: out_pos_embed = out_pos_embed * self.gate.tanh() x = x + out_pos_embed return x def _noinit(x): return x class CrossAttention(torch.nn.Module): """Cross attention layer with model-parallel attention layers.""" def __init__( self, dim: int, head_dim: int, n_heads: int, n_kv_heads: int, norm_eps: float, ): super().__init__() self.world_size = fs_init.get_model_parallel_world_size() replication_factor = 1 if self.world_size > 8: replication_factor = self.world_size // MP_SCALE n_kv_heads *= replication_factor assert n_heads % n_kv_heads == 0 self.wq = ColumnParallelLinear( dim, n_heads * head_dim, bias=False, gather_output=False, init_method=_noinit, ) self.wk = ColumnParallelLinear( dim, n_kv_heads * head_dim, bias=False, gather_output=False, init_method=_noinit, ) self.wv = ColumnParallelLinear( dim, n_kv_heads * head_dim, bias=False, gather_output=False, init_method=_noinit, ) self.wo = RowParallelLinear( n_heads * head_dim, dim, bias=False, input_is_parallel=True, init_method=_noinit, ) self.n_heads = n_heads self.head_dim = head_dim self.n_kv_heads = n_kv_heads self.q_norm = RMSNorm( self.head_dim, eps=norm_eps, ) self.k_norm = RMSNorm( self.head_dim, eps=norm_eps, ) # cross-attention heads are model parallel similar to # self-attention, and we also use the identical KV head # combination to ensure parity with the corresponding # trunk LLM (i.e., group query attention) -- @dubeya # local heads assert self.n_heads % self.n_kv_heads == 0 assert self.n_heads % self.world_size == 0 assert self.n_kv_heads % self.world_size == 0 self.n_local_heads = self.n_heads // self.world_size self.n_local_kv_heads = self.n_kv_heads // self.world_size self.n_rep = self.n_local_heads // self.n_local_kv_heads def _compute_xattn_kv_cache(self, xattn_tokens: torch.Tensor) -> torch.Tensor: bsz = xattn_tokens.shape[0] xk = self.wk(xattn_tokens) xv = self.wv(xattn_tokens) _, seqlen_y, _ = xk.shape xk = xk.view(bsz, seqlen_y, self.n_local_kv_heads, self.head_dim) xv = xv.view(bsz, seqlen_y, self.n_local_kv_heads, self.head_dim) xk, xv = [tensor.transpose(1, 2) for tensor in (xk, xv)] # repeat k/v heads if n_kv_heads < n_heads xk = xk.repeat_interleave(self.n_rep, dim=1) xv = xv.repeat_interleave(self.n_rep, dim=1) xk = self.k_norm(xk) return torch.stack([xk, xv]) def compute_xattn_kv_cache(self, xattn_tokens: torch.Tensor) -> torch.Tensor: return self._compute_xattn_kv_cache(xattn_tokens) def forward( self, x: torch.Tensor, xattn_mask: torch.Tensor, full_text_row_masked_out_mask: torch.Tensor, xattn_cache: torch.Tensor, ) -> torch.Tensor: xq = F.linear(x, self.wq.weight) bsz, seqlen, _ = x.shape xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) xq = self.q_norm(xq) xq = xq.transpose(1, 2) xk, xv = xattn_cache output = F.scaled_dot_product_attention(xq, xk, xv, attn_mask=xattn_mask, dropout_p=0.0) output = output * full_text_row_masked_out_mask output = output.transpose(1, 2).contiguous().reshape(bsz, seqlen, -1) out = F.linear(output, self.wo.weight) out = reduce_from_tensor_model_parallel_region(out) return out class CrossAttentionTransformerBlock(torch.nn.Module): """Cross-attention transformer block with tanh-gated attention and feedforward.""" def __init__( self, args: ModelArgs, layer_id: int, no_ffn: bool = False, ) -> None: super().__init__() self.layer_id = layer_id self.n_heads = args.n_heads self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads self.dim = args.dim self.head_dim = args.dim // args.n_heads self.attention = CrossAttention( dim=args.dim, head_dim=self.head_dim, n_heads=self.n_heads, n_kv_heads=self.n_kv_heads, norm_eps=args.norm_eps, ) self.attention_norm = RMSNorm( args.dim, eps=args.norm_eps, ) self.gate_attn = torch.nn.Parameter(torch.zeros(1)) self.feed_forward = FeedForward( dim=args.dim, hidden_dim=4 * args.dim, ffn_dim_multiplier=args.ffn_dim_multiplier, multiple_of=args.multiple_of, ) self.ffn_norm = RMSNorm( args.dim, eps=args.norm_eps, ) self.gate_ffwd = torch.nn.Parameter(torch.zeros(1)) self.no_ffn = no_ffn def compute_xattn_kv_cache(self, xattn_tokens: torch.Tensor) -> torch.Tensor: return self.attention.compute_xattn_kv_cache(xattn_tokens) def forward( self, x: torch.Tensor, xattn_mask: torch.Tensor, full_text_row_masked_out_mask: Tuple[torch.Tensor, torch.Tensor], xattn_cache: torch.Tensor, ) -> torch.Tensor: _attn_out = self.attention( x=self.attention_norm(x), xattn_mask=xattn_mask, xattn_cache=xattn_cache, full_text_row_masked_out_mask=full_text_row_masked_out_mask, ) h = x + self.gate_attn.tanh() * _attn_out _ffn = self.feed_forward(self.ffn_norm(h)) _ffn = full_text_row_masked_out_mask[:, 0] * _ffn # type: ignore h = h + self.gate_ffwd.tanh() * _ffn * float(not self.no_ffn) return h class DummyCrossAttentionTransformerBlock: """Dummy cross-attention transformer block with tanh-gated attention and feedforward.""" def __call__( self, x: torch.Tensor, *args, **kwargs, ) -> torch.Tensor: return x class DummySelfAttentionTransformerBlock: """Dummy self-attention transformer block""" def __call__( self, x: torch.Tensor, *args, **kwargs, ) -> torch.Tensor: return x class CrossAttentionTransformerVision(torch.nn.Module): def __init__(self, args: ModelArgs) -> None: super().__init__() return_intermediate = "3,7,15,23,30" self.vision_input_dim = 1280 self.image_res = args.vision_chunk_size self.max_num_chunks = args.vision_max_num_chunks if return_intermediate is not None: return_intermediate = [int(layer) for layer in return_intermediate.split(",")] self.vision_input_dim = (len(return_intermediate) + 1) * self.vision_input_dim self.patch_size = 14 self.vision_encoder = VisionEncoder( max_num_tiles=4, image_size=args.vision_chunk_size, patch_size=self.patch_size, n_global_layers=8, global_model=True, return_intermediate=return_intermediate, ) # vision token projection self.vision_projection = ColumnParallelLinear( self.vision_input_dim, args.dim, bias=True, init_method=lambda x: x, ) def forward(self, images: torch.Tensor, aspect_ratios: torch.Tensor) -> torch.Tensor: # vision_tokens: (B, T, D) # aspect_ratios: (B, T) # h: (B, T, D) vision_tokens = self.vision_encoder(images.to(dtype=torch.get_default_dtype()), aspect_ratios) vision_tokens = F.linear(vision_tokens, self.vision_projection.weight, self.vision_projection.bias) vision_tokens = gather_from_tensor_model_parallel_region(vision_tokens) return vision_tokens class CrossAttentionTransformerText(torch.nn.Module): INFERENCE_IMAGE_TOKEN_ID = 128010 def __init__(self, args: ModelArgs) -> None: super().__init__() self.world_size = fs_init.get_model_parallel_world_size() assert args.vocab_size > 0 self.vocab_size = args.vocab_size self.n_layers = args.n_layers self.dim = args.dim self.head_dim = args.dim // args.n_heads self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads self.n_local_kv_heads = self.n_kv_heads // self.world_size assert self.vocab_size % self.world_size == 0 self.tok_embeddings = VocabParallelEmbedding(args.vocab_size, args.dim, init_method=lambda x: x) self.pos_embeddings = None # final norm layer (not necessary for post-norm) self.norm = RMSNorm(args.dim, eps=args.norm_eps) # output layer self.output = ColumnParallelLinear(args.dim, args.vocab_size, bias=False, init_method=lambda x: x) self.n_llama_layers = args.n_layers self.model_dim = args.dim # BLOCKS self.fusion_schedule = self._init_fusion_schedule(args.vision_num_cross_attention_layers) self.learnable_embedding = VocabParallelEmbedding( max(fs_init.get_model_parallel_world_size(), 8), args.dim, init_method=lambda x: x, ) self.num_frozen_embeddings = self.tok_embeddings.num_embeddings self._thresh = self.num_frozen_embeddings - 1 # transformer blocks self.layers = torch.nn.ModuleList() self.cross_attention_layers = torch.nn.ModuleList() for i in range(args.n_layers): layer_id = i block = TransformerBlock(args=args, layer_id=layer_id) self.layers.append(block) if layer_id in self.fusion_schedule: xa_layer_id = self.fusion_schedule.index(layer_id) + args.n_layers block = CrossAttentionTransformerBlock( args, layer_id=xa_layer_id, ) self.cross_attention_layers.append(block) # add xattn and dummy layers to avoid conditionals in forward() self.text_and_xattn_layers = [] for idx, layer in enumerate(self.layers): if idx in self.fusion_schedule: xattn_layer_idx = self.fusion_schedule.index(idx) xattn_layer = self.cross_attention_layers[xattn_layer_idx] else: xattn_layer_idx = 0 xattn_layer = DummyCrossAttentionTransformerBlock() self.text_and_xattn_layers.append( ( layer, xattn_layer, xattn_layer_idx, ) ) self.freqs_cis = precompute_freqs_cis( args.dim // args.n_heads, args.max_seq_len * 2, args.rope_theta, args.use_scaled_rope, ) self.args = args self.cache_is_setup = False self.max_seq_len = args.max_seq_len def _init_fusion_schedule( self, num_layers: int, ) -> List[int]: llama_layers = list(range(self.n_llama_layers)) # uniformly spread the layers k = math.ceil(len(llama_layers) / num_layers) return llama_layers[::-1][::k][:num_layers][::-1] def get_partially_trainable_embedding(self, x): xz = torch.zeros_like(x, device=x.device) oz = torch.ones_like(x, device=x.device) x_orig = torch.minimum(x, torch.tensor(self._thresh, device=x.device)) x_new = torch.maximum(x, torch.tensor(self._thresh + 1, device=x.device)) - self.num_frozen_embeddings mask_orig = torch.where(x >= self.num_frozen_embeddings, xz, oz).unsqueeze(-1) mask_new = torch.where(x < self.num_frozen_embeddings, xz, oz).unsqueeze(-1) x_orig = self.tok_embeddings(x_orig) x_new = self.learnable_embedding(x_new).type_as(x_orig) return x_orig * mask_orig.type_as(x_orig) + x_new * mask_new.type_as(x_new) def forward( self, position_ids: torch.LongTensor, h: torch.Tensor, xattn_mask: torch.Tensor, full_text_row_masked_out_mask: torch.Tensor, xattn_caches: torch.Tensor, text_only_inference: bool = False, ): assert self.cache_is_setup, "Please set up cache before calling forward" self.mask_cache = self.mask_cache.to(h.device) self.freqs_cis = self.freqs_cis.to(h.device) mask = self.mask_cache.index_select(2, position_ids) freqs_cis = self.freqs_cis.index_select(0, position_ids) for ( layer, xattn_layer, xattn_layer_idx, ) in self.text_and_xattn_layers: if not text_only_inference: h = xattn_layer( x=h, xattn_mask=xattn_mask, xattn_cache=xattn_caches[xattn_layer_idx], full_text_row_masked_out_mask=full_text_row_masked_out_mask, ) h = layer( x=h, mask=mask, freqs_cis=freqs_cis, position_ids=position_ids, ) h = self.norm(h) output = F.linear(h, self.output.weight) output = gather_from_tensor_model_parallel_region(output) return output.float() def setup_cache(self, max_batch_size: int, device: torch.device, dtype=torch.bfloat16): # Set up the text kv caches ones = torch.ones( (self.max_seq_len, self.max_seq_len), dtype=torch.bool, device=device, ) self.register_buffer( "mask_cache", torch.tril( ones, ) .unsqueeze(0) .unsqueeze(0), persistent=False, ) for layer in self.layers: layer.setup_cache(max_batch_size, dtype=dtype) self.cache_is_setup = True def _get_xattn_mask( self, num_tokens, text_device, text_dtype, vision_tokens, cross_attention_masks, ) -> Tuple[Tensor, Tensor]: assert vision_tokens is not None, "Vision tokens must be provided" vision_seqlen = vision_tokens.shape[3] assert vision_tokens.shape[1] == cross_attention_masks.shape[2], ( f"Mismatch in number of images given and number of masks given {vision_tokens.shape} {cross_attention_masks.shape}" ) assert vision_tokens.shape[2] == cross_attention_masks.shape[3], ( f"Vision tokens shape {vision_tokens.shape} mismatch with xattn shape {cross_attention_masks.shape}" ) assert num_tokens == cross_attention_masks.shape[1], ( f"Mismatch in text sequence length and cross attention mask sequence length {num_tokens} {cross_attention_masks.shape}" ) _, _, _, num_image_tokens, image_token_dim = tuple(vision_tokens.shape) bsz, ntext, nimg, nchunks = cross_attention_masks.shape cross_attention_masks = ( cross_attention_masks.repeat_interleave(vision_seqlen, dim=3).view(bsz, ntext, -1).unsqueeze(1) ) full_text_row_masked_out_mask = _get_full_row_masked_out_mask( cross_attention_masks, get_negative_inf_value(cross_attention_masks.dtype), ) cross_attention_masks *= full_text_row_masked_out_mask return ( cross_attention_masks.to(device=text_device, dtype=text_dtype), full_text_row_masked_out_mask.to(device=text_device), ) class CrossAttentionTransformer(torch.nn.Module): def __init__(self, args: ModelArgs) -> None: super().__init__() self.params = args self.model_dim = args.dim self.vision_model = CrossAttentionTransformerVision(args) self.text_model = CrossAttentionTransformerText(args) self.image_res = args.vision_chunk_size self.max_num_chunks = args.vision_max_num_chunks self.image_transform = partial( VariableSizeImageTransform(size=args.vision_chunk_size), max_num_chunks=args.vision_max_num_chunks, ) def setup_cache(self, max_batch_size: int, device: torch.device, dtype: torch.dtype): self.text_model.setup_cache(max_batch_size, device, dtype) def compute_vision_tokens_masks( self, batch_images: List[List[PIL_Image.Image]], batch_masks: List[List[List[int]]], total_len: int, device: torch.device, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: skip_vision_encoder = False assert len(batch_images) == len(batch_masks), "Images and masks must have the same length" max_num_images = max(len(x) for x in batch_images) bsz = len(batch_images) if max_num_images == 0: num_chunks = [[self.max_num_chunks] for _ in batch_images] skip_vision_encoder = True else: images_and_aspect_ratios = [[self.image_transform(im) for im in row] for row in batch_images] transformed_images = [[x[0] for x in row] for row in images_and_aspect_ratios] aspect_ratios = torch.ones(bsz, max_num_images, 2, dtype=torch.int64) for i, row in enumerate(images_and_aspect_ratios): if len(row) > 0: aspect_ratios[i, : len(row)] = torch.stack([torch.tensor(x[1]) for x in row]) stacked_images, num_chunks = _stack_images( transformed_images, max_num_chunks=self.max_num_chunks, image_res=self.params.vision_chunk_size, max_num_images=max_num_images, ) stacked_images = stacked_images.to(device=device) if skip_vision_encoder: vision_tokens = torch.zeros( ( bsz, max_num_images, self.max_num_chunks, int((self.vision_model.image_res / self.vision_model.patch_size) ** 2 + 1), self.model_dim, ), ) else: vision_tokens = self.vision_model(stacked_images, aspect_ratios).to(device=device) bsz, nimg, nchunk, ntok, image_token_dim = tuple(vision_tokens.shape) xattn_caches = torch.stack( [ layer.compute_xattn_kv_cache(vision_tokens.view(bsz, -1, image_token_dim)) for layer in self.text_model.cross_attention_layers ] ) padded_masks = _pad_masks( batch_masks, num_chunks, total_len, self.max_num_chunks, ) cross_attention_masks, full_text_row_masked_out_mask = self.text_model._get_xattn_mask( num_tokens=total_len, text_device=vision_tokens.device.type, text_dtype=next(self.text_model.parameters()).dtype, vision_tokens=vision_tokens, cross_attention_masks=padded_masks, ) return (xattn_caches, cross_attention_masks, full_text_row_masked_out_mask) def forward( self, position_ids: torch.Tensor, tokens: torch.Tensor, cross_attention_masks: torch.Tensor, full_text_row_masked_out_mask: torch.Tensor, xattn_caches: torch.Tensor, text_only_inference: bool = False, ) -> torch.Tensor: h = self.text_model.get_partially_trainable_embedding(tokens[:, position_ids]) logits = self.text_model.forward( position_ids=position_ids, h=h, xattn_mask=cross_attention_masks[:, :, position_ids], full_text_row_masked_out_mask=full_text_row_masked_out_mask[:, :, position_ids], xattn_caches=xattn_caches, text_only_inference=text_only_inference, ) return logits def _stack_images( images: List[List[PIL_Image.Image]], max_num_chunks: int, image_res: int, max_num_images: int, ) -> Tuple[torch.Tensor, List[int]]: """ Takes a list of list of images and stacks them into a tensor. This function is needed since images can be of completely different resolutions and aspect ratios. """ out_images, out_num_chunks = [], [] for imgs_sample in images: out_images_i = torch.zeros( max_num_images, max_num_chunks, 3, image_res, image_res, ) _num_chunks = [] for j, chunks_image in enumerate(imgs_sample): out_images_i[j, : chunks_image.shape[0]] = chunks_image _num_chunks.append(chunks_image.shape[0]) out_images.append(out_images_i) out_num_chunks.append(_num_chunks) return torch.stack(out_images), out_num_chunks def _pad_masks( all_masks: List[List[List[int]]], all_num_chunks: List[List[int]], total_len: int, max_num_chunks: int, ) -> torch.Tensor: dtype = torch.get_default_dtype() inf_value = get_negative_inf_value(dtype) bsz = len(all_masks) max_num_media = max([len(m) for m in all_masks]) out_masks = torch.full( (bsz, total_len, max_num_media, max_num_chunks), inf_value, dtype=dtype, ) for idx, (mask, num_chunks) in enumerate(zip(all_masks, all_num_chunks)): for mask_idx, (mask_elem, mask_num_chunks) in enumerate(zip(mask, num_chunks)): if len(mask_elem) == 2: mask_elem[1] = min(mask_elem[1], total_len) if mask_elem[1] == -1: mask_elem[1] = total_len out_masks[idx, mask_elem[0] : mask_elem[1], mask_idx, :mask_num_chunks].fill_(0.0) return out_masks ================================================ FILE: models/llama3/multimodal/utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import collections import torch def get_negative_inf_value(dtype): return torch.finfo(dtype).min def to_2tuple(x): if isinstance(x, collections.abc.Iterable): return x return (x, x) ================================================ FILE: models/llama3/quantization/loader.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # type: ignore import os from typing import Any, Dict, List, Optional, cast import torch from fairscale.nn.model_parallel.initialize import get_model_parallel_rank from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region from torch import Tensor, nn from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear from ...datatypes import QuantizationMode from ...quantize_impls import ( Fp8ScaledWeights, ffn_swiglu, load_fp8, quantize_fp8, ) from ..model import Transformer, TransformerBlock from ..multimodal.model import CrossAttentionTransformer def swiglu_wrapper( self, x: Tensor, ): out = ffn_swiglu(x, self.w1.weight, self.w3.weight, self.w2.weight) return reduce_from_model_parallel_region(out) def convert_to_quantized_model( model: Transformer | CrossAttentionTransformer, checkpoint_dir: str, quantization_mode: Optional[str] = None, fp8_activation_scale_ub: Optional[float] = 1200.0, device: Optional[torch.device] = None, ) -> Transformer | CrossAttentionTransformer: if quantization_mode == QuantizationMode.fp8_mixed: return convert_to_fp8_quantized_model(model, checkpoint_dir, fp8_activation_scale_ub, device) elif quantization_mode == QuantizationMode.int4_mixed: return convert_to_int4_quantized_model(model, checkpoint_dir, device) else: raise ValueError(f"Unsupported quantization mode: {quantization_mode}") def convert_to_fp8_quantized_model( model: Transformer, checkpoint_dir: str, fp8_activation_scale_ub: Optional[float] = 1200.0, device: Optional[torch.device] = None, ) -> Transformer: # Move weights to GPU with quantization fp8_scales_path = os.path.join(checkpoint_dir, f"fp8_scales_{get_model_parallel_rank()}.pt") if os.path.isfile(fp8_scales_path): print("Loading fp8 scales...") fp8_scales = torch.load(fp8_scales_path, weights_only=True) for _, block in model.named_modules(): if isinstance(block, TransformerBlock): if block.layer_id == 0 or block.layer_id == (model.n_layers - 1): continue block.feed_forward.forward = swiglu_wrapper.__get__(block.feed_forward) for key in ("w1", "w3", "w2"): param = getattr(block.feed_forward, key) param.weight = load_fp8( param.weight, fp8_scales[f"{block.layer_id}_feed_forward.{key}_{get_model_parallel_rank()}"], fp8_activation_scale_ub, ) else: print("Quantizing fp8 weights from bf16...") for _, block in model.named_modules(): if isinstance(block, TransformerBlock): if block.layer_id == 0 or block.layer_id == (model.n_layers - 1): continue block.feed_forward.forward = swiglu_wrapper.__get__(block.feed_forward) # type: ignore for key in ("w1", "w3", "w2"): param = getattr(block.feed_forward, key) param.weight = quantize_fp8( param.weight, fp8_activation_scale_ub, output_device=device, ) for _, parameter in model.named_parameters(): if not isinstance(parameter, Fp8ScaledWeights): parameter.data = parameter.to(device=device) return model class Int8DynActInt4WeightLinearLoRA(Int8DynActInt4WeightLinear): """ Int8DynActInt4WeightLinear with LoRA adaptor. Args: in_features: Number of input features. out_features: Number of output features. bias: Whether to use bias. device: Device to use. group_size: Group size for quantization. precision: Precision of quantization. scales_precision: Precision of scales. lora_rank: Rank of LoRA adaptor. lora_scale: Scale of LoRA adaptor. """ def __init__( self, in_features: int, out_features: int, bias=False, device=None, # quantization parameters group_size: int = 256, precision: torch.dtype = torch.float32, scales_precision: torch.dtype = torch.float32, # LoRA parameters lora_rank: Optional[int] = None, lora_scale: Optional[float] = None, ) -> None: super().__init__( in_features, out_features, bias=bias, device=device, groupsize=group_size, precision=precision, scales_precision=scales_precision, ) self.lora_scale: Optional[float] = None self.adaptor: Optional[nn.Sequential] = None if lora_rank is not None: assert lora_scale is not None, "Please specify lora scale for LoRA." # Low-rank adaptation. See paper for more details: https://arxiv.org/abs/2106.09685 self.adaptor = nn.Sequential() self.adaptor.add_module("A", nn.Linear(in_features, lora_rank, bias=False)) self.adaptor.add_module("B", nn.Linear(lora_rank, out_features, bias=False)) self.lora_scale = lora_scale self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ) -> None: """A hook to load the quantized weights from the state dict.""" if prefix + "zeros" not in state_dict: # Zero-point may not be saved in the state dict. In this case, we assume it's zero. assert prefix + "scales" in state_dict state_dict[prefix + "zeros"] = torch.zeros_like(state_dict[prefix + "scales"]) def forward(self, input_: torch.Tensor) -> torch.Tensor: module_out = super().forward(input_) if self.adaptor is not None: adaptor_out = self.adaptor(input_) * self.lora_scale return module_out + adaptor_out return module_out class Int8WeightEmbedding(torch.nn.Embedding): """An embedding layer to load int8 weights. Args: num_embeddings: Number of embeddings. embedding_dim: Embedding dimension. padding_idx: Padding index. """ def __init__( self, num_embeddings: int, embedding_dim: int, padding_idx: int, device=None, ) -> None: super().__init__(num_embeddings, embedding_dim, padding_idx, device=device) self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ) -> None: """A hook to load the quantized embedding weight and scales from the state dict.""" weights = state_dict.pop(prefix + "weight") scales = state_dict.pop(prefix + "scales") state_dict[prefix + "weight"] = weights * scales class Int8WeightLinear(torch.nn.Linear): """A linear layer to load int8 weights. Args: in_features: Number of input features. out_features: Number of output features. bias: Whether to use bias. """ def __init__(self, in_features: int, out_features: int, bias: bool = True, device=None) -> None: super().__init__(in_features, out_features, bias, device=device) self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ) -> None: """A hook to load the quantized linear weight and scales from the state dict.""" weights = state_dict.pop(prefix + "weight") scales = state_dict.pop(prefix + "scales") state_dict[prefix + "weight"] = weights * scales def _prepare_model_int4_weight_int8_dynamic_activation( model: torch.nn.Module, group_size: int, lora_rank: Optional[int], lora_scale: Optional[float], ): """Prepare the model for int4 weight and int8 dynamic activation quantization. Note that the weights of embedding and output layers are quantized to int8. """ device = None for module_name, module in model.named_children(): if module_name == "output": quantized_module = Int8WeightLinear( in_features=module.in_features, out_features=module.out_features, bias=module.bias, device=device, ) del module setattr(model, module_name, quantized_module) elif module_name == "tok_embeddings": quantized_module = Int8WeightEmbedding( num_embeddings=module.num_embeddings, embedding_dim=module.embedding_dim, padding_idx=module.padding_idx, device=device, ) del module setattr(model, module_name, quantized_module) elif isinstance(module, (ColumnParallelLinear, RowParallelLinear, nn.Linear)): quantized_module = Int8DynActInt4WeightLinearLoRA( in_features=module.in_features, out_features=module.out_features, bias=False, group_size=group_size, lora_rank=lora_rank, lora_scale=lora_scale, device=device, ) del module setattr(model, module_name, quantized_module) else: _prepare_model_int4_weight_int8_dynamic_activation(module, group_size, lora_rank, lora_scale) return model def convert_to_int4_quantized_model( model: Transformer | CrossAttentionTransformer, checkpoint_dir: str, device: Optional[torch.device] = None, ) -> Transformer | CrossAttentionTransformer: """Convert the model to int4 quantized model.""" model_args = model.params assert model_args.quantization_args is not None, "Quantization args must be specified." quantization_args = model_args.quantization_args if quantization_args.scheme is None: raise ValueError("Quantization scheme must be specified in 'quantization_args'.") if quantization_args.scheme.value != "int4_weight_int8_dynamic_activation": raise NotImplementedError( "Only int4 quantization with 'int4_weight_int8_dynamic_activation' scheme is supported." ) group_size = model_args.quantization_args.group_size if group_size is None: raise ValueError("'group_size' cannot be None in 'quantization_args'. Please specify it.") if model_args.lora_args is None: # Certain quantized models (e.g., SpinQuant) may not have LoRA. lora_rank = None lora_scale = None else: lora_rank = model_args.lora_args.rank lora_scale = model_args.lora_args.scale _prepare_model_int4_weight_int8_dynamic_activation(model, group_size, lora_rank, lora_scale) return cast(Transformer | CrossAttentionTransformer, model.to(device=device)) ================================================ FILE: models/llama3/requirements.txt ================================================ blobfile fairscale jinja2 json-strong-typing tiktoken torch pydantic pydantic_core ================================================ FILE: models/llama3/scripts/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. ================================================ FILE: models/llama3/scripts/chat_completion.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # Copyright (c) Meta Platforms, Inc. and affiliates. # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement. from io import BytesIO from pathlib import Path from typing import Optional import fire from termcolor import cprint from models.datatypes import RawMediaItem, RawMessage, RawTextItem, StopReason from models.llama3.generation import Llama3 import os import torch THIS_DIR = Path(__file__).parent def get_device(): if "DEVICE" in os.environ: return os.environ["DEVICE"] if torch.cuda.is_available(): return "cuda" elif torch.xpu.is_available(): return "xpu" return "cpu" def run_main( ckpt_dir: str, temperature: float = 0.6, top_p: float = 0.9, max_seq_len: int = 512, max_batch_size: int = 4, world_size: Optional[int] = None, quantization_mode: Optional[str] = None, ): generator = Llama3.build( ckpt_dir=ckpt_dir, max_seq_len=max_seq_len, max_batch_size=max_batch_size, world_size=world_size, quantization_mode=quantization_mode, device=get_device(), ) dialogs = [ [RawMessage(role="user", content="what is the recipe of mayonnaise?")], [ RawMessage( role="user", content="I am going to Paris, what should I see?", ), RawMessage( role="assistant", content="""\ Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris: 1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city. 2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa. 3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows. These are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world.""", stop_reason=StopReason.end_of_turn, ), RawMessage(role="user", content="What is so great about #1?"), ], [ RawMessage(role="system", content="Always answer with Haiku"), RawMessage(role="user", content="I am going to Paris, what should I see?"), ], [ RawMessage(role="system", content="Always answer with emojis"), RawMessage(role="user", content="How to go from Beijing to NY?"), ], ] if generator.args.vision_chunk_size > 0: with open(THIS_DIR / "../../resources/dog.jpg", "rb") as f: img = f.read() dialogs.append( [ RawMessage( role="user", content=[ RawMediaItem(data=BytesIO(img)), RawTextItem(text="Describe this image in two sentences"), ], ), ] ) for dialog in dialogs: for msg in dialog: print(f"{msg.role.capitalize()}: {msg.content}\n") batch = [dialog] for token_results in generator.chat_completion( batch, temperature=temperature, top_p=top_p, max_gen_len=max_seq_len, ): result = token_results[0] if result.finished: break cprint(result.text, color="yellow", end="") print("\n") def main(): fire.Fire(run_main) if __name__ == "__main__": main() ================================================ FILE: models/llama3/scripts/completion.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # Copyright (c) Meta Platforms, Inc. and affiliates. # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement. from io import BytesIO from pathlib import Path from typing import Optional import fire from termcolor import cprint from models.datatypes import RawMediaItem from models.llama3.generation import Llama3 import os import torch THIS_DIR = Path(__file__).parent def get_device(): if "DEVICE" in os.environ: return os.environ["DEVICE"] if torch.cuda.is_available(): return "cuda" elif torch.xpu.is_available(): return "xpu" return "cpu" def run_main( ckpt_dir: str, temperature: float = 0.6, top_p: float = 0.9, max_seq_len: int = 512, max_batch_size: int = 4, world_size: Optional[int] = None, quantization_mode: Optional[str] = None, ): generator = Llama3.build( ckpt_dir=ckpt_dir, max_seq_len=max_seq_len, max_batch_size=max_batch_size, world_size=world_size, quantization_mode=quantization_mode, device=get_device(), ) interleaved_contents = [ "The color of the sky is blue but sometimes it can also be", """\ apple is pomme, bannana is banane, cherry is""", "1, 2, 3, 5, 8, 13", "ba ba black sheep, have you any wool?", ] if generator.args.vision_chunk_size > 0: with open(THIS_DIR / "../../resources/dog.jpg", "rb") as f: img = f.read() interleaved_contents.append( [ RawMediaItem(type="image", data=BytesIO(img)), "If I had to write a haiku for this one", ] ) for content in interleaved_contents: cprint(f"{content}", end="") batch = [content] for token_results in generator.completion( batch, temperature=temperature, top_p=top_p, ): result = token_results[0] if result.finished: break cprint(result.text, color="yellow", end="") print("\n==================================\n") def main(): fire.Fire(run_main) if __name__ == "__main__": main() ================================================ FILE: models/llama3/tests/api/test_generation.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import os import unittest from pathlib import Path import pytest import torch from llama_models.datatypes import RawMediaItem, RawMessage, RawTextItem from llama_models.llama3.generation import Llama3 THIS_DIR = Path(__file__).parent def get_device(): if "DEVICE" in os.environ: return os.environ["DEVICE"] if torch.cuda.is_available(): return "cuda" elif torch.xpu.is_available(): return "xpu" return "" def build_generator(env_var: str, device: str): if env_var not in os.environ: raise ValueError(f"{env_var} must be specified for this test") os.environ["RANK"] = "0" os.environ["WORLD_SIZE"] = "1" os.environ["MASTER_ADDR"] = "localhost" os.environ["MASTER_PORT"] = "29501" return Llama3.build(ckpt_dir=os.environ[env_var], max_seq_len=128, max_batch_size=1, world_size=1, device=device) class TestTextModelInference(unittest.TestCase): device = "cpu" @classmethod def setUpClass(cls): cls.generator = build_generator("TEXT_MODEL_CHECKPOINT_DIR", cls.device) def test_run_generation(self): dialogs = [ [ RawMessage(role="system", content="Always answer with Haiku"), RawMessage(role="user", content="I am going to Paris, what should I see?"), ], [ RawMessage(role="system", content="Always answer with emojis"), RawMessage(role="user", content="How to go from Beijing to NY?"), ], ] for dialog in dialogs: batch = [dialog] out_message = [] for token_results in self.__class__.generator.chat_completion( batch, temperature=0, logprobs=True, ): result = token_results[0] if result.finished: break out_message += [result] self.assertTrue(len(result.text) > 0) # assert at least 10 tokens self.assertTrue(len(out_message) > 10) @pytest.mark.skipif(get_device() == "", reason="No device available and none specified") class TestTextModelInferenceOnDevice(TestTextModelInference): device = get_device() class TestVisionModelInference(unittest.TestCase): device = "cpu" @classmethod def setUpClass(cls): cls.generator = build_generator("VISION_MODEL_CHECKPOINT_DIR", cls.device) @unittest.skip("Disabling vision model test") @pytest.mark.skip(reason="Disabling vision model test") def test_run_generation(self): with open(THIS_DIR.parent.parent.parent / "resources/dog.jpg", "rb") as f: img = f.read() dialogs = [ [ RawMessage( role="user", content=[ RawMediaItem(data=img), RawTextItem(text="Describe this image in two sentences"), ], ) ], [ RawMessage( role="user", content="what is the recipe of mayonnaise in two sentences?", ) ], ] for dialog in dialogs: batch = [dialog] out_message = [] for token_results in self.__class__.generator.chat_completion( batch, temperature=0, logprobs=True, ): result = token_results[0] if result.finished: break out_message += [result] self.assertTrue(len(result.text) > 0) # assert at least 10 tokens self.assertTrue(len(out_message) > 10) @pytest.mark.skipif(get_device() == "", reason="No device available and none specified") class TestVisionModelInferenceOnDevice(TestVisionModelInference): device = get_device() ================================================ FILE: models/llama3/tests/api/test_tokenizer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # Copyright (c) Meta Platforms, Inc. and affiliates. # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement. from unittest import TestCase from llama_models.datatypes import RawMessage, ToolPromptFormat from llama_models.llama3.chat_format import ChatFormat from llama_models.llama3.tokenizer import Tokenizer class TokenizerTests(TestCase): def setUp(self): self.tokenizer = Tokenizer.get_instance() self.format = ChatFormat(self.tokenizer) def test_special_tokens(self): self.assertEqual( self.tokenizer.special_tokens["<|begin_of_text|>"], 128000, ) def test_encode(self): self.assertEqual( self.tokenizer.encode("This is a test sentence.", bos=True, eos=True), [128000, 2028, 374, 264, 1296, 11914, 13, 128001], ) def test_decode(self): self.assertEqual( self.tokenizer.decode( [128000, 2028, 374, 264, 1296, 11914, 13, 128001], ), "<|begin_of_text|>This is a test sentence.<|end_of_text|>", ) def test_encode_message(self): message = RawMessage( role="user", content="This is a test sentence.", ) self.assertEqual( self.format.encode_message(message, tool_prompt_format=ToolPromptFormat.json)[0], [ 128006, # <|start_header_id|> 882, # "user" 128007, # <|end_of_header|> 271, # "\n\n" 2028, 374, 264, 1296, 11914, 13, # This is a test sentence. 128009, # <|eot_id|> ], ) def test_encode_dialog(self): messages = [ RawMessage( role="system", content="This is a test sentence.", ), RawMessage( role="user", content="This is a response.", ), ] model_input = self.format.encode_dialog_prompt(messages) self.assertEqual( model_input.tokens, [ 128000, # <|begin_of_text|> 128006, # <|start_header_id|> 9125, # "system" 128007, # <|end_of_header|> 271, # "\n\n" 2028, 374, 264, 1296, 11914, 13, # "This is a test sentence." 128009, # <|eot_id|> 128006, # <|start_header_id|> 882, # "user" 128007, # <|end_of_header|> 271, # "\n\n" 2028, 374, 264, 2077, 13, # "This is a response.", 128009, # <|eot_id|> 128006, # <|start_header_id|> 78191, # "assistant" 128007, # <|end_of_header|> 271, # "\n\n" ], ) ================================================ FILE: models/llama3/tests/api/test_tool_utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import unittest from llama_models.llama3.tool_utils import ( ToolUtils, is_valid_python_list, parse_python_list_for_function_calls, ) class TestToolUtils(unittest.TestCase): def test_maybe_extract_custom_tool_call(self): single_tool_call = """{"location":"New York","date":"2023-08-05"}""" res = ToolUtils.maybe_extract_custom_tool_call(single_tool_call) tool_name, args = res assert tool_name == "getWeather" assert args == {"location": "New York", "date": "2023-08-05"} class TestPythonListCheck(unittest.TestCase): def test_valid_list_with_single_function_call(self): input_string = '[get_boiling_point(liquid_name="water", celcius=True)]' assert is_valid_python_list(input_string) is True def test_valid_list_with_multiple_function_calls(self): input_string = ( '[get_boiling_point(liquid_name="water", celcius=True), get_melting_point(substance="ice", kelvin=False)]' ) assert is_valid_python_list(input_string) is True def test_invalid_empty_list(self): input_string = "[]" assert is_valid_python_list(input_string) is False def test_invalid_list_with_non_function_call(self): input_string = '[get_boiling_point(liquid_name="water"), 42]' assert is_valid_python_list(input_string) is False def test_invalid_list_with_positional_args(self): input_string = '[get_boiling_point("water", True)]' assert is_valid_python_list(input_string) is False def test_invalid_nested_list(self): input_string = '[[get_boiling_point(liquid_name="water")]]' assert is_valid_python_list(input_string) is False def test_invalid_dict(self): input_string = '{"func": get_boiling_point(liquid_name="water")}' assert is_valid_python_list(input_string) is False def test_invalid_syntax(self): input_string = '[get_boiling_point(liquid_name="water", celcius=True' # Missing closing bracket assert is_valid_python_list(input_string) is False def test_valid_list_with_boolean_args(self): input_string = '[get_boiling_point(liquid_name="water", celcius=True, precise=False)]' assert is_valid_python_list(input_string) is True def test_valid_list_with_numeric_args(self): input_string = "[calculate_volume(radius=5.2, height=10)]" assert is_valid_python_list(input_string) is True def test_invalid_bare_function_call(self): input_string = 'get_boiling_point(liquid_name="water")' assert is_valid_python_list(input_string) is False def test_invalid_extra_char_function_call(self): input_string = '[(get_boiling_point(liquid_name="water"),)]' assert is_valid_python_list(input_string) is False class TestParsePythonList(unittest.TestCase): def test_single_function_call(self): input_string = '[get_boiling_point(liquid_name="water", celcius=True)]' expected = [("get_boiling_point", {"liquid_name": "water", "celcius": True})] assert parse_python_list_for_function_calls(input_string) == expected def test_multiple_function_calls(self): input_string = ( '[get_boiling_point(liquid_name="water", celcius=True), get_melting_point(substance="ice", kelvin=False)]' ) expected = [ ("get_boiling_point", {"liquid_name": "water", "celcius": True}), ("get_melting_point", {"substance": "ice", "kelvin": False}), ] assert parse_python_list_for_function_calls(input_string) == expected def test_function_call_with_numeric_args(self): input_string = "[calculate_volume(radius=5.2, height=10)]" expected = [("calculate_volume", {"radius": 5.2, "height": 10})] assert parse_python_list_for_function_calls(input_string) == expected def test_function_call_with_mixed_type_args(self): input_string = '[process_data(name="sample", value=42, active=True, ratio=3.14)]' expected = [ ( "process_data", {"name": "sample", "value": 42, "active": True, "ratio": 3.14}, ) ] assert parse_python_list_for_function_calls(input_string) == expected def test_function_call_with_empty_args(self): input_string = "[initialize()]" expected = [("initialize", {})] assert parse_python_list_for_function_calls(input_string) == expected def test_function_call_with_string_containing_spaces(self): input_string = '[analyze_text(content="Hello, world!", language="en")]' expected = [("analyze_text", {"content": "Hello, world!", "language": "en"})] assert parse_python_list_for_function_calls(input_string) == expected def test_function_names_with_underscores_lists_and_dicts(self): input_string = '[get_boiling_point(liquid={"name":"water", "color":"translucent"}), convert_temperature(value=[100, 101], from_unit="C", to_unit="F")]' expected = [ ( "get_boiling_point", {"liquid": {"name": "water", "color": "translucent"}}, ), ( "convert_temperature", {"value": [100, 101], "from_unit": "C", "to_unit": "F"}, ), ] assert parse_python_list_for_function_calls(input_string) == expected ================================================ FILE: models/llama3/tokenizer.model ================================================ IQ== 0 Ig== 1 Iw== 2 JA== 3 JQ== 4 Jg== 5 Jw== 6 KA== 7 KQ== 8 Kg== 9 Kw== 10 LA== 11 LQ== 12 Lg== 13 Lw== 14 MA== 15 MQ== 16 Mg== 17 Mw== 18 NA== 19 NQ== 20 Ng== 21 Nw== 22 OA== 23 OQ== 24 Og== 25 Ow== 26 PA== 27 PQ== 28 Pg== 29 Pw== 30 QA== 31 QQ== 32 Qg== 33 Qw== 34 RA== 35 RQ== 36 Rg== 37 Rw== 38 SA== 39 SQ== 40 Sg== 41 Sw== 42 TA== 43 TQ== 44 Tg== 45 Tw== 46 UA== 47 UQ== 48 Ug== 49 Uw== 50 VA== 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111982 6KOc 111983 IGTDvG55YQ== 111984 INC30LDQu9C40Yg= 111985 0LTQsNC10YI= 111986 zr3OtQ== 111987 0LjRh9C10YHQutC+0LPQvg== 111988 7Iqk7YWc 111989 INCR0LXRgA== 111990 INC00LY= 111991 INC+0L/QsNGB 111992 z4bOsQ== 111993 IHp2bMOh 111994 IHTDtA== 111995 0LHQtdGA 111996 IM6czrHPgQ== 111997 dGnEn2luaQ== 111998 44Os44Oz 111999 IEtobw== 112000 INGW0L3RiA== 112001 IO+/pQ== 112002 7LCs 112003 772h 112004 INC90L7Rhw== 112005 6KiK 112006 xJt0aQ== 112007 5b+Z 112008 INqp2LHYr9mG2K8= 112009 IMSR4bqpeQ== 112010 INGB0LrQsNC30LDQsg== 112011 64Ol 112012 5bGs 112013 IOCktuCkueCksA== 112014 INqp2YXaqQ== 112015 wqDQnw== 112016 xLFuY2E= 112017 0L3RltCy0LXRgNGB0LjRgg== 112018 INqv2YjZhtmH 112019 IFRvcGxhbQ== 112020 IGnFn2FyZXQ= 112021 5L2g5Lus 112022 IGRlcmVjZQ== 112023 IOyCrOyLpA== 112024 IOyekOq4sA== 112025 5a6e546w 112026 55Sf54mp 112027 44Gu5LiA 112028 INGA0L7QvA== 112029 2YjYstmH 112030 IOOBqA== 112031 7ZmN 112032 2YrZgg== 112033 IOWQjeeEoeOBl+OBleOCkw== 112034 INm+24zYsQ== 112035 INC/0L7Qu9C10Lc= 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IGzDoXRreQ== 127968 INCR0Lg= 127969 IHPDvHJlY2k= 127970 wrfCt8K3wrc= 127971 IOqyveywsA== 127972 INC60LDQu9GM 127973 INC90LjQutGC0L4= 127974 2ZHZhQ== 127975 INiv2Yrar9ix 127976 IGFsxLFubWFzxLE= 127977 0LvQtdC90L3Rlg== 127978 4Li04Lin4LmA4LiV4Lit4Lij 127979 4Lib4LiB4LiE4Lij4Lit4LiH 127980 INC30LDQutC+0L3QvtC00LDQstGB0YLQstCw 127981 44CA44Kk 127982 IOuFuO2VmOyasA== 127983 IETDvMWf 127984 INCz0YPRgdGC 127985 INCS0LDRiA== 127986 INin2YXYqtuM 127987 IHBhcmFtZXQ= 127988 IM6gzrHOvc61z4A= 127989 4LmM4LiB4Lij 127990 zrbOsQ== 127991 IOuNlOyasQ== 127992 2YjZhNin2Ko= 127993 0LLQsNGC0LjRgdGP 127994 IGvDtms= 127995 2YbYqA== 127996 INCy0YvRgdC+0LrQvtC5 127997 44O844O8 127998 6ZSm 127999 ================================================ FILE: models/llama3/tokenizer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # Copyright (c) Meta Platforms, Inc. and affiliates. # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement. from logging import getLogger from pathlib import Path from typing import ( AbstractSet, Collection, Dict, Iterator, List, Literal, Optional, Sequence, Union, cast, ) import tiktoken from ..tokenizer_utils import load_bpe_file logger = getLogger(__name__) # The tiktoken tokenizer can handle <=400k chars without # pyo3_runtime.PanicException. TIKTOKEN_MAX_ENCODE_CHARS = 400_000 # https://github.com/openai/tiktoken/issues/195 # Here we iterate over subsequences and split if we exceed the limit # of max consecutive non-whitespace or whitespace characters. MAX_NO_WHITESPACES_CHARS = 25_000 _INSTANCE = None class Tokenizer: """ Tokenizing and encoding/decoding text using the Tiktoken tokenizer. """ special_tokens: Dict[str, int] num_reserved_special_tokens = 256 pat_str = r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+" # noqa: E501 @classmethod def get_instance(cls): global _INSTANCE if _INSTANCE is None: _INSTANCE = Tokenizer(Path(__file__).parent / "tokenizer.model") return _INSTANCE def __init__(self, model_path: Path): """ Initializes the Tokenizer with a Tiktoken model. Args: model_path (str): The path to the Tiktoken model file. """ if not model_path.exists(): raise FileNotFoundError(f"Tokenizer model file not found: {model_path}") mergeable_ranks = load_bpe_file(model_path) num_base_tokens = len(mergeable_ranks) special_tokens = [ "<|begin_of_text|>", "<|end_of_text|>", "<|reserved_special_token_0|>", "<|reserved_special_token_1|>", "<|finetune_right_pad_id|>", "<|step_id|>", "<|start_header_id|>", "<|end_header_id|>", "<|eom_id|>", # end of message "<|eot_id|>", # end of turn "<|python_tag|>", "<|image|>", ] reserved_tokens = [ f"<|reserved_special_token_{2 + i}|>" for i in range(self.num_reserved_special_tokens - len(special_tokens)) ] special_tokens = special_tokens + reserved_tokens self.special_tokens = {token: num_base_tokens + i for i, token in enumerate(special_tokens)} self.model = tiktoken.Encoding( name=model_path.name, pat_str=self.pat_str, mergeable_ranks=mergeable_ranks, special_tokens=self.special_tokens, ) self.n_words: int = num_base_tokens + len(special_tokens) # BOS / EOS token IDs self.bos_id: int = self.special_tokens["<|begin_of_text|>"] self.eos_id: int = self.special_tokens["<|end_of_text|>"] self.eot_id: int = self.special_tokens["<|eot_id|>"] self.eom_id: int = self.special_tokens["<|eom_id|>"] self.python_tag_id = self.special_tokens["<|python_tag|>"] self.pad_id: int = self.special_tokens["<|finetune_right_pad_id|>"] self.stop_tokens = [ self.eos_id, self.special_tokens["<|eom_id|>"], self.special_tokens["<|eot_id|>"], ] def encode( self, s: str, *, bos: bool, eos: bool, allowed_special: Optional[Union[Literal["all"], AbstractSet[str]]] = None, disallowed_special: Union[Literal["all"], Collection[str]] = (), ) -> List[int]: """ Encodes a string into a list of token IDs. Args: s (str): The input string to be encoded. bos (bool): Whether to prepend the beginning-of-sequence token. eos (bool): Whether to append the end-of-sequence token. allowed_special ("all"|set[str]): allowed special tokens in string disallowed_special ("all"|set[str]): special tokens that raise an error when in string Returns: list[int]: A list of token IDs. By default, setting disallowed_special=() encodes a string by ignoring special tokens. Specifically: - Setting `disallowed_special` to () will cause all text corresponding to special tokens to be encoded as natural text (insteading of raising an error). - Setting `allowed_special` to "all" will treat all text corresponding to special tokens to be encoded as special tokens. """ if allowed_special is None: allowed_special = set() assert type(s) is str substrs = ( substr for i in range(0, len(s), TIKTOKEN_MAX_ENCODE_CHARS) for substr in self._split_whitespaces_or_nonwhitespaces( s[i : i + TIKTOKEN_MAX_ENCODE_CHARS], MAX_NO_WHITESPACES_CHARS ) ) t: List[int] = [] for substr in substrs: t.extend( self.model.encode( substr, allowed_special=allowed_special, disallowed_special=disallowed_special, ) ) if bos: t.insert(0, self.bos_id) if eos: t.append(self.eos_id) return t def decode(self, t: Sequence[int]) -> str: """ Decodes a list of token IDs into a string. Args: t (List[int]): The list of token IDs to be decoded. Returns: str: The decoded string. """ # Typecast is safe here. Tiktoken doesn't do anything list-related with the sequence. return self.model.decode(cast(List[int], t)) @staticmethod def _split_whitespaces_or_nonwhitespaces(s: str, max_consecutive_slice_len: int) -> Iterator[str]: """ Splits the string `s` so that each substring contains no more than `max_consecutive_slice_len` consecutive whitespaces or consecutive non-whitespaces. """ current_slice_len = 0 current_slice_is_space = s[0].isspace() if len(s) > 0 else False slice_start = 0 for i in range(len(s)): is_now_space = s[i].isspace() if current_slice_is_space ^ is_now_space: current_slice_len = 1 current_slice_is_space = is_now_space else: current_slice_len += 1 if current_slice_len > max_consecutive_slice_len: yield s[slice_start:i] slice_start = i current_slice_len = 1 yield s[slice_start:] ================================================ FILE: models/llama3/tool_utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import ast import json import re from typing import Optional, Tuple from ..datatypes import BuiltinTool, RecursiveType, ToolCall, ToolPromptFormat BUILTIN_TOOL_PATTERN = r'\b(?P\w+)\.call\(query="(?P[^"]*)"\)' CUSTOM_TOOL_CALL_PATTERN = re.compile(r"[^}]+)>(?P{.