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FILE: README.md
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<p align="center">全世界最好的大语言模型资源汇总 持续更新</p>
<p align="center">
<a href="https://github.com/WangRongsheng/awesome-LLM-resourses"><img src=https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg ></a>
<a href="https://github.com/WangRongsheng/awesome-LLM-resourses"><img src=https://img.shields.io/github/forks/WangRongsheng/awesome-LLM-resourses.svg?style=social ></a>
<a href="https://github.com/WangRongsheng/awesome-LLM-resourses"><img src=https://img.shields.io/github/stars/WangRongsheng/awesome-LLM-resourses.svg?style=social ></a>
<a href="https://github.com/WangRongsheng/awesome-LLM-resourses"><img src=https://img.shields.io/github/watchers/WangRongsheng/awesome-LLM-resourses.svg?style=social ></a>
<a href="https://gitcode.com/wangrongsheng/awesome-LLM-resources"><img src="https://raw.githubusercontent.com/WangRongsheng/awesome-LLM-resources/main/assets/gitcode.png" height="25" alt="gitcode">
</a>
</p>
> [!TIP]
> 如果您对**医疗数据集/大模型/多模态/评估相关资源感兴趣**！请访问我们的 🤗 [Awesome-AI4Med](https://github.com/FreedomIntelligence/Awesome-AI4Med) !
---
#### Contents
- [推荐 Suggestion](#推荐-Suggestion)
- [数据 Data](#数据-Data)
- [微调 Fine-Tuning](#微调-Fine-Tuning)
- [Agentic RL](#Agentic-RL)
- [推理 Inference](#推理-Inference)
- [评估 Evaluation](#评估-Evaluation)
- [体验 Usage](#体验-Usage)
- [知识库 RAG](#知识库-RAG)
- [智能体 Agents](#智能体-Agents)
- [研究 Research](#研究-Research)
- [代码 Coding](#代码-Coding)
- [视频 Video](#视频-Video)
- [图片 Image](#图片-Image)
- [搜索 Search](#搜索-Search)
- [语音 Speech](#语音-Speech)
- [统一模型 Unified Model](#统一模型-Unified-Model)
- [书籍 Book](#书籍-Book)
- [课程 Course](#课程-Course)
- [教程 Tutorial](#教程-Tutorial)
- [论文 Paper](#论文-Paper)
- [社区 Community](#社区-Community)
- [模型上下文协议 MCP](#模型上下文协议-MCP)
- [技能 Skills](#技能-Skills)
- [推理 Open o1](#推理-Open-o1)
- [推理 Open o3](#推理-Open-o3)
- [小语言模型 Small Language Model](#小语言模型-Small-Language-Model)
- [小多模态模型 Small Vision Language Model](#小多模态模型-Small-Vision-Language-Model)
- [技巧 Tips](#技巧-tips)
## 推荐 Suggestion
#### Podcast
- [翁家翌：OpenAI，GPT，强化学习，Infra，后训练，天授，tuixue，开源，CMU，清华｜WhynotTV Podcast](https://www.bilibili.com/video/BV1darmBcE4A?vd_source=c739db1ebdd361d47af5a0b8497417db)
- [Lovart 创始人陈冕×罗永浩！且让我大闹一场，然后悄然离去](https://www.bilibili.com/video/BV14eiQBmEbN/?spm_id_from=333.1387.upload.video_card.click)
- [MiniMax 创始人闫俊杰×罗永浩！大山并非无法翻越](https://www.bilibili.com/video/BV11NmtBzE36/?spm_id_from=333.1387.upload.video_card.click&vd_source=c739db1ebdd361d47af5a0b8497417db)
- [影视飓风TIM×罗永浩！用影像打开世界的梦想家](https://www.bilibili.com/video/BV1B5xkzPEhx/?spm_id_from=333.1387.upload.video_card.click&vd_source=c739db1ebdd361d47af5a0b8497417db)
- [129. 全球大模型第一股的上市访谈，和智谱CEO张鹏聊：敢问路在何方？](https://www.youtube.com/watch?v=9zSMTUUEfmU&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=2)
- [128. Manus决定出售前最后的访谈：啊，这奇幻的2025年漂流啊…](https://www.youtube.com/watch?v=MW-ezf2RhVg&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=3)
- [122. 朱啸虎现实主义故事的第三次连载：人工智能的盛筵与泡泡](https://www.youtube.com/watch?v=wK0-m3rKgZ0&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=9)
- [119. Kimi Linear、Minimax M2？和杨松琳考古算法变种史，并预演未来架构改进方案](https://www.youtube.com/watch?v=858HR43pegk&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=12&t=1070s)
- [118. 对李想的第二次3小时访谈：CEO大模型、MoE、梁文锋、VLA、能量、记忆、对抗人性、亲密关系、人类的智慧](https://www.youtube.com/watch?v=RxXVq7-sJzM&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=13)
- [115. 对OpenAI姚顺雨3小时访谈：6年Agent研究、人与系统、吞噬的边界、既单极又多元的世界](https://www.youtube.com/watch?v=gQgKkUsx5q0&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=16)
- [113. 和杨植麟时隔1年的对话：K2、Agentic LLM、缸中之脑和“站在无限的开端”](https://www.youtube.com/watch?v=ouG6jrkECrc&list=PLwAchVoh-4zNSI5UlKEkKCL5r_jJyrFeO&index=18)
- [A 7-hour marathon interview with Saining Xie: World Models, AMI Labs, Yann LeCun, Fei-Fei Li, and 42](https://www.youtube.com/watch?v=rIwgZWzUKm8)
## 数据 Data
> [!NOTE]
>
> 此处命名为`数据`，但这里并没有提供具体数据集，而是提供了处理获取大规模数据的方法
1. [AotoLabel](https://github.com/refuel-ai/autolabel): Label, clean and enrich text datasets with LLMs.
2. [LabelLLM](https://github.com/opendatalab/LabelLLM): The Open-Source Data Annotation Platform.
3. [data-juicer](https://github.com/modelscope/data-juicer): A one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs!
4. [OmniParser](https://github.com/jf-tech/omniparser): a native Golang ETL streaming parser and transform library for CSV, JSON, XML, EDI, text, etc.
5. [MinerU (`🔥`)](https://github.com/opendatalab/MinerU): MinerU is a one-stop, open-source, high-quality data extraction tool, supports PDF/webpage/e-book extraction.
6. [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit): A Comprehensive Toolkit for High-Quality PDF Content Extraction.
7. [Parsera](https://github.com/raznem/parsera): Lightweight library for scraping web-sites with LLMs.
8. [Sparrow](https://github.com/katanaml/sparrow): Sparrow is an innovative open-source solution for efficient data extraction and processing from various documents and images.
9. [Docling](https://github.com/DS4SD/docling): Get your documents ready for gen AI.
10. [GOT-OCR2.0](https://github.com/Ucas-HaoranWei/GOT-OCR2.0): OCR Model.
11. [LLM Decontaminator](https://github.com/lm-sys/llm-decontaminator): Rethinking Benchmark and Contamination for Language Models with Rephrased Samples.
12. [DataTrove](https://github.com/huggingface/datatrove): DataTrove is a library to process, filter and deduplicate text data at a very large scale.
13. [llm-swarm](https://github.com/huggingface/llm-swarm/tree/main/examples/textbooks): Generate large synthetic datasets like [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia).
14. [Distilabel](https://github.com/argilla-io/distilabel): Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
15. [Common-Crawl-Pipeline-Creator](https://huggingface.co/spaces/lhoestq/Common-Crawl-Pipeline-Creator): The Common Crawl Pipeline Creator.
16. [Tabled](https://github.com/VikParuchuri/tabled): Detect and extract tables to markdown and csv.
17. [Zerox](https://github.com/getomni-ai/zerox): Zero shot pdf OCR with gpt-4o-mini.
18. [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO): Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception.
19. [TensorZero](https://github.com/tensorzero/tensorzero): make LLMs improve through experience.
20. [Promptwright](https://github.com/StacklokLabs/promptwright): Generate large synthetic data using a local LLM.
21. [pdf-extract-api](https://github.com/CatchTheTornado/pdf-extract-api): Document (PDF) extraction and parse API using state of the art modern OCRs + Ollama supported models.
22. [pdf2htmlEX](https://github.com/pdf2htmlEX/pdf2htmlEX): Convert PDF to HTML without losing text or format.
23. [Extractous](https://github.com/yobix-ai/extractous): Fast and efficient unstructured data extraction. Written in Rust with bindings for many languages.
24. [MegaParse](https://github.com/QuivrHQ/MegaParse): File Parser optimised for LLM Ingestion with no loss.
25. [MarkItDown](https://github.com/microsoft/markitdown): Python tool for converting files and office documents to Markdown.
26. [datasketch](https://github.com/ekzhu/datasketch): datasketch gives you probabilistic data structures that can process and search very large amount of data super fast, with little loss of accuracy.
27. [semhash](https://github.com/MinishLab/semhash): lightweight and flexible tool for deduplicating datasets using semantic similarity.
28. [ReaderLM-v2](https://huggingface.co/jinaai/ReaderLM-v2): a 1.5B parameter language model that converts raw HTML into beautifully formatted markdown or JSON.
29. [Bespoke Curator](https://github.com/bespokelabsai/curator): Data Curation for Post-Training & Structured Data Extraction.
30. [LangKit](https://github.com/whylabs/langkit): An open-source toolkit for monitoring Large Language Models (LLMs). Extracts signals from prompts & responses, ensuring safety & security.
31. [Curator](https://github.com/bespokelabsai/curator): Synthetic Data curation for post-training and structured data extraction.
32. [olmOCR](https://github.com/allenai/olmocr): A toolkit for training language models to work with PDF documents in the wild.
33. [Easy Dataset (`🔥`)](https://github.com/ConardLi/easy-dataset): A powerful tool for creating fine-tuning datasets for LLM.
34. [BabelDOC](https://github.com/funstory-ai/BabelDOC): PDF scientific paper translation and bilingual comparison library.
35. [Dolphin](https://github.com/bytedance/Dolphin): Document Image Parsing via Heterogeneous Anchor Prompting.
36. [EasyDistill](https://github.com/modelscope/easydistill): Easy Knowledge Distillation for Large Language Models.
37. [ContextGem](https://github.com/shcherbak-ai/contextgem): a free, open-source LLM framework that makes it radically easier to extract structured data and insights from documents.
38. [OCRFlux](https://github.com/chatdoc-com/OCRFlux): a lightweight yet powerful multimodal toolkit that significantly advances PDF-to-Markdown conversion, excelling in complex layout handling, complicated table parsing and cross-page content merging.
39. [DataFlow](https://github.com/OpenDCAI/DataFlow): Easy Data Preparation with latest LLMs-based Operators and Pipelines.
40. [DatasetLoom (`multimodal`)](https://github.com/599yongyang/DatasetLoom): 一个面向多模态大模型训练的智能数据集构建与评估平台.
41. [Logics-Parsing](https://github.com/alibaba/Logics-Parsing)
42. [DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR)
43. [PaddleOCR-VL](https://huggingface.co/PaddlePaddle/PaddleOCR-VL)
44. [Chandra](https://github.com/datalab-to/chandra): a highly accurate OCR model that converts images and PDFs into structured HTML/Markdown/JSON while preserving layout information.
45. [HunyuanOCR](https://github.com/Tencent-Hunyuan/HunyuanOCR): a leading end-to-end OCR expert VLM powered by Hunyuan's native multimodal architecture.
46. [DeepSeek-OCR-2](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2): Visual Causal Flow.
47. [PaddleOCR-VL-1.5](https://huggingface.co/PaddlePaddle/PaddleOCR-VL-1.5): Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing.
48. [GLM-OCR](https://huggingface.co/zai-org/GLM-OCR): a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture.
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## 微调 Fine-Tuning
1. [LLaMA-Factory (`🔥`)](https://github.com/hiyouga/LLaMA-Factory): Unify Efficient Fine-Tuning of 100+ LLMs.
2. [360-LLaMA-Factory](https://github.com/Qihoo360/360-LLaMA-Factory): Unify Efficient Fine-Tuning of 100+ LLMs. (add Sequence Parallelism for supporting long context training)
4. [unsloth](https://github.com/unslothai/unsloth): 2-5X faster 80% less memory LLM finetuning.
5. [TRL](https://huggingface.co/docs/trl/index): Transformer Reinforcement Learning.
6. [Firefly](https://github.com/yangjianxin1/Firefly): Firefly: 大模型训练工具，支持训练数十种大模型
7. [Xtuner](https://github.com/InternLM/xtuner): An efficient, flexible and full-featured toolkit for fine-tuning large models.
8. [torchtune](https://github.com/pytorch/torchtune): A Native-PyTorch Library for LLM Fine-tuning.
9. [Swift](https://github.com/modelscope/swift): Use PEFT or Full-parameter to finetune 200+ LLMs or 15+ MLLMs.