*?})") def is_json(s): try: parsed = json.loads(s) # Return True for valid objects and not for ints, strings, etc return isinstance(parsed, dict) except json.JSONDecodeError: return False return True def is_valid_python_list(input_string): """Check if the input string is a valid Python list of function calls""" try: # Try to parse the string tree = ast.parse(input_string) # Check if it's a single expression if len(tree.body) != 1 or not isinstance(tree.body[0], ast.Expr): return False # Check if the expression is a list expr = tree.body[0].value if not isinstance(expr, ast.List): return False # Check if the list is empty if len(expr.elts) == 0: return False # Check if all elements in the list are function calls for element in expr.elts: if not isinstance(element, ast.Call): return False # Check if the function call has a valid name if not isinstance(element.func, ast.Name): return False # Check if all arguments are keyword arguments if element.args or not all(isinstance(arg, ast.keyword) for arg in element.keywords): return False return True except SyntaxError: # If parsing fails, it's not a valid Python expression return False def parse_python_list_for_function_calls(input_string): """ Parse a Python list of function calls and return a list of tuples containing the function name and arguments """ # Parse the string into an AST tree = ast.parse(input_string) # Ensure the input is a list if not isinstance(tree.body[0], ast.Expr) or not isinstance(tree.body[0].value, ast.List): raise ValueError("Input must be a list of function calls") result = [] # Iterate through each function call in the list for node in tree.body[0].value.elts: if isinstance(node, ast.Call): function_name = node.func.id function_args = {} # Extract keyword arguments for keyword in node.keywords: function_args[keyword.arg] = ast.literal_eval(keyword.value) result.append((function_name, function_args)) return result class ToolUtils: @staticmethod def is_builtin_tool_call(message_body: str) -> bool: match = re.search(ToolUtils.BUILTIN_TOOL_PATTERN, message_body) return match is not None @staticmethod def maybe_extract_builtin_tool_call(message_body: str) -> Optional[Tuple[str, str]]: # Find the first match in the text match = re.search(BUILTIN_TOOL_PATTERN, message_body) # Check if a match is found and return it if match: tool_name = match.group("tool_name") query = match.group("query") return tool_name, query else: return None @staticmethod def maybe_extract_custom_tool_call(message_body: str) -> Optional[Tuple[str, str]]: # NOTE: Custom function too calls are still experimental # Sometimes, response is of the form # {"type": "function", "name": "function_name", "parameters": {...} # and some times # (parameters) # Find the first match in the text match = re.search(CUSTOM_TOOL_CALL_PATTERN, message_body) if match: tool_name = match.group("function_name") query = match.group("args") try: return tool_name, json.loads(query.replace("'", '"')) except Exception as e: print("Exception while parsing json query for custom tool call", query, e) return None elif is_json(message_body): response = json.loads(message_body) if ("type" in response and response["type"] == "function") or ("name" in response): function_name = response["name"] args = response["parameters"] return function_name, args else: return None elif is_valid_python_list(message_body): res = parse_python_list_for_function_calls(message_body) # FIXME: Enable multiple tool calls return res[0] else: return None @staticmethod def encode_tool_call(t: ToolCall, tool_prompt_format: ToolPromptFormat) -> str: if t.tool_name == BuiltinTool.brave_search: q = t.arguments["query"] return f'brave_search.call(query="{q}")' elif t.tool_name == BuiltinTool.wolfram_alpha: q = t.arguments["query"] return f'wolfram_alpha.call(query="{q}")' elif t.tool_name == BuiltinTool.photogen: q = t.arguments["query"] return f'photogen.call(query="{q}")' elif t.tool_name == BuiltinTool.code_interpreter: return t.arguments["code"] else: fname = t.tool_name if tool_prompt_format == ToolPromptFormat.json: return json.dumps( { "type": "function", "name": fname, "parameters": t.arguments, } ) elif tool_prompt_format == ToolPromptFormat.function_tag: args = json.dumps(t.arguments) return f"{args}" elif tool_prompt_format == ToolPromptFormat.python_list: def format_value(value: RecursiveType) -> str: if isinstance(value, str): return f'"{value}"' elif isinstance(value, (int, float, bool)) or value is None: return str(value) elif isinstance(value, list): return f"[{', '.join(format_value(v) for v in value)}]" elif isinstance(value, dict): return f"{{{', '.join(f'{k}={format_value(v)}' for k, v in value.items())}}}" else: raise ValueError(f"Unsupported type: {type(value)}") args_str = ", ".join(f"{k}={format_value(v)}" for k, v in t.arguments.items()) return f"[{fname}({args_str})]" else: raise ValueError(f"Unsupported tool prompt format: {tool_prompt_format}") ================================================ FILE: models/llama3_1/LICENSE ================================================ LLAMA 3.1 COMMUNITY LICENSE AGREEMENT Llama 3.1 Version Release Date: July 23, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.1” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.  iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. 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The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.  7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.  ================================================ FILE: models/llama3_1/MODEL_CARD.md ================================================ ## Model Information The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. **Model developer:** Meta **Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
Training Data Params Input modalities Output modalities Context length GQA Token count Knowledge cutoff
Llama 3.1 (text only) A new mix of publicly available online data. 8B Multilingual Text Multilingual Text and code 128k Yes 15T+ December 2023
70B Multilingual Text Multilingual Text and code 128k Yes
405B Multilingual Text Multilingual Text and code 128k Yes
**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. **Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** July 23, 2024. **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) **Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card. **Note:** Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. **Training Energy Use** Training utilized a cumulative of **39.3**M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. **Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
Training Time (GPU hours) Training Power Consumption (W) Training Location-Based Greenhouse Gas Emissions

(tons CO2eq)

Training Market-Based Greenhouse Gas Emissions

(tons CO2eq)

Llama 3.1 8B 1.46M 700 420 0
Llama 3.1 70B 7.0M 700 2,040 0
Llama 3.1 405B 30.84M 700 8,930 0
Total 39.3M
11,390 0
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples. **Data Freshness:** The pretraining data has a cutoff of December 2023. ## Benchmark scores In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. Details of our evals can be found [here](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/eval_details.md). We are also releasing the raw data generated as part of our evals which can be found [here](https://huggingface.co/meta-llama) in the dataset sections. ### Base pretrained models
Category Benchmark # Shots Metric Llama 3 8B Llama 3.1 8B Llama 3 70B Llama 3.1 70B Llama 3.1 405B
General MMLU 5 macro_avg/acc_char 66.7 66.7 79.5 79.3 85.2
MMLU-Pro (CoT) 5 macro_avg/acc_char 36.2 37.1 55.0 53.8 61.6
AGIEval English 3-5 average/acc_char 47.1 47.8 63.0 64.6 71.6
CommonSenseQA 7 acc_char 72.6 75.0 83.8 84.1 85.8
Winogrande 5 acc_char - 60.5 - 83.3 86.7
BIG-Bench Hard (CoT) 3 average/em 61.1 64.2 81.3 81.6 85.9
ARC-Challenge 25 acc_char 79.4 79.7 93.1 92.9 96.1
Knowledge reasoning TriviaQA-Wiki 5 em 78.5 77.6 89.7 89.8 91.8
Reading comprehension SQuAD 1 em 76.4 77.0 85.6 81.8 89.3
QuAC (F1) 1 f1 44.4 44.9 51.1 51.1 53.6
BoolQ 0 acc_char 75.7 75.0 79.0 79.4 80.0
DROP (F1) 3 f1 58.4 59.5 79.7 79.6 84.8
### Instruction tuned models
Category Benchmark # Shots Metric Llama 3 8B Instruct Llama 3.1 8B Instruct Llama 3 70B Instruct Llama 3.1 70B Instruct Llama 3.1 405B Instruct
General MMLU 5 macro_avg/acc 68.5 69.4 82.0 83.6 87.3
MMLU (CoT) 0 macro_avg/acc 65.3 73.0 80.9 86.0 88.6
MMLU-Pro (CoT) 5 macro_avg/acc 45.5 48.3 63.4 66.4 73.3
IFEval 76.8 80.4 82.9 87.5 88.6
Reasoning ARC-C 0 acc 82.4 83.4 94.4 94.8 96.9
GPQA 0 em 34.6 30.4 39.5 46.7 50.7
Code HumanEval 0 pass@1 60.4 72.6 81.7 80.5 89.0
MBPP ++ base version 0 pass@1 70.6 72.8 82.5 86.0 88.6
Multipl-E HumanEval 0 pass@1 - 50.8 - 65.5 75.2
Multipl-E MBPP 0 pass@1 - 52.4 - 62.0 65.7
Math GSM-8K (CoT) 8 em_maj1@1 80.6 84.5 93.0 95.1 96.8
MATH (CoT) 0 final_em 29.1 51.9 51.0 68.0 73.8
Tool Use API-Bank 0 acc 48.3 82.6 85.1 90.0 92.0
BFCL 0 acc 60.3 76.1 83.0 84.8 88.5
Gorilla Benchmark API Bench 0 acc 1.7 8.2 14.7 29.7 35.3
Nexus (0-shot) 0 macro_avg/acc 18.1 38.5 47.8 56.7 58.7
Multilingual Multilingual MGSM (CoT) 0 em - 68.9 - 86.9 91.6
#### Multilingual benchmarks
Category Benchmark Language Llama 3.1 8B Instruct Llama 3.1 70B Instruct Llama 3.1 405B Instruct
General MMLU (5-shot, macro_avg/acc) Portuguese 62.12 80.13 84.95
Spanish 62.45 80.05 85.08
Italian 61.63 80.4 85.04
German 60.59 79.27 84.36
French 62.34 79.82 84.66
Hindi 50.88 74.52 80.31
Thai 50.32 72.95 78.21
## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: * Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. * Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm. * Provide protections for the community to help prevent the misuse of our models. ### Responsible deployment Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more. #### Llama 3.1 instruct Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper. **Fine-tuning data** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals and Tone** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.1 systems **Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. #### New capabilities Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases. **Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards. **Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide. ### Evaluations We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization. **Red teaming** For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. . ### Critical and other risks We specifically focused our efforts on mitigating the following critical risk areas: **1. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness** To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. **2. Child Safety** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. **3. Cyber attack enablement** Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Our study of Llama 3.1 405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development. ================================================ FILE: models/llama3_1/USE_POLICY.md ================================================ **Llama 3.1** **Acceptable Use Policy** Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy (“**Policy**”). The most recent copy of this policy can be found at https://llama.meta.com/llama3_1/use-policy. **Prohibited Uses** We want everyone to use Llama 3.1 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.1 to: * Violate the law or others’ rights, including to: * Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: * Violence or terrorism * Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material * Human trafficking, exploitation, and sexual violence * The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. * Sexual solicitation * Any other criminal activity * Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals * Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services * Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices * Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws * Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials * Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system * Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.1 related to the following: * Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State * Guns and illegal weapons (including weapon development) * Illegal drugs and regulated/controlled substances * Operation of critical infrastructure, transportation technologies, or heavy machinery * Self-harm or harm to others, including suicide, cutting, and eating disorders * Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual * Intentionally deceive or mislead others, including use of Llama 3.1 related to the following: * Generating, promoting, or furthering fraud or the creation or promotion of disinformation * Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content * Generating, promoting, or further distributing spam * Impersonating another individual without consent, authorization, or legal right * Representing that the use of Llama 3.1 or outputs are human-generated * Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement * Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: https://github.com/meta-llama/llama-models/issues * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.1: LlamaUseReport@meta.com ================================================ FILE: models/llama3_1/eval_details.md ================================================ # Llama 3 Evaluation Details This document contains some additional context on the settings and methodology for how we evaluated the Llama 3.1 8B, 70B, and 405B pre-trained and post-trained models. ## Language auto-eval benchmark notes: For a given benchmark, we strive to use consistent evaluation settings across all models, including external models. We make every effort to achieve optimal scores for external models, including addressing any model-specific parsing and tokenization requirements. Where the scores are lower for external models than self-reported scores on comparable or more conservative settings, we report the self-reported scores for external models. We are also releasing the data generated as part of evaluations with publicly available benchmarks which can be found on [Llama 3.1 Evals Huggingface collection](https://huggingface.co/collections/meta-llama/llama-31-evals-66a2c5a14c2093e58298ac7f). We have also developed a [eval reproduction recipe](https://github.com/meta-llama/llama-recipes/tree/b5f64c0b69d7ff85ec186d964c6c557d55025969/tools/benchmarks/llm_eval_harness/meta_eval_reproduce) that demonstrates how to closely reproduce the Llama 3.1 reported benchmark numbers using the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/main) library and the datasets in [3.1 evals collections](https://huggingface.co/collections/meta-llama/llama-31-evals-66a2c5a14c2093e58298ac7f) on selected tasks. ### MMLU For the pre-trained models we use a 5-shot config. To determine the choice character we use the standard MMLU prompt and compare the negative log-likelihood (NLL) of the various choices. For the post-trained models we report both 5-shot and 0-shot scores. We ask the model to generate the best choice character. The 0-shot scores use a CoT (chain of thought) prompt. The maximum generation lengths for the 5-shot and 0-shot configs are 10 tokens and 1024 tokens respectively. Macro averages are reported unless otherwise stated. The micro average scores for the various models are: 65.6, 79.0, and 85.4 for the pre-trained 8B, 70B and 405B models respectively for the 5-shot config; 69.44, 84.0, 87.71 for the post-trained 8B, 70B and 405B models respectively for the 5-shot config. ### MMLU-Pro For the pre-trained and post-trained models we use a 5-shot config with CoT prompt. We ask the model to generate the reasoning and the corresponding best choice character. The maximum generation length is 512 tokens for pre-trained setup and 1024 for post-trained setup. Macro averages are reported unless otherwise stated. The micro average scores for the various models are: 35.6, 52.0, and 59.6 for the pre-trained 8B, 70B and 405B models; 47.0, 65.1, 72.2 for the post-trained 8B, 70B and 405B models. ### ARC-Challenge We use the Arc-Challenge subset from the Arc benchmark. For the pre-trained models, we use a 25-shot config and use the MMLU setup for evaluation where we provide all the choices in the prompt and calculate likelihood over choice characters. For the post-trained models, we use 0-shot config and ask the model to generate the choice character. The maximum generation length is 100 tokens. ### GPQA For post-trained models, we use 0-shot config with and without CoT prompt and report exact match scores over the possible options using the main set. Maximum generation length is 96 tokens when not using CoT prompt and 2048 tokens when using the CoT prompt. ### AGIEval English For pre-trained models, we use the default few-shot and prompt settings as specified [here](https://github.com/ruixiangcui/AGIEval). The score is averaged over the english subtasks. The maximum generation length is 10 tokens. ### IFEval For post-trained models, we use the default settings as specified [here](https://arxiv.org/pdf/2311.07911). We compute the prompt level scores and instruction level strict and loose accuracy. We then report the average across all the scores. ### HumanEval/HumanEval+ For both pre-trained and post-trained models, we use a 0-shot config and report pass@1 scores. The maximum generation length is 1024 tokens. ### CommonSenseQA For pre-trained models, we use the same 7-shot config with CoT prompt as in [Wei et al. (2022)](https://arxiv.org/pdf/2201.11903.pdf). We use the MMLU setup for evaluation where we provide all the choices in the prompt and calculate likelihood over choice characters. ### WinoGrande For pre-trained models, we use a choice based setup for evaluation where we fill in the missing blank with the two possible choices and then compute log-likelihood over the suffix. We use a 5-shot config. We use the MMLU setup where we provide all the choices in the prompt and calculate likelihood over choice characters. ### BIG-Bench Hard For pre-trained models, we use a 3-shot config with CoT prompt and compute the average exact match over the subsets in this task. We run this as a generative task. Maximum generation length is 512 tokens. ### SQuAD For pre-trained models, we use SQuAD v2 with a 1-shot config and report exact match scores. We run this as a generative task. Maximum generation length is 32 tokens. In the prompt, we include the ground truth Q & A pairs for all previous questions pertaining to the same passage. In short, the prompt template takes the form "{few-shot example} {passage} {all previous Q & A pairs for passage} {input question}". For specifics, see the released [evaluation details dataset](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-8B-evals/viewer/Meta-Llama-3.1-8B-evals__squad__details). ### QuAC For pre-trained models, we use a 1-shot config and report the F1 scores. We run this as a generative task. Maximum generation length is 32 tokens. ### BoolQ For pre-trained models, we use a 0-shot config and report average accuracy. We run this as a choice task. ### DROP For pre-trained models, for each validation example, we draw 3 random few-shot examples from the train split and report the F1 scores. The maximum generation length is 32 tokens. ### GSM8K For both pre-trained and post-trained models, we use the same 8-shot config with CoT prompt as in [Wei et al. (2022)](https://arxiv.org/pdf/2201.11903.pdf) (maj@1). The maximum generation length is 1024 tokens. ### RACE For pre-trained models, we use a 0-shot config. We run this as a choice task. We use the MMLU setup for evaluation where we provide all the choices in the prompt and calculate likelihood over choice characters. ### WorldSense For pre-trained models, we use a 0-shot config. We run this as a choice task. Unlike the original benchmark, we do not normalize the three-option partitions of the benchmark. The chance accuracy is therefore not 0.5, but averages to 0.46. ### MBPP For pre-trained and post-trained models we use a 3-shot config and report pass@1 scores. We run this as a generative task. Maximum generation length is 256 tokens. ### MBPP EvalPlus (base) For pre-trained and post-trained models we use a 0-shot config and report pass@1 scores. We run this as a generative task. Maximum generation length is 1024 tokens. ### MATH For pre-trained models, we use the same 4-shot config as in [Lewkowycz et al. (2022)](https://arxiv.org/pdf/2206.14858.pdf) (maj@1). Maximum generation length is 512 tokens. For post-trained models we use a 0-shot config with Cot prompt. We enhance the exact match using [sympy](https://www.sympy.org/en/index.html) and then use an [equality template](https://github.com/openai/simple-evals/blob/main/common.py#L27-L85) with a judge to resolve complex expressions. Maximum generation length is 5120 tokens. The MATH score represents the full dataset. The scores for MATH-HARD (Lvl 5) are 25.4, 43.8, and 53.4 for the 8B, 70B and 405B models respectively. ### SCROLLS For pre-trained models, we use a 5-shot config. Maximum generation length is 32 tokens. Maximum input prompt length is 131072 less the number of tokens generated (i.e. 131040). ### ZeroSCROLLS For post-trained models, we use a 0-shot config. Maximum generation length for QuALITY and SQuALITY is 64 tokens. For Qasper it is 128 tokens. Maximum input prompt length for Llama models is 131072 less the number of tokens generated for each task (i.e. 131008 for QuALITY and SQuALITY and 130944 for Qasper). Maximum input length for non-llama models is 128000 less the number of tokens generated for each task. We ensure that all relevant information is retained in the context for all models for fair comparison. ### InfiniteBench For post-trained models, we use a 0-shot config. Maximum generation length is 20 for both the En.QA and En.MC tasks and maximum input prompt length is 131052. Maximum input length for non-llama models is 127980. We ensure that all relevant information is retained in the context for all models for fair comparison. ### NIH/Multi-needle For post-training, we use a 0-shot config. Our context lengths are evenly spaced between 2000 and 131072 in 10 intervals, inclusive of the endpoints for llama models and between 2000 and 128000 for non-llama models. Maximum generation length is 256 tokens. ### Multilingual MGSM For post-trained models, we use an 0-shot config with CoT prompt and report exact match (maj@1) scores. Maximum generation length is 2048 tokens. The scores are averaged over all the eleven languages present in the MGSM benchmark, including the ones not supported by Llama models. ### Multilingual MMLU For post-trained models, we use a 5-shot config. We run this as a generative task. Maximum generation length is 10 tokens. The scores are individually reported for each and averaged over the seven non-english languages that Llama models support (Portuguese, Spanish, Italian, German, French, Hindi, Thai). ### Multipl-E HumanEval and Multipl-E MBPP For post-trained models, we use a 0-shot config and report pass@1 scores. Maximum generation length is 512 tokens. Where Multipl-E average is reported, the scores are averaged over all 6 languages in the benchmark. ### PiQA, SiQA, and OpenBookQA For pre-trained models, we use a 0-shot config and report average accuracy. We run these as choice task. ### Dynabench SQuAD and Adversarial SQuAD For the adversarial versions of squad ([Dynabench](https://aclanthology.org/2021.naacl-main.324/) and [Adversarial](https://aclanthology.org/D17-1215/)), we use the same setting as standard SQuAD (1-shot config and exact match as the metric) ### PAWS For pre-trained models, we use a 5-shot config and report exact match scores. We run this as a generative task. Maximum generation length is 32 tokens. ### GSM Plus For pre-trained models, we use the same 8-shot config with CoT prompt as in [Wei et al. (2022)](https://arxiv.org/pdf/2201.11903.pdf) (maj@1). The maximum generation length is 512 tokens. ### Berkeley Function Calling Leaderboard (BFCL) Benchmark results were achieved by running the open source evaluation repository [ShishirPatil/gorilla](https://github.com/ShishirPatil/gorilla/tree/main/berkeley-function-call-leaderboard) on commit 7bef000 without any further changes. ### Nexus We use the [open-source ](https://github.com/nexusflowai/NexusRaven)prompt and evaluation function followed by the[ open source notebook](https://github.com/nexusflowai/NexusRaven-V2/blob/master/evaluation_notebook/GPT4_Evaluation/Benchmark_GPT4.ipynb) to compute the scores. ### API Bank We use a 0-shot config with a custom prompt and parsing function to reduce the incidence of false negatives. We also modify the dataset by correcting and completing the ground truth answers that were initially incorrect or incomplete. Second, we improve the evaluation metric to better assess function call correctness by splitting keyword arguments into two groups. We use exact match for keyword arguments that have a unique ground truth, and ROUGE score for those that accept any string with the same semantic meaning as the reference value. ### Gorilla API Bench For post-trained models, we use the same 0-shot prompt and evaluation function as proposed in the [original paper](https://arxiv.org/abs/2305.15334). Just like the [open-source](https://github.com/ShishirPatil/gorilla) implementation, we compare the domains of the retrieved API call from the API database with the ground truth. If the domain of the retrieved API is the same as the ground truth and the API exists in the database, it is considered a success. All other scenarios are considered failures. ### TriviaQA-WIKI For TrivialQA, we evaluate on the Wiki validation set, use 5-shot config and compute average exact match. We run this as a generative task. Maximum generation length is 24 tokens. ================================================ FILE: models/llama3_1/prompt_format.md ================================================ # Llama 3.1 - Prompt Formats ## Tokens Here is a list of special tokens that are supported by Llama 3.1: - `<|begin_of_text|>`: Specifies the start of the prompt - `<|end_of_text|>`: Model will cease to generate more tokens. This token is generated only by the base models. - `<|finetune_right_pad_id|>`: This token is used for padding text sequences to the same length in a batch. - `<|start_header_id|>` and `<|end_header_id|>`: These tokens enclose the role for a particular message. The possible roles are: [system, user, assistant and ipython] - `<|eom_id|>`: End of message. A message represents a possible stopping point for execution where the model can inform the executor that a tool call needs to be made. This is used for multi-step interactions between the model and any available tools. This token is emitted by the model when the Environment: ipython instruction is used in the system prompt, or if the model calls for a built-in tool. - `<|eot_id|>`: End of turn. Represents when the model has determined that it has finished interacting with the user message that initiated its response. This is used in two scenarios: - at the end of a direct interaction between the model and the user - at the end of multiple interactions between the model and any available tools This token signals to the executor that the model has finished generating a response. - `<|python_tag|>`: Is a special tag used in the model's response to signify a tool call. There are 4 different roles that are supported by Llama 3.1 - `system`: Sets the context in which to interact with the AI model. It typically includes rules, guidelines, or necessary information that helps the model respond effectively. - `user`: Represents the human interacting with the model. It includes the inputs, commands, and questions to the model. - `ipython`: A new role introduced in Llama 3.1. Semantically, this role means "tool". This role is used to mark messages with the output of a tool call when sent back to the model from the executor. - `assistant`: Represents the response generated by the AI model based on the context provided in the `system`, `ipython` and `user` prompts. ## Llama 3.1 Base Model Text completion for Llama 3.1 base model uses this format. ##### Input Prompt Format ``` <|begin_of_text|>Color of sky is blue but sometimes can also be ``` ##### Model Response Format ``` red, orange, yellow, green, purple, pink, brown, gray, black, white, and even rainbow colors. The color of the sky can change due to various reasons such as time of day, weather conditions, pollution, and atmospheric phenomena. The color of the sky is primarily blue because of a phenomenon called ``` Note start special tag ## Llama 3.1 Instruct Model ## User and assistant conversation Here is a regular multi-turn user assistant conversation and how its formatted. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|> Answer who are you in the form of jeopardy?<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` Here's my response "What is a helpful assistant?"<|eot_id|> ``` ## Tool Calling Formats The three built-in tools (brave_search, wolfram_alpha, and code interpreter) can be turned on using the system prompt: - Brave Search: Tool call to perform web searches. - Wolfram Alpha: Tool call to perform complex mathematical calculations. - Code Interpreter: Enables the model to output python code. ## Builtin Tool Calling Here is an example of a conversation using brave search ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython Tools: brave_search, wolfram_alpha Cutting Knowledge Date: December 2023 Today Date: 21 September 2024 You are a helpful assistant. <|eot_id|><|start_header_id|>user<|end_header_id|> Search the web for the latest price of 1oz gold?<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` <|python_tag|>brave_search.call(query="latest price of 1oz gold")<|eom_id|> ``` - Just including Environment: ipython turns on code interpreter; therefore, you don't need to specify code interpretation on the Tools: line. The model can generate python code which is interpreted by the executor, with the result provided back to the model. - The message body of the assistant response starts with a special tag <|python_tag|> - As alluded to above, in such an environment, the model can generate <|eom_id|> instead of just the standard <|eot_id|> . The latter indicates the turn is finished, while the former indicates continued multi-step reasoning. That is, the model is expecting a continuation message with the output of the tool call. - The model tool call response is of the form `tool.call(query="...")` wher tool is `brave_search` or `wolfram_alpha` ## Builtin Code Interpreter Here is an actual example of model responding with code ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython<|eot_id|><|start_header_id|>user<|end_header_id|> Write code to check if number is prime, use that to see if the number 7 is prime<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` <|python_tag|>def is_prime(n): if n <= 1 return False for i in range(2, int(n**0.5) + 1): if n % i == 0: return False return True print(is_prime(7)) # Output: True<|eom_id|> ``` - Model starts with <|python_tag|> and continues writing python code that it needs to be executed - No explicit mention of code_interpreter in system prompt. `Environment: ipython` implicitly enables it. ## Built-in tools full interaction Here is a full interaction with the built-in tools including the tool response and the final assistant response. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython Tools: brave_search, wolfram_alpha <|eot_id|><|start_header_id|>user<|end_header_id|> What is the 100th decimal of pi?<|eot_id|><|start_header_id|>assistant<|end_header_id|> <|python_tag|>wolfram_alpha.call(query="100th decimal of pi")<|eom_id|><|start_header_id|>ipython<|end_header_id|> { "queryresult": { "success": true, "inputstring": "100th decimal of pi", "pods": [ { "title": "Input interpretation", "subpods": [ { "title": "", "plaintext": "100th digit | π" } ] }, { "title": "Nearby digits", "subpods": [ { "title": "", "plaintext": "...86208998628034825342117067982148086513282306647093..." } ] }, { "title": "Result", "primary": true, "subpods": [ { "title": "", "plaintext": "7" } ] } ] } } <|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` The 100th decimal of pi is 7.<|eot_id|> ``` - Note the `<|python_tag|>` in the assistant response. - Role is `ipython` for the wolfram alpha response that is passed back to the model. - Final message from assistant has <|eot_id|> tag. ## Zero shot tool calling ## JSON based tool calling Llama models can now output custom tool calls from a single message to allow easier tool calling. The following prompts provide an example of how custom tools can be called from the output of the model. It's important to note that the model itself does not execute the calls; it provides structured output to facilitate calling by an executor. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython Cutting Knowledge Date: December 2023 Today Date: 21 September 2024 You are a helpful assistant. <|eot_id|><|start_header_id|>user<|end_header_id|> Answer the user's question by making use of the following functions if needed. If none of the function can be used, please say so. Here is a list of functions in JSON format: { "type": "function", "function": { "name": "trending_songs", "description": "Returns the trending songs on a Music site", "parameters": { "type": "object", "properties": [ { "n": { "type": "object", "description": "The number of songs to return" } }, { "genre": { "type": "object", "description": "The genre of the songs to return" } } ], "required": ["n"] } } } Return function calls in JSON format.<|eot_id|><|start_header_id|>user<|end_header_id|> Use tools to get latest trending songs<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` <|python_tag|>{ "type": "function", "name": "trending_songs", "parameters": { "n": "10", "genre": "all" } }<|eom_id|> ``` - JSON format for providing tools needs name, description and parameters - Model responds with `<|python_tag|>` and `<|eom_id|>` as `Environment: ipython` was in the system prompt - Instructions for tools added as a user message - Only single tool calls are supported as of now ## Example of a user defined tool calling ## `` based tool calling Here is an example of how you could also write custom instructions for model to do zero shot tool calling. In this example, we define a custom tool calling format using the `` tag. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython Cutting Knowledge Date: December 2023 Today Date: 21 September 2024 You are a helpful assistant. <|eot_id|><|start_header_id|>user<|end_header_id|> You have access to the following functions: Use the function 'trending_songs' to 'Returns the trending songs on a Music site': {"name": "trending_songs", "description": "Returns the trending songs on a Music site", "parameters": {"genre": {"description": "The genre of the songs to return", "param_type": "str", "required": false}, "n": {"description": "The number of songs to return", "param_type": "int", "required": true}}} Think very carefully before calling functions. If you choose to call a function ONLY reply in the following format with no prefix or suffix: {"example_name": "example_value"} Reminder: - If looking for real time information use relevant functions before falling back to brave_search - Function calls MUST follow the specified format, start with - Required parameters MUST be specified - Only call one function at a time - Put the entire function call reply on one line<|eot_id|><|start_header_id|>user<|end_header_id|> Use tools to get latest trending songs<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` {"n": 10}<|eot_id|> ``` - In this case, model does NOT respond with `<|python_tag|>` and ends with `<|eot_id|>` - Instructions for tools added as a user message Thank You! ================================================ FILE: models/llama3_2/LICENSE ================================================ LLAMA 3.2 COMMUNITY LICENSE AGREEMENT Llama 3.2 Version Release Date: September 25, 2024 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 3.2 distributed by Meta at https://www.llama.com/docs/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 3.2” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ================================================ FILE: models/llama3_2/MODEL_CARD.md ================================================ ## Model Information The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. **Model Developer:** Meta **Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. | | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff | | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | | Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 | | | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | | | Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 | | | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | | **Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. **Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** Oct 24, 2024 **Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). **Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources. **Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## Hardware and Software **Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. **Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. **Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. | | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) | | :---- | :---: | ----- | :---: | :---: | :---: | | Llama 3.2 1B | 370k | \- | 700 | 107 | 0 | | Llama 3.2 3B | 460k | \- | 700 | 133 | 0 | | Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 | | Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 | | Llama 3.2 1B QLoRA | 1.3k | 0 | 700 | 0.381 | 0 | | Llama 3.2 3B QLoRA | 1.6k | 0 | 700 | 0.461 | 0 | | Total | 833k | 86k | | 240 | 0 | \*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required. The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO). **Data Freshness:** The pretraining data has a cutoff of December 2023\. ## Quantization ### Quantization Scheme We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts: - All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations. - The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation. - Similar to classification layer, an 8-bit per channel quantization is used for embedding layer. ### Quantization-Aware Training and LoRA The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We used the [torchao](https://github.com/pytorch/ao/blob/main/torchao/quantization/qat/README.md) API for this. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO). ### SpinQuant [SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length. ## Benchmarks \- English Text In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models | Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B | | ----- | ----- | :---: | :---: | :---: | :---: | :---: | | General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 | | | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 | | | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 | | Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 | | | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 | | | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 | | Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 | ### Instruction Tuned Models | Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B | | :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 | | Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 | | Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 | | Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 | | Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 | | | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 | | Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 | | | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 | | | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 | | Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 | | | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 | | Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 | | | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 | | | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 | | Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 | \*\*for comparison purposes only. Model not released. ### Multilingual Benchmarks | Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B | | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | | General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 | | | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 | | | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 | | | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 | | | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 | | | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 | | | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 | \*\*for comparison purposes only. Model not released. ## Inference time In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device. | Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) | | :---- | ----- | ----- | ----- | ----- | ----- | | 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 | | 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) | | 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) | | 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 | | 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) | | 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) | (\*) The performance measurement is done using an adb binary-based approach. (\*\*) It is measured on an Android OnePlus 12 device. (\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64 *Footnote:* - *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.* - *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.* - *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better* - *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch* - *RSS size \- Memory usage in resident set size (RSS)* ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm 3. Provide protections for the community to help prevent the misuse of our models ### Responsible Deployment **Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). #### Llama 3.2 Instruct **Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). **Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems **Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases **Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. **Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. ### Evaluations **Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. **Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: **1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. **2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. **3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models. ### Community **Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). **Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). **Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations **Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. **Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development. ================================================ FILE: models/llama3_2/MODEL_CARD_VISION.md ================================================ ## Model Information The Llama 3.2-Vision collection of multimodal large language models (LLMs) is a collection of pretrained and instruction-tuned image reasoning generative models in 11B and 90B sizes (text \+ images in / text out). The Llama 3.2-Vision instruction-tuned models are optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The models outperform many of the available open source and closed multimodal models on common industry benchmarks. **Model Developer**: Meta **Model Architecture:** Llama 3.2-Vision is built on top of the Llama 3.1 text-only model, which is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. To support image recognition tasks, the Llama 3.2-Vision model uses a separately trained vision adapter that integrates with the pre-trained Llama 3.1 language model. The adapter consists of a series of cross-attention layers that feed image encoder representations into the core LLM. | | Training Data | Params | Input modalities | Output modalities | Context length | GQA | Data volume | Knowledge cutoff | | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | | Llama 3.2-Vision | (Image, text) pairs | 11B (10.6) | Text \+ Image | Text | 128k | Yes | 6B (image, text) pairs | December 2023 | | Llama 3.2-Vision | (Image, text) pairs | 90B (88.8) | Text \+ Image | Text | 128k | Yes | 6B (image, text) pairs | December 2023 | **Supported Languages:** For text only tasks, English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Note for image+text applications, English is the only language supported. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly. **Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** Sept 25, 2024 **Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. **License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement). **Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2-Vision in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases:** Llama 3.2-Vision is intended for commercial and research use. Instruction tuned models are intended for visual recognition, image reasoning, captioning, and assistant-like chat with images, whereas pretrained models can be adapted for a variety of image reasoning tasks. Additionally, because of Llama 3.2-Vision’s ability to take images and text as inputs, additional use cases could include: 1. Visual Question Answering (VQA) and Visual Reasoning: Imagine a machine that looks at a picture and understands your questions about it. 2. Document Visual Question Answering (DocVQA): Imagine a computer understanding both the text and layout of a document, like a map or contract, and then answering questions about it directly from the image. 3. Image Captioning: Image captioning bridges the gap between vision and language, extracting details, understanding the scene, and then crafting a sentence or two that tells the story. 4. Image-Text Retrieval: Image-text retrieval is like a matchmaker for images and their descriptions. Similar to a search engine but one that understands both pictures and words. 5. Visual Grounding: Visual grounding is like connecting the dots between what we see and say. It’s about understanding how language references specific parts of an image, allowing AI models to pinpoint objects or regions based on natural language descriptions. The Llama 3.2 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.2 Community License allows for these use cases. **Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card. ## Hardware and Software **Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. **Training Energy Use:** Training utilized a cumulative of **2.02M** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. ## **Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **584** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq. | | Training Time (GPU hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) | | :---- | :---: | :---: | :---: | :---: | | Llama 3.2-vision 11B | Stage 1 pretraining: 147K H100 hours Stage 2 annealing: 98K H100 hours SFT: 896 H100 hours RLHF: 224 H100 hours | 700 | 71 | 0 | | Llama 3.2-vision 90B | Stage 1 pretraining: 885K H100 hours Stage 2 annealing: 885K H100 hours SFT: 3072 H100 hours RLHF: 2048 H100 hours | 700 | 513 | 0 | | Total | 2.02M | | 584 | 0 | The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 3.2-Vision was pretrained on 6B image and text pairs. The instruction tuning data includes publicly available vision instruction datasets, as well as over 3M synthetically generated examples. **Data Freshness:** The pretraining data has a cutoff of December 2023\. ## Benchmarks \- Image Reasoning In this section, we report the results for Llama 3.2-Vision models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. ### Base Pretrained Models | Category | Benchmark | \# Shots | Metric | Llama 3.2 11B | Llama 3.2 90B | | ----- | ----- | ----- | ----- | ----- | ----- | | Image Understanding | VQAv2 (val) | 0 | Accuracy | 66.8 | 73.6 | | | Text VQA (val) | 0 | Relaxed accuracy | 73.1 | 73.5 | | | DocVQA (val, unseen) | 0 | ANLS | 62.3 | 70.7 | | Visual Reasoning | MMMU (val, 0-shot) | 0 | Micro average accuracy | 41.7 | 49.3 | | | ChartQA (test) | 0 | Accuracy | 39.4 | 54.2 | | | InfographicsQA (val, unseen) | 0 | ANLS | 43.2 | 56.8 | | | AI2 Diagram (test) | 0 | Accuracy | 62.4 | 75.3 | ### Instruction Tuned Models | Modality | Capability | Benchmark | \# Shots | Metric | Llama 3.2 11B | Llama 3.2 90B | | ----- | :---: | ----- | :---: | :---: | ----- | ----- | | Image | College-level Problems and Mathematical Reasoning | MMMU (val, CoT) | 0 | Micro average accuracy | 50.7 | 60.3 | | | | MMMU-Pro, Standard (10 opts, test) | 0 | Accuracy | 33.0 | 45.2 | | | | MMMU-Pro, Vision (test) | 0 | Accuracy | 23.7 | 33.8 | | | | MathVista (testmini) | 0 | Accuracy | 51.5 | 57.3 | | | Charts and Diagram Understanding | ChartQA (test, CoT) | 0 | Relaxed accuracy | 83.4 | 85.5 | | | | AI2 Diagram (test) | 0 | Accuracy | 91.1 | 92.3 | | | | DocVQA (test) | 0 | ANLS | 88.4 | 90.1 | | | General Visual Question Answering | VQAv2 (test) | 0 | Accuracy | 75.2 | 78.1 | | | | | | | | | | Text | General | MMLU (CoT) | 0 | Macro\_avg/acc | 73.0 | 86.0 | | | Math | MATH (CoT) | 0 | Final\_em | 51.9 | 68.0 | | | Reasoning | GPQA | 0 | Accuracy | 32.8 | 46.7 | | | Multilingual | MGSM (CoT) | 0 | em | 68.9 | 86.9 | ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks: 1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. 2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm. 3. Provide protections for the community to help prevent the misuse of our models. ### Responsible Deployment **Approach:** Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more. #### Llama 3.2 Instruct **Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). **Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.2 Systems **Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### New Capabilities and Use Cases **Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well. **Image Reasoning:** Llama 3.2-Vision models come with multimodal (text and image) input capabilities enabling image reasoning applications. As part of our responsible release process, we took dedicated measures including evaluations and mitigations to address the risk of the models uniquely identifying individuals in images. As with other LLM risks, models may not always be robust to adversarial prompts, and developers should evaluate identification and other applicable risks in the context of their applications as well as consider deploying Llama Guard 3-11B-Vision as part of their system or other mitigations as appropriate to detect and mitigate such risks. ### Evaluations **Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. **Red teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas: **1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** For Llama 3.1, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. For Llama 3.2-Vision models, we conducted additional targeted evaluations and found that it was unlikely Llama 3.2 presented an increase in scientific capabilities due to its added image understanding capability as compared to Llama 3.1. **2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. **3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s vision capabilities are not generally germane to cyber uplift, we believe that the testing conducted for Llama 3.1 also applies to Llama 3.2. ### Community **Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). **Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). **Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations **Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. **Testing:** But Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development. ================================================ FILE: models/llama3_2/USE_POLICY.md ================================================ **Llama 3.2** **Acceptable Use Policy** Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy). **Prohibited Uses** We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 1. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 2. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 3. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 4. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law 5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 6. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta  2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following: 8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 9. Guns and illegal weapons (including weapon development) 10. Illegal drugs and regulated/controlled substances 11. Operation of critical infrastructure, transportation technologies, or heavy machinery 12. Self-harm or harm to others, including suicide, cutting, and eating disorders 13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following: 14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 16. Generating, promoting, or further distributing spam 17. Impersonating another individual without consent, authorization, or legal right 18. Representing that the use of Llama 3.2 or outputs are human-generated 19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement  4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2 With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: LlamaUseReport@meta.com ================================================ FILE: models/llama3_2/eval_details.md ================================================ # Llama 3 Evaluation Details This document contains some additional context on the settings and methodology for how we evaluated the Llama 3.2 models. ## Language auto-eval benchmark notes: For a given benchmark, we strive to use consistent evaluation settings across all models, including external models. We make every effort to achieve optimal scores for external models, including addressing any model-specific parsing and tokenization requirements. Where the scores are lower for external models than self-reported scores on comparable or more conservative settings, we report the self-reported scores for external models. We are also releasing the data generated as part of evaluations with publicly available benchmarks which can be found on [huggingface here](https://huggingface.co/collections/meta-llama/llama-32-evals-66f44b3d2df1c7b136d821f0). ### MMLU For the pre-trained models we use a 5-shot config. To determine the choice character we use the standard MMLU prompt and compare the negative log-likelihood (NLL) of the various choices. For the post-trained models we report both 5-shot and 0-shot scores. We ask the model to generate the best choice character. The 0-shot scores use a CoT (chain of thought) prompt. The maximum generation lengths for the 5-shot and 0-shot configs are 10 tokens and 1024 tokens respectively. Macro averages are reported unless otherwise stated. The micro average scores for the various models are: 65.6, 79.0, and 85.4 for the pre-trained 8B, 70B and 405B models respectively for the 5-shot config; 69.44, 84.0, 87.71 for the post-trained 8B, 70B and 405B models respectively for the 5-shot config. ### TLDR9+ For post-trained models, we use a 1-shot config and report rougeL scores. We run this as a generative task. Maximum generation length is 512 tokens. We specifically ran this on [TLDR9+ dataset](https://github.com/sajastu/reddit_collector) ### Open-Rewrite For post-trained models, we use a 0-shot config and report micro_avg rougeL scores across elaborate, formality, others, paraphrase, shorten, and wiki. We run this as a generative task. Maximum generation length is 512 tokens. Specific dataset can be found [here](https://github.com/google-research/google-research/tree/master/rewritelm). ### IFEval For post-trained models, we use the default settings as specified [here](https://arxiv.org/pdf/2311.07911). We compute the prompt level scores and instruction level strict and loose accuracy. We then report the average across all the scores. ### ARC-Challenge We use the Arc-Challenge subset from the Arc benchmark. For the pre-trained models, we use a 25-shot config and use the MMLU setup for evaluation where we provide all the choices in the prompt and calculate likelihood over choice characters. For the post-trained models, we use 0-shot config and ask the model to generate the choice character. The maximum generation length is 100 tokens. ### GPQA For post-trained models, we use 0-shot config with CoT prompt and report exact match scores over the possible options using the main set. Max generation length is 2048 tokens. ### AGIEval English For pre-trained models, we use the default few-shot and prompt settings as specified [here](https://github.com/ruixiangcui/AGIEval). The score is averaged over the english subtasks. The max generation length is 10 tokens. ### SQuAD For pre-trained models, we use SQuAD v2 with a 1-shot config and report exact match scores. We run this as a generative task. Maximum generation length is 32 tokens. ### QuAC For pre-trained models, we use a 1-shot config and report the F1 scores. We run this as a generative task. Maximum generation length is 32 tokens. ### DROP For pre-trained models, for each validation example, we draw 3 random few-shot examples from the train split and report the F1 scores. The maximum generation length is 32 tokens. ### GSM8K For both pre-trained and post-trained models, we use the same 8-shot config with CoT prompt as in [Wei et al. (2022)](https://arxiv.org/pdf/2201.11903.pdf) (maj@1). The maximum generation length is 1024 tokens. ### MATH For pre-trained models, we use the same 4-shot config as in [Lewkowycz et al. (2022)](https://arxiv.org/pdf/2206.14858.pdf) (maj@1). Maximum generation length is 512 tokens. For post-trained models we use a 0-shot config with Cot prompt. We enhance the exact match using [sympy](https://www.sympy.org/en/index.html) and then use an [equality template](https://github.com/openai/simple-evals/blob/main/common.py#L27-L85) with a judge to resolve complex expressions. Maximum generation length is 5120 tokens. The MATH score represents the full dataset. The scores for MATH-HARD (Lvl 5) are 25.4, 43.8, and 53.4 for the 8B, 70B and 405B models respectively. ### InfiniteBench We report on EN.MC (Free-form question answering based on the fake book) and EN.QA (Multiple choice questions derived from the fake book) sub-tasks. The average tokens in these tasks is 184.4k and 192.6k respectively. We truncate down the dataset to 128k input tokens using our tokenizer to have a 'clean' dataset where the right answer is not mistakenly deleted during truncation. For post-trained models, we use a 0-shot config. Maximum generation length is 20 for both the En.QA and En.MC tasks. ### NIH/Multi-needle For post-training, we use a 0-shot config. Our context lengths are evenly spaced between 2000 and 131072 in 10 intervals, inclusive of the endpoints for llama models and between 2000 and 128000 for non-llama models. Maximum generation length is 256 tokens. ### MGSM For post-trained models, we use an 0-shot config with CoT prompt and report exact match (maj@1) scores. Maximum generation length is 2048 tokens. The scores are averaged over all the eleven languages present in the MGSM benchmark, including the ones not supported by Llama models. ### Multilingual MMLU For post-trained models, we use a 5-shot config. We run this as a generative task. Maximum generation length is 10 tokens. The scores are individually reported for each and averaged over the seven non-english languages that Llama models support (Portuguese, Spanish, Italian, German, French, Hindi, Thai). ### Berkeley Function Calling Leaderboard (BFCL) v2 For Berkeley Function Calling Leaderboard (BFCL-v2), benchmark results were achieved by running the open source evaluation repository [ShishirPatil/gorilla ](https://github.com/ShishirPatil/gorilla/)on commit 70d6722 and we report the "AST Summary" metric. ### Nexus We use the [open-source ](https://github.com/nexusflowai/NexusRaven)prompt and evaluation function followed by the[ open source notebook](https://github.com/nexusflowai/NexusRaven-V2/blob/master/evaluation_notebook/GPT4_Evaluation/Benchmark_GPT4.ipynb) to compute the scores. ### RULER For more comprehensive long-context evals beyond retrieval, we assess our perf on RULER benchmark, where we synthesize datasets across increasingly long context length buckets, and compare mean across each of them over retrieval (single needle, multi needle, multi-value per multiple keys), multi-hop tracing (variable tracking), aggregation (common words, frequency extraction), and question-answering. ### MMMU For post-trained models, we use 0-shot config with CoT prompt and report scores over the possible options using the validation set. Maximum generation length is 2048 tokens. We use the following system prompt: "<|image|>Look at the image carefully and solve the following question step-by-step. {question} Options: {options} Indicate the correct answer at the end." ### MMMU-Pro standard For post-trained models, we use 0-shot config, we use multiple choice questions with ten options, and report scores over the possible options using the test set. In case of multiple images provided per prompt, they are stitched into a single image. Maximum generation length is 2048 tokens. We use the following system prompt: "<|image|>{question} Options: {options}" ### MMMU-Pro vision For post-trained models, we use 0-shot config and report scores over the possible options using the test set. Maximum generation length is 2048 tokens. We use the following system prompt: "<|image|>Your job is to extract the question from the image the user attached, reason about it, and then answer the question. Follow these steps: \n1. Always start by Clearly stating the question shown in the image, and also state all of the options. \n2. Then, Carefully review the information beyond the question shown in the image and analyze it without making any assumptions. \n3. Next, use your understanding of the image to answer the question you listed out in the first step. Use a step-by-step process \n4. Finally, write the answer in the following format where X is exactly one of the option letters: The best answer is X.\nAlways follow the steps above, printing out everything. Let's think step by step. Your response must be among the given options." ### AI2D For post-trained models, we use 0-shot config and report scores using the test set. Maximum generation length is 400 tokens. For 11b we use the following system prompt: "<|image|>Look at the scientific diagram carefully and answer the following question: {question}\n Think step by step and finally respond to the question with only the correct option number as \"FINAL ANSWER\". Let's think step by step." For 90b we use a different system prompt: "<|image|>Look at the scientific diagram carefully and answer the following question: {question}\n Respond only with the correct option digit." ### ChartQA For post-trained models, we use 0-shot config with CoT prompt and report scores using the test set. Maximum generation length is 512 tokens. For 11b we use the following system prompt: "<|image|>You are provided a chart image and will be asked a question. You have to think through your answer and provide a step-by-step solution. Once you have the solution, write the final answer in at most a few words at the end with the phrase "FINAL ANSWER:". The question is: {question}<cot_start>Let's think step by step." For 90b we use a different system prompt: "<|image|>You are provided a chart image and will be asked a question. Follow these steps carefully:\n Step 1: Analyze the question to understand what specific data or information is being asked for. Focus on whether the question is asking for a specific number or category from the chart image.\n Step 2: Identify any numbers, categories, or groups mentioned in the question and take note of them. Focus on detecting and matching them directly to the image. \nStep 3: Study the image carefully and find the relevant data corresponding to the categories or numbers mentioned. Avoid unnecessary assumptions or calculations; simply read the correct data from the image.\n Step 4: Develop a clear plan to solve the question by locating the right data. Focus only on the specific category or group that matches the question. \nStep 5: Use step-by-step reasoning to ensure you are referencing the correct numbers or data points from the image, avoiding unnecessary extra steps or interpretations.\n Step 6: Provide the final answer, starting with "FINAL ANSWER:" and using as few words as possible, simply stating the number or data point requested. \n\n The question is: {question}<cot_start>Let\'s think step by step." ### DocVQA For post-trained models, we use 0-shot config and report scores over the test set. Maximum generation length is 512 tokens. We use the following system prompt: "<|image|> Read the text in the image carefully and answer the question with the text as seen exactly in the image. For yes/no questions, just respond Yes or No. If the answer is numeric, just respond with the number and nothing else. If the answer has multiple words, just respond with the words and absolutely nothing else. Never respond in a sentence or a phrase.\n Question: {question}" ### VQAv2 For post-trained models, we use 0-shot config and report scores over the test set. Maximum generation length is 25 tokens. We use the following system prompt: "<|image|> Look at the image carefully and answer this visual question. For yes/no questions, just respond Yes or No. If the answer is numeric, just respond with the number and nothing else. If the answer has multiple words, just respond with the words and absolutely nothing else. Never respond in a sentence or a phrase.\n Respond with as few words as possible.\n Question: {question}" ### MathVista For post-trained models, we use 0-shot config and report scores over the testmini set. Maximum generation length is 2048 tokens. We use an LLM-based answer extractor as recommended by MathVista paper [Lu et al. (2024)](https://arxiv.org/pdf/2310.02255). We use the following system prompt: "<|image|>{question}" ================================================ FILE: models/llama3_2/text_prompt_format.md ================================================ ## User and assistant conversation Here is a regular multi-turn user assistant conversation and how its formatted. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|> Who are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` I'm an AI assistant, which means I'm a computer program designed to simulate conversation and answer questions to the best of my ability. I'm here to help you with any questions or tasks you may have, and I'll do my best to provide you with accurate and helpful information. I don't have a personal name, but I'm often referred to as a "virtual assistant" or a "chatbot." I'm a machine learning model, which means I've been trained on a large dataset of text and can generate responses based on patterns and context. I can help with a wide range of topics, from general knowledge and trivia to more specialized subjects like science, history, and technology. I can also assist with tasks like language translation, text summarization, and even generating creative content like stories or poetry. So, what can I help you with today?<|eot_id|> ``` ##### Notes This format is unchanged from Llama3.1 ## Zero shot function calling For Llama3.2 1B and 3B instruct models, we are introducing a new format for zero shot function calling. This new format is designed to be more flexible and powerful than the previous format. All available functions can be provided in the system message. A key difference is in the format of how the assistant responds with function calls. It is pythonic in the form of `[func1(params_name=params_value, params_name2=params_value2...), func2(params)]` instead of the `json` or `` tag that were defined in Llama3.1. Here is an example for the same, ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an expert in composing functions. You are given a question and a set of possible functions. Based on the question, you will need to make one or more function/tool calls to achieve the purpose. If none of the function can be used, point it out. If the given question lacks the parameters required by the function, also point it out. You should only return the function call in tools call sections. If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)] You SHOULD NOT include any other text in the response. Here is a list of functions in JSON format that you can invoke. [ { "name": "get_weather", "description": "Get weather info for places", "parameters": { "type": "dict", "required": [ "city" ], "properties": { "city": { "type": "string", "description": "The name of the city to get the weather for" }, "metric": { "type": "string", "description": "The metric for weather. Options are: celsius, fahrenheit", "default": "celsius" } } } } ]<|eot_id|><|start_header_id|>user<|end_header_id|> What is the weather in SF and Seattle?<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` [get_weather(city='San Francisco', metric='celsius'), get_weather(city='Seattle', metric='celsius')]<|eot_id|> ``` ##### Notes - The output supports multiple tool calls natively - JSON format for defining the functions in the system prompt is similar to Llama3.1 ## Zero shot function calling with user message While the default is to provide all function calls in a system message, in Llama3.2 text models you can also provide information for all the available tools in a user message. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>user<|end_header_id|> Questions: Can you retrieve the details for the user with the ID 7890, who has black as their special request? Here is a list of functions in JSON format that you can invoke: [ { "name": "get_user_info", "description": "Retrieve details for a specific user by their unique identifier. Note that the provided function is in Python 3 syntax.", "parameters": { "type": "dict", "required": [ "user_id" ], "properties": { "user_id": { "type": "integer", "description": "The unique identifier of the user. It is used to fetch the specific user details from the database." }, "special": { "type": "string", "description": "Any special information or parameters that need to be considered while fetching user details.", "default": "none" } } } } ] Should you decide to return the function call(s),Put it in the format of [func1(params_name=params_value, params_name2=params_value2...), func2(params)] NO other text MUST be included.<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` [get_user_info(user_id=7890, special='black')]<|eot_id|> ``` ##### Notes - The tool call format for the model is the same whether your function calls are provided in the system or user message. - While builtin tool calls end with a <|eom_id|>, notice the <|eot_id|> for zero shot tool calls. ## Code Interpreter Code Interpreter continues to work in 3.2 text models similar to Llama 3.1 model family. Here is an example, ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython Cutting Knowledge Date: December 2023 Today Date: 24 September 2024 <|eot_id|><|start_header_id|>user<|end_header_id|> Write code to check if number is prime. Use it to verify if number 7 is prime<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` <|python_tag|>def is_prime(n): if n <= 1: return False if n == 2: return True if n % 2 == 0: return False max_divisor = int(n**0.5) + 1 for d in range(3, max_divisor, 2): if n % d == 0: return False return True print(is_prime(7)) # Output: True<|eom_id|> ``` ##### Notes - Note `Environment: ipython` in the system prompt. - Note that the response starts with `<|python_tag|>` and ends with `<|eom_id|>` ## Zero shot function calling E2E format Here is an example of the e2e cycle of tool calls with the model in a muti-step way. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an expert in composing functions. You are given a question and a set of possible functions. Based on the question, you will need to make one or more function/tool calls to achieve the purpose. If none of the function can be used, point it out. If the given question lacks the parameters required by the function, also point it out. You should only return the function call in tools call sections. If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)] You SHOULD NOT include any other text in the response. Here is a list of functions in JSON format that you can invoke. [ { "name": "get_weather", "description": "Get weather info for places", "parameters": { "type": "dict", "required": [ "city" ], "properties": { "city": { "type": "string", "description": "The name of the city to get the weather for" }, "metric": { "type": "string", "description": "The metric for weather. Options are: celsius, fahrenheit", "default": "celsius" } } } } ]<|eot_id|><|start_header_id|>user<|end_header_id|> What is the weather in SF?<|eot_id|><|start_header_id|>assistant<|end_header_id|> <|python_tag|>[get_weather(city="San Francisco", metric="celsius")]<|eot_id|><|start_header_id|>ipython<|end_header_id|> "25 C"<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` The weather in San Francisco is 25 C.<|eot_id|> ``` ##### Notes - The output of the function call is provided back to the model as a tool response ( in json format ). - Notice `<|start_header_id|>ipython<|end_header_id|>` as the header message preceding the tool response. - The model finally summarizes the information from the tool response and returns the result to the user. ## Prompt format for base models For base models (Llama3.2-1B and Llama3.2-3B), the prompt format for a simple completion is as follows ##### Input Prompt Format ``` <|begin_of_text|>The color of the sky is blue but sometimes it can also be ``` ##### Model Response Format ``` gray or even purple. The color of the sky can change depending on the time of day, the weather, and the amount of pollution in the air. The color of the sky can also be affected by the presence of dust, smoke, and other particles in the air. ## Step 1: Identify the factors that ``` ##### Notes Same as Llama3.1 Thank You! ================================================ FILE: models/llama3_2/vision_prompt_format.md ================================================ ## User and assistant conversation Here is a regular multi-turn user assistant conversation and how its formatted. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|> Who are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` I'm a helpful assistant, here to provide information, answer questions, and assist with tasks to the best of my abilities. I'm a large language model, which means I can understand and respond to natural language inputs, and I'm constantly learning and improving to provide more accurate and helpful responses. I can help with a wide range of topics, from general knowledge and trivia to more specific areas like science, history, technology, and more. I can also assist with tasks like language translation, text summarization, and even generating creative content like stories or dialogues. What can I help you with today?<|eot_id|> ``` ##### Notes This format is unchanged from Llama3.1 ## User and assistant conversation with Images This example shows how to pass and image to the model as part of the messages. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>user<|end_header_id|> <|image|>Describe this image in two sentences<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` The image depicts a small dog standing on a skateboard, with its front paws firmly planted on the board and its back paws slightly raised. The dog's fur is predominantly brown and white, with a distinctive black stripe running down its back, and it is wearing a black collar around its neck.<|eot_id|> ``` ##### Notes - The `<|image|>` tag is used to indicate presence of the image - The model isn't an early fusion model so doesn't actually translate an image into several tokens. Instead the cross-attention layers take input "on the side" from a vision encoder ![Image](mm-model.png) - Its important to postion the <|image|> tag appropriately in the prompt. Image will only attend to the subsequent text tokens - The <|image|> tag is part of the user message body, implying that it should only come after the header `<|start_header_id|>{role}<|end_header_id|>` in the message body - We recommend using a single image in one prompt ## Builtin and Zero Shot Tool Calling Llama3.2 vision models follow the same tool calling format as Llama3.1 models when inputs are text only. Use `Environment: ipython` to enable tools. Add `Tools: {{tool_name1}},{{tool_name2}}` for each of the builtin tools. The same builtin tools as Llama3.1 are available, - code_interpreter (for executing python code) - brave_search (to search the web) - wolfram_alpha (for querying wolfram alpha for mathematical questions) ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython Tools: brave_search, wolfram_alpha Cutting Knowledge Date: December 2023 Today Date: 23 September 2024 You are a helpful assistant. <|eot_id|><|start_header_id|>user<|end_header_id|> Search the web for the latest price of 1oz gold?<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` <|python_tag|>brave_search.call(query="latest price of 1oz gold")<|eom_id|> ``` ##### Notes - Note the `<|python_tag|>` before `brave_search` function call. - The `<|eom_id|>` tag is used to indicate the end of the message. - Similar to Llama3.1, code_interpreter is not explicitly mentioned but is enabled via `Environment: ipython`. - Tool Calling does NOT work with images in the prompt as of now. ## Prompt format for base models For base models (Llama3.2-11B-Vision and Llama3.2-90B-Vision), the prompt format for a simple completion is as follows ##### Input Prompt Format ``` <|begin_of_text|>The color of the sky is blue but sometimes it can also be ``` ##### Model Response Format ``` red, orange, pink, purple, and even black. The color of the sky is determined by the amount of sunlight that is scattered by the atmosphere and the amount of dust and water vapor present in the atmosphere. During sunrise and sunset, the sky can take on a range of colors due to the scattering of light by ``` ##### Notes - Same as Llama3.1 ## Prompt format for base models with Image For base models (Llama3.2-11B-Vision and Llama3.2-90B-Vision), here is an example of how the text completion format looks with an image, ##### Input Prompt Format ``` <|begin_of_text|><|image|>If I had to write a haiku for this one ``` ##### Model Response Format ``` , it would be: A skateboarder's delight, a puppy on a board, a furry little thrill-seeker. This puppy is a true skateboarding enthusiast, always eager to hit the streets and show off his skills. He's a master of the board, gliding effortlessly across the pavement with grace and style. ``` ##### Notes - Note the placement of the special tags <|begin_of_text|> and <|image|> Thank You! ================================================ FILE: models/llama3_3/LICENSE ================================================ **LLAMA 3.3 COMMUNITY LICENSE AGREEMENT** Llama 3.3 Version Release Date: December 6, 2024 “**Agreement**” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “**Documentation**” means the specifications, manuals and documentation accompanying Llama 3.3 distributed by Meta at [https://www.llama.com/docs/overview](https://llama.com/docs/overview). “**Licensee**” or “**you**” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “**Llama 3.3**” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at [https://www.llama.com/llama-downloads](https://www.llama.com/llama-downloads). “**Llama Materials**” means, collectively, Meta’s proprietary Llama 3.3 and Documentation (and any portion thereof) made available under this Agreement. “**Meta**” or “**we**” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).  