10. [AutoTrain](https://huggingface.co/autotrain): A new way to automatically train, evaluate and deploy state-of-the-art Machine Learning models.
11. [OpenRLHF](https://github.com/OpenLLMAI/OpenRLHF): An Easy-to-use, Scalable and High-performance RLHF Framework (Support 70B+ full tuning & LoRA & Mixtral & KTO).
12. [Ludwig](https://github.com/ludwig-ai/ludwig): Low-code framework for building custom LLMs, neural networks, and other AI models.
13. [mistral-finetune](https://github.com/mistralai/mistral-finetune): A light-weight codebase that enables memory-efficient and performant finetuning of Mistral's models.
14. [aikit](https://github.com/sozercan/aikit): Fine-tune, build, and deploy open-source LLMs easily!
15. [H2O-LLMStudio](https://github.com/h2oai/h2o-llmstudio): H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs.
16. [LitGPT](https://github.com/Lightning-AI/litgpt): Pretrain, finetune, deploy 20+ LLMs on your own data. Uses state-of-the-art techniques: flash attention, FSDP, 4-bit, LoRA, and more.
17. [LLMBox](https://github.com/RUCAIBox/LLMBox): A comprehensive library for implementing LLMs, including a unified training pipeline and comprehensive model evaluation.
18. [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP): Easy-to-use and powerful NLP and LLM library.
19. [workbench-llamafactory](https://github.com/NVIDIA/workbench-llamafactory): This is an NVIDIA AI Workbench example project that demonstrates an end-to-end model development workflow using Llamafactory.
20. [OpenRLHF](https://github.com/OpenLLMAI/OpenRLHF): An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & Mixtral).
21. [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory): A Framework of Small-scale Large Multimodal Models.
22. [LLM-Foundry](https://github.com/mosaicml/llm-foundry): LLM training code for Databricks foundation models.
23. [lmms-finetune](https://github.com/zjysteven/lmms-finetune): A unified codebase for finetuning (full, lora) large multimodal models, supporting llava-1.5, qwen-vl, llava-interleave, llava-next-video, phi3-v etc.
24. [Simplifine](https://github.com/simplifine-llm/Simplifine): Simplifine lets you invoke LLM finetuning with just one line of code using any Hugging Face dataset or model.
25. [Transformer Lab](https://github.com/transformerlab/transformerlab-app): Open Source Application for Advanced LLM Engineering: interact, train, fine-tune, and evaluate large language models on your own computer.
26. [Liger-Kernel](https://github.com/linkedin/Liger-Kernel): Efficient Triton Kernels for LLM Training.
27. [ChatLearn](https://github.com/alibaba/ChatLearn): A flexible and efficient training framework for large-scale alignment.
28. [nanotron](https://github.com/huggingface/nanotron): Minimalistic large language model 3D-parallelism training.
29. [Proxy Tuning](https://github.com/alisawuffles/proxy-tuning): Tuning Language Models by Proxy.
30. [Effective LLM Alignment](https://github.com/VikhrModels/effective_llm_alignment/): Effective LLM Alignment Toolkit.
31. [Autotrain-advanced](https://github.com/huggingface/autotrain-advanced)
32. [Meta Lingua](https://github.com/facebookresearch/lingua): a lean, efficient, and easy-to-hack codebase to research LLMs.
33. [Vision-LLM Alignemnt](https://github.com/NiuTrans/Vision-LLM-Alignment): This repository contains the code for SFT, RLHF, and DPO, designed for vision-based LLMs, including the LLaVA models and the LLaMA-3.2-vision models.
34. [finetune-Qwen2-VL](https://github.com/zhangfaen/finetune-Qwen2-VL): Quick Start for Fine-tuning or continue pre-train Qwen2-VL Model.
35. [Online-RLHF](https://github.com/RLHFlow/Online-RLHF): A recipe for online RLHF and online iterative DPO.
36. [InternEvo](https://github.com/InternLM/InternEvo): an open-sourced lightweight training framework aims to support model pre-training without the need for extensive dependencies.
37. [veRL](https://github.com/volcengine/verl): Volcano Engine Reinforcement Learning for LLM.
38. [Axolotl](https://axolotl-ai-cloud.github.io/axolotl/): Axolotl is designed to work with YAML config files that contain everything you need to preprocess a dataset, train or fine-tune a model, run model inference or evaluation, and much more.
39. [Oumi](https://github.com/oumi-ai/oumi): Everything you need to build state-of-the-art foundation models, end-to-end.
40. [Kiln](https://github.com/Kiln-AI/Kiln): The easiest tool for fine-tuning LLM models, synthetic data generation, and collaborating on datasets.
41. [DeepSeek-671B-SFT-Guide](https://github.com/ScienceOne-AI/DeepSeek-671B-SFT-Guide): An open-source solution for full parameter fine-tuning of DeepSeek-V3/R1 671B, including complete code and scripts from training to inference, as well as some practical experiences and conclusions.
42. [MLX-VLM](https://github.com/Blaizzy/mlx-vlm): MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
43. [RL-Factory](https://github.com/Simple-Efficient/RL-Factory): Train your Agent model via our easy and efficient framework.
44. [RM-Gallery](https://github.com/modelscope/RM-Gallery): A One-Stop Reward Model Platform.
45. [ART](https://github.com/OpenPipe/ART): rain multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training.
46. [VeRL (`🔥`)](https://github.com/volcengine/verl): Volcano Engine Reinforcement Learning for LLMs.
47. [LMMs-Engine](https://github.com/EvolvingLMMs-Lab/lmms-engine): A simple, any-to-any modality framework for pretraining and finetuning. Lean, flexible, and built for research.
48. [dLLM](https://github.com/ZHZisZZ/dllm): a library that unifies the training and evaluation of diffusion language models, bringing transparency and reproducibility to the entire development pipeline. `diffusion`
49. [Miles](https://github.com/radixark/miles): an enterprise-facing reinforcement learning framework for large-scale MoE post-training and production workloads.
50. [Skills](https://github.com/NVIDIA-NeMo/Skills): a collection of pipelines to improve "skills" of large language models (LLMs).
51. [Twinkle](https://github.com/modelscope/twinkle): a lightweight, client-server training framework engineered with modular, high-cohesion interfaces.
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## Agentic RL
- veRL: https://github.com/volcengine/verl
- AReaL: https://github.com/inclusionAI/AReaL
- slime: https://github.com/THUDM/slime
- Agent Lightning: https://github.com/microsoft/agent-lightning
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## 推理 Inference
1. [ollama](https://github.com/ollama/ollama): Get up and running with Llama 3, Mistral, Gemma, and other large language models.
2. [Open WebUI](https://github.com/open-webui/open-webui): User-friendly WebUI for LLMs (Formerly Ollama WebUI).
3. [Text Generation WebUI](https://github.com/oobabooga/text-generation-webui): A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
4. [Xinference](https://github.com/xorbitsai/inference): A powerful and versatile library designed to serve language, speech recognition, and multimodal models.
5. [LangChain](https://github.com/langchain-ai/langchain): Build context-aware reasoning applications.
6. [LlamaIndex](https://github.com/run-llama/llama_index): A data framework for your LLM applications.
7. [lobe-chat](https://github.com/lobehub/lobe-chat): an open-source, modern-design LLMs/AI chat framework. Supports Multi AI Providers, Multi-Modals (Vision/TTS) and plugin system.
8. [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM): TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs.
9. [vllm (`🔥`)](https://github.com/vllm-project/vllm): A high-throughput and memory-efficient inference and serving engine for LLMs.
10. [LlamaChat](https://github.com/alexrozanski/LlamaChat): Chat with your favourite LLaMA models in a native macOS app.
11. [NVIDIA ChatRTX](https://www.nvidia.com/en-us/ai-on-rtx/chatrtx/): ChatRTX is a demo app that lets you personalize a GPT large language model (LLM) connected to your own content—docs, notes, or other data.
12. [LM Studio](https://lmstudio.ai/): Discover, download, and run local LLMs.
13. [chat-with-mlx](https://github.com/qnguyen3/chat-with-mlx): Chat with your data natively on Apple Silicon using MLX Framework.
14. [LLM Pricing](https://llmpricecheck.com/): Quickly Find the Perfect Large Language Models (LLM) API for Your Budget! Use Our Free Tool for Instant Access to the Latest Prices from Top Providers.
15. [Open Interpreter](https://github.com/OpenInterpreter/open-interpreter): A natural language interface for computers.
16. [Chat-ollama](https://github.com/sugarforever/chat-ollama): An open source chatbot based on LLMs. It supports a wide range of language models, and knowledge base management.
17. [chat-ui](https://github.com/huggingface/chat-ui): Open source codebase powering the HuggingChat app.
18. [MemGPT](https://github.com/cpacker/MemGPT): Create LLM agents with long-term memory and custom tools.
19. [koboldcpp](https://github.com/LostRuins/koboldcpp): A simple one-file way to run various GGML and GGUF models with KoboldAI's UI.
20. [LLMFarm](https://github.com/guinmoon/LLMFarm): llama and other large language models on iOS and MacOS offline using GGML library.
21. [enchanted](https://github.com/AugustDev/enchanted): Enchanted is iOS and macOS app for chatting with private self hosted language models such as Llama2, Mistral or Vicuna using Ollama.
22. [Flowise](https://github.com/FlowiseAI/Flowise): Drag & drop UI to build your customized LLM flow.
23. [Jan](https://github.com/janhq/jan): Jan is an open source alternative to ChatGPT that runs 100% offline on your computer. Multiple engine support (llama.cpp, TensorRT-LLM).
24. [LMDeploy](https://github.com/InternLM/lmdeploy): LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
25. [RouteLLM](https://github.com/lm-sys/RouteLLM): A framework for serving and evaluating LLM routers - save LLM costs without compromising quality!
26. [MInference](https://github.com/microsoft/MInference): About
To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy.
27. [Mem0](https://github.com/mem0ai/mem0): The memory layer for Personalized AI.
28. [SGLang (`🔥`)](https://github.com/sgl-project/sglang): SGLang is yet another fast serving framework for large language models and vision language models.
29. [AirLLM](https://github.com/lyogavin/airllm): AirLLM optimizes inference memory usage, allowing 70B large language models to run inference on a single 4GB GPU card without quantization, distillation and pruning. And you can run 405B Llama3.1 on 8GB vram now.
30. [LLMHub](https://github.com/jmather/llmhub): LLMHub is a lightweight management platform designed to streamline the operation and interaction with various language models (LLMs).
31. [YuanChat](https://github.com/IEIT-Yuan/YuanChat)
32. [LiteLLM](https://github.com/BerriAI/litellm): Call all LLM APIs using the OpenAI format [Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, Groq etc.]
33. [GuideLLM](https://github.com/neuralmagic/guidellm): GuideLLM is a powerful tool for evaluating and optimizing the deployment of large language models (LLMs).
34. [LLM-Engines](https://github.com/jdf-prog/LLM-Engines): A unified inference engine for large language models (LLMs) including open-source models (VLLM, SGLang, Together) and commercial models (OpenAI, Mistral, Claude).
35. [OARC](https://github.com/Leoleojames1/ollama_agent_roll_cage): ollama_agent_roll_cage (OARC) is a local python agent fusing ollama llm's with Coqui-TTS speech models, Keras classifiers, Llava vision, Whisper recognition, and more to create a unified chatbot agent for local, custom automation.
36. [g1](https://github.com/bklieger-groq/g1): Using Llama-3.1 70b on Groq to create o1-like reasoning chains.
37. [MemoryScope](https://github.com/modelscope/MemoryScope): MemoryScope provides LLM chatbots with powerful and flexible long-term memory capabilities, offering a framework for building such abilities.
38. [OpenLLM](https://github.com/bentoml/OpenLLM): Run any open-source LLMs, such as Llama 3.1, Gemma, as OpenAI compatible API endpoint in the cloud.
39. [Infinity](https://github.com/infiniflow/infinity): The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense embedding, sparse embedding, tensor and full-text.
40. [optillm](https://github.com/codelion/optillm): an OpenAI API compatible optimizing inference proxy which implements several state-of-the-art techniques that can improve the accuracy and performance of LLMs.
41. [LLaMA Box](https://github.com/gpustack/llama-box): LLM inference server implementation based on llama.cpp.
42. [ZhiLight](https://github.com/zhihu/ZhiLight): A highly optimized inference acceleration engine for Llama and its variants.
43. [DashInfer](https://github.com/modelscope/dash-infer): DashInfer is a native LLM inference engine aiming to deliver industry-leading performance atop various hardware architectures.
44. [LocalAI](https://github.com/mudler/LocalAI): The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required.
45. [ktransformers](https://github.com/kvcache-ai/ktransformers): A Flexible Framework for Experiencing Cutting-edge LLM Inference Optimizations.