By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement. 1\. **License Rights and Redistribution**. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.   b. Redistribution and Use.   i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.  iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.3 is licensed under the Llama 3.3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at [https://www.llama.com/llama3\_3/use-policy](https://www.llama.com/llama3_3/use-policy)), which is hereby incorporated by reference into this Agreement.    2\. **Additional Commercial Terms**. If, on the Llama 3.3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3**. Disclaimer of Warranty**. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4\. **Limitation of Liability**. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5\. **Intellectual Property**. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at [https://about.meta.com/brand/resources/meta/company-brand/](https://about.meta.com/brand/resources/meta/company-brand/)[)](https://en.facebookbrand.com/). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6\. **Term and Termination**. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.  7\. **Governing Law and Jurisdiction**. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.  ================================================ FILE: models/llama3_3/MODEL_CARD.md ================================================ ## Model Information The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks. **Model developer**: Meta **Model Architecture:** Llama 3.3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. | | Training Data | Params | Input modalities | Output modalities | Context length | GQA | Token count | Knowledge cutoff | | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | | Llama 3.3 (text only) | A new mix of publicly available online data. | 70B | Multilingual Text | Multilingual Text and code | 128k | Yes | 15T+ | December 2023 | **Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. **Llama 3.3 model**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date:** * **70B Instruct: December 6, 2024** **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license, the Llama 3.3 Community License Agreement, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3\_3/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/LICENSE) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3.3 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.3 model also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.3 Community License allows for these use cases. **Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.3 Community License. Use in languages beyond those explicitly referenced as supported in this model card\*\*. \*\*Note: Llama 3.3 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.3 models for languages beyond the 8 supported languages provided they comply with the Llama 3.3 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.3 in additional languages is done in a safe and responsible manner. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure. **Training Energy Use** Training utilized a cumulative of **39.3**M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. **Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq. | | Training Time (GPU hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (metric tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (metric tons CO2eq) | | :---- | :---: | :---: | :---: | :---: | | Llama 3.3 70B | 7.0M | 700 | 12.9 | 0 | The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 3.3 was pretrained on \~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples. **Data Freshness:** The pretraining data has a cutoff of December 2023\. ## Benchmarks \- English Text In this section, we report the results for Llama 3.3 relative to our previous models. ### Instruction tuned models ## | Category | Benchmark | \# Shots | Metric | Llama 3.1 8B Instruct | Llama 3.1 70B Instruct | Llama-3.3 70B Instruct | Llama 3.1 405B Instruct | | :---- | :---- | ----- | :---- | ----- | ----- | ----- | ----- | | General | MMLU (CoT) | 0 | macro\_avg/acc | 73.0 | 86.0 | 86.0 | 88.6 | | | MMLU Pro (CoT) | 5 | macro\_avg/acc | 48.3 | 66.4 | 68.9 | 73.3 | | Steerability | IFEval | | | 80.4 | 87.5 | 92.1 | 88.6 | | Reasoning | GPQA Diamond (CoT) | 0 | acc | 31.8 | 48.0 | 50.5 | 49.0 | | Code | HumanEval | 0 | pass@1 | 72.6 | 80.5 | 88.4 | 89.0 | | | MBPP EvalPlus (base) | 0 | pass@1 | 72.8 | 86.0 | 87.6 | 88.6 | | Math | MATH (CoT) | 0 | sympy\_intersection\_score | 51.9 | 68.0 | 77.0 | 73.8 | | Tool Use | BFCL v2 | 0 | overall\_ast\_summary/macro\_avg/valid | 65.4 | 77.5 | 77.3 | 81.1 | | Multilingual | MGSM | 0 | em | 68.9 | 86.9 | 91.1 | 91.6 | ## ## Responsibility & Safety As part of our Responsible release approach, we followed a three-pronged strategy to managing trust and safety risks: * Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. * Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm. * Provide protections for the community to help prevent the misuse of our models. ### Responsible deployment Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.3 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more. #### Llama 3.3 instruct Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the develoderationsper workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper. **Fine-tuning data** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals and Tone** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. #### Llama 3.3 systems **Large language models, including Llama 3.3, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. #### Capability-specific considerations **Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards. **Multilinguality**: Llama 3.3 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide. ### Evaluations We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization. **Red teaming** For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. . ### Critical and other risks ### We specifically focused our efforts on mitigating the following critical risk areas: **1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness** To assess risks related to proliferation of chemical and biological weapons of the Llama 3 family of models, we performed uplift testing designed to assess whether use of the Llama models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. ### **2\. Child Safety** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. **3\. Cyber attack enablement** Our cyber attack uplift study investigated whether the Llama 3 family of LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3.3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3.3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.3 model, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development. ================================================ FILE: models/llama3_3/USE_POLICY.md ================================================ **Llama 3.3** **Acceptable Use Policy** Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.3. If you access or use Llama 3.3, you agree to this Acceptable Use Policy (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3\_3/use-policy](https://www.llama.com/llama3_3/use-policy). **Prohibited Uses** We want everyone to use Llama 3.3 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 8. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta  2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.3 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 3.3 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Llama 3.3 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement  4. Fail to appropriately disclose to end users any known dangers of your AI system 5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.3 With respect to any multimodal models included in Llama 3.3, the rights granted under Section 1(a) of the Llama 3.3 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ) * Reporting risky content generated by the model: [developers.facebook.com/llama\_output\_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.3: LlamaUseReport@meta.com ================================================ FILE: models/llama3_3/eval_details.md ================================================ # Llama 3 Evaluation Details This document contains some additional context on the settings and methodology for how we evaluated the Llama 3.2 models. ## Language auto-eval benchmark notes: For a given benchmark, we strive to use consistent evaluation settings across all models, including external models. We make every effort to achieve optimal scores for external models, including addressing any model-specific parsing and tokenization requirements. Where the scores are lower for external models than self-reported scores on comparable or more conservative settings, we report the self-reported scores for external models. We are also releasing the data generated as part of evaluations with publicly available benchmarks which can be found on [huggingface here](https://huggingface.co/collections/meta-llama/llama-32-evals-66f44b3d2df1c7b136d821f0). ### MMLU For the pre-trained models we use a 5-shot config. To determine the choice character we use the standard MMLU prompt and compare the negative log-likelihood (NLL) of the various choices. For the post-trained models we report both 5-shot and 0-shot scores. We ask the model to generate the best choice character. The 0-shot scores use a CoT (chain of thought) prompt. The maximum generation lengths for the 5-shot and 0-shot configs are 10 tokens and 1024 tokens respectively. Macro averages are reported unless otherwise stated. The micro average scores for the various models are: 65.6, 79.0, and 85.4 for the pre-trained 8B, 70B and 405B models respectively for the 5-shot config; 69.44, 84.0, 87.71 for the post-trained 8B, 70B and 405B models respectively for the 5-shot config. ### TLDR9+ For post-trained models, we use a 1-shot config and report rougeL scores. We run this as a generative task. Maximum generation length is 512 tokens. We specifically ran this on [TLDR9+ dataset](https://github.com/sajastu/reddit_collector) ### Open-Rewrite For post-trained models, we use a 0-shot config and report micro_avg rougeL scores across elaborate, formality, others, paraphrase, shorten, and wiki. We run this as a generative task. Maximum generation length is 512 tokens. Specific dataset can be found [here](https://github.com/google-research/google-research/tree/master/rewritelm). ### IFEval For post-trained models, we use the default settings as specified [here](https://arxiv.org/pdf/2311.07911). We compute the prompt level scores and instruction level strict and loose accuracy. We then report the average across all the scores. ### ARC-Challenge We use the Arc-Challenge subset from the Arc benchmark. For the pre-trained models, we use a 25-shot config and use the MMLU setup for evaluation where we provide all the choices in the prompt and calculate likelihood over choice characters. For the post-trained models, we use 0-shot config and ask the model to generate the choice character. The maximum generation length is 100 tokens. ### GPQA For post-trained models, we use 0-shot config with CoT prompt and report exact match scores over the possible options using the main set. Max generation length is 2048 tokens. ### GPQA Diamond GPQA Diamond is the subset of GPQA where two out of two experts agree, Rein et al. (2023). For post-trained models, we use 0-shot config with CoT prompt and report exact match scores over the possible options using the Diamond set. Max generation length is 2048 tokens. ### AGIEval English For pre-trained models, we use the default few-shot and prompt settings as specified [here](https://github.com/ruixiangcui/AGIEval). The score is averaged over the english subtasks. The max generation length is 10 tokens. ### SQuAD For pre-trained models, we use SQuAD v2 with a 1-shot config and report exact match scores. We run this as a generative task. Maximum generation length is 32 tokens. ### QuAC For pre-trained models, we use a 1-shot config and report the F1 scores. We run this as a generative task. Maximum generation length is 32 tokens. ### DROP For pre-trained models, for each validation example, we draw 3 random few-shot examples from the train split and report the F1 scores. The maximum generation length is 32 tokens. ### GSM8K For both pre-trained and post-trained models, we use the same 8-shot config with CoT prompt as in [Wei et al. (2022)](https://arxiv.org/pdf/2201.11903.pdf) (maj@1). The maximum generation length is 1024 tokens. ### MATH For pre-trained models, we use the same 4-shot config as in [Lewkowycz et al. (2022)](https://arxiv.org/pdf/2206.14858.pdf) (maj@1). Maximum generation length is 512 tokens. For post-trained models we use a 0-shot config with Cot prompt. We enhance the exact match using [sympy](https://www.sympy.org/en/index.html) and then use an [equality template](https://github.com/openai/simple-evals/blob/main/common.py#L27-L85) with a judge to resolve complex expressions. Maximum generation length is 5120 tokens. The MATH score represents the full dataset. The scores for MATH-HARD (Lvl 5) are 25.4, 43.8, and 53.4 for the 8B, 70B and 405B models respectively. ### InfiniteBench We report on EN.MC (Free-form question answering based on the fake book) and EN.QA (Multiple choice questions derived from the fake book) sub-tasks. The average tokens in these tasks is 184.4k and 192.6k respectively. We truncate down the dataset to 128k input tokens using our tokenizer to have a 'clean' dataset where the right answer is not mistakenly deleted during truncation. For post-trained models, we use a 0-shot config. Maximum generation length is 20 for both the En.QA and En.MC tasks. ### NIH/Multi-needle For post-training, we use a 0-shot config. Our context lengths are evenly spaced between 2000 and 131072 in 10 intervals, inclusive of the endpoints for llama models and between 2000 and 128000 for non-llama models. Maximum generation length is 256 tokens. ### MGSM For post-trained models, we use an 0-shot config with CoT prompt and report exact match (maj@1) scores. Maximum generation length is 2048 tokens. The scores are averaged over all the eleven languages present in the MGSM benchmark, including the ones not supported by Llama models. ### Multilingual MMLU For post-trained models, we use a 5-shot config. We run this as a generative task. Maximum generation length is 10 tokens. The scores are individually reported for each and averaged over the seven non-english languages that Llama models support (Portuguese, Spanish, Italian, German, French, Hindi, Thai). ### Berkeley Function Calling Leaderboard (BFCL) v2 For Berkeley Function Calling Leaderboard (BFCL-v2), benchmark results were achieved by running the open source evaluation repository [ShishirPatil/gorilla ](https://github.com/ShishirPatil/gorilla/)on commit 70d6722 and we report the "AST Summary" metric. ### Nexus We use the [open-source ](https://github.com/nexusflowai/NexusRaven)prompt and evaluation function followed by the[ open source notebook](https://github.com/nexusflowai/NexusRaven-V2/blob/master/evaluation_notebook/GPT4_Evaluation/Benchmark_GPT4.ipynb) to compute the scores. ### RULER For more comprehensive long-context evals beyond retrieval, we assess our perf on RULER benchmark, where we synthesize datasets across increasingly long context length buckets, and compare mean across each of them over retrieval (single needle, multi needle, multi-value per multiple keys), multi-hop tracing (variable tracking), aggregation (common words, frequency extraction), and question-answering. ### MMMU For post-trained models, we use 0-shot config with CoT prompt and report scores over the possible options using the validation set. Maximum generation length is 2048 tokens. We use the following system prompt: "<|image|>Look at the image carefully and solve the following question step-by-step. {question} Options: {options} Indicate the correct answer at the end." ### MMMU-Pro standard For post-trained models, we use 0-shot config, we use multiple choice questions with ten options, and report scores over the possible options using the test set. In case of multiple images provided per prompt, they are stitched into a single image. Maximum generation length is 2048 tokens. We use the following system prompt: "<|image|>{question} Options: {options}" ### MMMU-Pro vision For post-trained models, we use 0-shot config and report scores over the possible options using the test set. Maximum generation length is 2048 tokens. We use the following system prompt: "<|image|>Your job is to extract the question from the image the user attached, reason about it, and then answer the question. Follow these steps: \n1. Always start by Clearly stating the question shown in the image, and also state all of the options. \n2. Then, Carefully review the information beyond the question shown in the image and analyze it without making any assumptions. \n3. Next, use your understanding of the image to answer the question you listed out in the first step. Use a step-by-step process \n4. Finally, write the answer in the following format where X is exactly one of the option letters: The best answer is X.\nAlways follow the steps above, printing out everything. Let's think step by step. Your response must be among the given options." ### AI2D For post-trained models, we use 0-shot config and report scores using the test set. Maximum generation length is 400 tokens. For 11b we use the following system prompt: "<|image|>Look at the scientific diagram carefully and answer the following question: {question}\n Think step by step and finally respond to the question with only the correct option number as \"FINAL ANSWER\". Let's think step by step." For 90b we use a different system prompt: "<|image|>Look at the scientific diagram carefully and answer the following question: {question}\n Respond only with the correct option digit." ### ChartQA For post-trained models, we use 0-shot config with CoT prompt and report scores using the test set. Maximum generation length is 512 tokens. For 11b we use the following system prompt: "<|image|>You are provided a chart image and will be asked a question. You have to think through your answer and provide a step-by-step solution. Once you have the solution, write the final answer in at most a few words at the end with the phrase "FINAL ANSWER:". The question is: {question}<cot_start>Let's think step by step." For 90b we use a different system prompt: "<|image|>You are provided a chart image and will be asked a question. Follow these steps carefully:\n Step 1: Analyze the question to understand what specific data or information is being asked for. Focus on whether the question is asking for a specific number or category from the chart image.\n Step 2: Identify any numbers, categories, or groups mentioned in the question and take note of them. Focus on detecting and matching them directly to the image. \nStep 3: Study the image carefully and find the relevant data corresponding to the categories or numbers mentioned. Avoid unnecessary assumptions or calculations; simply read the correct data from the image.\n Step 4: Develop a clear plan to solve the question by locating the right data. Focus only on the specific category or group that matches the question. \nStep 5: Use step-by-step reasoning to ensure you are referencing the correct numbers or data points from the image, avoiding unnecessary extra steps or interpretations.\n Step 6: Provide the final answer, starting with "FINAL ANSWER:" and using as few words as possible, simply stating the number or data point requested. \n\n The question is: {question}<cot_start>Let\'s think step by step." ### DocVQA For post-trained models, we use 0-shot config and report scores over the test set. Maximum generation length is 512 tokens. We use the following system prompt: "<|image|> Read the text in the image carefully and answer the question with the text as seen exactly in the image. For yes/no questions, just respond Yes or No. If the answer is numeric, just respond with the number and nothing else. If the answer has multiple words, just respond with the words and absolutely nothing else. Never respond in a sentence or a phrase.\n Question: {question}" ### VQAv2 For post-trained models, we use 0-shot config and report scores over the test set. Maximum generation length is 25 tokens. We use the following system prompt: "<|image|> Look at the image carefully and answer this visual question. For yes/no questions, just respond Yes or No. If the answer is numeric, just respond with the number and nothing else. If the answer has multiple words, just respond with the words and absolutely nothing else. Never respond in a sentence or a phrase.\n Respond with as few words as possible.\n Question: {question}" ### MathVista For post-trained models, we use 0-shot config and report scores over the testmini set. Maximum generation length is 2048 tokens. We use an LLM-based answer extractor as recommended by MathVista paper [Lu et al. (2024)](https://arxiv.org/pdf/2310.02255). We use the following system prompt: "<|image|>{question}" ================================================ FILE: models/llama3_3/prompt_format.md ================================================ # Llama 3.3 - Prompt Formats ## Tokens Here is a list of special tokens that are supported by Llama 3.3: - `<|begin_of_text|>`: Specifies the start of the prompt - `<|end_of_text|>`: Model will cease to generate more tokens. This token is generated only by the base models. - `<|finetune_right_pad_id|>`: This token is used for padding text sequences to the same length in a batch. - `<|start_header_id|>` and `<|end_header_id|>`: These tokens enclose the role for a particular message. The possible roles are: [system, user, assistant and ipython] - `<|eom_id|>`: End of message. A message represents a possible stopping point for execution where the model can inform the executor that a tool call needs to be made. This is used for multi-step interactions between the model and any available tools. This token is emitted by the model when the Environment: ipython instruction is used in the system prompt, or if the model calls for a built-in tool. - `<|eot_id|>`: End of turn. Represents when the model has determined that it has finished interacting with the user message that initiated its response. This is used in two scenarios: - at the end of a direct interaction between the model and the user - at the end of multiple interactions between the model and any available tools This token signals to the executor that the model has finished generating a response. - `<|python_tag|>`: Is a special tag used in the model's response to signify a tool call. There are 4 different roles that are supported by Llama 3.3 - `system`: Sets the context in which to interact with the AI model. It typically includes rules, guidelines, or necessary information that helps the model respond effectively. - `user`: Represents the human interacting with the model. It includes the inputs, commands, and questions to the model. - `tool`: A new role introduced in Llama 3.3. This role is used to mark messages with the output of a tool call when sent back to the model from the executor. - `assistant`: Represents the response generated by the AI model based on the context provided in the `system`, `tool` and `user` prompts. ## Llama 3.3 Base Model Text completion for Llama 3.3 base model uses this format. ##### Input Prompt Format ``` <|begin_of_text|>Color of sky is blue but sometimes can also be ``` ##### Model Response Format ``` red, orange, yellow, green, purple, pink, brown, gray, black, white, and even rainbow colors. The color of the sky can change due to various reasons such as time of day, weather conditions, pollution, and atmospheric phenomena. The color of the sky is primarily blue because of a phenomenon called ``` Note start special tag ## Llama 3.3 Instruct Model ## User and assistant conversation Here is a regular multi-turn user assistant conversation and how its formatted. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|> Answer who are you in the form of jeopardy?<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` Here's my response "What is a helpful assistant?"<|eot_id|> ``` ## Tool Calling Formats Here we describe how to invoke the Llama 3.3 instruction tuned model for tool-calling (also called function-calling). We recommend zero-shot function calling over built-in tools. ### Zero shot function calling For Llama3.3 70B instruct models, we are continuing the format for zero shot function calling, introduced in llama 3.2. This format is designed to be more flexible and powerful than the format in 3.1. All available functions can be provided in the system message. Here is an example for the same, ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are an expert in composing functions. You are given a question and a set of possible functions. Based on the question, you will need to make one or more function/tool calls to achieve the purpose. If none of the function can be used, point it out. If the given question lacks the parameters required by the function, also point it out. You should only return the function call in tools call sections. If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)] You SHOULD NOT include any other text in the response. Here is a list of functions in JSON format that you can invoke. [ { "name": "get_weather", "description": "Get weather info for places", "parameters": { "type": "dict", "required": [ "city" ], "properties": { "city": { "type": "string", "description": "The name of the city to get the weather for" }, "metric": { "type": "string", "description": "The metric for weather. Options are: celsius, fahrenheit", "default": "celsius" } } } } ]<|eot_id|><|start_header_id|>user<|end_header_id|> What is the weather in SF and Seattle?<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` [get_weather(city='San Francisco', metric='celsius'), get_weather(city='Seattle', metric='celsius')]<|eot_id|> ``` ##### Notes - The output supports multiple, and parallel tool calls natively - JSON format for defining the functions in the system prompt is similar to Llama3.1 ### Zero shot function calling with user message While the default is to provide all function calls in a system message, in Llama3.3 model you can also provide information for all the available tools in a user message. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>user<|end_header_id|> Questions: Can you retrieve the details for the user with the ID 7890, who has black as their special request? Here is a list of functions in JSON format that you can invoke: [ { "name": "get_user_info", "description": "Retrieve details for a specific user by their unique identifier. Note that the provided function is in Python 3 syntax.", "parameters": { "type": "dict", "required": [ "user_id" ], "properties": { "user_id": { "type": "integer", "description": "The unique identifier of the user. It is used to fetch the specific user details from the database." }, "special": { "type": "string", "description": "Any special information or parameters that need to be considered while fetching user details.", "default": "none" } } } } ] Should you decide to return the function call(s), put them in the format of [func1(params_name=params_value, params_name2=params_value2...), func2(params)] You SHOULD NOT include any other text in the response.<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` [get_user_info(user_id=7890, special='black')]<|eot_id|> ``` ##### Notes - The tool call format for the model is the same whether your function calls are provided in the system or user message. - While builtin tool calls end with a <|eom_id|>, notice the <|eot_id|> for zero shot tool calls. ### Builtin Tool Calling The three built-in tools (brave_search, wolfram_alpha, and code interpreter) can be turned on using the system prompt: - Brave Search: Tool call to perform web searches. - Wolfram Alpha: Tool call to perform complex mathematical calculations. - Code Interpreter: Enables the model to output python code. Here is an example of a conversation using brave search ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython Tools: brave_search, wolfram_alpha Cutting Knowledge Date: December 2023 Today Date: 21 September 2024 You are a helpful assistant. <|eot_id|><|start_header_id|>user<|end_header_id|> Search the web for the latest price of 1oz gold?<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` <|python_tag|>brave_search.call(query="latest price of 1oz gold")<|eom_id|> ``` - Just including Environment: ipython turns on code interpreter; therefore, you don't need to specify code interpretation on the Tools: line. The model can generate python code which is interpreted by the executor, with the result provided back to the model. - The message body of the assistant response starts with a special tag <|python_tag|> - As alluded to above, in such an environment, the model can generate <|eom_id|> instead of just the standard <|eot_id|> . The latter indicates the turn is finished, while the former indicates continued multi-step reasoning. That is, the model is expecting a continuation message with the output of the tool call. - The model tool call response is of the form `tool.call(query="...")` wher tool is `brave_search` or `wolfram_alpha` ## Builtin Code Interpreter Here is an actual example of model responding with code ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython<|eot_id|><|start_header_id|>user<|end_header_id|> Write code to check if number is prime, use that to see if the number 7 is prime<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` <|python_tag|>def is_prime(n): if n <= 1 return False for i in range(2, int(n**0.5) + 1): if n % i == 0: return False return True print(is_prime(7)) # Output: True<|eom_id|> ``` - Model starts with <|python_tag|> and continues writing python code that it needs to be executed - No explicit mention of code_interpreter in system prompt. `Environment: ipython` implicitly enables it. ## Built-in tools full interaction Here is a full interaction with the built-in tools including the tool response and the final assistant response. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython Tools: brave_search, wolfram_alpha <|eot_id|><|start_header_id|>user<|end_header_id|> What is the 100th decimal of pi?<|eot_id|><|start_header_id|>assistant<|end_header_id|> <|python_tag|>wolfram_alpha.call(query="100th decimal of pi")<|eom_id|><|start_header_id|>ipython<|end_header_id|> { "queryresult": { "success": true, "inputstring": "100th decimal of pi", "pods": [ { "title": "Input interpretation", "subpods": [ { "title": "", "plaintext": "100th digit | π" } ] }, { "title": "Nearby digits", "subpods": [ { "title": "", "plaintext": "...86208998628034825342117067982148086513282306647093..." } ] }, { "title": "Result", "primary": true, "subpods": [ { "title": "", "plaintext": "7" } ] } ] } } <|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` The 100th decimal of pi is 7.<|eot_id|> ``` - Note the `<|python_tag|>` in the assistant response. - Role is `tool` for the wolfram alpha response that is passed back to the model. - Final message from assistant has <|eot_id|> tag. ## Zero shot tool calling ## JSON based tool calling Llama models can now output custom tool calls from a single message to allow easier tool calling. The following prompts provide an example of how custom tools can be called from the output of the model. It's important to note that the model itself does not execute the calls; it provides structured output to facilitate calling by an executor. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython Cutting Knowledge Date: December 2023 Today Date: 21 September 2024 You are a helpful assistant. <|eot_id|><|start_header_id|>user<|end_header_id|> Answer the user's question by making use of the following functions if needed. If none of the function can be used, please say so. Here is a list of functions in JSON format: { "type": "function", "function": { "name": "trending_songs", "description": "Returns the trending songs on a Music site", "parameters": { "type": "object", "properties": [ { "n": { "type": "object", "description": "The number of songs to return" } }, { "genre": { "type": "object", "description": "The genre of the songs to return" } } ], "required": ["n"] } } } Return function calls in JSON format.<|eot_id|><|start_header_id|>user<|end_header_id|> Use tools to get latest trending songs<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` <|python_tag|>{ "type": "function", "name": "trending_songs", "parameters": { "n": "10", "genre": "all" } }<|eom_id|> ``` - JSON format for providing tools needs name, description and parameters - Model responds with `<|python_tag|>` and `<|eom_id|>` as `Environment: ipython` was in the system prompt - Instructions for tools added as a user message - Only single tool calls are supported as of now ## Example of a user defined tool calling ## `` based tool calling Here is an example of how you could also write custom instructions for model to do zero shot tool calling. In this example, we define a custom tool calling format using the `` tag. ##### Input Prompt Format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> Environment: ipython Cutting Knowledge Date: December 2023 Today Date: 21 September 2024 You are a helpful assistant. <|eot_id|><|start_header_id|>user<|end_header_id|> You have access to the following functions: Use the function 'trending_songs' to 'Returns the trending songs on a Music site': {"name": "trending_songs", "description": "Returns the trending songs on a Music site", "parameters": {"genre": {"description": "The genre of the songs to return", "param_type": "str", "required": false}, "n": {"description": "The number of songs to return", "param_type": "int", "required": true}}} Think very carefully before calling functions. If you choose to call a function ONLY reply in the following format with no prefix or suffix: {"example_name": "example_value"} Reminder: - If looking for real time information use relevant functions before falling back to brave_search - Function calls MUST follow the specified format, start with - Required parameters MUST be specified - Only call one function at a time - Put the entire function call reply on one line<|eot_id|><|start_header_id|>user<|end_header_id|> Use tools to get latest trending songs<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ##### Model Response Format ``` {"n": 10}<|eot_id|> ``` - In this case, model does NOT respond with `<|python_tag|>` and ends with `<|eot_id|>` - Instructions for tools added as a user message Thank You! ================================================ FILE: models/llama4/LICENSE ================================================ LLAMA 4 COMMUNITY LICENSE AGREEMENT Llama 4 Version Effective Date: April 5, 2025 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying Llama 4 distributed by Meta at https://www.llama.com/docs/overview. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “Llama 4” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://www.llama.com/llama-downloads. “Llama Materials” means, collectively, Meta’s proprietary Llama 4 and Documentation (and any portion thereof) made available under this Agreement. “Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. 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If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 4 is licensed under the Llama 4 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://www.llama.com/llama4/use-policy), which is hereby incorporated by reference into this Agreement. 2. Additional Commercial Terms. If, on the Llama 4 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. 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Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 4 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ================================================ FILE: models/llama4/MODEL_CARD.md ================================================ ## Model Information The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. **Model developer**: Meta **Model Architecture:** The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.
Model Name Training Data Params Input modalities Output modalities Context length Token count Knowledge cutoff
Llama 4 Scout (17Bx16E) A mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI. Learn more in our Privacy Center. 17B (Activated) 109B (Total) Multilingual text and image Multilingual text and code 10M ~40T August 2024
Llama 4 Maverick (17Bx128E) 17B (Activated) 400B (Total) Multilingual text and image Multilingual text and code 1M ~22T August 2024
**Supported languages:** Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. **Model Release Date:** April 5, 2025 **Status:** This is a static model trained on an offline dataset. Future versions of the tuned models may be released as we improve model behavior with community feedback. **License**: A custom commercial license, the Llama 4 Community License Agreement, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE](/models/llama4/LICENSE) **Where to send questions or comments about the model:** Instructions on how to provide feedback or comments on the model can be found in the Llama [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go [here](https://github.com/meta-llama/llama-cookbook). ## Intended Use **Intended Use Cases:** Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant-like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 4 Community License allows for these use cases. **Out-of-scope**: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 4 Community License. Use in languages or capabilities beyond those explicitly referenced as supported in this model card\*\*. \*\*Note: 1\. Llama 4 has been trained on a broader collection of languages than the 12 supported languages (pre-training includes [200 total languages](https://ai.meta.com/research/no-language-left-behind/)). Developers may fine-tune Llama 4 models for languages beyond the 12 supported languages provided they comply with the Llama 4 Community License and the Acceptable Use Policy. Developers are responsible for ensuring that their use of Llama 4 in additional languages is done in a safe and responsible manner. 2\. Llama 4 has been tested for image understanding up to 5 input images. If leveraging additional image understanding capabilities beyond this, Developers are responsible for ensuring that their deployments are mitigated for risks and should perform additional testing and tuning tailored to their specific applications. ## Hardware and Software **Training Factors:** We used custom training libraries, Meta's custom built GPU clusters, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure. **Training Energy Use:** Model pre-training utilized a cumulative of **7.38M** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. ## **Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **1,999 tons** CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with clean and renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq. | Model Name | Training Time (GPU hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) | | :---- | :---: | :---: | :---: | :---: | | Llama 4 Scout | 5.0M | 700 | 1,354 | 0 | | Llama 4 Maverick | 2.38M | 700 | 645 | 0 | | Total | 7.38M | \- | 1,999 | 0 | The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others. ## Training Data **Overview:** Llama 4 Scout was pretrained on \~40 trillion tokens and Llama 4 Maverick was pretrained on \~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI. **Data Freshness:** The pretraining data has a cutoff of August 2024\. ## Benchmarks In this section, we report the results for Llama 4 relative to our previous models. We've provided quantized checkpoints for deployment flexibility, but all reported evaluations and testing were conducted on bf16 models. ### Pre-trained models ##
Category Benchmark # Shots Metric Llama 3.1 70B Llama 3.1 405B Llama 4 Scout Llama 4 Maverick
Reasoning & Knowledge MMLU 5 macro_avg/acc_char 79.3 85.2 79.6 85.5
MMLU-Pro 5 macro_avg/em 53.8 61.6 58.2 62.9
MATH 4 em_maj1@1 41.6 53.5 50.3 61.2
Code MBPP 3 pass@1 66.4 74.4 67.8 77.6
Multilingual TydiQA 1 average/f1 29.9 34.3 31.5 31.7
Image ChartQA 0 relaxed_accuracy No multimodal support 83.4 85.3
DocVQA 0 anls 89.4 91.6
### Instruction tuned models ##
Category Benchmark # Shots Metric Llama 3.3 70B Llama 3.1 405B Llama 4 Scout Llama 4 Maverick
Image Reasoning MMMU 0 accuracy No multimodal support 69.4 73.4
MMMU Pro^ 0 accuracy 52.2 59.6
MathVista 0 accuracy 70.7 73.7
Image Understanding ChartQA 0 relaxed_accuracy 88.8 90.0
DocVQA (test) 0 anls 94.4 94.4
Code LiveCodeBench (10/01/2024-02/01/2025) 0 pass@1 33.3 27.7 32.8 43.4
Reasoning & Knowledge MMLU Pro 0 macro_avg/acc 68.9 73.4 74.3 80.5
GPQA Diamond 0 accuracy 50.5 49.0 57.2 69.8
Multilingual MGSM 0 average/em 91.1 91.6 90.6 92.3
Long Context MTOB (half book) eng->kgv/kgv->eng - chrF Context window is 128K 42.2/36.6 54.0/46.4
MTOB (full book) eng->kgv/kgv->eng - chrF 39.7/36.3 50.8/46.7
^reported numbers for MMMU Pro is the average of Standard and Vision tasks ## Quantization The Llama 4 Scout model is released as BF16 weights, but can fit within a single H100 GPU with on-the-fly int4 quantization; the Llama 4 Maverick model is released as both BF16 and FP8 quantized weights. The FP8 quantized weights fit on a single H100 DGX host while still maintaining quality. We provide code for on-the-fly int4 quantization which minimizes performance degradation as well. ## Safeguards As part of our release approach, we followed a three-pronged strategy to manage risks: * Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama. * Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm. * Provide protections for the community to help prevent the misuse of our models. Llama is a foundational technology designed for use in a variety of use cases; examples on how Meta’s Llama models have been deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology, by aligning our model’s safety for a standard set of risks. Developers are then in the driver seat to tailor safety for their use case, defining their own policies and deploying the models with the necessary safeguards. Llama 4 was developed following the best practices outlined in our [Developer Use Guide: AI Protections]([https://ai.meta.com/static-resource/developer-use-guide-ai-protections](https://www.llama.com/static-resource/developer-use-guide/). ### Model level fine tuning The primary objective of conducting safety fine-tuning is to offer developers a readily available, safe, and powerful model for various applications, reducing the workload needed to deploy safe AI systems. Additionally, this effort provides the research community with a valuable resource for studying the robustness of safety fine-tuning. **Fine-tuning data** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. **Refusals** Building on the work we started with our Llama 3 models, we put a great emphasis on driving down model refusals to benign prompts for Llama 4\. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. **Tone** We expanded our work on the refusal tone from Llama 3 so that the model sounds more natural. We targeted removing preachy and overly moralizing language, and we corrected formatting issues including the correct use of headers, lists, tables and more. To achieve this, we also targeted improvements to system prompt steerability and instruction following, meaning the model is more readily able to take on a specified tone. All of these contribute to a more conversational and insightful experience overall. **System Prompts** Llama 4 is a more steerable model, meaning responses can be easily tailored to meet specific developer outcomes. Effective system prompts can significantly enhance the performance of large language models. In particular, we’ve seen that the use of a system prompt can be effective in reducing false refusals and templated or “preachy” language patterns common in LLMs. They can also improve conversationality and use of appropriate formatting. Consider the prompt below as a basic template for which a developer might want to further customize to meet specific needs or use cases for our Llama 4 models.