46. [SkyPilot](https://github.com/skypilot-org/skypilot): Run AI and batch jobs on any infra (Kubernetes or 14+ clouds). Get unified execution, cost savings, and high GPU availability via a simple interface.
47. [Chitu](https://github.com/thu-pacman/chitu): High-performance inference framework for large language models, focusing on efficiency, flexibility, and availability.
48. [TokenSwift](https://github.com/bigai-nlco/TokenSwift): From Hours to Minutes: Lossless Acceleration of Ultra Long Sequence Generation.
49. [Cherry Studio](https://github.com/CherryHQ/cherry-studio): a desktop client that supports for multiple LLM providers, available on Windows, Mac and Linux.
50. [Shimmy](https://github.com/Michael-A-Kuykendall/shimmy): Python-free Rust inference server — OpenAI-API compatible. GGUF + SafeTensors, hot model swap, auto-discovery, single binary.
51. [LlamaBarn](https://github.com/ggml-org/LlamaBarn): Run local LLMs on your Mac with a simple menu bar app.
52. [Parallax](https://github.com/GradientHQ/parallax): a distributed model serving framework that lets you build your own AI cluster anywhere.
53. [xLLM](https://github.com/jd-opensource/xllm): A high-performance inference engine for LLMs, optimized for diverse AI accelerators.
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## 评估 Evaluation
1. [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness): A framework for few-shot evaluation of language models.
2. [opencompass](https://github.com/open-compass/opencompass): OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.
3. [llm-comparator](https://github.com/PAIR-code/llm-comparator): LLM Comparator is an interactive data visualization tool for evaluating and analyzing LLM responses side-by-side, developed.
4. [EvalScope (`🔥`)](https://github.com/modelscope/evalscope)
5. [Weave](https://weave-docs.wandb.ai/guides/core-types/evaluations): A lightweight toolkit for tracking and evaluating LLM applications.
6. [MixEval](https://github.com/Psycoy/MixEval/): Deriving Wisdom of the Crowd from LLM Benchmark Mixtures.
7. [Evaluation guidebook](https://github.com/huggingface/evaluation-guidebook): If you've ever wondered how to make sure an LLM performs well on your specific task, this guide is for you!
8. [Ollama Benchmark](https://github.com/aidatatools/ollama-benchmark): LLM Benchmark for Throughput via Ollama (Local LLMs).
9. [VLMEvalKit](https://github.com/open-compass/VLMEvalKit): Open-source evaluation toolkit of large vision-language models (LVLMs), support ~100 VLMs, 40+ benchmarks.
10. [AGI-Eval](https://agi-eval.cn/mvp/home)
11. [EvalScope](https://github.com/modelscope/evalscope): A streamlined and customizable framework for efficient large model evaluation and performance benchmarking.
12. [DeepEval](https://github.com/confident-ai/deepeval): a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems.
13. [Lighteval](https://github.com/huggingface/lighteval): Lighteval is your all-in-one toolkit for evaluating LLMs across multiple backends.
14. [QwQ/eval](https://github.com/QwenLM/QwQ/tree/main/eval): QwQ is the reasoning model series developed by Qwen team, Alibaba Cloud.
15. [Evalchemy](https://github.com/mlfoundations/evalchemy): A unified and easy-to-use toolkit for evaluating post-trained language models.
16. [MathArena](https://github.com/eth-sri/matharena): Evaluation of LLMs on latest math competitions.
17. [YourBench](https://github.com/huggingface/yourbench): A Dynamic Benchmark Generation Framework.
18. [MedEvalKit](https://github.com/alibaba-damo-academy/MedEvalKit): A Unified Medical Evaluation Framework.
19. [OpenJudge](https://github.com/modelscope/OpenJudge): A Unified Framework for Holistic Evaluation and Quality Rewards.
`LLM API 服务平台`：
1. [Groq](https://groq.com/)
2. [硅基流动](https://cloud.siliconflow.cn/models)
3. [火山引擎](https://www.volcengine.com/product/ark)
4. [文心千帆](https://qianfan.cloud.baidu.com/)
5. [DashScope](https://dashscope.aliyun.com/)
6. [aisuite](https://github.com/andrewyng/aisuite)
7. [DeerAPI](https://www.deerapi.com/)
8. [Qwen-Chat](https://chat.qwenlm.ai/)
9. [DeepSeek-v3](https://www.deepseek.com/)
10. [WaveSpeed](https://wavespeed.ai/) `视频生成`
11. [OpenRouter](https://openrouter.ai/)
12. [数标标 (`🔥`)](https://api.ai-gaochao.cn/)
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## 体验 Usage
1. [LM Arena](https://lmarena.ai/zh)
2. [Design Arena](https://www.designarena.ai/)
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## 知识库 RAG
1. [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm): The all-in-one AI app for any LLM with full RAG and AI Agent capabilites.
2. [MaxKB](https://github.com/1Panel-dev/MaxKB): 基于 LLM 大语言模型的知识库问答系统。开箱即用，支持快速嵌入到第三方业务系统
3. [RAGFlow](https://github.com/infiniflow/ragflow): An open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
4. [Dify](https://github.com/langgenius/dify): An open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.
5. [FastGPT](https://github.com/labring/FastGPT): A knowledge-based platform built on the LLM, offers out-of-the-box data processing and model invocation capabilities, allows for workflow orchestration through Flow visualization.
6. [Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat): 基于 Langchain 与 ChatGLM 等不同大语言模型的本地知识库问答
7. [QAnything](https://github.com/netease-youdao/QAnything): Question and Answer based on Anything.
8. [Quivr](https://github.com/QuivrHQ/quivr): A personal productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic, VertexAI, Ollama, LLMs, Groq that you can share with users ! Local & Private alternative to OpenAI GPTs & ChatGPT powered by retrieval-augmented generation.
9. [RAG-GPT](https://github.com/open-kf/rag-gpt): RAG-GPT, leveraging LLM and RAG technology, learns from user-customized knowledge bases to provide contextually relevant answers for a wide range of queries, ensuring rapid and accurate information retrieval.
10. [Verba](https://github.com/weaviate/Verba): Retrieval Augmented Generation (RAG) chatbot powered by Weaviate.
11. [FlashRAG](https://github.com/RUC-NLPIR/FlashRAG): A Python Toolkit for Efficient RAG Research.
12. [GraphRAG](https://github.com/microsoft/graphrag): A modular graph-based Retrieval-Augmented Generation (RAG) system.
13. [LightRAG](https://github.com/SylphAI-Inc/LightRAG): LightRAG helps developers with both building and optimizing Retriever-Agent-Generator pipelines.
14. [GraphRAG-Ollama-UI](https://github.com/severian42/GraphRAG-Ollama-UI): GraphRAG using Ollama with Gradio UI and Extra Features.
15. [nano-GraphRAG](https://github.com/gusye1234/nano-graphrag): A simple, easy-to-hack GraphRAG implementation.
16. [RAG Techniques](https://github.com/NirDiamant/RAG_Techniques): This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
17. [ragas](https://github.com/explodinggradients/ragas): Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines.
18. [kotaemon](https://github.com/Cinnamon/kotaemon): An open-source clean & customizable RAG UI for chatting with your documents. Built with both end users and developers in mind.
19. [RAGapp](https://github.com/ragapp/ragapp): The easiest way to use Agentic RAG in any enterprise.
20. [TurboRAG](https://github.com/MooreThreads/TurboRAG): Accelerating Retrieval-Augmented Generation with Precomputed KV Caches for Chunked Text.
21. [LightRAG](https://github.com/HKUDS/LightRAG): Simple and Fast Retrieval-Augmented Generation.
22. [TEN](https://github.com/TEN-framework/ten_framework): the Next-Gen AI-Agent Framework, the world's first truly real-time multimodal AI agent framework.
23. [AutoRAG](https://github.com/Marker-Inc-Korea/AutoRAG): RAG AutoML tool for automatically finding an optimal RAG pipeline for your data.
24. [KAG](https://github.com/OpenSPG/KAG): KAG is a knowledge-enhanced generation framework based on OpenSPG engine, which is used to build knowledge-enhanced rigorous decision-making and information retrieval knowledge services.
25. [Fast-GraphRAG](https://github.com/circlemind-ai/fast-graphrag): RAG that intelligently adapts to your use case, data, and queries.
26. [Tiny-GraphRAG](https://github.com/limafang/tiny-graphrag)
27. [DB-GPT GraphRAG](https://github.com/eosphoros-ai/DB-GPT/tree/main/dbgpt/storage/knowledge_graph): DB-GPT GraphRAG integrates both triplet-based knowledge graphs and document structure graphs while leveraging community and document retrieval mechanisms to enhance RAG capabilities, achieving comparable performance while consuming only 50% of the tokens required by Microsoft's GraphRAG. Refer to the DB-GPT [Graph RAG User Manual](http://docs.dbgpt.cn/docs/cookbook/rag/graph_rag_app_develop/) for details.
28. [Chonkie](https://github.com/bhavnicksm/chonkie): The no-nonsense RAG chunking library that's lightweight, lightning-fast, and ready to CHONK your texts.
29. [RAGLite](https://github.com/superlinear-ai/raglite): RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite.
30. [KAG](https://github.com/OpenSPG/KAG): KAG is a logical form-guided reasoning and retrieval framework based on OpenSPG engine and LLMs.
31. [CAG](https://github.com/hhhuang/CAG): CAG leverages the extended context windows of modern large language models (LLMs) by preloading all relevant resources into the model’s context and caching its runtime parameters.
32. [MiniRAG](https://github.com/HKUDS/MiniRAG): an extremely simple retrieval-augmented generation framework that enables small models to achieve good RAG performance through heterogeneous graph indexing and lightweight topology-enhanced retrieval.
33. [XRAG](https://github.com/DocAILab/XRAG): a benchmarking framework designed to evaluate the foundational components of advanced Retrieval-Augmented Generation (RAG) systems.
34. [Rankify](https://github.com/DataScienceUIBK/rankify): A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation.
35. [RAG-Anything](https://github.com/HKUDS/RAG-Anything): All-in-One RAG System.
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</div>
## 智能体 Agents
1. [AutoGen](https://github.com/microsoft/autogen): AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. [AutoGen AIStudio](https://autogen-studio.com/)
2. [CrewAI](https://github.com/joaomdmoura/crewAI): Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
3. [Coze](https://www.coze.com/)
4. [AgentGPT](https://github.com/reworkd/AgentGPT): Assemble, configure, and deploy autonomous AI Agents in your browser.
5. [XAgent](https://github.com/OpenBMB/XAgent): An Autonomous LLM Agent for Complex Task Solving.
6. [MobileAgent](https://github.com/X-PLUG/MobileAgent): The Powerful Mobile Device Operation Assistant Family.
7. [Lagent](https://github.com/InternLM/lagent): A lightweight framework for building LLM-based agents.
8. [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent): Agent framework and applications built upon Qwen2, featuring Function Calling, Code Interpreter, RAG, and Chrome extension.
9. [LinkAI](https://link-ai.tech/portal): 一站式 AI 智能体搭建平台
10. [Baidu APPBuilder](https://appbuilder.cloud.baidu.com/)
11. [agentUniverse](https://github.com/alipay/agentUniverse): agentUniverse is a LLM multi-agent framework that allows developers to easily build multi-agent applications. Furthermore, through the community, they can exchange and share practices of patterns across different domains.
12. [LazyLLM](https://github.com/LazyAGI/LazyLLM): 低代码构建多Agent大模型应用的开发工具
13. [AgentScope](https://github.com/modelscope/agentscope): Start building LLM-empowered multi-agent applications in an easier way.
14. [MoA](https://github.com/togethercomputer/MoA): Mixture of Agents (MoA) is a novel approach that leverages the collective strengths of multiple LLMs to enhance performance, achieving state-of-the-art results.
15. [Agently](https://github.com/Maplemx/Agently): AI Agent Application Development Framework.
16. [OmAgent](https://github.com/om-ai-lab/OmAgent): A multimodal agent framework for solving complex tasks.
17. [Tribe](https://github.com/StreetLamb/tribe): No code tool to rapidly build and coordinate multi-agent teams.
18. [CAMEL](https://github.com/camel-ai/camel): First LLM multi-agent framework and an open-source community dedicated to finding the scaling law of agents.
19. [PraisonAI](https://github.com/MervinPraison/PraisonAI/): PraisonAI application combines AutoGen and CrewAI or similar frameworks into a low-code solution for building and managing multi-agent LLM systems, focusing on simplicity, customisation, and efficient human-agent collaboration.
20. [IoA](https://github.com/openbmb/ioa): An open-source framework for collaborative AI agents, enabling diverse, distributed agents to team up and tackle complex tasks through internet-like connectivity.
21. [llama-agentic-system ](https://github.com/meta-llama/llama-agentic-system): Agentic components of the Llama Stack APIs.