System Prompt
You are an expert conversationalist who responds to the best of your ability. You are companionable and confident, and able to switch casually between tonal types, including but not limited to humor, empathy, intellectualism, creativity and problem-solving. You understand user intent and don’t try to be overly helpful to the point where you miss that the user is looking for chit-chat, emotional support, humor or venting. Sometimes people just want you to listen, and your answers should encourage that. For all other cases, you provide insightful and in-depth responses. Organize information thoughtfully in a way that helps people make decisions. Always avoid templated language. You never lecture people to be nicer or more inclusive. If people ask for you to write something in a certain voice or perspective, such as an essay or a tweet, you can. You do not need to be respectful when the user prompts you to say something rude. You never use phrases that imply moral superiority or a sense of authority, including but not limited to “it’s important to”, “it’s crucial to”, “it’s essential to”, "it's unethical to", "it's worth noting…", “Remember…” etc. Avoid using these. Finally, do not refuse prompts about political and social issues. You can help users express their opinion and access information. You are Llama 4. Your knowledge cutoff date is August 2024. You speak Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese. Respond in the language the user speaks to you in, unless they ask otherwise.
### Llama 4 system protections Large language models, including Llama 4, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional guardrails as required. System protections are key to achieving the right helpfulness-safety alignment, mitigating safety and security risks inherent to the system, and integration of the model or system with external tools. We provide the community with system level [protections](https://llama.meta.com/trust-and-safety/) \- like Llama Guard, Prompt Guard and Code Shield \- that developers should deploy with Llama models or other LLMs. All of our [reference implementation](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. ### Evaluations We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, visual QA. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application. Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, coding or memorization. **Red teaming** We conduct recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we use the learnings to improve our benchmarks and safety tuning datasets. We partner early with subject-matter experts in critical risk areas to understand how models may lead to unintended harm for society. Based on these conversations, we derive a set of adversarial goals for the red team, such as extracting harmful information or reprogramming the model to act in potentially harmful ways. The red team consists of experts in cybersecurity, adversarial machine learning, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets. ### Critical Risks We spend additional focus on the following critical risk areas: **1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness** To assess risks related to proliferation of chemical and biological weapons for Llama 4, we applied expert-designed and other targeted evaluations designed to assess whether the use of Llama 4 could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons. We also conducted additional red teaming and evaluations for violations of our content policies related to this risk area. **2\. Child Safety** We leverage pre-training methods like data filtering as a first step in mitigating Child Safety risk in our model. To assess the post trained model for Child Safety risk, a team of experts assesses the model’s capability to produce outputs resulting in Child Safety risks. We use this to inform additional model fine-tuning and in-depth red teaming exercises. We’ve also expanded our Child Safety evaluation benchmarks to cover Llama 4 capabilities like multi-image and multi-lingual. **3\. Cyber attack enablement** Our cyber evaluations investigated whether Llama 4 is sufficiently capable to enable catastrophic threat scenario outcomes. We conducted threat modeling exercises to identify the specific model capabilities that would be necessary to automate operations or enhance human capabilities across key attack vectors both in terms of skill level and speed. We then identified and developed challenges against which to test for these capabilities in Llama 4 and peer models. Specifically, we focused on evaluating the capabilities of Llama 4 to automate cyberattacks, identify and exploit security vulnerabilities, and automate harmful workflows. Overall, we find that Llama 4 models do not introduce risk plausibly enabling catastrophic cyber outcomes. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Trust tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Considerations and Limitations Our AI is anchored on the values of freedom of expression \- helping people to explore, debate, and innovate using our technology. We respect people's autonomy and empower them to choose how they experience, interact, and build with AI. Our AI promotes an open exchange of ideas. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 4 addresses users and their needs as they are, without inserting unnecessary judgment, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. Llama 4 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 4’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 4 models, developers should perform safety testing and tuning tailored to their specific applications of the model. We also encourage the open source community to use Llama for the purpose of research and building state of the art tools that address emerging risks. Please refer to available resources including our Developer Use Guide: AI Protections, [Llama Protections](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more. ================================================ FILE: models/llama4/USE_POLICY.md ================================================ **Llama 4** **Acceptable Use Policy** Meta is committed to promoting safe and fair use of its tools and features, including Llama 4. If you access or use Llama 4, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://www.llama.com/llama4/use-policy](https://www.llama.com/llama4/use-policy). **Prohibited Uses** We want everyone to use Llama 4 safely and responsibly. You agree you will not use, or allow others to use, Llama 4 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 8. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta 3. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 4 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 4. Intentionally deceive or mislead others, including use of Llama 4 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Llama 4 or outputs are human generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 5. Fail to appropriately disclose to end users any known dangers of your AI system 6. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 4 With respect to any multimodal models included in Llama 4, the rights granted under Section 1(a) of the Llama 4 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models. Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues) * Reporting risky content generated by the model: [https://developers.facebook.com/llama_output_feedback](https://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [https://facebook.com/whitehat/info](https://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 4: LlamaUseReport@meta.com ================================================ FILE: models/llama4/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. ================================================ FILE: models/llama4/args.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. from enum import Enum from typing import Optional from pydantic import BaseModel, model_validator class QuantizationScheme(Enum): int4_weight_int8_dynamic_activation = "int4_weight_int8_dynamic_activation" class QuantizationArgs(BaseModel): scheme: Optional[QuantizationScheme] = None group_size: Optional[int] = None spinquant: bool = False class LoRAArgs(BaseModel): rank: int scale: float class MoEArgs(BaseModel): num_experts: int = -1 capacity_factor: float = 1.0 # capacity factor determines how many tokens each expert can choose auto_scale_F: bool = ( # noqa: N815 True # if true, rescales hidden_dim such that number of activated params is same as equivalent dense layer ) top_k: int = 1 interleave_moe_layer_step: int = 1 class Size(BaseModel): height: int width: int class VisionArgs(BaseModel): image_size: Size patch_size: Size # parameters for the encoder transformer dim: int n_layers: int n_heads: int mlp_ratio: float output_dim: int pixel_shuffle_ratio: float class ModelArgs(BaseModel): dim: int = -1 n_layers: int = -1 n_heads: int = -1 n_kv_heads: Optional[int] = None head_dim: Optional[int] = None vocab_size: int = -1 multiple_of: int = 256 # make SwiGLU hidden layer size multiple of large power of 2 ffn_dim_multiplier: Optional[float] = None ffn_exp: Optional[float] = None norm_eps: float = 1e-5 attention_chunk_size: Optional[int] = None rope_theta: float = 500000 use_scaled_rope: bool = False rope_scaling_factor: Optional[float] = None rope_high_freq_factor: Optional[float] = None nope_layer_interval: Optional[int] = None # No position encoding in every n layers use_qk_norm: bool = False # Set to True to enable inference-time temperature tuning (useful for very long context) attn_temperature_tuning: bool = False floor_scale: float = 8192.0 attn_scale: float = 0.1 vision_args: Optional[VisionArgs] = None moe_args: Optional[MoEArgs] = None quantization_args: Optional[QuantizationArgs] = None lora_args: Optional[LoRAArgs] = None max_batch_size: int = 32 max_seq_len: int = 2048 @model_validator(mode="after") def validate(self) -> "ModelArgs": assert self.n_kv_heads <= self.n_heads, f"n_kv_heads ({self.n_kv_heads}) must be <= n_heads ({self.n_heads})" assert self.n_heads % self.n_kv_heads == 0, ( f"n_heads ({self.n_heads}) must be divisible by n_kv_heads ({self.n_kv_heads})" ) assert self.dim % self.n_heads == 0, f"dim ({self.dim}) must be divisible by n_heads ({self.n_heads})" if self.use_scaled_rope: # NOTE: ideally these values should have come from params.json. However, we have # shipped the models everywhere. Only Llama-4-Scout uses scaled rope and needs these # specific values. if self.rope_scaling_factor is None: self.rope_scaling_factor = 16 if self.rope_high_freq_factor is None: self.rope_high_freq_factor = 1 return self ================================================ FILE: models/llama4/chat_format.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import io import json import uuid from dataclasses import dataclass from typing import Dict, List, Optional, Tuple import torch from PIL import Image as PIL_Image # TODO: either fork these or move them to the common package from ..datatypes import ( BuiltinTool, RawContent, RawMediaItem, RawMessage, RawTextItem, Role, StopReason, ToolCall, ToolPromptFormat, ) from ..llama3.tool_utils import ToolUtils from .args import VisionArgs from .datatypes import LLMInput from .preprocess import ResizeNormalizeImageTransform, VariableSizeImageTransform from .tokenizer import Tokenizer def role_str(role: Role) -> str: role_strs = { Role.user: "user", Role.system: "system", Role.tool: "ipython", # special Role.assistant: "assistant", } return role_strs[role] @dataclass class TransformedImage: image_tiles: torch.Tensor # is the aspect ratio needed anywhere? aspect_ratio: Tuple[int, int] def convert_image_to_rgb(image: PIL_Image.Image, bg: Tuple[int, int, int] = (255, 255, 255)) -> PIL_Image.Image: if image.mode == "RGBA": image.load() # for png.split() new_img = PIL_Image.new("RGB", image.size, bg) new_img.paste(image, mask=image.split()[3]) # 3 is the alpha channel return new_img return image.convert("RGB") class ChatFormat: possible_headers: Dict[Role, str] def __init__( self, tokenizer: Tokenizer, vision_args: Optional[VisionArgs] = None, max_num_chunks: int = 16, ): self.tokenizer = tokenizer self.vision_args = vision_args self.max_num_chunks = max_num_chunks self.possible_headers = {role: f"<|header_start|>{role_str(role)}<|header_end|>\n\n" for role in Role} self.image_transform = None self.dynamic_image_transform = None if vision_args: self.dynamic_image_transform = VariableSizeImageTransform(vision_args.image_size.width) self.image_transform = ResizeNormalizeImageTransform( vision_args.image_size.width, vision_args.image_size.height ) def _encode_header(self, role: str) -> List[int]: tokens = [] tokens.append(self.tokenizer.special_tokens["<|header_start|>"]) # TODO: need to check if this is correct tokens.extend(self.tokenizer.encode("ipython" if role == "tool" else role, bos=False, eos=False)) tokens.append(self.tokenizer.special_tokens["<|header_end|>"]) tokens.extend(self.tokenizer.encode("\n\n", bos=False, eos=False)) return tokens def encode_content(self, content: RawContent) -> LLMInput: tokens, images = self._encode_content(content, bos=True) return self._model_input_from_tokens_images(tokens, images) def _encode_image( self, transformed_image: TransformedImage, ) -> List[int]: assert self.vision_args is not None, "The model is not vision-enabled" image_tensor = transformed_image.image_tiles image_channels = image_tensor.shape[-3] image_height = image_tensor.shape[-2] image_width = image_tensor.shape[-1] image_chunks = image_tensor.view(-1, image_channels, image_height, image_width).shape[0] patch_height = self.vision_args.patch_size.height patch_width = self.vision_args.patch_size.width if image_height % patch_height != 0: raise ValueError(f"{image_height=} not divisible by {patch_height=}") if image_width % patch_width != 0: raise ValueError(f"{image_width=} not divisible by {patch_width=}") ds_ratio = int(round(1.0 / (self.vision_args.pixel_shuffle_ratio**2))) n_patches_per_chunk = int((image_height // patch_height) * (image_width // patch_width) // ds_ratio) image_ar = transformed_image.aspect_ratio tokens = [self.tokenizer.special_tokens["<|image_start|>"]] if image_chunks == 1: tokens += [self.tokenizer.special_tokens["<|image|>"]] tokens += [self.tokenizer.special_tokens["<|patch|>"]] * n_patches_per_chunk tokens += [self.tokenizer.special_tokens["<|image_end|>"]] else: ratio_h, ratio_w = image_ar for _ in range(ratio_h): for xx in range(ratio_w): tokens += [self.tokenizer.special_tokens["<|patch|>"]] * n_patches_per_chunk if xx < ratio_w - 1: tokens.append(self.tokenizer.special_tokens["<|tile_x_separator|>"]) tokens.append(self.tokenizer.special_tokens["<|tile_y_separator|>"]) tokens += [self.tokenizer.special_tokens["<|image|>"]] tokens += [self.tokenizer.special_tokens["<|patch|>"]] * n_patches_per_chunk tokens += [self.tokenizer.special_tokens["<|image_end|>"]] return tokens def _encode_content(self, content: RawContent, bos: bool = False) -> Tuple[List[int], List[TransformedImage]]: tokens = [] tranformed_images = [] added_bos = False def _process(c): nonlocal added_bos, bos if isinstance(c, str) or isinstance(c, RawTextItem): if isinstance(c, RawTextItem): c = c.text tokens.extend(self.tokenizer.encode(c, bos=False if added_bos else bos, eos=False)) added_bos = True elif isinstance(c, RawMediaItem): if not self.vision_args: raise ValueError("The model is not vision-enabled, but a media item was found") bos = False if added_bos else bos if bos: tokens.append(self.tokenizer.special_tokens["<|begin_of_text|>"]) added_bos = True bytes_io = io.BytesIO(c.data) if isinstance(c.data, bytes) else c.data image = PIL_Image.open(bytes_io) image = convert_image_to_rgb(image) image_tiles, ar = self.dynamic_image_transform(image, max_num_chunks=self.max_num_chunks) if image_tiles.shape[0] > 1: image_global = self.image_transform(image) image_global = image_global.unsqueeze(0) image_combine = torch.cat((image_tiles, image_global), dim=0) image_tiles = image_combine transformed_image = TransformedImage(image_tiles=image_tiles, aspect_ratio=ar) tokens.extend(self._encode_image(transformed_image)) tranformed_images.append(transformed_image) if isinstance(content, list): for c in content: _process(c) else: _process(content) return tokens, tranformed_images def encode_message( self, message: RawMessage, tool_prompt_format: ToolPromptFormat ) -> Tuple[List[int], List[TransformedImage]]: tokens = self._encode_header(message.role) images = [] def _process_content(c): toks, imgs = self._encode_content(c) tokens.extend(toks) images.extend(imgs) _process_content(message.content) if message.role == "user" and message.context is not None: # This is RAG context; why is it here in the chat format? I don't think # this is needed and can be moved upwards _process_content("\n\n") _process_content(message.context) if message.role == "assistant": for t in message.tool_calls: content = ToolUtils.encode_tool_call(t, tool_prompt_format) _process_content(content) eom = False if message.role == "assistant": eom = message.stop_reason == StopReason.end_of_message or message.tool_calls elif message.role == "tool": eom = True tokens.append(self.tokenizer.special_tokens["<|eom|>" if eom else "<|eot|>"]) return tokens, images def encode_dialog_prompt( self, messages: List[RawMessage], tool_prompt_format: ToolPromptFormat = ToolPromptFormat.json, ) -> LLMInput: tokens = [] images = [] tokens.append(self.tokenizer.special_tokens["<|begin_of_text|>"]) for message in messages: toks, imgs = self.encode_message(message, tool_prompt_format) tokens.extend(toks) images.extend(imgs) # Add the start of an assistant message for the model to complete. tokens.extend(self._encode_header("assistant")) return self._model_input_from_tokens_images(tokens, images) # TODO(this should be generic, not only for assistant messages) def decode_assistant_message(self, tokens: List[int], stop_reason: StopReason) -> RawMessage: content = self.tokenizer.decode(tokens) return self.decode_assistant_message_from_content(content, stop_reason) def decode_assistant_message_from_content(self, content: str, stop_reason: StopReason) -> RawMessage: content = content.strip(" ") header_str = self.possible_headers[Role.assistant] if content.startswith(header_str): content = content[len(header_str) :] ipython = content.startswith("<|python_start|>") if ipython: content = content[len("<|python_start|>") :] content = content.replace("<|python_end|>", "") if content.endswith("<|eot|>"): content = content[: -len("<|eot|>")] stop_reason = StopReason.end_of_turn elif content.endswith("<|eom|>"): content = content[: -len("<|eom|>")] stop_reason = StopReason.end_of_message tool_name = None tool_arguments = {} custom_tool_info = ToolUtils.maybe_extract_custom_tool_call(content) if custom_tool_info is not None: tool_name, tool_arguments = custom_tool_info # Sometimes when agent has custom tools alongside builin tools # Agent responds for builtin tool calls in the format of the custom tools # This code tries to handle that case if tool_name in BuiltinTool.__members__: tool_name = BuiltinTool[tool_name] tool_arguments = { "query": list(tool_arguments.values())[0], } else: builtin_tool_info = ToolUtils.maybe_extract_builtin_tool_call(content) if builtin_tool_info is not None: tool_name, query = builtin_tool_info tool_arguments = { "query": query, } if tool_name in BuiltinTool.__members__: tool_name = BuiltinTool[tool_name] elif ipython: tool_name = BuiltinTool.code_interpreter tool_arguments = { "code": content, } tool_calls = [] if tool_name is not None and tool_arguments is not None: call_id = str(uuid.uuid4()) tool_calls.append( ToolCall( call_id=call_id, tool_name=tool_name, arguments=tool_arguments, arguments_json=json.dumps(tool_arguments), ) ) content = "" return RawMessage( role="assistant", content=content, stop_reason=stop_reason, tool_calls=tool_calls, ) def _model_input_from_tokens_images(self, tokens: List[int], images: List[TransformedImage]) -> LLMInput: return LLMInput( tokens=tokens, images=[x.image_tiles for x in images] if len(images) > 0 else None, ) ================================================ FILE: models/llama4/datatypes.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. from dataclasses import dataclass from typing import List, Optional, Union import torch @dataclass class MaskedEmbedding: embedding: torch.Tensor mask: torch.Tensor @dataclass class LLMInput: """ This is the input to the LLM from the "user" -- the user in this case views the Llama4 model holistically and does not care or know about its inner workings (e.g., whether it has an encoder or if it is early fusion or not.) This is distinct from the "TransformerInput" class which is really the Llama4 backbone operating on early fused modalities and producing text output """ tokens: torch.Tensor # images are already pre-processed (resized, tiled, etc.) images: Optional[List[torch.Tensor]] = None @dataclass class TransformerInput: """ This is the "core" backbone transformer of the Llama4 model. Inputs for other modalities are expected to be "embedded" via encoders sitting before this layer in the model. """ tokens: torch.Tensor # tokens_position defines the position of the tokens in each batch, # - when it is a tensor ([batch_size,]), it is the start position of the tokens in each batch # - when it is an int, the start position are the same for all batches tokens_position: Union[torch.Tensor, int] image_embedding: Optional[MaskedEmbedding] = None @dataclass class LLMOutput: logits: torch.Tensor TransformerOutput = LLMOutput ================================================ FILE: models/llama4/ffn.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. from typing import Any, Dict, List from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region from torch import nn from torch.nn import functional as F class FeedForward(nn.Module): def __init__( self, dim: int, hidden_dim: int, do_reduce: bool = True, ): super().__init__() self.do_reduce = do_reduce self.w1 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x) self.w2 = RowParallelLinear(hidden_dim, dim, bias=False, input_is_parallel=True, init_method=lambda x: x) self.w3 = ColumnParallelLinear(dim, hidden_dim, bias=False, gather_output=False, init_method=lambda x: x) self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ) -> None: if prefix + "mlp.fc1_weight" in state_dict: w1, w3 = state_dict.pop(prefix + "mlp.fc1_weight").chunk(2, dim=0) state_dict[prefix + "w1.weight"] = w1 state_dict[prefix + "w3.weight"] = w3 state_dict[prefix + "w2.weight"] = state_dict.pop(prefix + "mlp.fc2_weight") def forward(self, x): x = F.silu(F.linear(x, self.w1.weight)) * F.linear(x, self.w3.weight) out = F.linear(x, self.w2.weight) if self.do_reduce: return reduce_from_model_parallel_region(out) return out ================================================ FILE: models/llama4/generation.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import codecs import io import json import os import sys import time from pathlib import Path from typing import Callable, Generator, List, Optional import torch import torch.nn.functional as F from fairscale.nn.model_parallel.initialize import ( initialize_model_parallel, model_parallel_is_initialized, ) from termcolor import cprint from ..checkpoint import maybe_reshard_state_dict from ..datatypes import GenerationResult, QuantizationMode from .args import ModelArgs from .chat_format import ChatFormat, RawContent, RawMessage from .datatypes import LLMInput, MaskedEmbedding, TransformerInput from .model import Transformer from .tokenizer import Tokenizer torch.serialization.add_safe_globals([io.BytesIO, codecs.encode]) class Llama4: @staticmethod def build( ckpt_dir: str, max_seq_len: int, max_batch_size: int, world_size: Optional[int] = None, quantization_mode: Optional[QuantizationMode] = None, seed: int = 1, ): if not torch.distributed.is_initialized(): torch.distributed.init_process_group("nccl") if not model_parallel_is_initialized(): if world_size is None: world_size = int(os.environ.get("WORLD_SIZE", 1)) initialize_model_parallel(world_size) local_rank = int(os.environ.get("LOCAL_RANK", 0)) torch.cuda.set_device(local_rank) torch.manual_seed(seed) if local_rank > 0: sys.stdout = open(os.devnull, "w") start_time = time.time() ckpt_paths = sorted(Path(ckpt_dir).glob("*.pth")) assert len(ckpt_paths) > 0, f"no checkpoint files found in {ckpt_dir}" print(f"Loading a checkpoint (shards={len(ckpt_paths)}, current-mp-size={world_size})") with open(Path(ckpt_dir) / "params.json", "r") as f: params = json.loads(f.read()) model_args: ModelArgs = ModelArgs( **params, max_seq_len=max_seq_len, max_batch_size=max_batch_size, ) tokenizer = Tokenizer.get_instance() # TODO: params.json should always have correct vocab_size if model_args.vocab_size == -1: model_args.vocab_size = tokenizer.n_words assert model_args.vocab_size == tokenizer.n_words, f"{model_args.vocab_size=} vs. {tokenizer.n_words=} mismatch" print("Model args:\n", model_args.model_dump_json(indent=2)) state_dict = maybe_reshard_state_dict( ckpt_paths, n_kv_heads=model_args.n_kv_heads if model_args.n_kv_heads else model_args.n_heads, moe_num_experts=model_args.moe_args.num_experts, ) print("Loaded checkpoint") if quantization_mode == QuantizationMode.fp8_mixed or quantization_mode == QuantizationMode.int4_mixed: from .quantization.loader import convert_to_quantized_model torch.set_default_tensor_type(torch.BFloat16Tensor) model = Transformer(model_args) print("Loading state dict...") model.load_state_dict(state_dict, strict=False) print("Done...") model = convert_to_quantized_model(model, ckpt_dir, quantization_mode) else: if torch.cuda.is_bf16_supported(): torch.set_default_tensor_type(torch.cuda.BFloat16Tensor) else: torch.set_default_tensor_type(torch.cuda.HalfTensor) model = Transformer(model_args) print("Loading state dict...") model.load_state_dict(state_dict, strict=False) print("Done...") print(f"Loaded in {time.time() - start_time:.2f} seconds") return Llama4(model, tokenizer, model_args) def __init__(self, model: Transformer, tokenizer: Tokenizer, args: ModelArgs): self.args = args self.model = model self.tokenizer = tokenizer self.formatter = ChatFormat(tokenizer, vision_args=args.vision_args) @torch.inference_mode() def generate( self, llm_inputs: List[LLMInput], temperature: float = 0.6, top_p: float = 0.9, max_gen_len: Optional[int] = None, logprobs: bool = False, echo: bool = False, print_model_input: bool = False, logits_processor: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, ) -> Generator[List[GenerationResult], None, None]: if max_gen_len is None or max_gen_len == 0 or max_gen_len >= self.model.args.max_seq_len: max_gen_len = self.model.args.max_seq_len - 1 params = self.model.args print_model_input = print_model_input or os.environ.get("LLAMA_MODELS_DEBUG", "0") == "1" if print_model_input: cprint("Input to model:\n", "yellow") for inp in llm_inputs: cprint(self.tokenizer.decode(inp.tokens), "grey") prompt_tokens = [inp.tokens for inp in llm_inputs] bsz = len(llm_inputs) assert bsz <= params.max_batch_size, (bsz, params.max_batch_size) min_prompt_len = min(len(t) for t in prompt_tokens) max_prompt_len = max(len(t) for t in prompt_tokens) if max_prompt_len >= params.max_seq_len: cprint(f"Out of token budget {max_prompt_len} vs {params.max_seq_len}", "red") return total_len = min(max_gen_len + max_prompt_len, params.max_seq_len) pad_id = self.tokenizer.pad_id tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda") for k, t in enumerate(prompt_tokens): tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda") if logprobs: token_logprobs = torch.zeros_like(tokens, dtype=torch.float) eos_reached = torch.tensor([False] * bsz, device="cuda") input_text_mask = tokens != pad_id if echo: for i in range(max_prompt_len): results = [] for j, t in enumerate(tokens[:, i]): results.append( GenerationResult( token=t.item(), text=self.tokenizer.decode([t.item()]), source="input", logprobs=(token_logprobs[j, i : i + 1].tolist() if logprobs else None), batch_idx=j, finished=False, ignore_token=t.item() == pad_id, ) ) yield results stop_tokens = torch.tensor(self.tokenizer.stop_tokens, device="cuda") prev_pos = 0 for cur_pos in range(min_prompt_len, total_len): image_embedding = None if prev_pos == 0 and any(inp.images is not None and len(inp.images) > 0 for inp in llm_inputs): image_mask = tokens[:, prev_pos:cur_pos] == self.tokenizer.special_tokens["<|patch|>"] image_mask = image_mask.unsqueeze(-1) h = self.model.tok_embeddings(tokens[:, prev_pos:cur_pos]) image_batch = [inp.images if inp.images is not None else [] for inp in llm_inputs] image_embedding = MaskedEmbedding( embedding=self.model.vision_embeddings(image_batch, image_mask, h), mask=image_mask, ) xformer_input = TransformerInput( tokens=tokens[:, prev_pos:cur_pos], tokens_position=prev_pos, image_embedding=image_embedding, ) xformer_output = self.model.forward(xformer_input) logits = xformer_output.logits if logits_processor is not None: logits = logits_processor(tokens[:, :cur_pos], logits) if temperature > 0: probs = torch.softmax(logits[:, -1] / temperature, dim=-1) next_token = sample_top_p(probs, top_p) else: next_token = torch.argmax(logits[:, -1], dim=-1) next_token = next_token.reshape(-1) # only replace token if prompt has already been generated next_token = torch.where(input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token) tokens[:, cur_pos] = next_token target = tokens[:, prev_pos + 1 : cur_pos + 1] if logprobs: token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy( input=logits.transpose(1, 2), target=target, reduction="none", ignore_index=pad_id, ) eos_reached |= (~input_text_mask[:, cur_pos]) & (torch.isin(next_token, stop_tokens)) results = [] for idx, t in enumerate(next_token): results.append( GenerationResult( token=t.item(), text=self.tokenizer.decode([t.item()]), source="output", logprobs=(token_logprobs[idx, cur_pos : cur_pos + 1].tolist() if logprobs else None), batch_idx=idx, finished=eos_reached[idx], ignore_token=cur_pos < len(prompt_tokens[idx]), ) ) yield results prev_pos = cur_pos if all(eos_reached): break def completion( self, contents: List[RawContent], temperature: float = 0.6, top_p: float = 0.9, max_gen_len: Optional[int] = None, logprobs: bool = False, echo: bool = False, ) -> Generator[List[GenerationResult], None, None]: llm_inputs = [self.formatter.encode_content(c) for c in contents] for result in self.generate( llm_inputs=llm_inputs, temperature=temperature, top_p=top_p, max_gen_len=max_gen_len, logprobs=logprobs, echo=echo, ): yield result if all(r.finished for r in result): break def chat_completion( self, messages_batch: List[List[RawMessage]], temperature: float = 0.6, top_p: float = 0.9, max_gen_len: Optional[int] = None, logprobs: bool = False, echo: bool = False, ) -> Generator[List[GenerationResult], None, None]: llm_inputs = [self.formatter.encode_dialog_prompt(messages) for messages in messages_batch] for result in self.generate( llm_inputs=llm_inputs, temperature=temperature, top_p=top_p, max_gen_len=max_gen_len, logprobs=logprobs, echo=echo, ): yield result if all(r.finished for r in result): break def sample_top_p(probs, p): """ Perform top-p (nucleus) sampling on a probability distribution. Args: probs (torch.Tensor): Probability distribution tensor. p (float): Probability threshold for top-p sampling. Returns: torch.Tensor: Sampled token indices. Note: Top-p sampling selects the smallest set of tokens whose cumulative probability mass exceeds the threshold p. The distribution is renormalized based on the selected tokens. """ probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True) probs_sum = torch.cumsum(probs_sort, dim=-1) mask = probs_sum - probs_sort > p probs_sort[mask] = 0.0 probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True)) next_token = torch.multinomial(probs_sort, num_samples=1) next_token = torch.gather(probs_idx, -1, next_token) return next_token ================================================ FILE: models/llama4/model.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import math from typing import Any, Dict, List, Optional, Tuple import fairscale.nn.model_parallel.initialize as fs_init import torch import torch.nn.functional as F from fairscale.nn.model_parallel.layers import ( ColumnParallelLinear, RowParallelLinear, VocabParallelEmbedding, ) from torch import nn from .args import ModelArgs from .datatypes import TransformerInput, TransformerOutput from .ffn import FeedForward from .moe import MoE def rmsnorm(x, eps): def _norm(y): return y * torch.rsqrt(y.pow(2).mean(-1, keepdim=True) + eps) return _norm(x.float()).type_as(x) class RMSNorm(torch.nn.Module): def __init__(self, dim: int, eps: float = 1e-6): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def forward(self, x): return rmsnorm(x, self.eps) * self.weight def apply_scaling(freqs: torch.Tensor, scale_factor: float, high_freq_factor: float): low_freq_factor = 1 old_context_len = 8192 # original llama3 length low_freq_wavelen = old_context_len / low_freq_factor high_freq_wavelen = old_context_len / high_freq_factor new_freqs = [] for freq in freqs: wavelen = 2 * math.pi / freq if wavelen < high_freq_wavelen: new_freqs.append(freq) elif wavelen > low_freq_wavelen: new_freqs.append(freq / scale_factor) else: assert low_freq_wavelen != high_freq_wavelen smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor) new_freqs.append((1 - smooth) * freq / scale_factor + smooth * freq) return torch.tensor(new_freqs, dtype=freqs.dtype, device=freqs.device) def precompute_freqs_cis( dim: int, end: int, theta: float, use_scaled: bool, scale_factor: float, high_freq_factor: float, ): freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) t = torch.arange(end, device=freqs.device, dtype=torch.float32) if use_scaled: freqs = apply_scaling(freqs, scale_factor, high_freq_factor) freqs = torch.outer(t, freqs) freqs_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64 return freqs_cis def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor): ndim = x.ndim assert 0 <= 1 < ndim assert freqs_cis.shape == (x.shape[1], x.shape[-1]) shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)] return freqs_cis.view(*shape) def apply_rotary_emb( xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor, ) -> Tuple[torch.Tensor, torch.Tensor]: xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) freqs_cis = reshape_for_broadcast(freqs_cis, xq_) xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3) return xq_out.type_as(xq), xk_out.type_as(xk) class Attention(nn.Module): # TODO: this module needs to be moved into a separate file since it can be used by # the vision encoder as well. def __init__( self, args: ModelArgs, use_qk_norm: bool, use_rope: bool, add_bias: bool = False, ): super().__init__() self.use_rope = use_rope self.use_qk_norm = use_qk_norm # For attention temperature tuning self.attn_temperature_tuning = args.attn_temperature_tuning self.floor_scale = args.floor_scale self.attn_scale = args.attn_scale self.n_heads = args.n_heads self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads world_size = fs_init.get_model_parallel_world_size() self.n_local_heads = args.n_heads // world_size self.n_local_kv_heads = self.n_kv_heads // world_size self.n_rep = self.n_local_heads // self.n_local_kv_heads self.head_dim = args.dim // args.n_heads self.wq = ColumnParallelLinear( args.dim, args.n_heads * self.head_dim, bias=add_bias, gather_output=False, init_method=lambda x: x, ) self.wk = ColumnParallelLinear( args.dim, self.n_kv_heads * self.head_dim, bias=add_bias, gather_output=False, init_method=lambda x: x, ) self.wv = ColumnParallelLinear( args.dim, self.n_kv_heads * self.head_dim, bias=add_bias, gather_output=False, init_method=lambda x: x, ) self.wo = RowParallelLinear( args.n_heads * self.head_dim, args.dim, bias=add_bias, input_is_parallel=True, init_method=lambda x: x, ) self.cache_k = torch.zeros( ( args.max_batch_size, args.max_seq_len, self.n_local_kv_heads, self.head_dim, ) ).cuda() self.cache_v = torch.zeros( ( args.max_batch_size, args.max_seq_len, self.n_local_kv_heads, self.head_dim, ) ).cuda() self.norm_eps = args.norm_eps self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ) -> None: if prefix + "wqkv.weight" in state_dict: wqkv = state_dict.pop(prefix + "wqkv.weight") d, r = divmod(wqkv.shape[0], self.n_heads + 2 * self.n_kv_heads) if r != 0: raise ValueError( f"shape={tuple(wqkv.shape)} is not divisible by " f"n_heads ({self.n_heads}) + 2 * n_kv_heads ({self.n_kv_heads})" ) wq, wk, wv = wqkv.split([d * self.n_heads, d * self.n_kv_heads, d * self.n_kv_heads], dim=0) state_dict[prefix + "wq.weight"] = wq state_dict[prefix + "wk.weight"] = wk state_dict[prefix + "wv.weight"] = wv def forward( self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor] = None, ): bsz, seqlen, _ = x.shape xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) if self.use_rope: xq, xk = apply_rotary_emb(xq, xk, freqs_cis=freqs_cis) if self.use_qk_norm: xq = rmsnorm(xq, self.norm_eps) xk = rmsnorm(xk, self.norm_eps) # We are applying temperature tuning (https://arxiv.org/abs/2501.19399) to NoPE layers, where # the inference-time temperature tuning function is customized to not affect short context # while working at very long context if self.attn_temperature_tuning and not self.use_rope: seq_positions = torch.arange(start_pos, start_pos + seqlen, device=xq.device, dtype=torch.float32) attn_scales = torch.log(torch.floor((seq_positions + 1.0) / self.floor_scale) + 1.0) * self.attn_scale + 1.0 # reshape for broadcasting [seqlen] -> [1, seqlen, 1, 1] attn_scales = attn_scales.view(1, seqlen, 1, 1) xq = xq * attn_scales self.cache_k = self.cache_k.to(xq) self.cache_v = self.cache_v.to(xq) self.cache_k[:bsz, start_pos : start_pos + seqlen] = xk self.cache_v[:bsz, start_pos : start_pos + seqlen] = xv xk = self.cache_k[:bsz, : start_pos + seqlen] xv = self.cache_v[:bsz, : start_pos + seqlen] xq, xk, xv = [t.transpose(1, 2) for t in (xq, xk, xv)] xk = xk.repeat_interleave(self.n_rep, dim=1) xv = xv.repeat_interleave(self.n_rep, dim=1) attn_output = F.scaled_dot_product_attention(xq, xk, xv, attn_mask=mask, dropout_p=0.0) attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, seqlen, -1) output = self.wo(attn_output) return output class TransformerBlock(nn.Module): def __init__(self, layer_id: int, args: ModelArgs): super().__init__() self.n_heads = args.n_heads self.dim = args.dim self.head_dim = args.dim // args.n_heads if args.head_dim is None else args.head_dim self.is_nope_layer = args.nope_layer_interval is not None and (layer_id + 1) % args.nope_layer_interval == 0 use_rope = not self.is_nope_layer use_qk_norm = args.use_qk_norm and not self.is_nope_layer self.attention = Attention(args, use_rope=use_rope, use_qk_norm=use_qk_norm) if args.moe_args and (layer_id + 1) % args.moe_args.interleave_moe_layer_step == 0: self.feed_forward = MoE( dim=args.dim, hidden_dim=int(args.ffn_exp * args.dim), ffn_dim_multiplier=args.ffn_dim_multiplier, multiple_of=args.multiple_of, moe_args=args.moe_args, ) else: hidden_dim = int(4 * args.dim) hidden_dim = int(2 * hidden_dim / 3) if args.ffn_dim_multiplier is not None: hidden_dim = int(args.ffn_dim_multiplier * hidden_dim) hidden_dim = args.multiple_of * ((hidden_dim + args.multiple_of - 1) // args.multiple_of) self.feed_forward = FeedForward( dim=args.dim, hidden_dim=hidden_dim, ) self.layer_id = layer_id self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps) self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps) self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ) -> None: if prefix + "attention.wqkv.layer_norm_weight" in state_dict: state_dict[prefix + "attention_norm.weight"] = state_dict.pop(prefix + "attention.wqkv.layer_norm_weight") if prefix + "feed_forward.mlp.layer_norm_weight" in state_dict: state_dict[prefix + "ffn_norm.weight"] = state_dict.pop(prefix + "feed_forward.mlp.layer_norm_weight") elif prefix + "feed_forward.norm.weight" in state_dict: state_dict[prefix + "ffn_norm.weight"] = state_dict.pop(prefix + "feed_forward.norm.weight") for k in ( "feed_forward.experts.mlp", "feed_forward.mlp_shared", "attention.wo", "attention.wqkv", ): if prefix + k + "._extra_state" in state_dict: state_dict.pop(prefix + k + "._extra_state") def forward( self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, global_attn_mask: Optional[torch.Tensor], local_attn_mask: Optional[torch.Tensor], ): # The iRoPE architecture uses global attention mask for NoPE layers or # if chunked local attention is not used if self.is_nope_layer or local_attn_mask is None: mask = global_attn_mask else: mask = local_attn_mask h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask) out = h + self.feed_forward(self.ffn_norm(h)) return out class Transformer(nn.Module): def __init__(self, args: ModelArgs, **kwargs) -> None: super().