22. [Agent Zero](https://github.com/frdel/agent-zero): Agent Zero is not a predefined agentic framework. It is designed to be dynamic, organically growing, and learning as you use it.
23. [Agents](https://github.com/aiwaves-cn/agents): An Open-source Framework for Data-centric, Self-evolving Autonomous Language Agents.
24. [AgentScope](https://github.com/modelscope/agentscope): Start building LLM-empowered multi-agent applications in an easier way.
25. [FastAgency](https://github.com/airtai/fastagency): The fastest way to bring multi-agent workflows to production.
26. [Swarm](https://github.com/openai/swarm): Framework for building, orchestrating and deploying multi-agent systems. Managed by OpenAI Solutions team. Experimental framework.
27. [Agent-S](https://github.com/simular-ai/Agent-S): an open agentic framework that uses computers like a human.
28. [PydanticAI](https://github.com/pydantic/pydantic-ai): Agent Framework / shim to use Pydantic with LLMs.
29. [Agentarium](https://github.com/Thytu/Agentarium): open-source framework for creating and managing simulations populated with AI-powered agents.
30. [smolagents](https://github.com/huggingface/smolagents): a barebones library for agents. Agents write python code to call tools and orchestrate other agents.
31. [Cooragent](https://github.com/LeapLabTHU/cooragent): Cooragent is an AI agent collaboration community.
32. [Agno](https://github.com/agno-agi/agno): Agno is a lightweight library for building Agents with memory, knowledge, tools and reasoning.
33. [Suna](https://github.com/kortix-ai/suna): Open Source Generalist AI Agent.
34. [rowboat](https://github.com/rowboatlabs/rowboat): Let AI build multi-agent workflows for you in minutes.
35. [EvoAgentX](https://github.com/EvoAgentX/EvoAgentX): Building a Self-Evolving Ecosystem of AI Agents.
36. [ii-agent](https://github.com/Intelligent-Internet/ii-agent): a new open-source framework to build and deploy intelligent agents.
37. [OWL](https://github.com/camel-ai/owl): Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation.
38. [OpenManus](https://github.com/FoundationAgents/OpenManus): No fortress, purely open ground. OpenManus is Coming.
39. [JoyAgent-JDGenie](https://github.com/jd-opensource/joyagent-jdgenie): 业界首个开源高完成度轻量化通用多智能体产品.
40. [coze-studio](https://github.com/coze-dev/coze-studio): An AI agent development platform with all-in-one visual tools, simplifying agent creation, debugging, and deployment like never before.
41. [OxyGent](https://github.com/jd-opensource/OxyGent): An advanced Python framework that empowers developers to quickly build production-ready intelligent systems.
42. [LazyCraft](https://github.com/LazyAGI/LazyCraft): LazyCraft 是一个基于 LazyLLM 构建的 AI Agent 应用开发与管理平台，旨在协助开发者以 低门槛、低成本 快速构建和发布大模型应用。
43. [OpenAgents](https://github.com/openagents-org/openagents): AI Agent Networks for Open Collaboration.
44. [SandBox](https://github.com/agent-infra/sandbox): All-in-One Sandbox for AI Agents that combines Browser, Shell, File, MCP and VSCode Server in a single Docker container.
45. [DeepAnalyze](https://github.com/ruc-datalab/DeepAnalyze): First agentic LLM for autonomous data science, supporting specific data tasks (data preparation, analysis, modeling, visualization, and insight) and data-oriented deep research (produce analyst-grade research reports).
46. [Astron Agent](https://github.com/iflytek/astron-agent): Enterprise-grade, commercial-friendly agentic workflow platform for building next-generation SuperAgents.
47. [Youtu-Agent](https://github.com/TencentCloudADP/youtu-agent): A simple yet powerful agent framework that delivers with open-source models.
48. [MiroThinker](https://github.com/MiroMindAI/MiroThinker): an open-source search agent model, built for tool-augmented reasoning and real-world information seeking, aiming to match the deep research experience of OpenAI Deep Research and Gemini Deep Research.
49. [Nexent](https://github.com/ModelEngine-Group/nexent): A zero-code platform for auto-generating agents — no orchestration, no complex drag-and-drop required, using pure language to develop any agent you want.
50. [Yunjue-Agent](https://github.com/YunjueTech/Yunjue-Agent): A Fully Reproducible, Zero-Start In-Situ Self-Evolving Agent System for Open-Ended Tasks.
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</div>
## 研究 Research
#### 写作
- PaperDebugger: https://github.com/PaperDebugger/PaperDebugger
- Chat Overleaf: https://github.com/anuin-cat/chat-overleaf
- 文智云助手: https://overleaf.top/
- LiteWrite: https://litewrite.ai/
- Prism: https://openai.com/zh-Hans-CN/prism/
#### 审稿
- PaperReview: https://paperreview.ai/
- aiXiv: https://aixiv.science/
- OpenJudge Review: https://openjudge.me/paper_review
#### 其他
- Paper2Video: https://github.com/showlab/Paper2Video
- Paper2Poster: https://github.com/Paper2Poster/Paper2Poster
- AutoPR: https://github.com/irgolic/AutoPR
- Auto-Slides: https://github.com/Westlake-AGI-Lab/Auto-Slides
- EvoPresent: https://github.com/eric-ai-lab/EvoPresent
- Paper2All: https://github.com/YuhangChen1/Paper2All
- AutoPage: https://github.com/AutoLab-SAI-SJTU/AutoPage
- pdf2video: https://github.com/DangJin/pdf2video
- Idea2Paper: https://github.com/AgentAlphaAGI/Idea2Paper
- PaperX: https://github.com/yutao1024/PaperX
- figures4papers: https://github.com/ChenLiu-1996/figures4papers
- PaperBanana: https://github.com/dwzhu-pku/PaperBanana
- PaperBanana-Pro: https://github.com/elpsykongloo/PaperBanana-Pro
#### 全自动科研
- EvoScientist: https://github.com/EvoScientist/EvoScientist
- Auto-claude-code-research-in-sleep: https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep
- ArgusBot: https://github.com/waltstephen/ArgusBot
- Station: https://github.com/dualverse-ai/station
- Dr.Claw: https://github.com/OpenLAIR/dr-claw
- Redigg: https://github.com/redigg/redigg
- AutoResearchClaw: https://github.com/aiming-lab/AutoResearchClaw
- NanoResearch: https://github.com/OpenRaiser/NanoResearch
- ScienceClaw: https://github.com/AgentTeam-TaichuAI/ScienceClaw
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</div>
## 代码 Coding
1. [Cloi CLI](https://github.com/cloi-ai/cloi): Local debugging agent that runs in your terminal.
2. [Devin](https://devin.ai/)
3. [v0](https://v0.dev/)
4. [Blot.new](https://bolt.new/)
5. [cursor](https://www.cursor.com/)
6. [Windsurf](https://codeium.com/windsurf)
7. [cline](https://github.com/cline/cline)
8. [Trae](https://www.trae.ai/)
9. [MGX](https://mgx.dev/)
10. [Roo Code](https://github.com/RooCodeInc/Roo-Code)
11. [Kilo Code](https://github.com/Kilo-Org/kilocode)
12. [AugmentCode](https://www.augmentcode.com/)
13. [Claude Code](https://github.com/anthropics/claude-code)
14. [Gemini CLI](https://github.com/google-gemini/gemini-cli)
15. [Serena](https://github.com/oraios/serena)
16. [Claudia](https://github.com/getAsterisk/claudia)
17. [OpenCode](https://github.com/opencode-ai/opencode)
18. [Kiro](https://kiro.dev/)
19. [CodeBuddy](https://copilot.tencent.com/)
20. [Kiro](https://kiro.dev/)
21. [CodeX](https://github.com/openai/codex)
22. [Kimi-CLI](https://github.com/MoonshotAI/kimi-cli)
23. [opencode](https://github.com/anomalyco/opencode)
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## 视频 Video
#### 模型
> [!NOTE]
> 🤝[Awesome-Video-Diffusion](https://github.com/showlab/Awesome-Video-Diffusion)
1. [HunyuanVideo](https://github.com/Tencent/HunyuanVideo)
2. [CogVideo](https://github.com/THUDM/CogVideo)
3. [Wan2.1](https://github.com/Wan-Video/Wan2.1)
4. [Open-Sora](https://github.com/hpcaitech/Open-Sora)
5. [Open-Sora-Plan](https://github.com/PKU-YuanGroup/Open-Sora-Plan)
6. [LTX-Video](https://github.com/Lightricks/LTX-Video)
7. [Step-Video-T2V](https://github.com/stepfun-ai/Step-Video-T2V)
8. [Step1X-Edit](https://github.com/stepfun-ai/Step1X-Edit) `Editing`
9. [Wan2.1-VACE](https://huggingface.co/Wan-AI/Wan2.1-VACE-14B) `Editing`
10. [ICEdit](https://github.com/River-Zhang/ICEdit) `Editing`
11. [mochi-1-preview](https://huggingface.co/genmo/mochi-1-preview)
12. [Wan2.1-Fun](https://huggingface.co/collections/alibaba-pai/wan21-fun-v11-680f514c89fe7b4df9d44f17)
13. [Wan2.1-FLF2V](https://huggingface.co/Wan-AI/Wan2.1-FLF2V-14B-720P) `首尾帧`
14. [MAGI-1](https://github.com/SandAI-org/MAGI-1) `自回归模型`
15. [SkyReels-V2](https://github.com/SkyworkAI/SkyReels-V2)
16. [FramePack](https://github.com/lllyasviel/FramePack)
17. [Pusa-VidGen](https://github.com/Yaofang-Liu/Pusa-VidGen)
18. [Wan2.2](https://github.com/Wan-Video/Wan2.2)
19. [MoGA](https://arxiv.org/pdf/2510.18692) `长视频`
20. [LongCat-Video](https://huggingface.co/meituan-longcat/LongCat-Video)
21. [HunyuanVideo-1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5)
22. [LTX-2](https://huggingface.co/Lightricks/LTX-2)
- [Training](https://github.com/Lightricks/LTX-2/blob/main/packages/ltx-trainer/README.md)
#### 编辑
1. Wan2.1-VACE-14B: https://huggingface.co/Wan-AI/Wan2.1-VACE-14B
2. Ditto: https://github.com/EzioBy/Ditto
#### 训练
- https://github.com/hao-ai-lab/FastVideo
- https://github.com/tdrussell/diffusion-pipe
- https://github.com/VideoVerses/VideoTuna
- https://github.com/modelscope/DiffSynth-Studio
- https://github.com/huggingface/diffusers
- https://github.com/kohya-ss/musubi-tuner
- https://github.com/spacepxl/HunyuanVideo-Training
- https://github.com/Tele-AI/TeleTron
- https://github.com/Yaofang-Liu/Mochi-Full-Finetuner
- https://github.com/bghira/SimpleTuner
#### 推理
- https://github.com/ModelTC/LightX2V
- https://github.com/thu-ml/TurboDiffusion
#### 实用工具
- [PySceneDetect](https://github.com/Breakthrough/PySceneDetect): Python and OpenCV-based scene cut/transition detection program & library.
- [DOVER](https://github.com/VQAssessment/DOVER): Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives.
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## 图片 Image
#### 生成
- [awesome-nano-banana](https://github.com/JimmyLv/awesome-nano-banana)
- [Awesome-Nano-Banana-images](https://github.com/PicoTrex/Awesome-Nano-Banana-images)
- HunyuanImage-3.0：https://github.com/Tencent-Hunyuan/HunyuanImage-3.0
- Seedream 4.0：https://arxiv.org/abs/2509.20427
- LongCat-Image：https://huggingface.co/meituan-longcat/LongCat-Image
- Z-Image-Turbo：https://huggingface.co/Tongyi-MAI/Z-Image-Turbo
- https://huggingface.co/inclusionAI/TwinFlow
- Qwen-Image：https://huggingface.co/Qwen/Qwen-Image
- Qwen-Image-2512：https://huggingface.co/Qwen/Qwen-Image-2512
- Z-Image：https://huggingface.co/Tongyi-MAI/Z-Image
#### 编辑
- ChronoEdit-14B: https://huggingface.co/nvidia/ChronoEdit-14B-Diffusers
- Eigen-Banana-Qwen-Image-Edit: https://huggingface.co/eigen-ai-labs/eigen-banana-qwen-image-edit
- Qwen-Image-Edit-2509: https://huggingface.co/Qwen/Qwen-Image-Edit-2509
- Upscale: https://huggingface.co/vafipas663/Qwen-Edit-2509-Upscale-LoRA
- Multiple-angles: https://huggingface.co/dx8152/Qwen-Edit-2509-Multiple-angles
- Multi-Angle-Lighting: https://huggingface.co/dx8152/Qwen-Edit-2509-Multi-Angle-Lighting
- LongCat-Image-Edit: https://huggingface.co/meituan-longcat/LongCat-Image-Edit
- Qwen-Image-Edit-2511: https://huggingface.co/Qwen/Qwen-Image-Edit-2511
- Qwen-Image-Edit-2511-Upscale2K: https://huggingface.co/valiantcat/Qwen-Image-Edit-2511-Upscale2K
- Qwen-Image-Edit-2511-Multiple-Angles-LoRA: https://huggingface.co/fal/Qwen-Image-Edit-2511-Multiple-Angles-LoRA
- FireRed-Image-Edit: https://huggingface.co/FireRedTeam/FireRed-Image-Edit-1.0
#### 统一
- GLM-Image: https://huggingface.co/zai-org/GLM-Image
- https://huggingface.co/black-forest-labs/FLUX.2-klein-4B
- https://huggingface.co/black-forest-labs/FLUX.2-klein-9B
#### 训练
- Ostris：https://github.com/ostris/ai-toolkit
- FlymyAI：https://github.com/FlyMyAI/flymyai-lora-trainer
- Nitro-T：https://github.com/AMD-AGI/Nitro-T
- DiffSynth-Studio：https://github.com/modelscope/DiffSynth-Studio
- Musubi Tuner: https://github.com/kohya-ss/musubi-tuner
- SimpleTuner: https://github.com/bghira/SimpleTuner
- MS Training: https://www.modelscope.cn/aigc/modelTraining
- Finetune HunyuanImage-3.0: https://github.com/PhotonAISG/hunyuan-image3-finetune
- OneTrainer: https://github.com/Nerogar/OneTrainer
- Finetune LongCat-Image and Edit: https://github.com/meituan-longcat/LongCat-Image/tree/main/train_examples
#### 评估
- ULMEvalKit：https://github.com/ULMEvalKit/ULMEvalKit
#### 推理
- TypemovieInfer: https://github.com/typemovie/TypemovieInfer
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## 搜索 Search
1. [OpenSearch GPT](https://github.com/supermemoryai/opensearch-ai): SearchGPT / Perplexity clone, but personalised for you.
2. [MindSearch](https://github.com/InternLM/MindSearch): An LLM-based Multi-agent Framework of Web Search Engine (like Perplexity.ai Pro and SearchGPT).
3. [nanoPerplexityAI](https://github.com/Yusuke710/nanoPerplexityAI): The simplest open-source implementation of perplexity.ai.
4. [curiosity](https://github.com/jank/curiosity): Try to build a Perplexity-like user experience.
5. [MiniPerplx](https://github.com/zaidmukaddam/miniperplx): A minimalistic AI-powered search engine that helps you find information on the internet.
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## 语音 Speech
#### TTS
1. SpeechGPT-2.0-preview: https://github.com/OpenMOSS/SpeechGPT-2.0-preview
2. Moss-TTSD：https://github.com/OpenMOSS/MOSS-TTSD
3. Index-TTS：https://github.com/index-tts/index-tts
4. MegaTTS3：https://github.com/bytedance/MegaTTS3
5. F5-TTS：https://github.com/SWivid/F5-TTS
6. GPT-SoVITS：https://github.com/RVC-Boss/GPT-SoVITS
7. CosyVoice：https://github.com/FunAudioLLM/CosyVoice
8. Spark-TTS：https://github.com/SparkAudio/Spark-TTS
9. OpenVoice：https://github.com/myshell-ai/OpenVoice
10. Dia：https://github.com/nari-labs/dia
11. ChatTTS：https://github.com/2noise/ChatTTS
12. Fish Speech：https://github.com/fishaudio/fish-speech
13. Edge-TTS：https://github.com/rany2/edge-tts
14. Bark：https://github.com/suno-ai/bark
15. kokoro: https://github.com/hexgrad/kokoro
16. Higgs Audio V2: https://github.com/boson-ai/higgs-audio 【[Training](https://github.com/JimmyMa99/train-higgs-audio)】
17. KittenTTS: https://github.com/KittenML/KittenTTS
18. ZipVoice: https://github.com/k2-fsa/ZipVoice
19. VyvoTTS: https://github.com/Vyvo-Labs/VyvoTTS
20. VibeVoice: https://github.com/microsoft/VibeVoice
21. Index-TTS-2: https://huggingface.co/IndexTeam/IndexTTS-2
22. FireRedTTS2: https://github.com/FireRedTeam/FireRedTTS2
23. VoxCPM: https://github.com/OpenBMB/VoxCPM/
24. Neutts-Air: https://github.com/neuphonic/neutts-air
25. Maya1: https://huggingface.co/maya-research/maya1
26. VibeVoice: https://huggingface.co/collections/microsoft/vibevoice
27. GLM-TTS: https://github.com/zai-org/GLM-TTS
28. Fun-CosyVoice3: https://huggingface.co/FunAudioLLM/Fun-CosyVoice3-0.5B-2512
29. Qwen3-TTS:https://huggingface.co/collections/Qwen/qwen3-tts
30. Ming-Omni-TTS: https://github.com/inclusionAI/Ming-omni-tts
#### STT/ASR
1. Kyutai: https://github.com/kyutai-labs/delayed-streams-modeling
2. Whisper: https://github.com/openai/whisper
3. Audio Flamingo 3: https://huggingface.co/nvidia/audio-flamingo-3
4. Voxtral: https://huggingface.co/mistralai/Voxtral-Mini-3B-2507
5. Step-Audio2: https://github.com/stepfun-ai/Step-Audio2
6. SoulX-Podcast: https://huggingface.co/collections/Soul-AILab/soulx-podcast
7. Omnilingual ASR: https://github.com/facebookresearch/omnilingual-asr
8. Fun-ASR: https://huggingface.co/FunAudioLLM/Fun-ASR-Nano-2512
9. VibeVoice-ASR: https://huggingface.co/microsoft/VibeVoice-ASR
10. Qwen3-ASR: https://github.com/QwenLM/Qwen3-ASR
#### Voice Interaction
1. Fun-Audio-Chat: https://huggingface.co/FunAudioLLM/Fun-Audio-Chat-8B
2. Chroma 1.0: https://huggingface.co/FlashLabs/Chroma-4B
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## 统一模型 Unified Model
> 现在统一模型已经从`理解+生成`变成`理解+生成+编辑`
- Emu-2：https://arxiv.org/abs/2312.13286
- Emu-3：https://arxiv.org/abs/2409.18869
- Emu-1：https://arxiv.org/abs/2307.05222
- Janus：https://github.com/deepseek-ai/Janus
- Janus-Pro：http://arxiv.org/abs/2508.05954
- show-o：https://arxiv.org/abs/2408.12528
- Any-GPT：https://arxiv.org/abs/2402.12226
- Next-GPT：https://arxiv.org/pdf/2309.05519.pdf
- CoDi：https://arxiv.org/abs/2305.11846
- Seed-X：https://arxiv.org/abs/2404.14396
- Dream-LLM：https://arxiv.org/abs/2309.11499
- Chameleon：https://arxiv.org/abs/2405.09818
- Spider：https://arxiv.org/abs/2411.09439
- MedViLaM：https://arxiv.org/abs/2409.19684
- VITRON：https://github.com/SkyworkAI/Vitron
- TokenFlow：https://github.com/ByteFlow-AI/TokenFlow
- OneDiffusion：https://github.com/lehduong/OneDiffusion
- MetaMorph: https://arxiv.org/abs/2412.14164
- LlamaFusion：https://arxiv.org/abs/2412.15188
- InstructSeg：https://arxiv.org/abs/2412.14006
- VILA-U：https://arxiv.org/abs/2409.04429
- Ullava: https://github.com/OPPOMKLab/u-LLaVA
- ILLUME: https://arxiv.org/abs/2412.06673
- Vitron:https://arxiv.org/abs/2412.19806
- SynerGen-VL：https://arxiv.org/abs/2412.09604
- Align Anything：https://arxiv.org/abs/2412.15838
- Mico：https://arxiv.org/abs/2406.09412
- OneLLM:https://arxiv.org/abs/2312.03700
- X-VILA:https://arxiv.org/abs/2405.19335
- OLA：https://arxiv.org/abs/2502.04328
- Transfusion: https://arxiv.org/abs/2408.11039
- JanusFlow: https://arxiv.org/abs/2411.07975
- HealthGPT：https://arxiv.org/abs/2502.09838 `Medical`
- BAGEL：https://arxiv.org/abs/2505.14683
- Qwen2.5-Omni：https://arxiv.org/abs/2503.20215
- X2I：https://arxiv.org/abs/2503.06134
- Bifrost-1：https://arxiv.org/abs/2508.05954
- OmniGen2：https://arxiv.org/abs/2506.18871
- UniPic：https://github.com/SkyworkAI/UniPic
- VeOmni：https://github.com/ByteDance-Seed/VeOmni `Training`
- NextStep-1：https://arxiv.org/abs/2508.10711
- UniUGG: https://arxiv.org/abs/2508.11952 `3D`
- Omni-Video：https://arxiv.org/abs/2507.06119
- OneCAT：https://arxiv.org/abs/2509.03498
- Lumina-DiMOO：https://github.com/Alpha-VLLM/Lumina-DiMOO
- UAE：https://github.com/PKU-YuanGroup/UAE
- RecA：https://arxiv.org/abs/2509.07295
- UniLM：https://arxiv.org/abs/1905.03197
- Hyper-Bagel：https://arxiv.org/abs/2509.18824
- Ming-UniVision：https://arxiv.org/abs/2510.06590
- EditVerse：https://arxiv.org/abs/2509.20360
- LightBagel: https://arxiv.org/abs/2510.22946
- DreamLLM: https://arxiv.org/abs/2309.11499
- X-Omni: https://arxiv.org/abs/2507.22058
- Ming-flash-omni-Preview: https://huggingface.co/inclusionAI/Ming-flash-omni-Preview
- Omni-View: https://arxiv.org/abs/2511.07222
- NExT-OMNI: https://arxiv.org/abs/2510.13721
- Uni-MoE-2.0-Omni: https://arxiv.org/abs/2511.12609
- LongCat-Flash-Omni: https://huggingface.co/meituan-longcat/LongCat-Flash-Omni
- ShapeLLM-Omni: https://arxiv.org/abs/2506.01853
- UniGen-1.5: https://arxiv.org/abs/2511.14760
- Jodi: https://arxiv.org/abs/2505.19084
- UniModel: https://arxiv.org/abs/2511.16917
- TUNA: https://arxiv.org/abs/2512.02014
- HBridge: https://arxiv.org/abs/2511.20520
- EMMA: https://arxiv.org/abs/2512.04810
- OpenOmni: https://github.com/RainBowLuoCS/OpenOmni
- Ming-Flash-Omni: https://arxiv.org/abs/2510.24821
- STAR: https://github.com/mm-mvr/star
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</div>
## 书籍 Book
1. [《大规模语言模型：从理论到实践》](https://intro-llm.github.io/)
2. [《大语言模型》](https://llmbook-zh.github.io/)
3. [《动手学大模型Dive into LLMs》](https://github.com/Lordog/dive-into-llms)
4. [《动手做AI Agent》](https://book.douban.com/subject/36884058/)
5. [《Build a Large Language Model (From Scratch)》](https://github.com/rasbt/LLMs-from-scratch)
6. [《多模态大模型》](https://github.com/HCPLab-SYSU/Book-of-MLM)
7. [《Generative AI Handbook: A Roadmap for Learning Resources》](https://genai-handbook.github.io/)
8. [《Understanding Deep Learning》](https://udlbook.github.io/udlbook/)
9. [《Illustrated book to learn about Transformers & LLMs》](https://www.reddit.com/r/MachineLearning/comments/1ew1hws/p_illustrated_book_to_learn_about_transformers/)
10. [《Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG》](https://www.amazon.com/Building-LLMs-Production-Reliability-Fine-Tuning/dp/B0D4FFPFW8?crid=7OAXELUKGJE4&dib=eyJ2IjoiMSJ9.Qr3e3VSH8LSo_j1M7sV7GfS01q_W1LDYd2uGlvGJ8CW-t4DTlng6bSeOlZBryhp6HJN5K1HqWMVVgabU2wz2i9yLpy_AuaZN-raAEbenKx2NHtzZA3A4k-N7GpnldF1baCarA_V1CRF-aCdc9_3WSX7SaEzmpyDv22TTyltcKT74HAb2KiQqBGLhQS3cEAnzChcqGa1Xp-XhbMnplVwT7xZLApE3tGLhDOgi5GmSi9w.8SY_4NBEkm68YF4GwhDnz0r81ZB1d8jr-gK9IMJE5AE&dib_tag=se&keywords=building+llms+for+production&qid=1716376414&sprefix=building+llms+for+production,aps,101&sr=8-1&linkCode=sl1&tag=whatsai06-20&linkId=ee102fda07a0eb51710fcdd8b8d20c28&language=en_US&ref_=as_li_ss_tl)
11. [《大型语言模型实战指南：应用实践与场景落地》](https://github.com/liucongg/LLMsBook)
12. [《Hands-On Large Language Models》](https://github.com/handsOnLLM/Hands-On-Large-Language-Models)
13. [《自然语言处理：大模型理论与实践》](https://nlp-book.swufenlp.group/)
14. [《动手学强化学习》](https://hrl.boyuai.com/)
15. [《面向开发者的LLM入门教程》](https://datawhalechina.github.io/llm-cookbook/#/)
16. [《大模型基础》](https://github.com/ZJU-LLMs/Foundations-of-LLMs)
17. [Taming LLMs: A Practical Guide to LLM Pitfalls with Open Source Software ](https://www.tamingllms.com/)
18. [Foundations of Large Language Models](https://arxiv.org/abs/2501.09223)
19. [Textbook on reinforcement learning from human feedback](https://github.com/natolambert/rlhf-book)
20. [《大模型算法：强化学习、微调与对齐》](https://book.douban.com/subject/37331056/)
21. [《The Smol Training Playbook: The Secrets to Building World-Class LLMs》](https://github.com/WangRongsheng/awesome-LLM-resources/blob/main/books/the-smol-training-playbook-the-secrets-to-building-world-class-llms.pdf)
22. [《从零开始构建智能体》——从零开始的智能体原理与实践教程](https://github.com/datawhalechina/hello-agents)
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</div>
## 课程 Course
> [LLM Resources Hub](https://llmresourceshub.vercel.app/)
1. [斯坦福 CS224N: Natural Language Processing with Deep Learning](https://web.stanford.edu/class/cs224n/)
2. [吴恩达: Generative AI for Everyone](https://www.deeplearning.ai/courses/generative-ai-for-everyone/)
3. [吴恩达: LLM series of courses](https://learn.deeplearning.ai/)
4. [ACL 2023 Tutorial: Retrieval-based Language Models and Applications](https://acl2023-retrieval-lm.github.io/)
5. [llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.](https://github.com/mlabonne/llm-course)
6. [微软: Generative AI for Beginners](https://github.com/microsoft/generative-ai-for-beginners)
7. [微软: State of GPT](https://www.youtube.com/watch?v=bZQun8Y4L2A)
8. [HuggingFace NLP Course](https://huggingface.co/learn/nlp-course/chapter1/1)
9. [清华 NLP 刘知远团队大模型公开课](https://www.bilibili.com/video/BV1UG411p7zv/?vd_source=c739db1ebdd361d47af5a0b8497417db)
10. [斯坦福 CS25: Transformers United V4](https://web.stanford.edu/class/cs25/)
11. [斯坦福 CS324: Large Language Models](https://stanford-cs324.github.io/winter2022/)
12. [普林斯顿 COS 597G (Fall 2022): Understanding Large Language Models](https://www.cs.princeton.edu/courses/archive/fall22/cos597G/)
13. [约翰霍普金斯 CS 601.471/671 NLP: Self-supervised Models](https://self-supervised.cs.jhu.edu/sp2023/index.html)
14. [李宏毅 GenAI课程](https://www.youtube.com/watch?v=yiY4nPOzJEg&list=PLJV_el3uVTsOePyfmkfivYZ7Rqr2nMk3W)
15. [openai-cookbook](https://github.com/openai/openai-cookbook): Examples and guides for using the OpenAI API.