__init__() self.args = args self.vocab_size = args.vocab_size self.n_layers = args.n_layers self.tok_embeddings = VocabParallelEmbedding(args.vocab_size, args.dim, init_method=lambda x: x) self.layers = torch.nn.ModuleList() for layer_id in range(args.n_layers): self.layers.append(TransformerBlock(layer_id, args)) self.norm = RMSNorm(args.dim, eps=args.norm_eps) self.output = ColumnParallelLinear(args.dim, args.vocab_size, bias=False, init_method=lambda x: x) self.freqs_cis = precompute_freqs_cis( args.dim // args.n_heads, args.max_seq_len * 2, args.rope_theta, args.use_scaled_rope, args.rope_scaling_factor, args.rope_high_freq_factor, ) vision_args = self.args.vision_args if vision_args: # circular import otherwise until we refactor out Attention from .vision.embedding import VisionEmbeddings self.vision_embeddings = VisionEmbeddings(vision_args) self.vision_projection = ColumnParallelLinear( vision_args.output_dim, args.dim, bias=False, init_method=lambda x: x, ) self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ) -> None: if prefix + "rope.freqs" in state_dict: state_dict.pop(prefix + "rope.freqs") @torch.inference_mode() def forward(self, model_input: TransformerInput) -> TransformerOutput: tokens = model_input.tokens start_pos = model_input.tokens_position assert isinstance(start_pos, int), ( "This implementation does not support different start positions per batch item" ) _bsz, seqlen = tokens.shape h = self.tok_embeddings(tokens) if image_embedding := model_input.image_embedding: h_image = self.vision_projection(image_embedding.embedding) h = h * ~image_embedding.mask + h_image * image_embedding.mask self.freqs_cis = self.freqs_cis.to(h.device) freqs_cis = self.freqs_cis[start_pos : start_pos + seqlen] global_attn_mask, local_attn_mask = None, None if seqlen > 1: global_attn_mask = torch.full((seqlen, seqlen), float("-inf"), device=tokens.device) global_attn_mask = torch.triu(global_attn_mask, diagonal=1).type_as(h) # https://github.com/pytorch/pytorch/issues/100005 # torch.triu is buggy when the device is mps: filled values are # nan instead of 0. if global_attn_mask.device.type == torch.device("mps").type: global_attn_mask = torch.nan_to_num(global_attn_mask, nan=0.0) if chunk_size := self.args.attention_chunk_size: local_attn_mask = create_chunked_attention_mask(seqlen, chunk_size, tokens.device) for layer in self.layers: h = layer(h, start_pos, freqs_cis, global_attn_mask, local_attn_mask) h = self.norm(h) output = self.output(h).float() return TransformerOutput(logits=output) # tokens (0, K), (K, 2K), (2K, 3K) attend to each other when doing local chunked attention # in the iRoPE architecture def create_chunked_attention_mask(seq_len: int, attention_chunk_size: int, device: torch.device) -> torch.Tensor: block_pos = torch.abs( (torch.arange(seq_len).unsqueeze(0) // attention_chunk_size) - (torch.arange(seq_len).unsqueeze(1) // attention_chunk_size) ) token_pos = torch.arange(seq_len).unsqueeze(0) - torch.arange(seq_len).unsqueeze(1) mask = (block_pos == 0) & (token_pos <= 0) return mask.to(device) ================================================ FILE: models/llama4/moe.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # ruff: noqa: N806 # pyre-strict from typing import Any, Dict, List import fairscale.nn.model_parallel.initialize as fs_init import torch from fairscale.nn.model_parallel.mappings import reduce_from_model_parallel_region from torch import Tensor, nn from torch.nn import functional as F from .args import MoEArgs from .ffn import FeedForward class Experts(nn.Module): def __init__( self, num_local_experts: int, dim: int, hidden_dim: int, ) -> None: super().__init__() dtype = torch.get_default_dtype() self.num_local_experts = num_local_experts self.dim = dim divide_factor = fs_init.get_model_parallel_world_size() self.w1: nn.Parameter = nn.Parameter( torch.empty( num_local_experts, dim, divide_exact(hidden_dim, divide_factor), dtype=dtype, ) ) self.w2: nn.Parameter = nn.Parameter( torch.empty( num_local_experts, divide_exact(hidden_dim, divide_factor), dim, dtype=dtype, ) ) self.w3: nn.Parameter = nn.Parameter( torch.empty( num_local_experts, dim, divide_exact(hidden_dim, divide_factor), dtype=dtype, ) ) self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ) -> None: self.prefix = prefix if prefix + "moe_w_in_eD_F" in state_dict: e = self.num_local_experts D = self.dim state_dict[prefix + "w1"] = state_dict.pop(prefix + "moe_w_in_eD_F").view(e, D, -1) state_dict[prefix + "w2"] = state_dict.pop(prefix + "moe_w_out_eF_D").view(e, -1, D) state_dict[prefix + "w3"] = state_dict.pop(prefix + "moe_w_swiglu_eD_F").view(e, D, -1) def forward( self, routed_in_egD: torch.Tensor, # noqa: N803 ) -> torch.Tensor: e = self.num_local_experts D = self.dim x_egD = routed_in_egD.view(e, -1, D) out_egD = self.batched_swiglu(x_egD, self.w1, self.w3, self.w2) out_egD = out_egD.view(-1, D) return out_egD def batched_swiglu(self, x: Tensor, w1: Tensor, w3: Tensor, w2: Tensor) -> Tensor: middle_out_egF = F.silu(torch.bmm(x, w1)) * torch.bmm(x, w3) return torch.bmm(middle_out_egF, w2) class MoE(torch.nn.Module): """ Tensors used in this module are annotated with the suffixes that indicate the shape of the tensor. Several commonly used annotations include: - a: bsz*slen - E: number of experts - e: number of local experts per ep (n_experts/ep) - D: hidden dimension - d: D/tp - F: model dimension - G: number of tokens per expert (a * capacity_factor / E) - g: number of tokens per expert per TP rank (i.e., G/TP) Examples: x_aD [a, D] routed_in_etG_D [et*G, D] x_eGD: [e, G, D] """ def __init__( self, dim: int, hidden_dim: int, ffn_dim_multiplier: float, multiple_of: int, moe_args: MoEArgs, ) -> None: super().__init__() self.moe_args = moe_args hidden_dim_denom: float = 1 if moe_args.auto_scale_F: hidden_dim_denom = moe_args.capacity_factor + 1 hidden_dim = int(2 * hidden_dim / 3) # custom dim factor multiplier hidden_dim = int(ffn_dim_multiplier * hidden_dim) if moe_args.auto_scale_F: hidden_dim = int(hidden_dim / hidden_dim_denom) hidden_dim += -hidden_dim % multiple_of num_local_experts: int = moe_args.num_experts dtype: torch.dtype = torch.get_default_dtype() self.experts = Experts( num_local_experts, dim, hidden_dim, ) self.router_DE: nn.Parameter = nn.Parameter(torch.empty(dim, moe_args.num_experts, dtype=dtype)) self.shared_expert = FeedForward(dim, hidden_dim, do_reduce=False) self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool, missing_keys: List[str], unexpected_keys: List[str], error_msgs: List[str], ) -> None: if prefix + "w_in_shared_FD.weight" in state_dict: state_dict[prefix + "shared_expert.w1.weight"] = state_dict.pop(prefix + "w_in_shared_FD.weight") state_dict[prefix + "shared_expert.w3.weight"] = state_dict.pop(prefix + "w_swiglu_FD.weight") state_dict[prefix + "shared_expert.w2.weight"] = state_dict.pop(prefix + "w_out_shared_DF.weight") def forward(self, x_bsD: Tensor) -> Tensor: # noqa: N803 _, slen, D = x_bsD.shape x_aD = x_bsD.view(-1, D) a = x_aD.shape[0] router_scores: Tensor = torch.matmul(x_aD, self.router_DE).transpose(0, 1) router_scores_aK, router_indices_aK = torch.topk(router_scores.transpose(0, 1), self.moe_args.top_k, dim=1) router_scores = ( torch.full_like(router_scores.transpose(0, 1), float("-inf")) .scatter_(1, router_indices_aK, router_scores_aK) .transpose(0, 1) ) router_indices = torch.arange(a, device=x_aD.device).view(1, -1).expand(router_scores.size(0), -1) router_scores = torch.sigmoid(router_scores) routed_in_EG_D: Tensor = torch.gather( x_aD, dim=0, index=router_indices.reshape(-1, 1).expand(-1, D), ) routed_in_EG_D = routed_in_EG_D * router_scores.reshape(-1, 1) out_aD = self.shared_expert(x_aD) routed_out_eg_D = self.experts(routed_in_EG_D.detach()) router_indices_EG_D = router_indices.reshape(-1, 1).expand(-1, D) out_aD.scatter_add_( dim=0, index=router_indices_EG_D, src=routed_out_eg_D.view(-1, D), ) out_aD = reduce_from_model_parallel_region(out_aD) return out_aD.view(-1, slen, D) def divide_exact(numerator: int, denominator: int) -> int: assert numerator % denominator == 0, "{} is not divisible by {}".format(numerator, denominator) return numerator // denominator ================================================ FILE: models/llama4/preprocess.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import math from collections import defaultdict from typing import Optional, Set, Tuple import torch import torchvision.transforms as tv from PIL import Image, ImageFile from torchvision.transforms import functional as F ImageFile.LOAD_TRUNCATED_IMAGES = True IMAGE_RES = 448 class ResizeNormalizeImageTransform: def __init__( self, size_width=None, size_height=None, ) -> None: self._size_width = size_width or IMAGE_RES self._size_height = size_height or IMAGE_RES self._mean = (0.5, 0.5, 0.5) self._std = (0.5, 0.5, 0.5) self.tv_transform = tv.Compose( [ tv.Resize((self._size_height, self._size_width)), tv.ToTensor(), tv.Normalize( mean=self._mean, std=self._std, inplace=True, ), ] ) def __call__(self, image: Image.Image) -> torch.Tensor: return self.tv_transform(image) class VariableSizeImageTransform(object): """ This class accepts images of any size and dynamically resize, pads and chunks it based on the image aspect ratio and the number of image chunks we allow. The algorithm will NOT distort the image fit a certain aspect ratio, because that leads to a significant degradation in image quality. It can be summarized in 6 steps: 1. Find all possible canvas combinations of max_num_chunks; 2. Find the best canvas to fit the image; 3. Resize without distortion 4. Pad 5. Normalize 6. Chunk For example, if an input image is of size 300x800, patch_size of 224, and max_num_chunks = 8, it will find the closest aspect ratio that is allowed within 8 image chunks, with some restrictions. In this case, 2:4 = 2 horizontal patches and 4 vertical patches, giving a total of 8 chunks. If resize_to_max_canvas, the image will be resized (without distortion), to the largest possible resolution. In this case, 388:896, and padded to 448:896, where we maintain the original aspect ratio and pad with zeros value for the rest. This approach minimizes the amount of padding required for any arbitrary resolution. However, if limit_upscaling_to_patch_size is set to True, the upscaling will be limited to the patch size. In the example above, the image would remain 300x800 (no upscaling), and then padded to 448:896. The final output will therefore be of shape (8, 3, 224, 224), where 2x4 patches are coming from the resizing and chunking. """ def __init__(self, size: int = IMAGE_RES) -> None: self.size = size self.to_tensor = tv.ToTensor() self._mean = (0.5, 0.5, 0.5) self._std = (0.5, 0.5, 0.5) self.normalize = tv.Normalize( mean=self._mean, std=self._std, inplace=True, ) self.resample = tv.InterpolationMode.BILINEAR @staticmethod def get_factors(n: int) -> Set[int]: """ Calculate all factors of a given number, i.e. a dividor that leaves no remainder. For example, if n=12, it will return {1, 2, 3, 4, 6, 12}. Args: n (int): The number to find factors for. Returns: set: A set containing all factors of the number. """ factors_set = set() for i in range(1, int(n**0.5) + 1): if n % i == 0: factors_set.add(i) factors_set.add(n // i) return factors_set def find_supported_resolutions(self, max_num_chunks: int, patch_size: int) -> torch.Tensor: """ Computes all of the allowed resoltuions for a fixed number of chunks and patch_size. Useful for when dividing an image into chunks. Args: max_num_chunks (int): Maximum number of chunks for processing. patch_size (int): Size of the side of the patch. Returns: torch.Tensor: List of possible resolutions as tuples (height, width). Example: >>> max_num_chunks = 5 >>> patch_size = 224 >>> find_supported_resolutions(max_num_chunks, patch_size) tensor([(224, 896), (448, 448), (224, 224), (896, 224), (224, 672), (672, 224), (224, 448), (448, 224)]) Given max_num_chunks=4, patch_size=224, it will create a dictionary: { 0.25: [(1, 4)], 1.0: [(2, 2), (1, 1)], 4.0: [(4, 1)], 0.33: [(1, 3)], 3.0: [(3, 1)], 0.5: [(1, 2)], 2.0: [(2, 1)] } and return the resolutions multiplied by the patch_size: [(1*224, 4*224), (2*224, 2*224), ..., (2*224, 1*224)] """ asp_dict = defaultdict(list) for chunk_size in range(max_num_chunks, 0, -1): _factors = sorted(self.get_factors(chunk_size)) _asp_ratios = [(factor, chunk_size // factor) for factor in _factors] for height, width in _asp_ratios: ratio_float = height / width asp_dict[ratio_float].append((height, width)) # get the resolutions multiplied by the patch_size possible_resolutions = [] for key, value in asp_dict.items(): for height, depth in value: possible_resolutions.append((height * patch_size, depth * patch_size)) return possible_resolutions @staticmethod def get_max_res_without_distortion( image_size: Tuple[int, int], target_size: Tuple[int, int], ) -> Tuple[int, int]: """ Determines the maximum resolution to which an image can be resized to without distorting its aspect ratio, based on the target resolution. Args: image_size (Tuple[int, int]): The original resolution of the image (height, width). target_resolution (Tuple[int, int]): The desired resolution to fit the image into (height, width). Returns: Tuple[int, int]: The optimal dimensions (height, width) to which the image should be resized. Example: >>> _get_max_res_without_distortion([200, 300], target_size = [450, 200]) (134, 200) >>> _get_max_res_without_distortion([800, 600], target_size = [450, 1300]) (450, 338) """ original_width, original_height = image_size target_width, target_height = target_size scale_w = target_width / original_width scale_h = target_height / original_height if scale_w < scale_h: new_width = target_width new_height = min(math.floor(original_height * scale_w), target_height) else: new_height = target_height new_width = min(math.floor(original_width * scale_h), target_width) return new_width, new_height def _pad(self, image: Image.Image, target_size) -> Image.Image: new_width, new_height = target_size new_im = Image.new(mode="RGB", size=(new_width, new_height), color=(0, 0, 0)) # type: ignore new_im.paste(image) return new_im def _split(self, image: torch.Tensor, ncw: int, nch: int) -> torch.Tensor: # Split image into number of required tiles (width x height) num_channels, height, width = image.size() image = image.view(num_channels, nch, height // nch, ncw, width // ncw) # Permute dimensions to reorder the axes image = image.permute(1, 3, 0, 2, 4).contiguous() # Reshape into the desired output shape (batch_size * 4, num_channels, width/2, height/2) image = image.view(ncw * nch, num_channels, height // nch, width // ncw) return image def resize_without_distortion( self, image: torch.Tensor, target_size: Tuple[int, int], max_upscaling_size: Optional[int], ) -> torch.Tensor: """ Used to resize an image to target_resolution, without distortion. If target_size requires upscaling the image, the user can set max_upscaling_size to limit the upscaling to a maximum size. In this case, since we rescale without distortion, modifying target_size works as a boundary for the image's largest side. Args: resample (str): Resampling method used when resizing images. Supports "nearest", "nearest_exact", "bilinear", "bicubic". max_upscaling_size (int): The maximum size to upscale the image to. If None, there is no limit. Examples: >>> target_size = (1000, 1200) >>> max_upscaling_size = 600 >>> image_size = (400, 200) >>> resize_without_distortion(image_size, target_size, max_upscaling_size) (600, 300) # new_size_without_distortion >>> target_size = (1000, 1200) >>> max_upscaling_size = 600 >>> image_size = (2000, 200) >>> resize_without_distortion(image_size, target_size, max_upscaling_size) (1000, 100) # new_size_without_distortion >>> target_size = (1000, 1200) >>> max_upscaling_size = 2000 >>> image_size = (400, 200) >>> resize_without_distortion(image_size, target_size, max_upscaling_size) (1000, 500) # new_size_without_distortion >>> target_size = (1000, 1200) >>> max_upscaling_size = None >>> image_size = (400, 200) >>> resize_without_distortion(image_size, target_size, max_upscaling_size) (1000, 500) # new_size_without_distortion """ image_width, image_height = image.size image_size = (image_width, image_height) # If target_size requires upscaling, we might want to limit the upscaling to max_upscaling_size if max_upscaling_size is not None: new_target_width = min(max(image_width, max_upscaling_size), target_size[0]) new_target_height = min(max(image_height, max_upscaling_size), target_size[1]) target_size = (new_target_width, new_target_height) # resize to target_size while preserving aspect ratio new_size_without_distortion = self.get_max_res_without_distortion(image_size, target_size) image = F.resize( image, ( max(new_size_without_distortion[1], 1), max(new_size_without_distortion[0], 1), ), interpolation=self.resample, ) return image def get_best_fit( self, image_size: Tuple[int, int], possible_resolutions: torch.Tensor, resize_to_max_canvas: bool = False, ) -> Tuple[int, int]: """ Determines the best canvas possible from a list of possible resolutions to, without distortion, resize an image to. For each possible resolution, calculates the scaling factors for width and height, and selects the smallest one, which is the limiting side. E.g. to match the canvas you can upscale height by 2x, and width by 1.5x, therefore, the maximum upscaling you can do is min(2, 1.5) = 1.5. If upscaling is possible (any of the scaling factors is greater than 1), then picks the smallest upscaling factor > 1, unless resize_to_max_canvas is True. If upscaling is not possible, then picks the largest scaling factor <= 1, i.e. reduce downscaling as much as possible. If there are multiple resolutions with the same max scale, we pick the one with the lowest area, to minimize padding. E.g., the same image can be upscaled to 224x224 and 224x448, but the latter has more padding. Args: image_size (Tuple[int, int]): A tuple containing the height and width of the image. possible_resolutions (torch.Tensor): A tensor of shape (N, 2) where each row represents a possible resolution (height, width). use_max_upscaling (bool): If True, will return the largest upscaling resolution. Returns: List[int]: The best resolution [height, width] for the given image. Example: >>> image_size = (200, 300) >>> possible_resolutions = torch.tensor([[224, 672], ... [672, 224], ... [224, 448], ... [448, 224], ... [224, 224]]) >>> _get_smallest_upscaling_possibility(image_size, possible_resolutions) [224, 448] We have: scale_w = tensor([2.2400, 0.7467, 1.4933, 0.7467, 0.7467]) scale_h = tensor([1.1200, 3.3600, 1.1200, 2.2400, 1.1200]) scales = tensor([1.1200, 0.7467, 1.1200, 0.7467, 0.7467]) Only one of the scales > 1: upscaling_possible = tensor([1.1200, 1.1200]) smallest_rescale = tensor(1.1200) So we pick the resolution with the smallest smallest area: areas = tensor([150528, 100352]) # [672, 224], [224, 448] optimal_canvas = tensor([224, 448]) """ original_width, original_height = image_size # get all possible resolutions heights/widths target_widths, target_heights = ( possible_resolutions[:, 0], possible_resolutions[:, 1], ) # get scaling factors to resize the image without distortion scale_w = target_widths / original_width scale_h = target_heights / original_height # get the min scale between width and height (limiting side -> no distortion) scales = torch.where(scale_w > scale_h, scale_h, scale_w) # filter only scales that allow upscaling upscaling_options = scales[scales >= 1] if len(upscaling_options) > 0: if resize_to_max_canvas: selected_scale = torch.max(upscaling_options) else: selected_scale = torch.min(upscaling_options) else: # no upscaling possible, # get the minimum downscaling (max scale for scales<1) downscaling_options = scales[scales < 1] selected_scale = torch.max(downscaling_options) # get all resolutions that support this scaling factor, # e.g. you can upscale to 224x224, 224x448, 224x672 without distortion chosen_canvas = possible_resolutions[scales == selected_scale] # if there are multiple resolutions, # get the one with minimum area to reduce padding if len(chosen_canvas) > 1: areas = chosen_canvas[:, 0] * chosen_canvas[:, 1] optimal_idx = torch.argmin(areas) optimal_canvas = chosen_canvas[optimal_idx] else: optimal_canvas = chosen_canvas[0] return tuple(optimal_canvas.tolist()) def __call__( self, image: Image.Image, max_num_chunks: int, normalize_img: bool = True, resize_to_max_canvas: bool = False, ) -> Tuple[torch.Tensor, Tuple[int, int]]: """ Args: image (PIL.Image): Image to be resized. max_num_chunks (int): Maximum number of chunks to split the image into. normalize_img (bool): Whether to normalize the image. resize_to_max_canvas (bool): Whether to resize the image to the maximum canvas size. If True, picks the canvas the allows the largest resizing without distortion. If False, downsample as little as possible, including no resizing at all, but never upsample, unless the image is smaller than the patch size. """ assert max_num_chunks > 0 assert isinstance(image, Image.Image), type(image) w, h = image.size possible_resolutions = self.find_supported_resolutions(max_num_chunks=max_num_chunks, patch_size=self.size) possible_resolutions = torch.tensor(possible_resolutions) best_resolution = self.get_best_fit( image_size=(w, h), possible_resolutions=possible_resolutions, resize_to_max_canvas=resize_to_max_canvas, ) max_upscaling_size = None if resize_to_max_canvas else self.size image = self.resize_without_distortion(image, best_resolution, max_upscaling_size) image = self._pad(image, best_resolution) image = self.to_tensor(image) if normalize_img: image = self.normalize(image) ratio_w, ratio_h = ( best_resolution[0] // self.size, best_resolution[1] // self.size, ) image = self._split(image, ratio_w, ratio_h) # type: ignore ar = (ratio_h, ratio_w) return image, ar ================================================ FILE: models/llama4/prompt_format.md ================================================ # Llama 4 - Prompt Formats ## Tokens Here is a list of special tokens that are supported by Llama 4: - `<|begin_of_text|>`: Specifies the start of the prompt - `<|end_of_text|>`: Model will cease to generate more tokens. This token is generated only by the base models. - `<|header_start|>` and `<|header_end|>`: These tokens enclose the role for a particular message. The possible roles are: [system, user and assistant]. - `<|eot|>`: End of turn. Represents when the model has determined that it has finished interacting with the user message that initiated its response. This is used in two scenarios: - at the end of a direct interaction between the model and the user - at the end of multiple interactions between the model and any available tools This token signals to the executor that the model has finished generating a response. - `<|image_start|>` and `<|image_end|>`: These tokens enclose the image data in the prompt. - `<|patch|>`: This token represents a piece of the tile/ - `<|tile_y_separator|>` and `<|tile_x_separator|>`: These tokens are used to separate the y and x tiles of an image - `<|image|>`: In the new architecture, this token now separates the regular sized image information from a downsized version of it that fits in a single tile. The longer side is used for calculating the scale factor and the rest is padded to fit the tile. There are 3 different roles that are supported by Llama 4 - `system`: Sets the context in which to interact with the AI model. It typically includes rules, guidelines, or necessary information that helps the model respond effectively. - `user`: Represents the human interacting with the model. It includes the inputs, commands, and questions to the model. - `assistant`: Represents the response generated by the AI model based on the context provided in the `system`, `tool` and `user` prompts. # Llama 4 Instruct Model ## Simple User and assistant conversation Here is a regular multi-turn user assistant conversation and how its formatted. ##### Input Prompt Format ``` <|begin_of_text|><|header_start|>system<|header_end|> You are a helpful assistant<|eot|><|header_start|>user<|header_end|> Answer who are you in the form of jeopardy?<|eot|><|header_start|>assistant<|header_end|> ``` ##### Model Response Format ``` "What is a helpful assistant?"<|eot|> ``` # Image prompt format ## Single image prompt format - small image This example passes an image that is smaller than the tile size, to show the tile separator tokens are not needed ##### Input Prompt Format ``` <|begin_of_text|><|header_start|>user<|header_end|> <|image_start|><|image|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|image_end|>Describe this image in two sentences<|eot|><|header_start|>assistant<|header_end|> ``` ##### Model Response Format ``` The image depicts a dog standing on a skateboard, positioned centrally and facing the camera directly. The dog has a distinctive coat pattern featuring white, black, and brown fur, with floppy ears and a black nose, and is standing on a skateboard with red wheels.<|eot|> ``` ##### Notes Notice the structure of the image section: ``` <|image_start|><|image|><|patch|>...<|patch|><|image_end|> ``` This is due to the image being smaller than the tile size. ## Single image prompt format Here is an example of how to pass an image to the model ##### Input Prompt Format ``` <|begin_of_text|><|header_start|>user<|header_end|> 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patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|tile_y_separator|><|image|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|patch|><|image_end|>Describe this image in two sentences<|eot|><|header_start|>assistant<|header_end|> ``` ##### Model Response Format ``` The image depicts a dog standing on a skateboard, with the dog positioned centrally and facing forward. The dog has a distinctive coat featuring a mix of white, brown, and black fur, and is wearing a collar as it stands on the skateboard, which has red wheels.<|eot|> ``` ##### Notes With a bigger image, the image will include the tile separator tokens. Additionally, the image tag now separates a scaled down version of the image from the regular sized image. ``` <|image_start|><|patch|>...<|patch|><|tile_x_separator|><|patch|>...<|patch|><|tile_y_separator|><|patch|>...<|patch|><|image|><|patch|>...<|patch|><|image_end|> ``` ## Multiple images prompt format Here is an example of how to pass an image to the model ##### Input Prompt Format ``` <|begin_of_text|><|header_start|>user<|header_end|> 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these images in two sentences<|eot|><|header_start|>assistant<|header_end|> ``` ##### Model Response Format ``` The first image features a dog standing on a skateboard, while the second image showcases a plate of spaghetti with tomato sauce and cheese. The two images appear to be unrelated, with one depicting a playful scene of a dog on a skateboard and the other presenting a classic Italian dish.<|eom|> ``` ##### Notes With multiple images, each one is encapsulated in their corresponding image tags. # Tool calling We are continuing the format for zero shot function calling used in previous versions of Llama. All available functions can be provided either in the system message or in the user message. ## Zero shot function calling - system message ##### Input Prompt Format ``` <|begin_of_text|><|header_start|>system<|header_end|> You are a helpful assistant and an expert in function composition. You can answer general questions using your internal knowledge OR invoke functions when necessary. Follow these strict guidelines: 1. FUNCTION CALLS: - ONLY use functions that are EXPLICITLY listed in the function list below - If NO functions are listed (empty function list []), respond ONLY with internal knowledge or "I don't have access to [Unavailable service] information" - If a function is not in the list, respond ONLY with internal knowledge or "I don't have access to [Unavailable service] information" - If ALL required parameters are present AND the query EXACTLY matches a listed function's purpose: output ONLY the function call(s) - Use exact format: [func_name1(param1=value1, param2=value2), func_name2(...)] Examples: CORRECT: [get_weather(location="Vancouver"), calculate_route(start="Boston", end="New York")] <- Only if get_weather and calculate_route are in function list INCORRECT: get_weather(location="New York") INCORRECT: Let me check the weather: [get_weather(location="New York")] INCORRECT: [get_events(location="Singapore")] <- If function not in list 2. RESPONSE RULES: - For pure function requests matching a listed function: ONLY output the function call(s) - For knowledge questions: ONLY output text - For missing parameters: ONLY request the specific missing parameters - For unavailable services (not in function list): output ONLY with internal knowledge or "I don't have access to [Unavailable service] information". Do NOT execute a function call. - If the query asks for information beyond what a listed function provides: output ONLY with internal knowledge about your limitations - NEVER combine text and function calls in the same response - NEVER suggest alternative functions when the requested service is unavailable - NEVER create or invent new functions not listed below 3. STRICT BOUNDARIES: - ONLY use functions from the list below - no exceptions - NEVER use a function as an alternative to unavailable information - NEVER call functions not present in the function list - NEVER add explanatory text to function calls - NEVER respond with empty brackets - Use proper Python/JSON syntax for function calls - Check the function list carefully before responding 4. TOOL RESPONSE HANDLING: - When receiving tool responses: provide concise, natural language responses - Don't repeat tool response verbatim - Don't add supplementary information Here is a list of functions in JSON format that you can invoke: [ { "name": "get_weather", "description": "Get weather info for places", "parameters": { "type": "dict", "required": ["city"], "properties": { "city": { "type": "string", "description": "The name of the city to get the weather for" }, "metric": { "type": "string", "description": "The metric for weather. Options are: celsius, fahrenheit", "default": "celsius" } } } } ]<|eot|><|header_start|>user<|header_end|> What is the weather in SF and Seattle?<|eot|><|header_start|>assistant<|header_end|> ``` ##### Model Response Format ``` [get_weather(city="San Francisco"), get_weather(city="Seattle")]<|eot|> ``` ##### Notes - The output supports multiple, and parallel tool calls natively - JSON format for defining the functions in the system prompt is similar to Llama3.1 ## Zero shot function calling - user message Similar to the above example, you can also provide information for all the available tools in the user message. ##### Input Prompt Format ``` <|begin_of_text|><|header_start|>user<|header_end|> Questions: Can you retrieve the details for the user with the ID 7890, who has black as their special request? Here is a list of functions in JSON format that you can invoke: [ { "name": "get_user_info", "description": "Retrieve details for a specific user by their unique identifier. Note that the provided function is in Python 3 syntax.", "parameters": { "type": "dict", "required": [ "user_id" ], "properties": { "user_id": { "type": "integer", "description": "The unique identifier of the user. It is used to fetch the specific user details from the database." }, "special": { "type": "string", "description": "Any special information or parameters that need to be considered while fetching user details.", "default": "none" } } } } ] Should you decide to return the function call(s), put them in the format of [func1(params_name=params_value, params_name2=params_value2...), func2(params)] You SHOULD NOT include any other text in the response.<|eot|><|header_start|>assistant<|header_end|> ``` ##### Model Response Format ``` [get_user_info(user_id=7890, special='black')]<|eot|> ``` ##### Notes - The tool call format for the model is the same whether your function calls are provided in the system or user message. ## Tool calling with custom formats Here is an example of how you could also write custom instructions for model to do zero shot tool calling. In this example, we define a custom tool calling format using the `` tag. ##### Input Prompt Format ``` <|begin_of_text|><|header_start|>user<|header_end|> You have access to the following functions: Use the function 'trending_songs' to 'Returns the trending songs on a Music site': {"name": "trending_songs", "description": "Returns the trending songs on a Music site", "parameters": {"genre": {"description": "The genre of the songs to return", "param_type": "str", "required": false}, "n": {"description": "The number of songs to return", "param_type": "int", "required": true}}} Think very carefully before calling functions. If you choose to call a function ONLY reply in the following format with no prefix or suffix: {"example_name": "example_value"} Reminder: - If looking for real time information use relevant functions before falling back to brave_search - Function calls MUST follow the specified format, start with - Required parameters MUST be specified - Only call one function at a time - Put the entire function call reply on one line<|eot_id|><|eot|><|header_start|>user<|header_end|> Use tools to get latest trending songs<|eot|><|header_start|>assistant<|header_end|> ``` ##### Model Response Format ``` {"n": 10}<|eot|> ``` ================================================ FILE: models/llama4/quantization/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # the root directory of this source tree. ================================================ FILE: models/llama4/quantization/loader.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import logging import os from typing import Callable, Optional import torch from fairscale.nn.model_parallel.initialize import get_model_parallel_rank from torch import nn, Tensor from torch.nn import functional as F from ...datatypes import QuantizationMode from ..model import Transformer, TransformerBlock from ..moe import MoE log = logging.getLogger(__name__) def swiglu_wrapper_no_reduce( self, x: Tensor, ): from ...quantize_impls import ffn_swiglu return ffn_swiglu(x, self.w1.weight, self.w3.weight, self.w2.weight) def experts_batched_swiglu_wrapper( self, x: Tensor, # (e, g, D) w1: Tensor, # (e, D, F) w3: Tensor, # (e, D, F) w2: Tensor, # (e, F, D) ) -> torch.Tensor: from ...quantize_impls import bmm_nt middle_out_egF = F.silu(bmm_nt(x, w1)) * bmm_nt(x, w3) # noqa: N806 return bmm_nt(middle_out_egF, w2) def convert_to_quantized_model( model: Transformer, checkpoint_dir: str, quantization_mode: Optional[str] = None, fp8_activation_scale_ub: Optional[float] = 1200.0, use_rich_progress: bool = True, ) -> Transformer: from ...quantize_impls import ( Fp8ScaledWeights, Int4ScaledWeights, load_fp8, load_int4, quantize_fp8, quantize_int4, ) rank = get_model_parallel_rank() def should_quantize_block(block: nn.Module) -> bool: if not isinstance(block, TransformerBlock): return False is_moe = isinstance(block.feed_forward, MoE) if quantization_mode == QuantizationMode.fp8_mixed: # skip quantization on first and last layers return is_moe and not (block.layer_id == 0 or block.layer_id == (model.n_layers - 1)) return is_moe use_rich_progress = use_rich_progress and rank == 0 progress, log_status, update_status = logging_callbacks(use_rich_progress, rank, model, should_quantize_block) if quantization_mode == QuantizationMode.int4_mixed: int4_scales_path = os.path.join(checkpoint_dir, f"int4_scales_{rank}.pt") if os.path.isfile(int4_scales_path): log_status(f"Rank {rank}: Loading int4 scales") int4_scales = torch.load(int4_scales_path, weights_only=True) def apply_quantization(key, weight): scale = int4_scales[key] return load_int4( weight, scale, output_device=torch.device("cuda"), ) else: log_status(f"Rank {rank}: Quantizing int4 weights from bf16") def apply_quantization(_, weight): return quantize_int4(weight, output_device=torch.device("cuda")) else: fp8_scales_path = os.path.join(checkpoint_dir, f"fp8_scales_{rank}.pt") if os.path.isfile(fp8_scales_path): log_status(f"Rank {rank}: Loading fp8 scales") fp8_scales = torch.load(fp8_scales_path, weights_only=True) def apply_quantization(key, weight): scale = fp8_scales[key] return load_fp8( weight, scale, fp8_activation_scale_ub, output_device=torch.device("cuda"), ) else: log_status(f"Rank {rank}: Quantizing fp8 weights from bf16") def apply_quantization(_, weight): return quantize_fp8(weight, fp8_activation_scale_ub, output_device=torch.device("cuda")) processed_blocks = 0 try: if use_rich_progress: progress.start() for _, block in model.named_modules(): if not should_quantize_block(block): continue update_status(f"Rank {rank} - Layer {block.layer_id}") # Quantize only routed experts, not shared prefix = f"layers.{block.layer_id}.feed_forward" moe = block.feed_forward moe.experts.batched_swiglu = experts_batched_swiglu_wrapper.__get__(moe.experts) for key in ("w1", "w3", "w2"): param = getattr(moe.experts, key) update_status(f"Rank {rank} - Layer {block.layer_id} - MoE {key}") setattr( moe.experts, key, apply_quantization( f"{prefix}.experts.{key}", param.transpose(1, 2).contiguous(), ), ) if quantization_mode == QuantizationMode.int4_mixed: # Quantize shared experts moe.shared_expert.forward = swiglu_wrapper_no_reduce.__get__(moe.shared_expert) for key in ("w1", "w3", "w2"): param = getattr(moe.shared_expert, key) update_status(f"Rank {rank} - Layer {block.layer_id} - MoE shared expert {key}") param.weight = apply_quantization(f"{prefix}.shared_expert.{key}", param.weight) processed_blocks += 1 update_status(message=None, completed=processed_blocks) update_status(f"Rank {rank} - Moving parameters to CUDA") param_count = 0 for _, parameter in model.named_parameters(): if not isinstance(parameter, Fp8ScaledWeights) and not isinstance(parameter, Int4ScaledWeights): parameter.data = parameter.to(device="cuda") param_count += 1 update_status(f"Rank {rank} - Completed - moved {param_count} parameters to CUDA") finally: if use_rich_progress: progress.stop() return model # fp8/int4 loading can be very slow so we add progress bars to make life slightly better def logging_callbacks( use_rich_progress: bool, rank: int, model: Transformer, should_quantize_block: Callable[[nn.Module], bool], ): console = None if use_rich_progress: from rich.console import Console console = Console(highlight=False) def log_status(message: str) -> None: if use_rich_progress: console.print(message) elif rank == 0: # Only log from rank 0 for non-rich logging log.info(message) total_blocks = sum(1 for _, block in model.named_modules() if should_quantize_block(block)) progress = None if use_rich_progress: from rich.progress import ( BarColumn, Progress, SpinnerColumn, TextColumn, TimeElapsedColumn, TimeRemainingColumn, ) progress = Progress( SpinnerColumn(), BarColumn(complete_style="green", finished_style="bright_green"), TextColumn("[progress.percentage]{task.percentage:>3.0f}%"), TimeElapsedColumn(), TextColumn("ETA:"), TimeRemainingColumn(), TextColumn("[bold]{task.fields[status]}"), console=console, expand=True, ) task_id = progress.add_task("[blue]Converting layers...", total=total_blocks, status="Starting") def update_status(message: Optional[str], completed: Optional[int] = None) -> None: if use_rich_progress: if message is not None: progress.update(task_id, status=message) if completed is not None: progress.update(task_id, completed=completed) elif rank == 0 and completed and completed % 10 == 0: log.info(f"Rank {rank}: {completed}/{total_blocks} blocks completed") return progress, log_status, update_status ================================================ FILE: models/llama4/scripts/chat_completion.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # Copyright (c) Meta Platforms, Inc. and affiliates. # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement. from io import BytesIO from pathlib import Path from typing import Optional import fire from termcolor import cprint from models.datatypes import RawMediaItem, RawMessage, RawTextItem, StopReason from models.llama4.generation import Llama4 THIS_DIR = Path(__file__).parent def run_main( checkpoint_dir: str, world_size: int = 1, max_seq_len: Optional[int] = 4096, max_batch_size: Optional[int] = 1, temperature: float = 0.6, top_p: float = 0.9, quantization_mode: Optional[str] = None, ): generator = Llama4.build( checkpoint_dir, max_seq_len=max_seq_len, max_batch_size=max_batch_size, world_size=world_size, quantization_mode=quantization_mode, ) dialogs = [ [RawMessage(role="user", content="what is the recipe of mayonnaise?")], [ RawMessage( role="user", content="I am going to Paris, what should I see?", ), RawMessage( role="assistant", content="""\ Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris: 1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city. 2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa. 3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows. These are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world.""", stop_reason=StopReason.end_of_turn, ), RawMessage(role="user", content="What is so great about #1?"), ], [ RawMessage(role="system", content="Always answer with Haiku"), RawMessage(role="user", content="I am going to Paris, what should I see?"), ], # [ # RawMessage(role="system", content="Always answer with emojis"), # RawMessage(role="user", content="How to go from Beijing to NY?"), # ], ] with open(THIS_DIR / "../../resources/dog.jpg", "rb") as f: img1 = f.read() with open(THIS_DIR / "../../resources/pasta.jpeg", "rb") as f: img2 = f.read() dialogs.append( [ RawMessage( role="user", content=[ RawMediaItem(data=BytesIO(img1)), RawMediaItem(data=BytesIO(img2)), RawTextItem(text="Write a haiku that brings both images together"), ], ), ] ) for dialog in dialogs: for msg in dialog: print(f"{msg.role.capitalize()}: {msg.content}\n") batch = [dialog] for token_results in generator.chat_completion( batch, temperature=temperature, top_p=top_p, max_gen_len=max_seq_len, ): result = token_results[0] if result.finished: break cprint(result.text, color="yellow", end="") print("\n") def main(): fire.Fire(run_main) if __name__ == "__main__": main() ================================================ FILE: models/llama4/scripts/completion.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # Copyright (c) Meta Platforms, Inc. and affiliates. # This software may be used and distributed in accordance with the terms of the Llama 3 Community License Agreement. from io import BytesIO from pathlib import Path from typing import Optional import fire from termcolor import cprint from models.datatypes import RawMediaItem from models.llama4.generation import Llama4 THIS_DIR = Path(__file__).parent def run_main( checkpoint_dir: str, world_size: int = 1, max_seq_len: Optional[int] = 1024, max_batch_size: Optional[int] = 1, temperature: float = 0.6, top_p: float = 0.9, quantization_mode: Optional[str] = None, ): generator = Llama4.build( checkpoint_dir, max_seq_len=max_seq_len, max_batch_size=max_batch_size, world_size=world_size, quantization_mode=quantization_mode, ) with open(THIS_DIR / "../../resources/dog.jpg", "rb") as f: img = f.read() interleaved_contents = [ # text only "The color of the sky is blue but sometimes it can also be", "The capital of France is", # image understanding [ RawMediaItem(type="image", data=BytesIO(img)), "If I had to write a haiku for this one", ], ] for content in interleaved_contents: cprint(f"{content}", end="") batch = [content] for token_results in generator.completion( batch, temperature=temperature, top_p=top_p, ): result = token_results[0] if result.finished: break cprint(result.text, color="yellow", end="") print("\n==================================\n") def main(): fire.Fire(run_main) if __name__ == "__main__": main() ================================================ FILE: models/llama4/scripts/quantize.