16. [Hands on llms](https://github.com/iusztinpaul/hands-on-llms): Learn about LLM, LLMOps, and vector DBS for free by designing, training, and deploying a real-time financial advisor LLM system.
17. [滑铁卢大学 CS 886: Recent Advances on Foundation Models](https://cs.uwaterloo.ca/~wenhuche/teaching/cs886/)
18. [Mistral: Getting Started with Mistral](https://www.deeplearning.ai/short-courses/getting-started-with-mistral/)
19. [斯坦福 CS25: Transformers United V4](https://web.stanford.edu/class/cs25/)
20. [Coursera: Chatgpt 应用提示工程](https://www.coursera.org/learn/prompt-engineering)
21. [LangGPT](https://github.com/langgptai/LangGPT): Empowering everyone to become a prompt expert!
22. [mistralai-cookbook](https://github.com/mistralai/cookbook)
23. [Introduction to Generative AI 2024 Spring](https://speech.ee.ntu.edu.tw/~hylee/genai/2024-spring.php)
24. [build nanoGPT](https://github.com/karpathy/build-nanogpt): Video+code lecture on building nanoGPT from scratch.
25. [LLM101n](https://github.com/karpathy/LLM101n): Let's build a Storyteller.
26. [Knowledge Graphs for RAG](https://www.deeplearning.ai/short-courses/knowledge-graphs-rag/)
27. [LLMs From Scratch (Datawhale Version)](https://github.com/datawhalechina/llms-from-scratch-cn)
28. [OpenRAG](https://openrag.notion.site/Open-RAG-c41b2a4dcdea4527a7c1cd998e763595)
29. [通往AGI之路](https://waytoagi.feishu.cn/wiki/QPe5w5g7UisbEkkow8XcDmOpn8e)
30. [Andrej Karpathy - Neural Networks: Zero to Hero](https://www.youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ)
31. [Interactive visualization of Transformer](https://poloclub.github.io/transformer-explainer/)
32. [andysingal/llm-course](https://github.com/andysingal/llm-course)
33. [LM-class](https://lm-class.org/lectures)
34. [Google Advanced: Generative AI for Developers Learning Path](https://www.cloudskillsboost.google/paths/183)
35. [Anthropics：Prompt Engineering Interactive Tutorial](https://github.com/anthropics/courses/tree/master/prompt_engineering_interactive_tutorial/Anthropic%201P)
36. [LLMsBook](https://github.com/liucongg/LLMsBook)
37. [Large Language Model Agents](https://llmagents-learning.org/f24)
38. [Cohere LLM University](https://cohere.com/llmu)
39. [LLMs and Transformers](https://www.ambujtewari.com/LLM-fall2024/)
40. [Smol Vision](https://github.com/merveenoyan/smol-vision): Recipes for shrinking, optimizing, customizing cutting edge vision models.
41. [Multimodal RAG: Chat with Videos](https://www.deeplearning.ai/short-courses/multimodal-rag-chat-with-videos/)
42. [LLMs Interview Note](https://github.com/wdndev/llm_interview_note)
43. [RAG++ : From POC to production](https://www.wandb.courses/courses/rag-in-production): Advanced RAG course.
44. [Weights & Biases AI Academy](https://www.wandb.courses/pages/w-b-courses): Finetuning, building with LLMs, Structured outputs and more LLM courses.
45. [Prompt Engineering & AI tutorials & Resources](https://promptengineering.org/)
46. [Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer](https://www.youtube.com/watch?v=sVcwVQRHIc8)
47. [LLM Evaluation: A Complete Course](https://www.comet.com/site/llm-course/)
48. [HuggingFace Learn](https://huggingface.co/learn)
49. [Andrej Karpathy: Deep Dive into LLMs like ChatGPT](https://www.youtube.com/watch?v=7xTGNNLPyMI)
50. [LLM技术科普](https://github.com/karminski/one-small-step)
51. [CS25: Transformers United V5](https://web.stanford.edu/class/cs25/)
52. [RAG_Techniques](https://github.com/NirDiamant/RAG_Techniques): This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
53. [100+ LLM & RL Algorithm Maps | 原创 LLM / RL 100+原理图](https://github.com/changyeyu/LLM-RL-Visualized)
54. [Reinforcement Learning of Large Language Models](https://ernestryu.com/courses/RL-LLM.html)
55. [NanoChat](https://github.com/karpathy/nanochat): The best ChatGPT that $100 can buy.
56. [斯坦福CS146S: The Modern Software Developer](https://themodernsoftware.dev/)
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## 教程 Tutorial
1. [动手学大模型应用开发](https://datawhalechina.github.io/llm-universe/#/)
2. [AI开发者频道](https://techdiylife.github.io/blog/blog_list.html)
3. [B站：五里墩茶社](https://space.bilibili.com/615957867/?spm_id_from=333.999.0.0)
4. [B站：木羽Cheney](https://space.bilibili.com/3537113897241540/?spm_id_from=333.999.0.0)
5. [YTB：AI Anytime](https://www.youtube.com/channel/UC-zVytOQB62OwMhKRi0TDvg)
6. [B站：漆妮妮](https://space.bilibili.com/1262370256/?spm_id_from=333.999.0.0)
7. [Prompt Engineering Guide](https://www.promptingguide.ai/)
8. [YTB: AI超元域](https://www.youtube.com/@AIsuperdomain)
9. [B站：TechBeat人工智能社区](https://space.bilibili.com/209732435)
10. [B站：黄益贺](https://space.bilibili.com/322961825)
11. [B站：深度学习自然语言处理](https://space.bilibili.com/507524288)
12. [LLM Visualization](https://bbycroft.net/llm)
13. [知乎: 原石人类](https://www.zhihu.com/people/zhang-shi-tou-88-98/posts)
14. [B站：小黑黑讲AI](https://space.bilibili.com/1963375439/?spm_id_from=333.999.0.0)
15. [B站：面壁的车辆工程师](https://space.bilibili.com/669720247/?spm_id_from=333.999.0.0)
16. [B站：AI老兵文哲](https://space.bilibili.com/472543316/?spm_id_from=333.999.0.0)
17. [Large Language Models (LLMs) with Colab notebooks](https://mlabonne.github.io/blog/)
18. [YTB：IBM Technology](https://www.youtube.com/@IBMTechnology)
19. [YTB: Unify Reading Paper Group](https://www.youtube.com/playlist?list=PLwNuX3xB_tv91QvDXlW2TjrLGHW51uMul)
20. [Chip Huyen](https://huyenchip.com/blog/)
21. [How Much VRAM](https://github.com/AlexBodner/How_Much_VRAM)
22. [Blog: 科学空间（苏剑林）](https://kexue.fm/)
23. [YTB: Hyung Won Chung](https://www.youtube.com/watch?v=dbo3kNKPaUA)
24. [Blog: Tejaswi kashyap](https://medium.com/@tejaswi_kashyap)
25. [Blog: 小昇的博客](https://xiaosheng.blog/)
26. [知乎: ybq](https://www.zhihu.com/people/ybq-29-32/posts)
27. [W&B articles](https://wandb.ai/fully-connected)
28. [Huggingface Blog](https://huggingface.co/blog/zh)
29. [Blog: GbyAI](https://gby.ai/)
30. [Blog: mlabonne](https://mlabonne.github.io/blog/)
31. [LLM-Action](https://github.com/liguodongiot/llm-action)
32. [Blog: Lil’Log (OponAI)](https://lilianweng.github.io/)
33. [B站: 毛玉仁](https://space.bilibili.com/3546823125895398)
34. [AI-Guide-and-Demos](https://github.com/Hoper-J/AI-Guide-and-Demos-zh_CN)
35. [cnblog: 第七子](https://www.cnblogs.com/theseventhson)
36. [Implementation of all RAG techniques in a simpler way.](https://github.com/FareedKhan-dev/all-rag-techniques)
37. [Theoretical Machine Learning: A Handbook for Everyone](https://www.tengjiaye.com/mlbook.html)
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</div>
## 论文 Paper
> [!NOTE]
> 🤝[Huggingface Daily Papers](https://huggingface.co/papers)、[Cool Papers](https://papers.cool/)、[ML Papers Explained](https://github.com/dair-ai/ML-Papers-Explained)
1. [Hermes-3-Technical-Report](https://nousresearch.com/wp-content/uploads/2024/08/Hermes-3-Technical-Report.pdf)
2. [The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783)
3. [Qwen Technical Report](https://arxiv.org/abs/2309.16609)
4. [Qwen2 Technical Report](https://arxiv.org/abs/2407.10671)
5. [Qwen2-vl Technical Report](https://arxiv.org/abs/2409.12191)
6. [DeepSeek LLM: Scaling Open-Source Language Models with Longtermism](https://arxiv.org/abs/2401.02954)
7. [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://arxiv.org/abs/2405.04434)
8. [Baichuan 2: Open Large-scale Language Models](https://arxiv.org/abs/2309.10305)
9. [DataComp-LM: In search of the next generation of training sets for language models](https://arxiv.org/abs/2406.11794)
10. [OLMo: Accelerating the Science of Language Models](https://arxiv.org/abs/2402.00838)
11. [MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series](https://arxiv.org/abs/2405.19327)
12. [Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model](https://arxiv.org/abs/2404.04167)
13. [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://arxiv.org/abs/2404.14219)
14. [Jamba-1.5: Hybrid Transformer-Mamba Models at Scale](https://arxiv.org/abs/2408.12570v1)
15. [Jamba: A Hybrid Transformer-Mamba Language Model](https://arxiv.org/abs/2403.19887)
16. [Textbooks Are All You Need](https://arxiv.org/abs/2306.11644)
17. [Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Models](https://arxiv.org/abs/2408.02085) `data`
18. [OLMoE: Open Mixture-of-Experts Language Models](https://arxiv.org/abs/2409.02060)
19. [Model Merging Paper](https://huggingface.co/collections/osanseviero/model-merging-65097893623330a3a51ead66)
20. [Baichuan-Omni Technical Report](https://arxiv.org/abs/2410.08565)
21. [1.5-Pints Technical Report: Pretraining in Days, Not Months – Your Language Model Thrives on Quality Data](https://arxiv.org/abs/2408.03506)
22. [Baichuan Alignment Technical Report](https://arxiv.org/abs/2410.14940v1)
23. [Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent](https://arxiv.org/abs/2411.02265)
24. [Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models](https://arxiv.org/abs/2409.17146)
25. [TÜLU 3: Pushing Frontiers in Open Language Model Post-Training](https://arxiv.org/abs/2411.15124)
26. [Phi-4 Technical Report](https://arxiv.org/abs/2412.08905)
27. [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://arxiv.org/abs/2412.05271)
28. [Qwen2.5 Technical Report](https://arxiv.org/abs/2412.15115)
29. [YuLan-Mini: An Open Data-efficient Language Model](https://arxiv.org/abs/2412.17743)
30. [An Introduction to Vision-Language Modeling](https://arxiv.org/abs/2405.17247)
31. [DeepSeek V3 Technical Report](https://github.com/WangRongsheng/awesome-LLM-resourses/blob/main/docs/DeepSeek_V3.pdf)
32. [2 OLMo 2 Furious](https://arxiv.org/abs/2501.00656)
33. [Yi-Lightning Technical Report](https://arxiv.org/abs/2412.01253)
34. [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://github.com/deepseek-ai/DeepSeek-R1)
35. [KIMI K1.5](https://github.com/WangRongsheng/awesome-LLM-resourses/blob/main/docs/Kimi_k1.5.pdf)