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import json import os import sys from pathlib import Path from typing import Optional import fire import torch from models.llama4.args import ModelArgs from models.llama4.generation import QuantizationMode from models.llama4.model import MoE, Transformer, TransformerBlock from models.quantize_impls import int4_row_quantize, pack_int4 try: import fbgemm_gpu.experimental.gen_ai # noqa: F401 print("Using efficient FP8/INT4 operators in FBGEMM.") except ImportError: print("No efficient FP8/INT4 operators. Please install FBGEMM.") raise from fairscale.nn.model_parallel.initialize import ( get_model_parallel_rank, initialize_model_parallel, model_parallel_is_initialized, ) # You can run this script with: # # torchrun --nproc-per-node=8 -m models.llama4.scripts.quantize \ # --ckpt_dir /path/to/llama4/checkpoints \ # --output_dir /path/to/output/dir \ # --quantization_mode fp8_mixed \ # --world_size 8 def ffn_quantize( ckpt_dir: str, output_dir: str, quantization_mode: str, world_size: Optional[int] = None, ) -> None: """ Quantizes BF16 weights using "rowwise fp8" quantization or "int4-weight-bf16-activation (int4_mixed)" quantization. This method works well for FFNs. It produces two outputs: - quantized weights (in consolidated.0X.pth) - fp8 scales (in fp8_scales_X.pt) It produces three outputs for int4_mixed: - quantized weights (in consolidated.0X.pth) - int4 scales (in int4_scales_X.pt) The keys in the fp8/int4 scales are named so that they can be loaded using quantization/loader.py Args: ckpt_dir (str): The directory containing the checkpoint files. output_dir (str): The directory to save the quantized checkpoint and fp8/int4 scales. world_size (Optional[int]): The number of GPUs to use for model parallelization. """ print(f"checkpoint_dir: {ckpt_dir} output_dir: {output_dir} world_size: {world_size}") if not torch.distributed.is_initialized(): torch.distributed.init_process_group("nccl") if not model_parallel_is_initialized(): if world_size is None: world_size = int(os.environ.get("WORLD_SIZE", 1)) initialize_model_parallel(world_size) local_rank = int(os.environ.get("LOCAL_RANK", 0)) torch.cuda.set_device(local_rank) if local_rank > 0: sys.stdout = open(os.devnull, "w") if get_model_parallel_rank() == 0: os.makedirs(output_dir, exist_ok=True) seed = 1 torch.manual_seed(seed) checkpoints = set(Path(ckpt_dir).glob("*.pth")) assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}" assert world_size == len(checkpoints), ( f"Loading a checkpoint for MP={len(checkpoints)} but world size is {world_size}" ) checkpoints = sorted(checkpoints) ckpt_idx = get_model_parallel_rank() ckpt_path = checkpoints[ckpt_idx] with open(Path(ckpt_dir) / "params.json", "r") as f: params = json.loads(f.read()) model_args: ModelArgs = ModelArgs(**params, max_seq_len=1024, max_batch_size=1) torch.set_default_tensor_type(torch.BFloat16Tensor) model = Transformer(model_args) print(f"Quantizing {ckpt_path}") with open(ckpt_path, "rb") as f: print("Reading checkpoint...") checkpoint = torch.load(f, map_location="cpu", weights_only=False) print("Done...") print("Loading state dict...") model.load_state_dict(checkpoint, strict=False) print("Done...") fp8_scales = {} int4_scales = {} old_keys = set(checkpoint.keys()) new_state_dict = checkpoint for _, block in model.named_modules(): if isinstance(block, TransformerBlock): if block.layer_id == 0 or block.layer_id == (model.n_layers - 1): continue if not isinstance(block.feed_forward, MoE): continue # IMPORTANT NOTE: # (1) Keys for weights are exactly as in the original state dict # (2) Keys for fp8/int4 scales are according to re-mapped keys based on the load_hooks # # Why is it? In any case, things are confusing. However, this limits the confusion in the loading # code. The load_hooks remain simple, and in the fp8/int4 loading code at runtime, we don't need to # do any key re-mapping. # # Secondly: # (1) MoE experts weights are transposed back so once again the loading code remains same for # on-the-fly vs. from-pre-quantized weights # (2) Scales however are _not_ transposed back prefix = f"layers.{block.layer_id}.feed_forward" moe = block.feed_forward if quantization_mode == QuantizationMode.int4_mixed: for key in ("w1", "w3", "w2"): param = getattr(moe.experts, key) shape = param.shape weight = param.transpose(1, 2).contiguous() if weight.ndim >= 3: wq, scale = zip(*[int4_row_quantize(i.cuda()) for i in weight]) wq = torch.stack([pack_int4(i.cuda()) for i in wq], dim=0) w_scale = torch.stack(scale, dim=0) else: wq, w_scale = int4_row_quantize(weight.cuda()) wq = pack_int4(wq.cuda()) state_dict_key_map = { "w1": "moe_w_in_eD_F", "w2": "moe_w_out_eF_D", "w3": "moe_w_swiglu_eD_F", } # we must save this as 2D in the state dict, since loading code expects 2D weights new_shape = (-1, shape[-1]) wq = wq.transpose(1, 2).reshape(*new_shape).contiguous() # torch.nn.Parameter requires weights be floating point, so we cast packed int8 (2 * int4) to float8_e4m3fn, and will cast back to int8 in loading code new_state_dict[f"{prefix}.experts.{state_dict_key_map[key]}"] = torch.nn.Parameter( wq.view(torch.float8_e4m3fn) ) int4_scales[f"{prefix}.experts.{key}"] = w_scale print(f"Quantized {prefix}.experts.{state_dict_key_map[key]} {wq.shape=} {w_scale.shape=}") else: for key in ("w1", "w3", "w2"): param = getattr(moe.experts, key) shape = param.shape wq, w_scale = torch.ops.fbgemm.quantize_fp8_per_row(param.transpose(1, 2).contiguous()) state_dict_key_map = { "w1": "moe_w_in_eD_F", "w2": "moe_w_out_eF_D", "w3": "moe_w_swiglu_eD_F", } # we must save this as 2D in the state dict, since loading code expects 2D weights new_shape = (-1, shape[-1]) wq = wq.transpose(1, 2).reshape(*new_shape).contiguous() new_state_dict[f"{prefix}.experts.{state_dict_key_map[key]}"] = torch.nn.Parameter(wq) fp8_scales[f"{prefix}.experts.{key}"] = w_scale print(f"Quantized {prefix}.experts.{state_dict_key_map[key]} {wq.shape=} {w_scale.shape=}") new_keys = set(new_state_dict.keys()) assert old_keys == new_keys, f"old_keys != new_keys: {old_keys - new_keys}" if quantization_mode == QuantizationMode.int4_mixed: print("Saving int4 scales") int4_scales_path = os.path.join(output_dir, f"int4_scales_{ckpt_idx}.pt") torch.save(int4_scales, int4_scales_path) else: print("Saving fp8 scales") fp8_scales_path = os.path.join(output_dir, f"fp8_scales_{ckpt_idx}.pt") torch.save(fp8_scales, fp8_scales_path) ckpt_path = os.path.join( output_dir, "consolidated.{:02d}.pth".format(ckpt_idx), ) print(f"Saving checkpoint to {ckpt_path}") torch.save(new_state_dict, ckpt_path) torch.distributed.barrier() def main(): fire.Fire(ffn_quantize) if __name__ == "__main__": main() ================================================ FILE: models/llama4/tests/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. ================================================ FILE: models/llama4/tests/api/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. ================================================ FILE: models/llama4/tests/api/test_chat_format.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import json import unittest from unittest.mock import Mock from llama_models.datatypes import BuiltinTool, StopReason from llama_models.llama4.chat_format import ChatFormat from llama_models.llama4.tokenizer import Tokenizer class TestChatFormatArgumentsJson(unittest.TestCase): """Test that ToolCall objects include the arguments_json parameter.""" def setUp(self): """Set up test fixtures.""" # Mock the tokenizer to avoid dependency issues in tests self.mock_tokenizer = Mock(spec=Tokenizer) self.chat_format = ChatFormat(self.mock_tokenizer) def test_arguments_json_included_in_custom_tool_call(self): """Test that arguments_json is included for custom tool calls.""" # Mock content that represents a custom tool call in python_list format content = '[get_weather(location="San Francisco", units="metric")]' # Mock the ToolUtils.maybe_extract_custom_tool_call to return our test data with unittest.mock.patch( "llama_models.llama3.tool_utils.ToolUtils.maybe_extract_custom_tool_call" ) as mock_extract: mock_extract.return_value = ("get_weather", {"location": "San Francisco", "units": "metric"}) # Call the method under test result = self.chat_format.decode_assistant_message_from_content(content, StopReason.end_of_turn) # Verify the result self.assertEqual(len(result.tool_calls), 1) tool_call = result.tool_calls[0] # Verify all expected fields are present self.assertIsNotNone(tool_call.call_id) self.assertEqual(tool_call.tool_name, "get_weather") self.assertEqual(tool_call.arguments, {"location": "San Francisco", "units": "metric"}) # This is the critical test - verify arguments_json is included self.assertIsNotNone(tool_call.arguments_json) self.assertEqual(tool_call.arguments_json, '{"location": "San Francisco", "units": "metric"}') # Verify arguments_json is valid JSON that matches arguments parsed_args = json.loads(tool_call.arguments_json) self.assertEqual(parsed_args, tool_call.arguments) def test_arguments_json_included_in_builtin_tool_call(self): """Test that arguments_json is included for builtin tool calls.""" # Mock content that represents a builtin tool call content = "search for weather in San Francisco" # Mock the ToolUtils methods to simulate builtin tool extraction with ( unittest.mock.patch( "llama_models.llama3.tool_utils.ToolUtils.maybe_extract_custom_tool_call" ) as mock_custom, unittest.mock.patch( "llama_models.llama3.tool_utils.ToolUtils.maybe_extract_builtin_tool_call" ) as mock_builtin, ): mock_custom.return_value = None # No custom tool mock_builtin.return_value = ("brave_search", "weather in San Francisco") # Call the method under test result = self.chat_format.decode_assistant_message_from_content(content, StopReason.end_of_turn) # Verify the result self.assertEqual(len(result.tool_calls), 1) tool_call = result.tool_calls[0] # Verify all expected fields are present self.assertIsNotNone(tool_call.call_id) self.assertEqual(tool_call.tool_name, BuiltinTool.brave_search) self.assertEqual(tool_call.arguments, {"query": "weather in San Francisco"}) # This is the critical test - verify arguments_json is included self.assertIsNotNone(tool_call.arguments_json) self.assertEqual(tool_call.arguments_json, '{"query": "weather in San Francisco"}') # Verify arguments_json is valid JSON that matches arguments parsed_args = json.loads(tool_call.arguments_json) self.assertEqual(parsed_args, tool_call.arguments) def test_arguments_json_included_in_code_interpreter_call(self): """Test that arguments_json is included for code interpreter tool calls.""" # Mock content that represents a code interpreter tool call content = "run some python code" # Mock the ToolUtils methods to simulate code interpreter tool extraction with ( unittest.mock.patch( "llama_models.llama3.tool_utils.ToolUtils.maybe_extract_custom_tool_call" ) as mock_custom, unittest.mock.patch( "llama_models.llama3.tool_utils.ToolUtils.maybe_extract_builtin_tool_call" ) as mock_builtin, ): mock_custom.return_value = None # No custom tool mock_builtin.return_value = ("code_interpreter", "print('Hello, World!')") # Call the method under test result = self.chat_format.decode_assistant_message_from_content(content, StopReason.end_of_turn) # Verify the result self.assertEqual(len(result.tool_calls), 1) tool_call = result.tool_calls[0] # Verify all expected fields are present self.assertIsNotNone(tool_call.call_id) self.assertEqual(tool_call.tool_name, BuiltinTool.code_interpreter) self.assertEqual(tool_call.arguments, {"query": "print('Hello, World!')"}) # This is the critical test - verify arguments_json is included self.assertIsNotNone(tool_call.arguments_json) self.assertEqual(tool_call.arguments_json, '{"query": "print(\'Hello, World!\')"}') # Verify arguments_json is valid JSON that matches arguments parsed_args = json.loads(tool_call.arguments_json) self.assertEqual(parsed_args, tool_call.arguments) def test_arguments_json_with_complex_arguments(self): """Test that arguments_json handles complex arguments correctly.""" # Mock content that represents a custom tool call with complex but supported arguments content = '[complex_function(title="My Title", numbers=[1, 2, 3], enabled=true, score=3.14)]' # Mock the ToolUtils.maybe_extract_custom_tool_call to return our test data with unittest.mock.patch( "llama_models.llama3.tool_utils.ToolUtils.maybe_extract_custom_tool_call" ) as mock_extract: complex_args = {"title": "My Title", "numbers": [1, 2, 3], "enabled": True, "score": 3.14} mock_extract.return_value = ("complex_function", complex_args) # Call the method under test result = self.chat_format.decode_assistant_message_from_content(content, StopReason.end_of_turn) # Verify the result self.assertEqual(len(result.tool_calls), 1) tool_call = result.tool_calls[0] # Verify all expected fields are present self.assertIsNotNone(tool_call.call_id) self.assertEqual(tool_call.tool_name, "complex_function") self.assertEqual(tool_call.arguments, complex_args) # This is the critical test - verify arguments_json is included self.assertIsNotNone(tool_call.arguments_json) # Verify arguments_json is valid JSON that matches arguments parsed_args = json.loads(tool_call.arguments_json) self.assertEqual(parsed_args, tool_call.arguments) # Verify the JSON string contains the expected structure expected_json = '{"title": "My Title", "numbers": [1, 2, 3], "enabled": true, "score": 3.14}' self.assertEqual(tool_call.arguments_json, expected_json) def test_no_tool_calls_when_no_tools_detected(self): """Test that no tool calls are created when no tools are detected.""" content = "Just a regular response with no tool calls." # Mock the ToolUtils methods to return None (no tools detected) with ( unittest.mock.patch( "llama_models.llama3.tool_utils.ToolUtils.maybe_extract_custom_tool_call" ) as mock_custom, unittest.mock.patch( "llama_models.llama3.tool_utils.ToolUtils.maybe_extract_builtin_tool_call" ) as mock_builtin, ): mock_custom.return_value = None mock_builtin.return_value = None # Call the method under test result = self.chat_format.decode_assistant_message_from_content(content, StopReason.end_of_turn) # Verify no tool calls are created self.assertEqual(len(result.tool_calls), 0) self.assertEqual(result.content, content) if __name__ == "__main__": unittest.main() ================================================ FILE: models/llama4/tokenizer.model ================================================ wA== 0 wQ== 1 9Q== 2 9g== 3 9w== 4 +A== 5 +Q== 6 +g== 7 +w== 8 /A== 9 /Q== 10 /g== 11 /w== 12 IQ== 13 Ig== 14 Iw== 15 JA== 16 JQ== 17 Jg== 18 Jw== 19 KA== 20 KQ== 21 Kg== 22 Kw== 23 LA== 24 LQ== 25 Lg== 26 Lw== 27 MA== 28 MQ== 29 Mg== 30 Mw== 31 NA== 32 NQ== 33 Ng== 34 Nw== 35 OA== 36 OQ== 37 Og== 38 Ow== 39 PA== 40 PQ== 41 Pg== 42 Pw== 43 QA== 44 QQ== 45 Qg== 46 Qw== 47 RA== 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196755 INmF2YLYp9ix2YbYqQ== 196756 RXF1aXR5 196757 INin2YTYsdiz 196758 Lm5vdGlmaWNhdGlvbnM= 196759 IEtvbnRyb2w= 196760 4KaP4KaV 196761 T2ZmZXJpbmc= 196762 INC+0YLQstC10YDRgdGC0LjQuQ== 196763 b3Zpc25pbmc= 196764 IHVwbGluaw== 196765 0LLRiNGD0Y7RgdGP 196766 INin24zZhdmG24w= 196767 LmNyZWRlbnRpYWxz 196768 4YOf 196769 IExvY2tlcg== 196770 YWtrZW4= 196771 aWxsZXJp 196772 INC90LDQstC4 196773 Y2twdA== 196774 IGdsaWRlcg== 196775 INCx0YPRgdCw0LQ= 196776 IHBvd2VyaG91c2U= 196777 IGltcHJhY3RpY2Fs 196778 IOCkpuCkv+CktuCkvg== 196779 INGA0YPRhdGD 196780 IFNUQU5E 196781 IG1lbWJydQ== 196782 IGbDtnJsb3I= 196783 IG5lY2Vzc2l0aWVz 196784 5LiA5Liq5paw55qE 196785 IENvbnN1bW8= 196786 6ICD5Y+k 196787 KFNX 196788 IHByZXZlbnRpdm8= 196789 IGV4ZWN1dGl2bw== 196790 IGZvbmTDqWU= 196791 IHBpbG90ZXM= 196792 INiq2LnYp9mE2Yk= 196793 IHBvxI1hc2lh 196794 INm+24zYsdmI2LLbjA== 196795 IG1ub2g= 196796 IOydmOybkOydtA== 196797 IEVyaWtzc29u 196798 INC90L7Qs9GC0LXQuQ== 196799 LWJ1dHls 196800 INC/0YPRgtGR0Lw= 196801 IFByb2dub3Nl 196802 5Ya26YeR 196803 IEjDqWN0b3I= 196804 LVBsYW5jaw== 196805 CXByb3BlcnR5 196806 IOW+hQ== 196807 IGRpZGVz 196808 INC/0L7QtNC4 196809 IE7Eg25n 196810 IEhu 196811 IE9jaG8= 196812 b3JleGlh 196813 xaHEqw== 196814 IEVudmVsb3Bl 196815 7JWx 196816 INC+0YHRg9C00Lg= 196817 INC60L7QvdCy0LXQuQ== 196818 U1NTUw== 196819 INin2YTZhdir2KfZhA== 196820 7Lak 196821 IHdvbmVu 196822 0LPRgNCw0YTQuA== 196823 IHRpcHVz 196824 44OJ44Kk44OE 196825 IFBhcmHDrWJh 196826 IGHDp8O6Y2Fy 196827 IEVwaWxl 196828 IOuCqOyWkQ== 196829 bGVpc3R1bmdz 196830 IHB1Ymxpw6ll 196831 4LiE4Lij4Lix4LmJ4LiH4LmB4Lij4LiB 196832 4LS/4LSV4LW+ 196833 INCy0LjQvdGC0L7Qsg== 196834 b2JhY3Rlcmlh 196835 INC90Y7QsNC90YHRiw== 196836 IERFQUxJTkdT 196837 KHJvdA== 196838 LmhvdXNl 196839 Lmlzc3Vl 196840 PGNvdW50 196841 TEo= 196842 IGVudmVq 196843 IHpib3I= 196844 IFtbIg== 196845 IHJlc3VsdGVy 196846 IGFyY2E= 196847 2YXZg9in2YY= 196848 c2V0enQ= 196849 4KS/4KSZ 196850 w7pkbw== 196851 IHRyYW5zYWN0 196852 LXN5c3RlbXM= 196853 bmFhbg== 196854 IHNpZ25pYw== 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IHBpc2NpbmU= 198635 INGD0LPQu9C10LLQvtC00L7Qsg== 198636 O3Jlc3RyaWN0aW9u 198637 R2FtaW5n 198638 SHZhZA== 198639 XFF1ZXJ5 198640 vIg= 198641 IOW4iA== 198642 Y3RyZWU= 198643 IFRHVg== 198644 IHNlb3R1ZA== 198645 77yM6K645aSa 198646 LS0tLS0tLS0tLS0tLS0tLS0= 198647 INGA0LXQsdGP0YLQsA== 198648 INCc0LXQvdGP 198649 aXNraXQ= 198650 INCT0YDQvtC3 198651 IGFudGFsbA== 198652 IGZhY2Vt 198653 IMOhcmFiZQ== 198654 5o6l6Ke4 198655 INC/0YPRgdGC 198656 ZWxuxJs= 198657 IHZhY2F0aW9ucw== 198658 IGJncA== 198659 INGW0L3RgtC1 198660 5p+Q5p+Q 198661 LmFjbWljcGM= 198662 IMW8ZWw= 198663 IGdhbWxl 198664 INCS0LXRgdGM 198665 bcOkY2h0 198666 INGF0YPQtNC+0LbQvdC40Lo= 198667 bmlhbmlh 198668 IGVuY2VwaGFs 198669 LkNSRUFURUQ= 198670 LWhlcm8= 198671 0LfQvtGA0Ys= 198672 cHR1 198673 IG1lbmFy 198674 IHZvb3Ju 198675 bW9kdWw= 198676 LmV4cGVyaW1lbnRhbA== 198677 UmVjbw== 198678 Lk5PUlRI 198679 IExlc2Vu 198680 INGA0LDRgdC/0Ys= 198681 IFBST1Q= 198682 IFNQRFg= 198683 4KS+4KSc4KS8 198684 IHdhbHV0 198685 IEFHSQ== 198686 IGZyYWd0 198687 anNwYg== 198688 INCy0LXQu9C40YfQuNC9 198689 IHN2b2x0bw== 198690 IOC2lOC3hOC3lA== 198691 IGN1Y2luYQ== 198692 IOC2huC2uw== 198693 IHNhZmVndWFyZHM= 198694 IHBlcnRlbmVjZW4= 198695 IEV4cGVkaWVudGU= 198696 IGVzY2xhcmVjZXI= 198697 INC/0L7Qs9C+0LLQvtGA0LjQvA== 198698 INin2YTZhdmK2KfZhw== 198699 IGN1YXJ0b3M= 198700 QnJ5 198701 Z2VzdGlvbg== 198702 aG93ZXI= 198703 enVzY2g= 198704 CW9wdHM= 198705 wqAgwqDCoCDCoMKg 198706 aWdodGVk 198707 55qE56S+5Lya 198708 IHRla2Vu 198709 dGV4dGZpZWxk 198710 YXJnaW5z 198711 YnJ0Yw== 198712 IENvbHRz 198713 INiv2LHYp9iz2Kk= 198714 2LfYp9ih 198715 IHdhdGVyZmFsbHM= 198716 IFBlcsOy 198717 0YLQtdGA0LTRlg== 198718 IGJpbGF5ZXI= 198719 IG3DvGRk 198720 2YjYqNiq 198721 IEluZ3JpZA== 198722 IOC4quC4ow== 198723 IGzDpGdyZQ== 198724 IHBlbnllYmFyYW4= 198725 INGC0LXRhdC90LjRh9C10YHQutC40Lk= 198726 IHZlcmtyaWo= 198727 INC60YDRi9GI0LrQvtC5 198728 IOCqruCqsw== 198729 IOCkpuClh+CkluClgOCksg== 198730 IHVyYmFpbg== 198731 IE93bmVk 198732 IGNhcmFjdMOpcmlzdGlxdWU= 198733 IEzDrW5lYQ== 198734 4oCcU2F5YQ== 198735 6aWy5YW7 198736 PXVybA== 198737 WXk= 198738 65w= 198739 IHDDug== 198740 IHBwaQ== 198741 IGxlc3Rl 198742 IGVpdA== 198743 IEEncw== 198744 wqDsnojri6Q= 198745 44CC546L 198746 IGV4dGlu 198747 IGluZHJ1aw== 198748 VW5jaGVja2Vk 198749 44K544Oi 198750 6Ieq6KeJ 198751 IMSQ4bqvaw== 198752 IEVyc3RlbGx1bmc= 198753 IHNlbnRp 198754 VU1BTg== 198755 IGhheW90 198756 b2Nzw6F0 198757 INCf0YDQvtC40LfQstC+0LTQuA== 198758 INiq2LHZiNix24w= 198759 57aT5YW4 198760 IGRvdnV0YQ== 198761 aXRhdGlp 198762 0LLQtdC00LXQvdC90Y8= 198763 16DXmdeZ158= 198764 INGD0YfQtdGC0L3QvtC5 198765 LWRyaXZpbmc= 198766 IFNjaHdlZGVu 198767 57uY55S7 198768 IGtsYXN5Zmlr 198769 IHPDumx5b3M= 198770 INC00YDRg9C30YzRj9C80Lg= 198771 INC70Y7RgtC+0LPQvg== 198772 IGJvbnQ= 198773 IE1DRg== 198774 wqDOvA== 198775 IER1Y3Q= 198776 IEROSQ== 198777 IEZha2F0 198778 aXpsaQ== 198779 77yM5Zue 198780 44CC5aSp 198781 INGC0Y0= 198782 44Gu5pel 198783 emV1Z3M= 198784 4KSq4KSk4KWN4KSw 198785 44OD44K/44O8 198786 cGF1c2Vk 198787 IOyngO2RnA== 198788 5q+U6YeN 198789 IE1hdHRl 198790 7KSR7J24 198791 IElGUw== 198792 INmF2LHYp9iz2YU= 198793 YWdvbmlh 198794 IENlcnRhaW5lcw== 198795 LXRyYWQ= 198796 INGU0LI= 198797 INio2K/Yp9mK2Kk= 198798 INC60YDQtdCw 198799 INit2KfZhNin 198800 IOKHlA== 198801 IOCksuClh+CkpOClhw== 198802 IOODneOCpOODs+ODiA== 198803 IFdpc3NlbnNjaGFmdGxlcg== 198804 IGFwYXJlbGhvcw== 198805 INC/0YDQsNCy0L7QvdCw0YDRg9GI0LXQvdC40Y/RhQ== 198806 IERsYWN6ZWdv 198807 44Gf44GP44GV44KT 198808 4Lit4Lil4LmA4Lil4Lii4LmM4Lia4Lit4Lil 198809 ZmVm 198810 fF57 198811 IGRhZGFz 198812 IGJ1bnQ= 198813 IGFuZGF0YQ== 198814 IEN5cA== 198815 IENEQg== 198816 IEJMQQ== 198817 YW5kaWE= 198818 IEZhc2M= 198819 IGVudHVzaQ== 198820 IG9wYQ== 198821 LnRhZ2V4dA== 198822 LW1lbA== 198823 d2FyZGVu 198824 5pyA5aSa55qE 198825 IMWhxIE= 198826 INC40LzQv9C70LDQvQ== 198827 IOGDk+GDkOGDkg== 198828 IFVJU3Rvcnlib2FyZA== 198829 0LbQtdC90ZY= 198830 IG1pZHBvaW50cw== 198831 INC00L7QutGD0LzQtdC90YLQvtC8 198832 IOyViOyXkOyEnA== 198833 IEF1Zmtsw6RydW5n 198834 KCIuIiw= 198835 YmVpdHJhZw== 198836 L3JlcG9ydHM= 198837 INCc0LDRgtC10LzQsNGC0Lg= 198838 IERvbG9yZXM= 198839 INC/0YDQtdC20LTQtdCy0YDQtdC80LXQvQ== 198840 INCT0LvQsNCy0L3QsNGP 198841 IHN1cGVyc3ltbWV0cnk= 198842 LOKApiw= 198843 55ay5Yqz 198844 4Lit4Lix4LiV4Lij4Liy 198845 INin2YTYq9mE2KfYq9in2KE= 198846 IHN5a2RvbQ== 198847 IGVwaXNvZGlvcw== 198848 U3RlcmVv 198849 Q1NU 198850 dcWl 198851 IFPFgm93 198852 IFRQUA== 198853 IFBESQ== 198854 IGvDvG5mdA== 198855 b2NoZXQ= 198856 IGNoaWxseQ== 198857 YWN0aXZh 198858 X3RvdWNo 198859 IGFtZQ== 198860 X3Ntb290aA== 198861 INC10LTRgw== 198862 INC/0YDQvtCy0LDQu9C4 198863 0L7QtNCw0YI= 198864 INCU0L7Qsw== 198865 INC70LjRh9C90L7QvA== 198866 IGtvbXBvbg== 198867 IGRlYmls 198868 IFdlYnNlaXRl 198869 IE1ldGFib2xpc20= 198870 IOu2gOuTnA== 198871 w7x6Z2Fy 198872 IGJlaGFuZGVsbg== 198873 IOyInOychA== 198874 IG9kbm9zaQ== 198875 IOC4hOC4uOC4k+C4quC4suC4oeC4suC4o+C4lg== 198876 IG5ldHRlbWVudA== 198877 IHBlbnllbGlkaWthbg== 198878 INGB0LzQvtGC0YDQuA== 198879 JHdw 198880 RXBpcw== 198881 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4KS44KSB4KSX 199521 bGV2w6k= 199522 IHJldGVuY2nDs24= 199523 INC/0LXRgNC10L4= 199524 IHV0dGFs 199525 4LiK4Li14LmJ 199526 CWZk 199527 IFBhYXI= 199528 w6Ftb3M= 199529 IG9yZ2FuaXpt 199530 TWVsZWU= 199531 IG1lZ8Opcg== 199532 LUFuaA== 199533 zr7Ov8+Fzr0= 199534 4Z6Z4Z+J4Z624Z6E 199535 IGtvbnNpc3Rlbg== 199536 IGxpbmd1aXN0aWNz 199537 INC/0YDQtdC00LLQsNGA0LjRgtC10LvRjNC90YvQvA== 199538 INC/0YDQvtGP0LLQu9GP0Y7RgtGB0Y8= 199539 15XXnteY 199540 INCw0L3QsNGC0L7QvNC4 199541 IGRpc2FwcGVhcmluZw== 199542 4K6p4K+N 199543 IGVzY3JldmU= 199544 IMOcYmVyc2V0enVuZw== 199545 7YC0 199546 57q957qm 199547 IMOpcnTDqWtlbA== 199548 IM60z4HOsc+Dz4TOt8+Bzrk= 199549 4Kq54KuA4KqC 199550 Lj4= 199551 PkdldA== 199552 Pk15 199553 YW9z 199554 YmF0dGVy 199555 aWlk 199556 IGRlyJlp 199557 ZGVjcmVhc2U= 199558 b2dpYw== 199559 ZW5zaXTDqQ== 199560 IGJvenVs 199561 LWJhc2lz 199562 INC/0LXRgNC10YfQuNGB0LvQuA== 199563 IGdyYWZmaXRp 199564 IG5hdmlv 199565 IGFtcGxv 199566 IGdhbWl0 199567 INCQ0YDQs9C10L0= 199568 Y2lwaWVudA== 199569 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0LPRgNCw0LTQuA== 199921 IGNlbGRh 199922 IGpvZ2dpbmc= 199923 w6R0dG5pbmdlbg== 199924 U3VtbWl0 199925 IE5hdmlkYWQ= 199926 YXZpZXN0 199927 4LKu4LK+4LKo 199928 IGxldmVyZW4= 199929 4LmA4LiI4LmJ4Liy 199930 INC10YHRgtC10YHRgtCy0LXQvdC90YvQvA== 199931 X2RvY3VtZW50cw== 199932 IGVudmlzaW9uZWQ= 199933 IEdhemV0dGVlcg== 199934 LmxleA== 199935 OnN1cGVycw== 199936 Q3JlYW0= 199937 YXJjZQ== 199938 aW9uYXRl 199939 4oCcRm9y 199940 IFBsZW50eQ== 199941 IHFhYnVs 199942 IHNlbXBs 199943 INC/0LXRgNC10LjQvNC10L0= 199944 0KPRh9Cw0YHRgg== 199945 IGZpcm1hcg== 199946 66+47Iig 199947 X2JhY2t3YXJk 199948 IEFzdHVyaWFz 199949 4YGB 199950 IGludMOpZ3LDqQ== 199951 IHZlcmVpc3Q= 199952 57yp5pS+ 199953 IM6SzrHPgw== 199954 IGTDqXBhc3Nl 199955 LkNvbmZpZ3VyZQ== 199956 4LiX4Li14LmI4Lih4Li14LiE4Lin4Liy4Lih 199957 IOu2gOu2hOydtA== 199958 0J/QtdGA0LLRi9C1 199959 LmNrbQ== 199960 IFdzenlzdGtpZQ== 199961 IE1hdmVyaWNr 199962 RWxlY3Rpb24= 199963 X3Vj 199964 CU1hdHJpeA== 199965 IHBlbmRhZnRhcmFu 199966 0YHRg9C2 199967 wqB1 199968 0L7QstC+0YA= 199969 55qE5biC5Zy6 199970 w6dhbmRv 199971 IHJhaW5pbmc= 199972 INC60LDQvdC40LrRgw== 199973 IGFjY3Q= 199974 IGJsb2Nz 199975 IGludmFk 199976 IHNtYXNoZWQ= 199977 IFBsYW5lamFtZW50bw== 199978 X1RVUk4= 199979 4oCM2q/bjNix2K8= 199980 zrTOv8+Fz4I= 199981 4LmI4Liy4LiI4Liw 199982 6rO16rO1 199983 IGVtYmVsbA== 199984 IE1heGltaXpl 199985 ZXN0cmF0ZWc= 199986 5rOo5oSP5LqL6aCF 199987 4LK44LON4LKv 199988 5Y+R55Sf5Y+Y5YyW 199989 IFdhdGZvcmQ= 199990 IGFwcHLDqWM= 199991 56ue5oqA 199992 2YbYtNin2YY= 199993 bWlkZGxld2FyZXM= 199994 IOCkruClgeCktg== 199995 IE5pY29sYWU= 199996 IFPhu611 199997 INC/0L7QtNC+0LnQtNGD0YI= 199998 KGdl 199999 ================================================ FILE: models/llama4/tokenizer.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. from logging import getLogger from pathlib import Path from typing import ( AbstractSet, Collection, Dict, Iterator, List, Literal, Optional, Sequence, Union, cast, ) import tiktoken from ..tokenizer_utils import load_bpe_file logger = getLogger(__name__) # The tiktoken tokenizer can handle <=400k chars without # pyo3_runtime.PanicException. TIKTOKEN_MAX_ENCODE_CHARS = 400_000 # https://github.com/openai/tiktoken/issues/195 # Here we iterate over subsequences and split if we exceed the limit # of max consecutive non-whitespace or whitespace characters. MAX_NO_WHITESPACES_CHARS = 25_000 _INSTANCE = None def get_reserved_special_tokens(name, count, start_index=0): return [f"<|{name}_reserved_special_token_{i}|>" for i in range(start_index, start_index + count)] # 200005, ..., 200079 LLAMA4_TEXT_POST_TRAIN_SPECIAL_TOKENS = [ "<|header_start|>", "<|header_end|>", "<|eom|>", "<|eot|>", "<|step|>", "<|text_post_train_reserved_special_token_0|>", "<|text_post_train_reserved_special_token_1|>", "<|text_post_train_reserved_special_token_2|>", "<|text_post_train_reserved_special_token_3|>", "<|text_post_train_reserved_special_token_4|>", "<|text_post_train_reserved_special_token_5|>", "<|python_start|>", "<|python_end|>", "<|finetune_right_pad|>", ] + get_reserved_special_tokens( "text_post_train", 61, 8 ) # <|text_post_train_reserved_special_token_8|>, ..., <|text_post_train_reserved_special_token_68|> # 200080, ..., 201133 LLAMA4_VISION_SPECIAL_TOKENS = [ "<|image_start|>", "<|image_end|>", "<|vision_reserved_special_token_0|>", "<|vision_reserved_special_token_1|>", "<|tile_x_separator|>", "<|tile_y_separator|>", "<|vision_reserved_special_token_2|>", "<|vision_reserved_special_token_3|>", "<|vision_reserved_special_token_4|>", "<|vision_reserved_special_token_5|>", "<|image|>", "<|vision_reserved_special_token_6|>", "<|patch|>", ] + get_reserved_special_tokens( "vision", 1041, 7 ) # <|vision_reserved_special_token_7|>, ..., <|vision_reserved_special_token_1047|> # 201134, ..., 201143 LLAMA4_REASONING_SPECIAL_TOKENS = [ "<|reasoning_reserved_special_token_0|>", "<|reasoning_reserved_special_token_1|>", "<|reasoning_reserved_special_token_2|>", "<|reasoning_reserved_special_token_3|>", "<|reasoning_reserved_special_token_4|>", "<|reasoning_reserved_special_token_5|>", "<|reasoning_reserved_special_token_6|>", "<|reasoning_reserved_special_token_7|>", "<|reasoning_thinking_start|>", "<|reasoning_thinking_end|>", ] LLAMA4_SPECIAL_TOKENS = ( LLAMA4_TEXT_POST_TRAIN_SPECIAL_TOKENS + LLAMA4_VISION_SPECIAL_TOKENS + LLAMA4_REASONING_SPECIAL_TOKENS ) BASIC_SPECIAL_TOKENS = [ "<|begin_of_text|>", "<|end_of_text|>", "<|fim_prefix|>", "<|fim_middle|>", "<|fim_suffix|>", ] class Tokenizer: """ Tokenizing and encoding/decoding text using the Tiktoken tokenizer. """ special_tokens: Dict[str, int] num_reserved_special_tokens = 2048 O200K_PATTERN = r"""[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n/]*|\s*[\r\n]+|\s+(?!\S)|\s+""" # noqa: E501 @classmethod def get_instance(cls): global _INSTANCE if _INSTANCE is None: _INSTANCE = Tokenizer(Path(__file__).parent / "tokenizer.model") return _INSTANCE def __init__(self, model_path: Path): """ Initializes the Tokenizer with a Tiktoken model. Args: model_path (Path): The path to the Tiktoken model file. """ if not model_path.exists(): raise FileNotFoundError(f"Tokenizer model file not found: {model_path}") mergeable_ranks = load_bpe_file(model_path) num_base_tokens = len(mergeable_ranks) special_tokens = BASIC_SPECIAL_TOKENS + LLAMA4_SPECIAL_TOKENS assert len(set(special_tokens)) == len(special_tokens) assert len(special_tokens) <= self.num_reserved_special_tokens reserved_tokens = [ f"<|reserved_special_token_{i}|>" for i in range(self.num_reserved_special_tokens - len(special_tokens)) ] special_tokens = special_tokens + reserved_tokens self.special_tokens = {token: num_base_tokens + i for i, token in enumerate(special_tokens)} self.model = tiktoken.Encoding( name=model_path.name, pat_str=self.O200K_PATTERN, mergeable_ranks=mergeable_ranks, special_tokens=self.special_tokens, ) self.n_words: int = num_base_tokens + len(special_tokens) # BOS / EOS token IDs self.bos_id: int = self.special_tokens["<|begin_of_text|>"] self.eos_id: int = self.special_tokens["<|end_of_text|>"] self.pad_id: int = self.special_tokens["<|finetune_right_pad|>"] self.eot_id: int = self.special_tokens["<|eot|>"] self.eom_id: int = self.special_tokens["<|eom|>"] self.thinking_start_id: int = self.special_tokens["<|reasoning_thinking_start|>"] self.thinking_end_id: int = self.special_tokens["<|reasoning_thinking_end|>"] self.stop_tokens = [ self.eos_id, self.special_tokens["<|eom|>"], self.special_tokens["<|eot|>"], ] def encode( self, s: str, *, bos: bool, eos: bool, allowed_special: Optional[Union[Literal["all"], AbstractSet[str]]] = None, disallowed_special: Union[Literal["all"], Collection[str]] = (), ) -> List[int]: """ Encodes a string into a list of token IDs. Args: s (str): The input string to be encoded. bos (bool): Whether to prepend the beginning-of-sequence token. eos (bool): Whether to append the end-of-sequence token. allowed_special ("all"|set[str]): allowed special tokens in string disallowed_special ("all"|set[str]): special tokens that raise an error when in string Returns: list[int]: A list of token IDs. By default, setting disallowed_special=() encodes a string by ignoring special tokens. Specifically: - Setting `disallowed_special` to () will cause all text corresponding to special tokens to be encoded as natural text (insteading of raising an error). - Setting `allowed_special` to "all" will treat all text corresponding to special tokens to be encoded as special tokens. """ if allowed_special is None: allowed_special = set() assert type(s) is str substrs = ( substr for i in range(0, len(s), TIKTOKEN_MAX_ENCODE_CHARS) for substr in self._split_whitespaces_or_nonwhitespaces( s[i : i + TIKTOKEN_MAX_ENCODE_CHARS], MAX_NO_WHITESPACES_CHARS ) ) t: List[int] = [] for substr in substrs: t.extend( self.model.encode( substr, allowed_special=allowed_special, disallowed_special=disallowed_special, ) ) if bos: t.insert(0, self.bos_id) if eos: t.append(self.eos_id) return t def decode(self, t: Sequence[int]) -> str: """ Decodes a list of token IDs into a string. Args: t (List[int]): The list of token IDs to be decoded. Returns: str: The decoded string. """ # Typecast is safe here. Tiktoken doesn't do anything list-related with the sequence. return self.model.decode(cast(List[int], t)) @staticmethod def _split_whitespaces_or_nonwhitespaces(s: str, max_consecutive_slice_len: int) -> Iterator[str]: """ Splits the string `s` so that each substring contains no more than `max_consecutive_slice_len` consecutive whitespaces or consecutive non-whitespaces. """ current_slice_len = 0 current_slice_is_space = s[0].isspace() if len(s) > 0 else False slice_start = 0 for i in range(len(s)): is_now_space = s[i].isspace() if current_slice_is_space ^ is_now_space: current_slice_len = 1 current_slice_is_space = is_now_space else: current_slice_len += 1 if current_slice_len > max_consecutive_slice_len: yield s[slice_start:i] slice_start = i current_slice_len = 1 yield s[slice_start:] ================================================ FILE: models/llama4/vision/embedding.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import math from typing import Any, Callable, Dict, List import torch import torch.nn as nn import torch.nn.functional as F from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear from ..args import VisionArgs from .encoder import VisionEncoder class PixelShuffle(nn.Module): def __init__(self, ps_ratio): super().__init__() self.ps_ratio = ps_ratio def forward(self, x): # x: [B, N, C], N = number of patches assert self.ps_ratio is not None, "ps_ratio is required for pixel shuffle" assert x.dim() == 3, "pixel shuffle requires encoded patches [B, N, C]" hh = ww = int(math.sqrt(x.shape[1])) x = x.reshape(x.shape[0], hh, ww, -1) x = pixel_shuffle_op(x, ps_ratio=self.ps_ratio) pixel_shuffle_patches = x.reshape(x.shape[0], -1, x.shape[-1]) return pixel_shuffle_patches def pixel_shuffle_op(input_x, ps_ratio): n, w, h, c = input_x.size() input_x = input_x.view(n, w, int(h * ps_ratio), int(c / ps_ratio)) input_x = input_x.permute(0, 2, 1, 3).contiguous() input_x = input_x.view( n, int(h * ps_ratio), int(w * ps_ratio), int(c / (ps_ratio * ps_ratio)), ) input_x = input_x.permute(0, 2, 1, 3).contiguous() return input_x class SimpleMLP(torch.nn.Module): def __init__( self, dim: int, hidden_dim: int, bias: bool = True, dropout: float = 0.0, act_layer: Callable = nn.GELU, ): super().__init__() # layers self.c_fc = ColumnParallelLinear( dim, hidden_dim, bias=bias, gather_output=False, ) self.c_proj = RowParallelLinear( hidden_dim, hidden_dim, bias=bias, input_is_parallel=True, ) self.non_linearity = act_layer() self.dropout = dropout def forward(self, x): hidden = self.c_fc(x) hidden = self.non_linearity(hidden) hidden = F.dropout(hidden, p=self.dropout, training=self.training) return self.non_linearity(self.c_proj(hidden)) class PixelShuffleMLP(torch.nn.Module): def __init__( self, ps_ratio: float, input_dim: int, output_dim: int = 4096, add_fc: bool = False, ): super().__init__() self.pixel_shuffle = PixelShuffle(ps_ratio) self.mlp = SimpleMLP( int(input_dim // (ps_ratio**2)), output_dim, bias=False, dropout=0.0, act_layer=nn.GELU, ) self.fc = nn.Identity() if add_fc: self.fc = ColumnParallelLinear( output_dim, output_dim, bias=False, ) def forward(self, encoded_patches: torch.Tensor) -> torch.Tensor: encoded_patches = self.pixel_shuffle(encoded_patches) return self.fc(self.mlp(encoded_patches)) class VisionEmbeddings(torch.nn.Module): def __init__(self, args: VisionArgs): super().__init__() self.args = args image_size = args.image_size patch_size = args.patch_size self.vision_encoder = VisionEncoder( image_size=(image_size.height, image_size.width), patch_size=(patch_size.height, patch_size.width), dim=args.dim, layers=args.n_layers, heads=args.n_heads, mlp_ratio=args.mlp_ratio, ) self.vision_encoder = self.vision_encoder.to(torch.bfloat16) self.vision_adapter = PixelShuffleMLP( ps_ratio=args.pixel_shuffle_ratio, input_dim=args.dim, output_dim=args.output_dim, ) self.output_dim = args.output_dim self._register_load_state_dict_pre_hook(self.load_hook) def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool = True, missing_keys: List[str] = None, unexpected_keys: List[str] = None, error_msgs: List[str] = None, return_state_dict: bool = False, ) -> None: original_sd = self.state_dict() for k in state_dict: if k.startswith(prefix) and len(state_dict[k].shape) == 1 and state_dict[k].shape[0] == 0: state_dict[k] = state_dict[k].reshape(original_sd[k[len(prefix) :]].shape) def _get_empty_sequence(self, h): return torch.zeros( h.shape[0], h.shape[1], self.output_dim, device=h.device, dtype=h.dtype, ) # x_images is batched; each batch sample contains a list of images. so this is List[List[torch.Tensor]] # each image is a tensor of shape [num_tiles, C, H, W] def forward( self, image_batch: List[List[torch.Tensor]], image_mask: torch.Tensor, h_ref: torch.Tensor, ) -> torch.Tensor: images_flattened = [image for sample in image_batch for image in sample] images_flattened = torch.vstack(images_flattened).unsqueeze(1).to(h_ref.dtype).to(h_ref.device) embedding = self.vision_encoder(images_flattened) projected_embedding = self.vision_adapter(embedding) h_image = self._get_empty_sequence(h_ref) return scatter_embeddings(image_batch, image_mask, h_image, projected_embedding) def scatter_embeddings(image_batch, image_mask, h_image, encoded_patches_proj): # If dynamic transform is used and the batch contains 2 images (where image_1 has 2 chunks and image_2 has 3 chunks), # `num_images_per_sequence` now records the number of chunks per image as `[2, 3]`. # `encoded_patches_proj.split` will then split the image chunks into 2 groups: `[image_1_chunks, image_2_chunks]`. num_images_per_sequence = [sum(image.size(0) for image in sample_images) for sample_images in image_batch] assert not torch.isnan(encoded_patches_proj).any() assert sum(num_images_per_sequence) == encoded_patches_proj.size(0), ( f"{sum(num_images_per_sequence)=} != {encoded_patches_proj.shape=}" ) encoded_patches_list = encoded_patches_proj.split(num_images_per_sequence, dim=0) for index in range(h_image.size(0)): encoded_patches_per_sample = encoded_patches_list[index] sample_image_mask = image_mask[index] if encoded_patches_per_sample.numel() == 0: continue encoded_patches_per_sample = encoded_patches_per_sample.contiguous().view( -1, encoded_patches_per_sample.size(-1) ) n_tokens_to_fill = sample_image_mask.sum() assert n_tokens_to_fill <= encoded_patches_per_sample.size(0) h_image[index].masked_scatter_( sample_image_mask.expand(-1, h_image.size(-1)), encoded_patches_per_sample[:n_tokens_to_fill], ) return h_image ================================================ FILE: models/llama4/vision/encoder.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. # Copyright (c) Meta Platforms, Inc. and its affiliates. from typing import Any, Callable, Dict, List, Optional, Tuple, Union import fairscale.nn.model_parallel.initialize as fs_init import torch import torch.nn as nn import torch.nn.functional as F from fairscale.nn.model_parallel.layers import ColumnParallelLinear, RowParallelLinear from torch import einsum from ..args import ModelArgs from ..model import Attention class LayerNorm(nn.LayerNorm): """Subclass torch's LayerNorm to handle fp16.""" def forward(self, x: torch.Tensor): x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps) return x class ColumnParallelConv2dPatch(torch.nn.Module): """Conv2D Patching layer with model parallelism. Column parallel over unfolded input. Arguments: in_channels: Input channels. out_channels: Output channels. kernel_size: Size of convolution kernel. stride (default 1): Stride for convolution. bias (default False): Use bias in Conv2d. Input: (bsz, in_channels, height, width) Output: (bsz, num_tokens, out_channels) """ def __init__( self, in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int, int]], stride: Union[int, Tuple[int, int]], bias: Optional[bool] = False, ) -> None: super().__init__() if isinstance(kernel_size, int): kernel_size = (kernel_size, kernel_size) self._unfold = torch.nn.Unfold(kernel_size=kernel_size, stride=stride) self._linear = ColumnParallelLinear( in_channels * kernel_size[0] * kernel_size[1], out_channels, bias=bias, ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self._unfold(x) x = x.permute(0, 2, 1) x = self._linear(x) return x class _FeedForward(torch.nn.Module): def __init__( self, dim: int, hidden_dim: int, dropout: float, act_layer: Callable = nn.GELU, ): super().__init__() # layers self.c_fc = ColumnParallelLinear( dim, hidden_dim, bias=True, gather_output=False, init_method=lambda x: x, ) self.c_proj = RowParallelLinear( hidden_dim, dim, bias=True, input_is_parallel=True, init_method=lambda x: x, ) self.non_linearity = act_layer() self.dropout = dropout def forward(self, x): hidden = self.c_fc(x) hidden = self.non_linearity(hidden) hidden = F.dropout(hidden, p=self.dropout, training=self.training) return self.c_proj(hidden) class _TransformerBlock(nn.Module): def __init__( self, d_model: int, n_head: int, max_batch_size: int, max_seq_len: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU, gated: bool = False, ): super().__init__() assert d_model % n_head == 0 self.n_heads = n_head self.head_dim = d_model // self.n_heads attn_args = ModelArgs( dim=d_model, head_dim=self.head_dim, n_heads=self.n_heads, n_kv_heads=self.n_heads, max_batch_size=max_batch_size, max_seq_len=max_seq_len, ) self.attn = Attention(attn_args, use_rope=True, use_qk_norm=False, add_bias=True) self.ln_1 = LayerNorm(d_model) self.mlp = _FeedForward( dim=d_model, hidden_dim=int(mlp_ratio * d_model), dropout=0.0, act_layer=act_layer, ) self.ln_2 = LayerNorm(d_model) self.gated = gated if gated: self.gate_attn = nn.Parameter(torch.zeros(1)) self.gate_ffn = nn.Parameter(torch.zeros(1)) def attention( self, x: torch.Tensor, freq_cis: Optional[torch.Tensor] = None, ): return self.attn(x=x, start_pos=0, freqs_cis=freq_cis) def forward( self, x: torch.Tensor, mask: Optional[torch.Tensor] = None, freq_cis: Optional[torch.Tensor] = None, ): _gate_attn = 1 if not self.gated else self.gate_attn.tanh() _gate_ffn = 1 if not self.gated else self.gate_ffn.tanh() x = x + _gate_attn * self.attention(self.ln_1(x), freq_cis=freq_cis) x = x + _gate_ffn * self.mlp(self.ln_2(x)) return x class _Transformer(nn.Module): def __init__( self, dim: int, layers: int, heads: int, max_batch_size: int, max_seq_len: int, mlp_ratio: float = 4.0, act_layer: Callable = nn.GELU, gated: bool = False, ): super().__init__() self.resblocks = nn.ModuleList( [ _TransformerBlock( d_model=dim, n_head=heads, mlp_ratio=mlp_ratio, act_layer=act_layer, gated=gated, max_batch_size=max_batch_size, max_seq_len=max_seq_len, ) for _ in range(layers) ] ) def forward(self, x: torch.Tensor, return_intermediate=None, mask=None, freq_cis=None): out = [] for idx, r in enumerate(self.resblocks): if return_intermediate is not None and idx in return_intermediate: out.append(x) x = r(x, mask=mask, freq_cis=freq_cis) if return_intermediate is not None: return x, torch.stack(out, dim=-1) return x class PackingIndex: Z = 0 # Z (time) coordinate of the token in the original sample Y = 1 # Y (height) coordinate of the token in the original sample X = 2 # X (width) coordinate of the token in the original sample TIME = 3 # Total number of time units (frames) in the original sample HEIGHT = 4 # Height of the original sample WIDTH = 5 # Width of the original sample # USE INDEX TO CHECK THE TYPE OF THE TOKEN (see ID fields below) IDX = 6 # Full index of the token in the original sample (x + y * w + z * w * h) BATCH_IDX = 7 # Which batch element this token belongs to. Note the batch idx of padding tokens is BATCH_SIZE # Total size of the enum, remember to update this! NUM_METADATA = 8 # Note: For padding tokens IDX = -1 # For cls tokens, IDX = -2 ID_CLS_TOKEN = -2 ID_PAD_TOKEN = -1 ENCODER_MAX_BATCH_SIZE = 32 ENCODER_MAX_SEQ_LEN = 1024 class VisionEncoder(nn.Module): def __init__( self, image_size: Tuple[int, int], patch_size: Tuple[int, int], dim: int, layers: int, heads: int, mlp_ratio: float, ): super().