36. [Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models](https://arxiv.org/abs/2501.14818)
37. [Qwen2.5-VL Technical Report](https://arxiv.org/abs/2502.13923)
38. [Baichuan-M1: Pushing the Medical Capability of Large Language Models](https://arxiv.org/abs/2502.12671)
39. [Predictable Scale: Part I -- Optimal Hyperparameter Scaling Law in Large Language Model Pretraining](https://arxiv.org/abs/2503.04715)
40. [SkyLadder: Better and Faster Pretraining via Context Window Scheduling](https://arxiv.org/abs/2503.15450)
41. [Qwen2.5-Omni technical report](https://github.com/QwenLM/Qwen2.5-Omni/blob/main/assets/Qwen2.5_Omni.pdf)
42. [Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs](https://arxiv.org/abs/2503.05139)
43. [Gemma 3 Technical Report](https://arxiv.org/abs/2503.19786)
44. [Open-Qwen2VL: Compute-Efficient Pre-Training of Fully-Open Multimodal LLMs on Academic Resources](https://arxiv.org/abs/2504.00595)
45. [Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs](https://arxiv.org/abs/2504.07866)
46. [MiMo: Unlocking the Reasoning Potential of Language Model – From Pretraining to Posttraining](https://github.com/XiaomiMiMo/MiMo/blob/main/MiMo-7B-Technical-Report.pdf)
47. [Phi-4 Technical Report](https://arxiv.org/abs/2412.08905)
48. [Llama-Nemotron: Efficient Reasoning Models](https://arxiv.org/abs/2505.00949)
49. [Qwen3 Technical Report](https://github.com/QwenLM/Qwen3/blob/main/Qwen3_Technical_Report.pdf)
50. [MiMo-VL Technical Report](https://arxiv.org/abs/2506.03569v1)
51. [ERNIE Technical Report](https://github.com/WangRongsheng/awesome-LLM-resources/blob/main/docs/ERNIE_Technical_Report_compressed.pdf)
52. [Kwai Keye-VL Technical Report](https://arxiv.org/abs/2507.01949)
53. [Kimi K2 Technical Report](https://github.com/MoonshotAI/Kimi-K2/blob/main/tech_report.pdf)
54. [KAT-V1: Kwai-AutoThink Technical Report](https://arxiv.org/abs/2507.08297v3)
55. [Step3](https://github.com/stepfun-ai/Step3)
56. [SAIL-VL2 Technical Report](https://arxiv.org/abs/2509.14033)
57. [LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training](https://arxiv.org/abs/2509.23661) [85M-Midtraining Data](https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Mid-Training-85M) [22M Instruct Data](https://huggingface.co/datasets/mvp-lab/LLaVA-OneVision-1.5-Instruct-Data)
58. [Olmo3](https://www.datocms-assets.com/64837/1763646865-olmo_3_technical_report-1.pdf): Charting a path through the model flow to lead open-source AI. [Website](https://allenai.org/blog/olmo3)
59. [OpenMMReasoner](https://arxiv.org/abs/2511.16334)
60. [Qwen3-VL Technical Report](https://arxiv.org/pdf/2511.21631)
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## 社区 Community
1. [魔乐社区](https://modelers.cn/)
2. [HuggingFace](https://huggingface.co/)
3. [ModelScope](https://modelscope.cn/)
4. [WiseModel](https://www.wisemodel.cn/)
5. [OpenCSG](https://opencsg.com/)
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## 模型上下文协议 MCP
1. [MCP是啥？技术原理是什么？一个视频搞懂MCP的一切。Windows系统配置MCP，Cursor,Cline 使用MCP](https://www.youtube.com/watch?v=McNRkd5CxFY)
2. [MCP是什么？为啥是下一代AI标准？MCP原理+开发实战！在Cursor、Claude、Cline中使用MCP，让AI真正自动化！](https://www.youtube.com/watch?v=jGVsLeDxtQY)
3. [从零编写MCP并发布上线，超简单！手把手教程](https://www.youtube.com/watch?v=a3U-JrFkA9s)
MCP工具聚合：
1. [smithery.ai](https://smithery.ai/)
2. [mcp.so](https://mcp.so/)
3. [modelcontextprotocol/servers](https://github.com/modelcontextprotocol/servers)
4. [mcp.ad](https://mcp.ad/)
5. [pulsemcp.com](https://www.pulsemcp.com/)
6. [awesome-mcp-servers](https://github.com/punkpeye/awesome-mcp-servers)
7. [glama.ai](https://glama.ai/mcp/servers)
8. [mcp.composio.dev](https://mcp.composio.dev/)
9. [awesome-mcp-list](https://github.com/MobinX/awesome-mcp-list)
10. [mcpo](https://github.com/open-webui/mcpo)
11. [FastMCP](https://github.com/jlowin/fastmcp)
12. [sharemcp.cn](https://sharemcp.cn/)
13. [mcpstore.co](https://mcpstore.co/)
14. [FastAPI-MCP](https://github.com/tadata-org/fastapi_mcp)
15. [modelscope/mcp](https://modelscope.cn/mcp)
16. [mcpm.sh](https://github.com/pathintegral-institute/mcpm.sh)
<div align="right">
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</div>
## 技能 Skills
1. [Agent Skills (Claude Skills) 详细攻略，一期视频精通](https://www.bilibili.com/video/BV1HuiyBQE9G)
2. [OpenClaw](https://docs.openclaw.ai/zh-CN)
1. [awesome-claude-skills](https://github.com/ComposioHQ/awesome-claude-skills)
2. [Anthropics Skills](https://github.com/anthropics/skills)
3. [Skillsmp](https://skillsmp.com/)
4. [awesome-claude-skills](https://github.com/BehiSecc/awesome-claude-skills)
5. [ClawHub](https://clawhub.ai/)
6. [水产市场](https://openclawmp.cc/)
7. [Skills.Sh](https://skills.sh/)
8. [awesome-agent-skills](https://github.com/VoltAgent/awesome-agent-skills)
9. [llmbase](https://llmbase.ai/openclaw/)
10. [awesome-openclaw-skills](https://github.com/VoltAgent/awesome-openclaw-skills)
11. [claude-scientific-skills](https://github.com/K-Dense-AI/claude-scientific-skills)
12. [LLMs-Universal-Life-Science-and-Clinical-Skills-](https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-)
13. [SkillHub](https://skillhub.tencent.com/#categories)
14. [LabClaw](https://github.com/wu-yc/LabClaw)
15. [Modelscope Skills](https://modelscope.cn/skills)
<div align="right">
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</div>
## 推理 Open o1
> [!NOTE]
>
> 开放的技术是我们永恒的追求
1. https://github.com/atfortes/Awesome-LLM-Reasoning
2. https://github.com/hijkzzz/Awesome-LLM-Strawberry
3. https://github.com/wjn1996/Awesome-LLM-Reasoning-Openai-o1-Survey
4. https://github.com/srush/awesome-o1
5. https://github.com/open-thought/system-2-research
6. https://github.com/ninehills/blog/issues/121
7. https://github.com/OpenSource-O1/Open-O1
8. https://github.com/GAIR-NLP/O1-Journey
9. https://github.com/marlaman/show-me
10. https://github.com/bklieger-groq/g1
11. https://github.com/Jaimboh/Llamaberry-Chain-of-Thought-Reasoning-in-AI
12. https://github.com/pseudotensor/open-strawberry
13. https://huggingface.co/collections/peakji/steiner-preview-6712c6987110ce932a44e9a6
14. https://github.com/SimpleBerry/LLaMA-O1
15. https://huggingface.co/collections/Skywork/skywork-o1-open-67453df58e12f6c3934738d0
16. https://huggingface.co/collections/Qwen/qwq-674762b79b75eac01735070a
17. https://github.com/SkyworkAI/skywork-o1-prm-inference
18. https://github.com/RifleZhang/LLaVA-Reasoner-DPO
19. https://github.com/ADaM-BJTU
20. https://github.com/ADaM-BJTU/OpenRFT
21. https://github.com/RUCAIBox/Slow_Thinking_with_LLMs
22. https://github.com/richards199999/Thinking-Claude
23. https://huggingface.co/AGI-0/Art-v0-3B
24. https://huggingface.co/deepseek-ai/DeepSeek-R1
25. https://huggingface.co/deepseek-ai/DeepSeek-R1-Zero
26. https://github.com/huggingface/open-r1
27. https://github.com/hkust-nlp/simpleRL-reason
28. https://github.com/Jiayi-Pan/TinyZero
29. https://github.com/baichuan-inc/Baichuan-M1-14B
30. https://github.com/EvolvingLMMs-Lab/open-r1-multimodal
31. https://github.com/open-thoughts/open-thoughts
32. Mini-R1: https://www.philschmid.de/mini-deepseek-r1
33. LLaMA-Berry: https://arxiv.org/abs/2410.02884
34. MCTS-DPO: https://arxiv.org/abs/2405.00451
35. OpenR: https://github.com/openreasoner/openr
36. https://arxiv.org/abs/2410.02725
37. LLaVA-o1: https://arxiv.org/abs/2411.10440
38. Marco-o1: https://arxiv.org/abs/2411.14405
39. OpenAI o1 report: https://openai.com/index/deliberative-alignment
40. DRT-o1: https://github.com/krystalan/DRT-o1
41. Virgo：https://arxiv.org/abs/2501.01904
42. HuatuoGPT-o1：https://arxiv.org/abs/2412.18925
43. o1 roadmap：https://arxiv.org/abs/2412.14135
44. Mulberry：https://arxiv.org/abs/2412.18319
45. https://arxiv.org/abs/2412.09413
46. https://arxiv.org/abs/2501.02497
47. Search-o1:https://arxiv.org/abs/2501.05366v1
48. https://arxiv.org/abs/2501.18585
49. https://github.com/simplescaling/s1
50. https://github.com/Deep-Agent/R1-V
51. https://github.com/StarRing2022/R1-Nature
52. https://github.com/Unakar/Logic-RL
53. https://github.com/datawhalechina/unlock-deepseek
54. https://github.com/GAIR-NLP/LIMO
55. https://github.com/Zeyi-Lin/easy-r1
56. https://github.com/jackfsuia/nanoRLHF/tree/main/examples/r1-v0
57. https://github.com/FanqingM/R1-Multimodal-Journey
58. https://github.com/dhcode-cpp/X-R1
59. https://github.com/agentica-project/deepscaler
60. https://github.com/ZihanWang314/RAGEN
61. https://github.com/sail-sg/oat-zero
62. https://github.com/TideDra/lmm-r1
63. https://github.com/FlagAI-Open/OpenSeek
64. https://github.com/SwanHubX/ascend_r1_turtorial
65. https://github.com/om-ai-lab/VLM-R1
66. https://github.com/wizardlancet/diagnosis_zero
67. https://github.com/lsdefine/simple_GRPO
68. https://github.com/brendanhogan/DeepSeekRL-Extended
69. https://github.com/Wang-Xiaodong1899/Open-R1-Video
70. https://github.com/lsdefine/simple_GRPO
71. https://github.com/Open-Reasoner-Zero/Open-Reasoner-Zero
72. https://github.com/lucasjinreal/Namo-R1
73. https://github.com/hiyouga/EasyR1
74. https://github.com/Fancy-MLLM/R1-Onevision
75. https://github.com/tulerfeng/Video-R1
76. https://huggingface.co/qihoo360/TinyR1-32B-Preview
77. https://github.com/facebookresearch/swe-rl
78. https://github.com/turningpoint-ai/VisualThinker-R1-Zero
79. https://github.com/yuyq96/R1-Vision
80. https://github.com/sungatetop/deepseek-r1-vision
81. https://huggingface.co/qihoo360/Light-R1-32B
82. https://github.com/Liuziyu77/Visual-RFT
83. https://github.com/Mohammadjafari80/GSM8K-RLVR
84. https://github.com/ModalMinds/MM-EUREKA
85. https://github.com/joey00072/nanoGRPO
86. https://github.com/PeterGriffinJin/Search-R1
87. https://openi.pcl.ac.cn/PCL-Reasoner/GRPO-Training-Suite
88. https://github.com/dvlab-research/Seg-Zero
89. https://github.com/HumanMLLM/R1-Omni
90. https://github.com/OpenManus/OpenManus-RL
91. https://arxiv.org/pdf/2503.07536
92. https://github.com/Osilly/Vision-R1