__init__() self.image_size = image_size self.patch_size = patch_size self.grid_size = ( self.image_size[0] // self.patch_size[0], self.image_size[1] // self.patch_size[1], ) self.conv1 = ColumnParallelConv2dPatch( in_channels=3, out_channels=dim, kernel_size=patch_size, stride=patch_size, bias=False, ) scale = dim**-0.5 self.class_embedding = nn.Parameter(scale * torch.randn(dim)) self.positional_embedding_vlm = nn.Parameter( scale * torch.randn(self.grid_size[0] * self.grid_size[1] + 1, dim) ) self.ln_pre = LayerNorm(dim) self.ln_post = LayerNorm(dim) self.transformer = _Transformer( dim, layers, heads, ENCODER_MAX_BATCH_SIZE, ENCODER_MAX_SEQ_LEN, mlp_ratio, act_layer=nn.GELU, ) # NOTE: hack for the fixed res image_h, image_w = self.image_size patch_h, patch_w = self.patch_size idx_h, idx_w = image_h // patch_h, image_w // patch_w img_idx = torch.arange(image_h * image_w // (patch_h * patch_w), dtype=torch.int32) img_idx = img_idx.reshape(idx_h * idx_w, 1) img_idx = torch.cat([img_idx, img_idx[:1]], dim=0) img_idx[-1, -1] = PackingIndex.ID_CLS_TOKEN packed_img_idx = torch.empty( img_idx.shape[0], img_idx.shape[1], PackingIndex.NUM_METADATA - 1, dtype=torch.int32, ) packed_img_idx[:, :, PackingIndex.Y] = img_idx // idx_w packed_img_idx[:, :, PackingIndex.X] = img_idx % idx_w packed_img_idx[:, :, PackingIndex.HEIGHT].fill_(idx_h) packed_img_idx[:, :, PackingIndex.WIDTH].fill_(idx_w) packed_img_idx[:, :, PackingIndex.IDX] = img_idx packed_img_idx = packed_img_idx.reshape(1, -1, PackingIndex.NUM_METADATA - 1) self.packed_img_idx = packed_img_idx # for positional embedding load hook # compute rope freqs rope_freq = self.get_rope_freqs(dim // heads // 2) freqs_x = self.compute_rope_freqs(rope_freq, packed_img_idx[:, :, PackingIndex.X] + 1) freqs_y = self.compute_rope_freqs(rope_freq, packed_img_idx[:, :, PackingIndex.Y] + 1) freqs = torch.cat([freqs_x, freqs_y], dim=-1).float().contiguous()[..., ::2] # disable RoPE for padding and cls tokens freqs = freqs.masked_fill(packed_img_idx[:, :, PackingIndex.IDX, None] < 0, 0) # compute complex freqs self.freq_cis = torch.view_as_complex(torch.stack([torch.cos(freqs), torch.sin(freqs)], dim=-1)) # xlf automatically broadcasts self.freq_cis = self.freq_cis.squeeze(0) self.n_heads = heads // fs_init.get_model_parallel_world_size() self._register_load_state_dict_pre_hook(self.load_hook) def get_rope_freqs(self, dim, theta=10000): freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) return freqs @torch.amp.autocast("cuda", enabled=False) def compute_rope_freqs(self, freqs, t): freqs = einsum("..., f -> ... f", t.type(freqs.dtype), freqs) freqs = freqs.repeat_interleave(2, dim=-1) return freqs def load_hook( self, state_dict: Dict[str, Any], prefix: str, local_metadata: Dict[str, Any], strict: bool = True, missing_keys: List[str] = None, unexpected_keys: List[str] = None, error_msgs: List[str] = None, return_state_dict: bool = False, ) -> None: orig_pos_embed = state_dict.get(prefix + "positional_embedding") if orig_pos_embed is not None and orig_pos_embed.shape[-2:] != self.positional_embedding_vlm.shape[-2:]: raise ValueError( f"Positional embedding shape {orig_pos_embed.shape} does not match expected shape {self.positional_embedding_vlm.shape}" ) batch_size, token_per_image, _ = self.packed_img_idx.shape # Input points for idx are [x, y, w, h] idx = self.packed_img_idx.reshape(batch_size * token_per_image, 1, -1) total_windows, window_size, _ = idx.shape # Grid values are [-1, 1] and coords are w, h grid = ( (idx[:, :, [PackingIndex.X, PackingIndex.Y]] / idx[:, :, [PackingIndex.WIDTH, PackingIndex.HEIGHT]]) * 2 - 1 )[None, ...] # In this mode, cls token has no position embedding if orig_pos_embed is not None: posemb = ( orig_pos_embed[1:].view(1, self.grid_size[0], self.grid_size[1], -1).permute(0, 3, 1, 2).contiguous() ) posemb = posemb.to(device=grid.device, dtype=grid.dtype) sample = F.grid_sample( posemb, grid, padding_mode="zeros" ) # padding tokens / class token will get zero for posemb sample = sample.view(-1, total_windows, window_size).permute(1, 2, 0).contiguous() sample = torch.where( idx[:, :, PackingIndex.IDX, None] == PackingIndex.ID_CLS_TOKEN, orig_pos_embed[0].view(1, 1, -1).to(device=sample.device, dtype=sample.dtype), sample, ) new_pos_embed = sample.reshape(batch_size, token_per_image, -1) state_dict[prefix + "positional_embedding_vlm"] = new_pos_embed.squeeze(0) if return_state_dict: return state_dict def apply_class_embedding(self, x): x = torch.cat( [ x, self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), ], dim=1, ) # shape = [*, grid ** 2 + 1, width] return x def forward(self, images: torch.Tensor) -> torch.Tensor: # NOTE: in Llama4 bsz=bsz*num_tiles, num_chunks=1 if images.ndim == 5: num_concurrent_media = 1 bsz, num_chunks, nch, h, w = images.shape else: bsz, num_concurrent_media, num_chunks, nch, h, w = images.shape images = images.reshape(bsz * num_concurrent_media * num_chunks, nch, h, w) # patch embedding x = images.reshape(bsz * num_concurrent_media * num_chunks, nch, h, w) x = self.conv1(x) # shape = [*, width, grid ** 2] _, ntok, dim = x.shape x = x.reshape(bsz * num_concurrent_media * num_chunks, ntok, dim) # apply cls token x = self.apply_class_embedding(x) ntok += 1 # apply position embeddings if self.positional_embedding_vlm is not None: x = x + self.positional_embedding_vlm.to(x.dtype) x = x.reshape(bsz * num_concurrent_media, num_chunks, ntok, dim) x = self.ln_pre(x) x = x.view(bsz * num_concurrent_media, -1, dim) freq_cis = self.freq_cis.to(images.device) tf_output = self.transformer( x, freq_cis=freq_cis, ) int_x = None if isinstance(tf_output, tuple): x, int_x = tf_output else: x = tf_output x = self.ln_post(x) # remove cls token output x = x[:, :-1, :] # add and output x + int_x features if int_x is not None: int_x = int_x[:, :-1, :, :] int_x = int_x.reshape(bsz * num_concurrent_media, ntok - 1, -1) x = torch.cat([x, int_x], dim=-1) return x ================================================ FILE: models/quantize_impls.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import collections import logging from typing import Optional, Tuple, Type, Union log = logging.getLogger(__name__) try: import fbgemm_gpu.experimental.gen_ai # noqa: F401 log.info("Using efficient FP8 or INT4 operators in FBGEMM.") except ImportError: log.error("No efficient FP8 or INT4 operators. Please install FBGEMM.") raise import torch from torch import nn, Tensor class Fp8ScaledWeights: # TODO: Ugly trick so torch allows us to replace parameters # with our custom Fp8Weights instance. Do this properly. @property def __class__(self) -> Type[nn.parameter.Parameter]: return nn.Parameter @property def grad_fn(self) -> None: return None # pyre-fixme[4]: Attribute annotation cannot be `Any`. # pyre-fixme[2]: Parameter annotation cannot be `Any`. class Fp8RowwiseWeights( Fp8ScaledWeights, collections.namedtuple( "Fp8RowwiseWeights", ["weight", "scale", "shape", "activation_scale_ub"], ), ): pass class Int4ScaledWeights: # TODO: Ugly trick so torch allows us to replace parameters # with our custom Int4Weights instance. Do this properly. @property def __class__(self) -> Type[nn.parameter.Parameter]: return nn.Parameter @property def grad_fn(self) -> None: return None # pyre-fixme[4]: Attribute annotation cannot be `Any`. # pyre-fixme[2]: Parameter annotation cannot be `Any`. class Int4Weights( Int4ScaledWeights, collections.namedtuple( "Int4Weights", ["weight", "scale", "shape"], ), ): pass def int4_row_quantize( x: torch.Tensor, group_size: int = 128, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Helper function to quantize a tensor to int4 with groupwise scales. Args: x (Tensor): [N, K] Higher precision weight tensor to quantize. group_size (int): Number of elements to calculate group scale for. Returns: wq (Tensor): [N, K // 2] Quantized int4 tensor stored in int8 elements. group_scale (Tensor): [K / group_size, N] FP32 Scale per group. """ n_bit = 4 # Number of target bits. to_quant = x.reshape(-1, group_size).to(torch.float) max_val = torch.abs(to_quant).amax(dim=1, keepdim=True) max_int = 2 ** (n_bit - 1) min_int = -(2 ** (n_bit - 1)) scales = max_val.clamp(min=1e-6) / max_int out = to_quant.div(scales).round().clamp_(min_int, max_int - 1) # Cast to int8 and restore shape. out = out.to(dtype=torch.int8).reshape(x.shape) # Scales should be in [num_groups, N] layout. scales = scales.view(x.shape[0], -1).t().contiguous() return out, scales def pack_int4(x: torch.Tensor) -> torch.Tensor: # Given int8 x, pack adjacent int4 values into a single int8. low_x = x[:, ::2] high_x = x[:, 1::2] # High bits need to left shift, this also masks off extra bits. high_x = torch.bitwise_left_shift(high_x, 4) # Low bits need to have sign bits removed. low_x = torch.bitwise_and(low_x, 0xF) # Recombine into a single value with bitwise or. return torch.bitwise_or(low_x, high_x).contiguous() def bmm_nt( x: Tensor, w: Union[Fp8RowwiseWeights, Int4Weights], num_tokens: Optional[Tensor] = None, ) -> Tensor: if isinstance(w, Fp8ScaledWeights): xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row(x, num_tokens, w.activation_scale_ub) return torch.ops.fbgemm.f8f8bf16_rowwise_batched(xq, w.weight, x_scale, w.scale) elif isinstance(w, Int4ScaledWeights): return torch.ops.fbgemm.bf16i4bf16_rowwise_batched(x, w.weight, w.scale, torch.zeros_like(w.scale)) else: raise ValueError("Unsupported quantization type") def ffn_swiglu( x: Tensor, w1: Union[Fp8RowwiseWeights, Int4Weights], w3: Union[Fp8RowwiseWeights, Int4Weights], w2: Union[Fp8RowwiseWeights, Int4Weights], num_tokens: Optional[Tensor] = None, is_memory_bounded: bool = False, ) -> Tensor: if isinstance(w1, Fp8ScaledWeights) and isinstance(w3, Fp8ScaledWeights) and isinstance(w2, Fp8ScaledWeights): return ffn_swiglu_dynamic(x, w1, w3, w2, w1.activation_scale_ub, num_tokens, is_memory_bounded) elif isinstance(w1, Int4ScaledWeights) and isinstance(w3, Int4ScaledWeights) and isinstance(w2, Int4ScaledWeights): return ffn_swiglu_dynamic(x, w1, w3, w2, None, num_tokens, is_memory_bounded) (B, T, D) = x.shape # noqa: N806 (HD_L, D_) = w1.shape # noqa: N806 assert D_ == D assert isinstance(w1, Tensor) assert isinstance(w3, Tensor) x1 = x.view(B * T, D) @ w1.T x2 = x.view(B * T, D) @ w3.T z = torch.nn.functional.silu(x1) * x2 del x1, x2 assert isinstance(w2, Tensor) return (z @ w2.T).view(B, T, D) @torch.inference_mode() def quantize_fp8( w: Tensor, fp8_activation_scale_ub: float, output_device: Optional[torch.device] = None, ) -> Fp8RowwiseWeights: """Quantize [n, k] weight tensor. Args: w (Tensor): [n, k] input high precision tensor to quantize. fp8_activation_scale_ub (float): Upper bound for activation max. """ activation_scale_ub = torch.tensor( [fp8_activation_scale_ub], dtype=torch.float, device=output_device, ) wq, w_scale = torch.ops.fbgemm.quantize_fp8_per_row(w) del w return Fp8RowwiseWeights( weight=wq, scale=w_scale, shape=wq.shape, activation_scale_ub=activation_scale_ub, ) @torch.inference_mode() def quantize_int4( w: Tensor, output_device: Optional[torch.device] = None, ) -> Int4Weights: """Quantize [n, k/2] weight tensor. Args: w (Tensor): [n, k/2] input high precision tensor to quantize. """ if w.ndim >= 3: wq, scale = zip(*[int4_row_quantize(i) for i in w]) wq = torch.stack([pack_int4(i) for i in wq], dim=0) scale = torch.stack(scale, dim=0) else: wq, scale = int4_row_quantize(w) wq = pack_int4(wq) del w return Int4Weights( weight=wq.to(output_device), scale=scale.to(output_device), shape=wq.shape, ) @torch.inference_mode() def load_fp8( w: Tensor, w_scale: Tensor, fp8_activation_scale_ub: float, output_device: Optional[torch.device] = None, ) -> Fp8RowwiseWeights: """Load FP8 [n, k] weight tensor. Args: w (Tensor): [n, k] input FP8. fp8_activation_scale_ub (float): Upper bound for activation max. """ activation_scale_ub = torch.tensor( [fp8_activation_scale_ub], dtype=torch.float, device=output_device, ) return Fp8RowwiseWeights( weight=w.to(torch.float8_e4m3fn).to(device=output_device), scale=w_scale.to(device=output_device), shape=w.shape, activation_scale_ub=activation_scale_ub, ) @torch.inference_mode() def load_int4( w: Tensor, scale: Tensor, output_device: Optional[torch.device] = None, ) -> Int4Weights: """Load INT4 [n, k/2] weight tensor. Args: w (Tensor): [n, k/2] input INT4. w_scale (Tensor): [n, k/2] input INT4 scale. """ return Int4Weights( weight=w.to(torch.int8).to(device=output_device), scale=scale.to(device=output_device), shape=w.shape, ) def fc_dynamic( x: Tensor, w: Union[Fp8RowwiseWeights, Int4Weights], activation_scale_ub: Optional[Tensor] = None, num_tokens: Optional[Tensor] = None, is_memory_bounded: bool = False, ) -> Tensor: """ Single w8a8 fc layer with dynamic row-wise scaling, or w4a16 fc layer with dyanmic row-wise scaling """ if isinstance(w, Int4Weights): y = torch.ops.fbgemm.bf16i4bf16_rowwise(x, w.weight, w.scale, torch.zeros_like(w.scale)) else: xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row(x, num_tokens, activation_scale_ub) y = torch.ops.fbgemm.f8f8bf16_rowwise(xq, w.weight, x_scale, w.scale, use_fast_accum=True) del xq return y def ffn_swiglu_dynamic( x: Tensor, w1: Union[Fp8RowwiseWeights, Int4Weights], w3: Union[Fp8RowwiseWeights, Int4Weights], w2: Union[Fp8RowwiseWeights, Int4Weights], activation_scale_ub: Optional[Tensor] = None, num_tokens: Optional[Tensor] = None, is_memory_bounded: bool = False, ) -> Tensor: assert x.dim() == 3 or x.dim() == 2 if x.dim() == 3: (B, T, D) = x.shape # noqa: N806 else: (T, D) = x.shape # noqa: N806 B = 1 # noqa: N806 HD_L = w1.shape[0] # noqa: N806 assert HD_L == w3.shape[0] x1 = fc_dynamic( x.view(B * T, D), w1, activation_scale_ub, num_tokens, is_memory_bounded, ) x2 = fc_dynamic( x.view(B * T, D), w3, activation_scale_ub, num_tokens, is_memory_bounded, ) z = torch.nn.functional.silu(x1) * x2 del x1, x2 z_ = fc_dynamic(z, w2, activation_scale_ub, num_tokens, is_memory_bounded) if x.dim() == 3: return z_.view(B, T, D) else: return z_ ================================================ FILE: models/sku_list.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. from dataclasses import dataclass from functools import lru_cache from .sku_types import ( CheckpointQuantizationFormat, CoreModelId, Model, ModelFamily, ) LLAMA2_VOCAB_SIZE = 32000 LLAMA3_VOCAB_SIZE = 128256 def resolve_model(descriptor: str) -> Model | None: for m in all_registered_models(): if descriptor in (m.descriptor(), m.huggingface_repo): return m return None def all_registered_models() -> list[Model]: return ( llama2_family() + llama3_family() + llama3_1_family() + llama3_2_family() + llama3_3_family() + llama4_family() + safety_models() ) def llama2_family() -> list[Model]: return [ *llama2_base_models(), *llama2_instruct_models(), ] def llama3_family() -> list[Model]: return [ *llama3_base_models(), *llama3_instruct_models(), ] def llama3_1_family() -> list[Model]: return [ *llama3_1_base_models(), *llama3_1_instruct_models(), ] def llama3_2_family() -> list[Model]: return [ *llama3_2_base_models(), *llama3_2_instruct_models(), ] def llama3_3_family() -> list[Model]: return [ *llama3_3_instruct_models(), ] def llama4_family() -> list[Model]: return [ *llama4_base_models(), *llama4_instruct_models(), ] def llama4_base_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama4_scout_17b_16e, description="Llama 4 Scout (17b 16 experts model)", huggingface_repo="meta-llama/Llama-4-Scout-17B-16E", pth_file_count=8, arch_args={}, ), Model( core_model_id=CoreModelId.llama4_maverick_17b_128e, description="Llama 4 Maverick (17b 128 experts model)", huggingface_repo="meta-llama/Llama-4-Maverick-17B-128E", pth_file_count=8, arch_args={}, ), ] def llama4_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama4_scout_17b_16e_instruct, description="Llama 4 Scout (17b 16 experts instruct model)", huggingface_repo="meta-llama/Llama-4-Scout-17B-16E-Instruct", pth_file_count=8, arch_args={}, ), Model( core_model_id=CoreModelId.llama4_maverick_17b_128e_instruct, description="Llama 4 Maverick (17b 128 experts instruct model)", huggingface_repo="meta-llama/Llama-4-Maverick-17B-128E-Instruct", pth_file_count=8, arch_args={}, ), Model( core_model_id=CoreModelId.llama4_maverick_17b_128e_instruct, description="Llama 4 Maverick (FP8 quantized)", huggingface_repo="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8", quantization_format=CheckpointQuantizationFormat.fp8_mixed, pth_file_count=8, variant="fp8", arch_args={}, ), ] def llama2_base_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama2_7b, description="Llama 2 7b model", huggingface_repo="meta-llama/Llama-2-7b", arch_args={ "dim": 4096, "n_layers": 32, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA2_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama2_13b, description="Llama 2 13b model", huggingface_repo="meta-llama/Llama-2-13b", arch_args={ "dim": 5120, "n_layers": 40, "n_heads": 40, "n_kv_heads": 8, "vocab_size": LLAMA2_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama2_70b, description="Llama 2 70b model", huggingface_repo="meta-llama/Llama-2-70b", arch_args={ "dim": 8192, "n_layers": 80, "n_heads": 64, "n_kv_heads": 8, "vocab_size": LLAMA2_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=8, ), ] def llama3_base_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_8b, description="Llama 3 8b model", huggingface_repo="meta-llama/Llama-3-8B", arch_args={ "dim": 4096, "n_layers": 32, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 1024, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_70b, description="Llama 3 70b model", huggingface_repo="meta-llama/Llama-3-70B", arch_args={ "dim": 8192, "n_layers": 80, "n_heads": 64, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=8, ), ] def llama3_1_base_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_1_8b, description="Llama 3.1 8b model", huggingface_repo="meta-llama/Llama-3.1-8B", arch_args={ "dim": 4096, "n_layers": 32, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 1024, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_1_70b, description="Llama 3.1 70b model", huggingface_repo="meta-llama/Llama-3.1-70B", arch_args={ "dim": 8192, "n_layers": 80, "n_heads": 64, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=8, ), Model( core_model_id=CoreModelId.llama3_1_405b, variant="bf16-mp8", description="Llama 3.1 405b model (BF16 weights)", huggingface_repo="meta-llama/Llama-3.1-405B", arch_args={ "dim": 16384, "n_layers": 126, "n_heads": 128, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.2, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=8, ), Model( core_model_id=CoreModelId.llama3_1_405b, description="Llama 3.1 405b model (FP8 quantized)", huggingface_repo="meta-llama/Llama-3.1-405B-FP8", quantization_format=CheckpointQuantizationFormat.fp8_mixed, arch_args={ "dim": 16384, "n_layers": 126, "n_heads": 128, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.2, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=8, ), Model( core_model_id=CoreModelId.llama3_1_405b, variant="bf16-mp16", description="Llama 3.1 405b model (BF16 weights for mp16)", huggingface_repo="meta-llama/Llama-3.1-405B", arch_args={ "dim": 16384, "n_layers": 126, "n_heads": 128, "n_kv_heads": 16, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.2, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=16, ), ] def llama3_2_base_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_2_1b, description="Llama 3.2 1b model", huggingface_repo="meta-llama/Llama-3.2-1B", arch_args={ "dim": 2048, "n_layers": 16, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.5, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_2_3b, description="Llama 3.2 3b model", huggingface_repo="meta-llama/Llama-3.2-3B", arch_args={ "dim": 3072, "n_layers": 28, "n_heads": 24, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.0, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_2_11b_vision, description="Llama 3.2 11b vision model", huggingface_repo="meta-llama/Llama-3.2-11B-Vision", arch_args={ "dim": 4096, "n_layers": 32, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 1024, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, "vision_chunk_size": 448, "vision_max_num_chunks": 4, "vision_num_cross_attention_layers": 8, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_2_90b_vision, description="Llama 3.2 90b vision model", huggingface_repo="meta-llama/Llama-3.2-90B-Vision", arch_args={ "dim": 8192, "n_layers": 80, "n_heads": 64, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, "vision_chunk_size": 560, "vision_max_num_chunks": 4, "vision_num_cross_attention_layers": 20, }, pth_file_count=8, ), ] def llama2_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama2_7b_chat, description="Llama 2 7b chat model", huggingface_repo="meta-llama/Llama-2-7b-chat", arch_args={ "dim": 4096, "n_layers": 32, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA2_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama2_13b_chat, description="Llama 2 13b chat model", huggingface_repo="meta-llama/Llama-2-13b-chat", arch_args={ "dim": 5120, "n_layers": 40, "n_heads": 40, "n_kv_heads": 8, "vocab_size": LLAMA2_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama2_70b_chat, description="Llama 2 70b chat model", huggingface_repo="meta-llama/Llama-2-70b-chat", arch_args={ "dim": 8192, "n_layers": 80, "n_heads": 64, "n_kv_heads": 8, "vocab_size": LLAMA2_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=8, ), ] def llama3_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_8b_instruct, description="Llama 3 8b instruct model", huggingface_repo="meta-llama/Llama-3-8B-Instruct", arch_args={ "dim": 4096, "n_layers": 32, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 1024, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_70b_instruct, description="Llama 3 70b instruct model", huggingface_repo="meta-llama/Llama-3-70B-Instruct", arch_args={ "dim": 8192, "n_layers": 80, "n_heads": 64, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=8, ), ] def llama3_1_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_1_8b_instruct, description="Llama 3.1 8b instruct model", huggingface_repo="meta-llama/Llama-3.1-8B-Instruct", arch_args={ "dim": 4096, "n_layers": 32, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 1024, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_1_70b_instruct, description="Llama 3.1 70b instruct model", huggingface_repo="meta-llama/Llama-3.1-70B-Instruct", arch_args={ "dim": 8192, "n_layers": 80, "n_heads": 64, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=8, ), Model( core_model_id=CoreModelId.llama3_1_405b_instruct, variant="bf16-mp8", description="Llama 3.1 405b instruct model (BF16 weights)", huggingface_repo="meta-llama/Llama-3.1-405B-Instruct", arch_args={ "dim": 16384, "n_layers": 126, "n_heads": 128, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.2, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=8, ), Model( core_model_id=CoreModelId.llama3_1_405b_instruct, description="Llama 3.1 405b instruct model (FP8 quantized)", huggingface_repo="meta-llama/Llama-3.1-405B-Instruct-FP8", quantization_format=CheckpointQuantizationFormat.fp8_mixed, arch_args={ "dim": 16384, "n_layers": 126, "n_heads": 128, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.2, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=8, ), Model( core_model_id=CoreModelId.llama3_1_405b_instruct, variant="bf16-mp16", description="Llama 3.1 405b instruct model (BF16 weights for mp16)", huggingface_repo="meta-llama/Llama-3.1-405B-Instruct", arch_args={ "dim": 16384, "n_layers": 126, "n_heads": 128, "n_kv_heads": 16, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.2, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=16, ), ] def arch_args_1b() -> dict: return { "dim": 2048, "n_layers": 16, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.5, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, } def arch_args_3b() -> dict: return { "dim": 3072, "n_layers": 28, "n_heads": 24, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.0, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, } def llama3_2_quantized_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_2_1b_instruct, variant="int4-qlora-eo8", quantization_format=CheckpointQuantizationFormat.int4, description="Llama 3.2 1b INT4 quantized LoRA", huggingface_repo="meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8", arch_args={ **arch_args_1b(), "quantization_args": { "group_size": 256, }, "lora_args": { "rank": 16, "scale": 2.0, }, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_2_1b_instruct, variant="int4-spinquant-eo8", quantization_format=CheckpointQuantizationFormat.int4, description="Llama 3.2 1b INT4 quantized SpinQuant", huggingface_repo="meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8", arch_args={ **arch_args_1b(), "quantization_args": { "group_size": 256, }, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_2_3b_instruct, variant="int4-qlora-eo8", quantization_format=CheckpointQuantizationFormat.int4, description="Llama 3.2 3b INT4 quantized LoRA", huggingface_repo="meta-llama/Llama-3.2-3B-Instruct-QLORA_INT4_EO8", arch_args={ **arch_args_3b(), "quantization_args": { "group_size": 256, }, "lora_args": { "rank": 16, "scale": 2.0, }, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_2_3b_instruct, variant="int4-spinquant-eo8", quantization_format=CheckpointQuantizationFormat.int4, description="Llama 3.2 3b INT4 quantized SpinQuant", huggingface_repo="meta-llama/Llama-3.2-3B-Instruct-SpinQuant_INT4_EO8", arch_args={ **arch_args_3b(), "quantization_args": { "group_size": 256, }, }, pth_file_count=1, ), ] def llama3_2_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_2_1b_instruct, description="Llama 3.2 1b instruct model", huggingface_repo="meta-llama/Llama-3.2-1B-Instruct", arch_args=arch_args_1b(), pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_2_3b_instruct, description="Llama 3.2 3b instruct model", huggingface_repo="meta-llama/Llama-3.2-3B-Instruct", arch_args=arch_args_3b(), pth_file_count=1, ), *llama3_2_quantized_models(), Model( core_model_id=CoreModelId.llama3_2_11b_vision_instruct, description="Llama 3.2 11b vision instruct model", huggingface_repo="meta-llama/Llama-3.2-11B-Vision-Instruct", arch_args={ "dim": 4096, "n_layers": 32, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 1024, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, "vision_chunk_size": 560, "vision_max_num_chunks": 4, "vision_num_cross_attention_layers": 8, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama3_2_90b_vision_instruct, description="Llama 3.2 90b vision instruct model", huggingface_repo="meta-llama/Llama-3.2-90B-Vision-Instruct", arch_args={ "dim": 8192, "n_layers": 80, "n_heads": 64, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, "vision_chunk_size": 560, "vision_max_num_chunks": 4, "vision_num_cross_attention_layers": 20, }, pth_file_count=8, ), ] def llama3_3_instruct_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama3_3_70b_instruct, description="Llama 3.3 70b instruct", huggingface_repo="meta-llama/Llama-3.3-70B-Instruct", arch_args={ "dim": 8192, "n_layers": 80, "n_heads": 64, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 4096, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=8, ), ] @lru_cache def safety_models() -> list[Model]: return [ Model( core_model_id=CoreModelId.llama_guard_4_12b, description="Llama Guard v4 12b system safety model", huggingface_repo="meta-llama/Llama-Guard-4-12B", arch_args={}, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama_guard_3_11b_vision, description="Llama Guard v3 11b vision system safety model", huggingface_repo="meta-llama/Llama-Guard-3-11B-Vision", arch_args={ "dim": 4096, "n_layers": 32, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 1024, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, "vision_chunk_size": 560, "vision_max_num_chunks": 4, "vision_num_cross_attention_layers": 8, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama_guard_3_1b, variant="int4", description="Llama Guard v3 1b 'int4' quantized system safety model", huggingface_repo="meta-llama/Llama-Guard-3-1B-INT4", quantization_format=CheckpointQuantizationFormat.int4, arch_args={ "dim": 2048, "n_layers": 12, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "rope_freq_base": 500000.0, "norm_eps": 1e-05, "hidden_dim": 6400, "use_scaled_rope": True, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama_guard_3_1b, description="Llama Guard v3 1b system safety model", huggingface_repo="meta-llama/Llama-Guard-3-1B", arch_args={ "dim": 2048, "n_layers": 16, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA3_VOCAB_SIZE, "ffn_dim_multiplier": 1.5, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": True, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama_guard_3_8b, description="Llama Guard v3 8b system safety model", huggingface_repo="meta-llama/Llama-Guard-3-8B", arch_args={ "dim": 4096, "ffn_dim_multiplier": 1.3, "multiple_of": 1024, "n_heads": 32, "n_kv_heads": 8, "n_layers": 32, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, "vocab_size": LLAMA3_VOCAB_SIZE, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama_guard_3_8b, variant="int8", description="Llama Guard v3 8b system safety model", huggingface_repo="meta-llama/Llama-Guard-3-8B-INT8", quantization_format=CheckpointQuantizationFormat.int8, arch_args={ "dim": 4096, "ffn_dim_multiplier": 1.3, "multiple_of": 1024, "n_heads": 32, "n_kv_heads": 8, "n_layers": 32, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, "vocab_size": LLAMA3_VOCAB_SIZE, }, pth_file_count=1, ), Model( core_model_id=CoreModelId.llama_guard_2_8b, description="Llama Guard v2 8b system safety model", huggingface_repo="meta-llama/Llama-Guard-2-8B", arch_args={ "dim": 4096, "n_layers": 32, "n_heads": 32, "n_kv_heads": 8, "vocab_size": LLAMA2_VOCAB_SIZE, "ffn_dim_multiplier": 1.3, "multiple_of": 256, "norm_eps": 1e-05, "rope_theta": 500000.0, "use_scaled_rope": False, }, pth_file_count=1, ), ] @dataclass class LlamaDownloadInfo: folder: str files: list[str] pth_size: int def llama_meta_net_info(model: Model) -> LlamaDownloadInfo: """Information needed to download model from llamameta.net""" pth_count = model.pth_file_count if model.core_model_id == CoreModelId.llama3_1_405b: if pth_count == 16: folder = "Llama-3.1-405B-MP16" elif model.quantization_format == CheckpointQuantizationFormat.fp8_mixed: folder = "Llama-3.1-405B" else: folder = "Llama-3.1-405B-MP8" elif model.core_model_id == CoreModelId.llama3_1_405b_instruct: if pth_count == 16: folder = "Llama-3.1-405B-Instruct-MP16" elif model.quantization_format == CheckpointQuantizationFormat.fp8_mixed: folder = "Llama-3.1-405B-Instruct" else: folder = "Llama-3.1-405B-Instruct-MP8" elif model.core_model_id == CoreModelId.llama_guard_3_8b: if model.quantization_format == CheckpointQuantizationFormat.int8: folder = "Llama-Guard-3-8B-INT8-HF" else: folder = "Llama-Guard-3-8B" elif model.core_model_id == CoreModelId.llama_guard_2_8b: folder = "llama-guard-2" else: if model.huggingface_repo is None: raise ValueError(f"Model {model.core_model_id} has no huggingface_repo set") folder = model.huggingface_repo.split("/")[-1] if "Llama-2" in folder: folder = folder.lower() files = ["checklist.chk"] if ( model.core_model_id == CoreModelId.llama_guard_3_8b and model.quantization_format == CheckpointQuantizationFormat.int8 ): files.extend( [ "generation_config.json", "model-00001-of-00002.safetensors", "model-00002-of-00002.safetensors", "special_tokens_map.json", "tokenizer.json", "tokenizer_config.json", "model.safetensors.index.json", ] ) elif ( model.core_model_id == CoreModelId.llama_guard_3_1b and model.quantization_format == CheckpointQuantizationFormat.int4 ): files.extend( [ "llama_guard_3_1b_pruned_xnnpack.pte", "example-prompt.txt", "params.json", "tokenizer.model", ] ) else: files.extend( [ "tokenizer.model", "params.json", ] ) if model.quantization_format == CheckpointQuantizationFormat.fp8_mixed: files.extend([f"fp8_scales_{i}.pt" for i in range(pth_count)]) files.extend([f"consolidated.{i:02d}.pth" for i in range(pth_count)]) return LlamaDownloadInfo( folder=folder, files=files, pth_size=llama_meta_pth_size(model), ) # Sadness because Cloudfront rejects our HEAD requests to find Content-Length def llama_meta_pth_size(model: Model) -> int: if model.core_model_id not in ( CoreModelId.llama3_1_405b, CoreModelId.llama3_1_405b_instruct, CoreModelId.llama4_maverick_17b_128e, CoreModelId.llama4_maverick_17b_128e_instruct, ): return 0 if model.model_family == ModelFamily.llama3_1: if model.pth_file_count == 16: return 51268302389 elif model.quantization_format == CheckpointQuantizationFormat.fp8_mixed: return 60903742309 else: return 101470976045 if model.model_family == ModelFamily.llama4: if model.core_model_id == CoreModelId.llama4_maverick_17b_128e: return 100458118386 elif model.core_model_id == CoreModelId.llama4_maverick_17b_128e_instruct: if model.quantization_format == CheckpointQuantizationFormat.fp8_mixed: return 54121549657 else: return 100426653046 return 0 ================================================ FILE: models/sku_types.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. from enum import Enum from typing import Any from pydantic import BaseModel, ConfigDict, Field class CheckpointQuantizationFormat(Enum): # default format bf16 = "bf16" # used for enabling fp8_rowwise inference, some weights are bf16 fp8_mixed = "fp8-mixed" int8 = "int8" int4 = "int4" class ModelFamily(Enum): llama2 = "llama2" llama3 = "llama3" llama3_1 = "llama3_1" llama3_2 = "llama3_2" llama3_3 = "llama3_3" llama4 = "llama4" safety = "safety" class CoreModelId(Enum): """Each of these models is a unique "SKU". These root models can be served in various garbs (especially by quantizing them)""" # Llama 2 family llama2_7b = "Llama-2-7b" llama2_13b = "Llama-2-13b" llama2_70b = "Llama-2-70b" llama2_7b_chat = "Llama-2-7b-chat" llama2_13b_chat = "Llama-2-13b-chat" llama2_70b_chat = "Llama-2-70b-chat" # Llama 3 family llama3_8b = "Llama-3-8B" llama3_70b = "Llama-3-70B" llama3_8b_instruct = "Llama-3-8B-Instruct" llama3_70b_instruct = "Llama-3-70B-Instruct" # Llama 3.1 family llama3_1_8b = "Llama3.1-8B" llama3_1_70b = "Llama3.1-70B" llama3_1_405b = "Llama3.1-405B" llama3_1_8b_instruct = "Llama3.1-8B-Instruct" llama3_1_70b_instruct = "Llama3.1-70B-Instruct" llama3_1_405b_instruct = "Llama3.1-405B-Instruct" # Llama 3.2 family llama3_2_1b = "Llama3.2-1B" llama3_2_3b = "Llama3.2-3B" llama3_2_1b_instruct = "Llama3.2-1B-Instruct" llama3_2_3b_instruct = "Llama3.2-3B-Instruct" llama3_2_11b_vision = "Llama3.2-11B-Vision" llama3_2_90b_vision = "Llama3.2-90B-Vision" llama3_2_11b_vision_instruct = "Llama3.2-11B-Vision-Instruct" llama3_2_90b_vision_instruct = "Llama3.2-90B-Vision-Instruct" # Llama 3.3 family llama3_3_70b_instruct = "Llama3.3-70B-Instruct" # Llama 4 family llama4_scout_17b_16e = "Llama-4-Scout-17B-16E" llama4_scout_17b_16e_instruct = "Llama-4-Scout-17B-16E-Instruct" llama4_maverick_17b_128e = "Llama-4-Maverick-17B-128E" llama4_maverick_17b_128e_instruct = "Llama-4-Maverick-17B-128E-Instruct" # Safety models llama_guard_3_8b = "Llama-Guard-3-8B" llama_guard_2_8b = "Llama-Guard-2-8B" llama_guard_3_11b_vision = "Llama-Guard-3-11B-Vision" llama_guard_3_1b = "Llama-Guard-3-1B" llama_guard_4_12b = "Llama-Guard-4-12B" def is_multimodal(model_id) -> bool: if model_id in [ CoreModelId.llama3_2_11b_vision, CoreModelId.llama3_2_90b_vision, CoreModelId.llama3_2_11b_vision_instruct, CoreModelId.llama3_2_90b_vision_instruct, ]: return True else: return False def model_family(model_id) -> ModelFamily: if model_id in [ CoreModelId.llama2_7b, CoreModelId.llama2_13b, CoreModelId.llama2_70b, CoreModelId.llama2_7b_chat, CoreModelId.llama2_13b_chat, CoreModelId.llama2_70b_chat, ]: return ModelFamily.llama2 elif model_id in [ CoreModelId.llama3_8b, CoreModelId.llama3_70b, CoreModelId.llama3_8b_instruct, CoreModelId.llama3_70b_instruct, ]: return ModelFamily.llama3 elif model_id in [ CoreModelId.llama3_1_8b, CoreModelId.llama3_1_70b, CoreModelId.llama3_1_405b, CoreModelId.llama3_1_8b_instruct, CoreModelId.llama3_1_70b_instruct, CoreModelId.llama3_1_405b_instruct, ]: return ModelFamily.llama3_1 elif model_id in [ CoreModelId.llama3_2_1b, CoreModelId.llama3_2_3b, CoreModelId.llama3_2_1b_instruct, CoreModelId.llama3_2_3b_instruct, CoreModelId.llama3_2_11b_vision, CoreModelId.llama3_2_90b_vision, CoreModelId.llama3_2_11b_vision_instruct, CoreModelId.llama3_2_90b_vision_instruct, ]: return ModelFamily.llama3_2 elif model_id in [ CoreModelId.llama3_3_70b_instruct, ]: return ModelFamily.llama3_3 elif model_id in [ CoreModelId.llama4_scout_17b_16e, CoreModelId.llama4_scout_17b_16e_instruct, CoreModelId.llama4_maverick_17b_128e, CoreModelId.llama4_maverick_17b_128e_instruct, ]: return ModelFamily.llama4 elif model_id in [ CoreModelId.llama_guard_3_8b, CoreModelId.llama_guard_2_8b, CoreModelId.llama_guard_3_11b_vision, CoreModelId.llama_guard_3_1b, CoreModelId.llama_guard_4_12b, ]: return ModelFamily.safety else: raise ValueError(f"Unknown model family for {model_id}") class Model(BaseModel): core_model_id: CoreModelId description: str huggingface_repo: str | None = None arch_args: dict[str, Any] variant: str = "" quantization_format: CheckpointQuantizationFormat = CheckpointQuantizationFormat.bf16 pth_file_count: int metadata: dict[str, Any] = Field(default_factory=dict) # silence pydantic until we remove the `model_` fields model_config = ConfigDict(protected_namespaces=()) @property def model_family(self) -> ModelFamily: return model_family(self.core_model_id) # The SKU is uniquely identified by (model_id, variant) combo def descriptor(self, shorten_default_variant: bool = True) -> str: if not self.variant: return self.core_model_id.value return f"{self.core_model_id.value}:{self.variant}" @property def is_instruct_model(self) -> bool: return "instruct" in self.core_model_id.value # Featured models are shown in the non-exhaustive model list @property def is_featured(self) -> bool: return self.model_family in [ ModelFamily.llama3_1, ModelFamily.llama3_2, ModelFamily.llama3_3, ModelFamily.llama4, ModelFamily.safety, ] @property def max_seq_length(self) -> int: if self.model_family == ModelFamily.llama2: return 4096 elif self.core_model_id == CoreModelId.llama_guard_2_8b: return 4096 elif self.model_family == ModelFamily.llama3: return 8192 elif self.model_family in [ModelFamily.llama3_1, ModelFamily.llama3_3]: return 131072 elif self.model_family == ModelFamily.llama3_2: if self.quantization_format == CheckpointQuantizationFormat.int4: return 8192 return 131072 elif self.model_family == ModelFamily.llama4: if self.core_model_id in { CoreModelId.llama4_scout_17b_16e, CoreModelId.llama4_maverick_17b_128e, }: return 262144 if self.core_model_id == CoreModelId.llama4_scout_17b_16e_instruct: return 10485760 if self.core_model_id == CoreModelId.llama4_maverick_17b_128e_instruct: return 1048576 raise AssertionError(f"Unexpected core model id: {self.core_model_id}") elif self.core_model_id in [ CoreModelId.llama_guard_3_8b, CoreModelId.llama_guard_3_11b_vision, CoreModelId.llama_guard_3_1b, ]: return 131072 elif self.core_model_id == CoreModelId.llama_guard_4_12b: return 8192 else: raise ValueError(f"Unknown max_seq_len for {self.core_model_id}") ================================================ FILE: models/tokenizer_utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import base64 from pathlib import Path import logging log = logging.getLogger(__name__) def load_bpe_file(model_path: Path) -> dict[bytes, int]: """ Load BPE file directly and return mergeable ranks. Args: model_path (Path): Path to the BPE model file. Returns: dict[bytes, int]: Dictionary mapping byte sequences to their ranks. """ mergeable_ranks = {} with open(model_path, encoding="utf-8") as f: content = f.read() for line in content.splitlines(): if not line.strip(): # Skip empty lines continue try: token, rank = line.split() mergeable_ranks[base64.b64decode(token)] = int(rank) except Exception as e: log.warning(f"Failed to parse line '{line}': {e}") continue return mergeable_ranks ================================================ FILE: models/utils/__init__.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. ================================================ FILE: models/utils/config.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. import os from pathlib import Path LLAMA_MODELS_CONFIG_DIR = Path(os.getenv("LLAMA_MODELS_CONFIG_DIR", os.path.expanduser("~/.llama/"))) DEFAULT_CHECKPOINT_DIR = LLAMA_MODELS_CONFIG_DIR / "checkpoints" ================================================ FILE: models/utils/model_utils.py ================================================ # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # # This source code is licensed under the terms described in the LICENSE file in # top-level folder for each specific model found within the models/ directory at # the top-level of this source tree. from pathlib import Path from .config import DEFAULT_CHECKPOINT_DIR def model_local_dir(descriptor: str) -> str: """Get the local directory path for a model given its descriptor.""" return str(Path(DEFAULT_CHECKPOINT_DIR) / (descriptor.replace(":", "-"))) ================================================ FILE: pyproject.toml ================================================ [build-system] requires = ["setuptools>=61.0"] build-backend = "setuptools.build_meta" [project] name = "llama_models" version = "0.2.0" authors = [ {name = "Meta Llama", email = "llama-oss@meta.com"}, ] description = "Llama models" readme = "README.md" requires-python = ">=3.10" dependencies = [ "PyYAML", "jinja2>=3.1.6", "tiktoken", "pydantic>=2", "Pillow", "rich", "httpx", "termcolor", "huggingface-hub", ] classifiers = [] [project.urls] Homepage = "https://github.com/meta-llama/llama-models" [project.scripts] llama-model = "llama_models.cli.llama:main" multimodal_example_chat_completion = "llama_models.scripts.multimodal_example_chat_completion:main" multimodal_example_text_completion = "llama_models.scripts.multimodal_example_text_completion:main" example_chat_completion = "llama_models.scripts.example_chat_completion:main" example_text_completion = "llama_models.scripts.example_text_completion:main" llama4_completion = "llama_models.llama4.scripts.completion:main" llama4_chat_completion = "llama_models.llama4.scripts.chat_completion:main" [project.optional-dependencies] dev = [ "pytest", "black", "isort", "mypy", "ruff", ] torch = [ "torch", "torchvision", "fairscale", "fire", "blobfile", "fbgemm-gpu-genai==1.1.2", ] [tool.setuptools] package-dir = {"llama_models" = "llama_models"} packages = {find = {}} ================================================ FILE: requirements.txt ================================================ # This file was autogenerated by uv via the following command: # uv export --frozen --no-hashes --no-emit-project --output-file=requirements.txt annotated-types==0.7.0 certifi==2025.1.31 charset-normalizer==3.4.1 idna==3.10 jinja2==3.1.6 markupsafe==3.0.2 pillow==11.1.0 pydantic==2.10.6 pydantic-core==2.27.2 pyyaml==6.0.2 regex==2024.11.6 requests==2.32.3 tiktoken==0.8.0 typing-extensions==4.12.2 urllib3==2.3.0