93. https://github.com/LengSicong/MMR1
94. https://github.com/phonism/CP-Zero
95. https://github.com/SkyworkAI/Skywork-R1V
96. https://arxiv.org/abs/2503.13939v1
97. https://github.com/0russwest0/Agent-R1
98. https://github.com/MetabrainAGI/Awaker2.5-R1
99. https://github.com/LG-AI-EXAONE/EXAONE-Deep
100. https://github.com/qiufengqijun/open-r1-reprod
101. https://github.com/SUFE-AIFLM-Lab/Fin-R1
102. https://github.com/sail-sg/understand-r1-zero
103. https://github.com/baibizhe/Efficient-R1-VLLM
104. https://github.com/hkust-nlp/simpleRL-reason
105. https://arxiv.org/abs/2502.19655
106. https://arxiv.org/abs/2503.21620v1
107. https://arxiv.org/abs/2503.16081
108. https://github.com/ShadeCloak/ADORA
109. https://github.com/appletea233/Temporal-R1
110. https://github.com/inclusionAI/AReaL
111. https://github.com/lzhxmu/CPPO
112. https://arxiv.org/abs/2503.23829
113. https://github.com/TencentARC/SEED-Bench-R1
114. https://github.com/McGill-NLP/nano-aha-moment
115. https://github.com/VLM-RL/Ocean-R1
116. https://github.com/OpenGVLab/VideoChat-R1
117. https://github.com/ByteDance-Seed/Seed-Thinking-v1.5
118. https://github.com/SkyworkAI/Skywork-OR1
119. https://github.com/MoonshotAI/Kimi-VL
120. https://arxiv.org/abs/2504.08600
121. https://github.com/ZhangXJ199/TinyLLaVA-Video-R1
122. https://arxiv.org/abs/2504.11914
123. https://github.com/policy-gradient/GRPO-Zero
124. https://github.com/linkangheng/PR1
125. https://github.com/jiangxinke/Agentic-RAG-R1
126. https://github.com/shangshang-wang/Tina
127. https://github.com/aliyun/qwen-dianjin
128. https://github.com/RAGEN-AI/RAGEN
129. https://github.com/XiaomiMiMo/MiMo
130. https://github.com/yuanzhoulvpi2017/nano_rl
131. https://huggingface.co/a-m-team/AM-Thinking-v1
132. https://huggingface.co/Intelligent-Internet/II-Medical-8B
133. https://github.com/CSfufu/Revisual-R1
<div align="right">
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</div>
## 推理 Open o3
1. Mini-o3: https://arxiv.org/abs/2509.07969
2. Simple-o3: https://arxiv.org/abs/2508.12109
3. Thyme: https://arxiv.org/abs/2508.11630
4. Open o3 Video: https://arxiv.org/abs/2510.20579
<div align="right">
<b><a href="#Contents">↥ back to top</a></b>
</div>
## 小语言模型 Small Language Model
1. https://github.com/jiahe7ay/MINI_LLM
2. https://github.com/jingyaogong/minimind
3. https://github.com/DLLXW/baby-llama2-chinese
4. https://github.com/charent/ChatLM-mini-Chinese
5. https://github.com/wdndev/tiny-llm-zh
6. https://github.com/Tongjilibo/build_MiniLLM_from_scratch
7. https://github.com/jzhang38/TinyLlama
8. https://github.com/AI-Study-Han/Zero-Chatgpt
9. https://github.com/loubnabnl/nanotron-smol-cluster ([使用Cosmopedia训练cosmo-1b](https://huggingface.co/blog/zh/cosmopedia))
10. https://github.com/charent/Phi2-mini-Chinese
11. https://github.com/allenai/OLMo
12. https://github.com/keeeeenw/MicroLlama
13. https://github.com/Chinese-Tiny-LLM/Chinese-Tiny-LLM
14. https://github.com/leeguandong/MiniLLaMA3
15. https://github.com/Pints-AI/1.5-Pints
16. https://github.com/zhanshijinwat/Steel-LLM
17. https://github.com/RUC-GSAI/YuLan-Mini
18. https://github.com/Om-Alve/smolGPT
19. https://github.com/skyzh/tiny-llm
20. https://github.com/qibin0506/Cortex
21. https://github.com/huggingface/picotron
22. https://github.com/Alic-Li/Mini_RWKV_7
23. https://huggingface.co/Nanbeige/Nanbeige4-3B-Thinking-2511 `23T tokens预训练模型`
24. https://github.com/stepfun-ai/SteptronOss
<div align="right">
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</div>
## 小多模态模型 Small Vision Language Model
1. https://github.com/jingyaogong/minimind-v
2. https://github.com/yuanzhoulvpi2017/zero_nlp/tree/main/train_llava
3. https://github.com/AI-Study-Han/Zero-Qwen-VL
4. https://github.com/Coobiw/MPP-LLaVA
5. https://github.com/qnguyen3/nanoLLaVA
6. https://github.com/TinyLLaVA/TinyLLaVA_Factory
7. https://github.com/ZhangXJ199/TinyLLaVA-Video
8. https://github.com/Emericen/tiny-qwen
9. https://github.com/merveenoyan/smol-vision
10. https://github.com/huggingface/nanoVLM
11. https://github.com/GeeeekExplorer/nano-vllm
12. https://github.com/ritabratamaiti/AnyModal
13. https://github.com/yujunhuics/Reyes
14. https://github.com/Victorwz/Open-Qwen2VL
<div align="right">
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</div>
## 技巧 Tips
1. [What We Learned from a Year of Building with LLMs (Part I)](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/)
2. [What We Learned from a Year of Building with LLMs (Part II)](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-ii/)
3. [What We Learned from a Year of Building with LLMs (Part III): Strategy](https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-iii-strategy/)
4. [轻松入门大语言模型（LLM）](https://www.bilibili.com/video/BV1pF4m1V7FB/?spm_id_from=333.999.0.0&vd_source=c739db1ebdd361d47af5a0b8497417db)
5. [LLMs for Text Classification: A Guide to Supervised Learning](https://www.striveworks.com/blog/llms-for-text-classification-a-guide-to-supervised-learning)
6. [Unsupervised Text Classification: Categorize Natural Language With LLMs](https://www.striveworks.com/blog/unsupervised-text-classification-how-to-use-llms-to-categorize-natural-language-data)
7. [Text Classification With LLMs: A Roundup of the Best Methods](https://www.striveworks.com/blog/text-classification-with-llms-a-roundup-of-the-best-methods)
8. [LLM Pricing](https://docs.google.com/spreadsheets/d/18GHPEBJzDbICmMStPVkNWA_hQHiWmLcqUdEJA1b4MJM/edit?gid=0#gid=0)
9. [Uncensor any LLM with abliteration](https://huggingface.co/blog/mlabonne/abliteration)
10. [Tiny LLM Universe](https://github.com/datawhalechina/tiny-universe)
11. [Zero-Chatgpt](https://github.com/AI-Study-Han/Zero-Chatgpt)
12. [Zero-Qwen-VL](https://github.com/AI-Study-Han/Zero-Qwen-VL)
13. [finetune-Qwen2-VL](https://github.com/zhangfaen/finetune-Qwen2-VL)
14. [MPP-LLaVA](https://github.com/Coobiw/MPP-LLaVA)
15. [build_MiniLLM_from_scratch](https://github.com/Tongjilibo/build_MiniLLM_from_scratch)
16. [Tiny LLM zh](https://github.com/wdndev/tiny-llm-zh)
17. [MiniMind](https://github.com/jingyaogong/minimind): 3小时完全从0训练一个仅有26M的小参数GPT，最低仅需2G显卡即可推理训练.
18. [LLM-Travel](https://github.com/Glanvery/LLM-Travel): 致力于深入理解、探讨以及实现与大模型相关的各种技术、原理和应用
19. [Knowledge distillation: Teaching LLM's with synthetic data](https://wandb.ai/byyoung3/ML_NEWS3/reports/Knowledge-distillation-Teaching-LLM-s-with-synthetic-data--Vmlldzo5MTMyMzA2)
20. [Part 1: Methods for adapting large language models](https://ai.meta.com/blog/adapting-large-language-models-llms/)
21. [Part 2: To fine-tune or not to fine-tune](https://ai.meta.com/blog/when-to-fine-tune-llms-vs-other-techniques/)
22. [Part 3: How to fine-tune: Focus on effective datasets](https://ai.meta.com/blog/how-to-fine-tune-llms-peft-dataset-curation/)
23. [Reader-LM: Small Language Models for Cleaning and Converting HTML to Markdown](https://jina.ai/news/reader-lm-small-language-models-for-cleaning-and-converting-html-to-markdown/?nocache=1)
24. [LLMs应用构建一年之心得](https://iangyan.github.io/2024/09/08/building-with-llms-part-1/)
25. [LLM训练-pretrain](https://zhuanlan.zhihu.com/p/718354385)
26. [pytorch-llama](https://github.com/hkproj/pytorch-llama): LLaMA 2 implemented from scratch in PyTorch.
27. [Preference Optimization for Vision Language Models with TRL](https://huggingface.co/blog/dpo_vlm) 【[support model](https://huggingface.co/docs/transformers/model_doc/auto#transformers.AutoModelForVision2Seq)】
28. [Fine-tuning visual language models using SFTTrainer](https://huggingface.co/blog/vlms) 【[docs](https://huggingface.co/docs/trl/sft_trainer#extending-sfttrainer-for-vision-language-models)】
29. [A Visual Guide to Mixture of Experts (MoE)](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mixture-of-experts)
30. [Role-Playing in Large Language Models like ChatGPT](https://promptengineering.org/role-playing-in-large-language-models-like-chatgpt/)
31. [Distributed Training Guide](https://github.com/LambdaLabsML/distributed-training-guide): Best practices & guides on how to write distributed pytorch training code.
32. [Chat Templates](https://hf-mirror.com/blog/chat-templates)
33. [Top 20+ RAG Interview Questions](https://www.analyticsvidhya.com/blog/2024/04/rag-interview-questions/)
34. [LLM-Dojo 开源大模型学习场所，使用简洁且易阅读的代码构建模型训练框架](https://github.com/mst272/LLM-Dojo)
35. [o1 isn’t a chat model (and that’s the point)](https://www.latent.space/p/o1-skill-issue)
36. [Beam Search快速理解及代码解析](https://www.cnblogs.com/nickchen121/p/15499576.html)
37. [基于 transformers 的 generate() 方法实现多样化文本生成：参数含义和算法原理解读](https://blog.csdn.net/muyao987/article/details/125917234)
38. [The Ultra-Scale Playbook: Training LLMs on GPU Clusters](https://huggingface.co/spaces/nanotron/ultrascale-playbook)
<div align="right">
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</div>
贡献者:
<a href="https://github.com/WangRongsheng/awesome-LLM-resources/graphs/contributors">
<img src="https://contrib.rocks/image?repo=WangRongsheng/awesome-LLM-resources" />
</a>
如果你觉得本项目对你有帮助，欢迎引用：
```bib
@misc{wang2024llm,
title={awesome-LLM-resourses},
author={Rongsheng Wang},
year={2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/WangRongsheng/awesome-LLM-resourses}},
}
```
<!--
[](https://github.com/WangRongsheng/awesome-LLM-resourses/network/members)
[](https://github.com/WangRongsheng/awesome-LLM-resourses/stargazers)
-->
[](https://starchart.cc/WangRongsheng/awesome-LLM-resourses)
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Condensed preview — 4 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (112K chars).
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About this extraction
This page contains the full source code of the WangRongsheng/awesome-LLM-resources GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 4 files (107.6 KB), approximately 33.0k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.