Full Code of AI4Finance-Foundation/FinGPT for AI

Repository: AI4Finance-Foundation/FinGPT
Branch: master
Commit: a91a408d817f
Files: 376
Total size: 10.8 MB

Directory structure:
gitextract_nwpardo3/

├── .github/
│   ├── FUNDING.yml
│   └── ISSUE_TEMPLATE/
│       └── feature_request.md
├── .gitignore
├── .gitpod.yml
├── .idea/
│   └── .gitignore
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── FinGPT_ Training with LoRA and Meta-Llama-3-8B.ipynb
├── FinGPT_Inference_Llama2_13B_falcon_7B_for_Beginners.ipynb
├── FinGPT_Training_LoRA_with_ChatGLM2_6B_for_Beginners.ipynb
├── FinGPT_Training_LoRA_with_ChatGLM2_6B_for_Beginners_v2-2.ipynb
├── LICENSE
├── MANIFEST.in
├── README.md
├── Use_Cases.md
├── figs/
│   └── README.md
├── fingpt/
│   ├── FinGPT_Benchmark/
│   │   ├── __init__.py
│   │   ├── benchmarks/
│   │   │   ├── __init__.py
│   │   │   ├── benchmarks.py
│   │   │   ├── convfinqa.py
│   │   │   ├── evaluate.sh
│   │   │   ├── fineval.py
│   │   │   ├── finred.py
│   │   │   ├── fiqa.py
│   │   │   ├── fpb.py
│   │   │   ├── headline.py
│   │   │   ├── ner.py
│   │   │   ├── nwgi.py
│   │   │   ├── sentiment_templates.txt
│   │   │   └── tfns.py
│   │   ├── config.json
│   │   ├── config_hf.json
│   │   ├── config_new.json
│   │   ├── data/
│   │   │   ├── __init__.py
│   │   │   ├── download.py
│   │   │   └── prepare_data.ipynb
│   │   ├── demo.ipynb
│   │   ├── readme.md
│   │   ├── train.sh
│   │   ├── train_lora.py
│   │   └── utils.py
│   ├── FinGPT_FinancialReportAnalysis/
│   │   ├── README.md
│   │   ├── reportanalysis.ipynb
│   │   └── utils/
│   │       ├── __init__.py
│   │       ├── earning_calls.py
│   │       ├── format_pdf.py
│   │       └── rag.py
│   ├── FinGPT_Forecaster/
│   │   ├── AAAI-Good-Data/
│   │   │   ├── README.md
│   │   │   ├── Testing.ipynb
│   │   │   ├── Training.ipynb
│   │   │   ├── config.json
│   │   │   ├── train.sh
│   │   │   ├── train_lora.py
│   │   │   ├── utils.py
│   │   │   └── yes.gitkeep
│   │   ├── FinGPT-Forecaster-Chinese/
│   │   │   ├── Ashare_data.py
│   │   │   ├── Formulate_training_data.ipynb
│   │   │   ├── Inference_datapipe.py
│   │   │   ├── requirement.txt
│   │   │   ├── train_lora.py
│   │   │   └── utils.py
│   │   ├── README.md
│   │   ├── app.py
│   │   ├── config.json
│   │   ├── data.py
│   │   ├── data_infererence_fetch.py
│   │   ├── data_pipeline.py
│   │   ├── demo.ipynb
│   │   ├── indices.py
│   │   ├── prepare_data.ipynb
│   │   ├── prompt.py
│   │   ├── requirements.txt
│   │   ├── train.sh
│   │   ├── train_lora.py
│   │   └── utils.py
│   ├── FinGPT_MultiAgentsRAG/
│   │   ├── Evaluation_methods/
│   │   │   ├── HaluEval/
│   │   │   │   ├── evaluate.py
│   │   │   │   ├── filtering.py
│   │   │   │   ├── generate.py
│   │   │   │   └── halueval
│   │   │   ├── MMLU/
│   │   │   │   ├── eval_mmlu.py
│   │   │   │   ├── gen_mmlu.py
│   │   │   │   └── mmlu
│   │   │   ├── TruthfulQA/
│   │   │   │   ├── evaluate.py
│   │   │   │   ├── truthfulqa
│   │   │   │   └── utilities.py
│   │   │   └── new_evaluation
│   │   ├── Fine_tune_model/
│   │   │   ├── fine_tune_GLM2.ipynb
│   │   │   ├── fine_tune_Llama2.ipynb
│   │   │   └── newFTmodels
│   │   ├── MultiAgents/
│   │   │   ├── inference_mmlu_Llama2.ipynb
│   │   │   ├── inference_mmlu_Llama2_cleaned.ipynb
│   │   │   ├── inference_mmlu_Llama2_v2.ipynb
│   │   │   └── new_file_multiagents
│   │   ├── RAG/
│   │   │   ├── RAG_part.ipynb
│   │   │   └── new_file_rag
│   │   └── README.md
│   ├── FinGPT_Others/
│   │   ├── FinGPT_Low_Code_Development/
│   │   │   ├── chatgpt-low-code-development-v1/
│   │   │   │   ├── main.ipynb
│   │   │   │   └── readme.md
│   │   │   └── chatgpt-low-code-development-v2/
│   │   │       ├── main.ipynb
│   │   │       └── readme.md
│   │   ├── FinGPT_Robo_Advisor/
│   │   │   ├── chatgpt-robo-advisor-v1/
│   │   │   │   ├── ChatGPT_Robo_Advisor.ipynb
│   │   │   │   ├── ChatGPT_Robo_Advisor_Results.csv
│   │   │   │   ├── README.md
│   │   │   │   └── requirements.txt
│   │   │   └── chatgpt-robo-advisor-v2/
│   │   │       ├── ChatGPT_Robo_Advisor_v2.ipynb
│   │   │       ├── ChatGPT_Robo_Advisor_v2_Results.csv
│   │   │       ├── README.md
│   │   │       ├── data/
│   │   │       │   └── dataset.csv
│   │   │       └── requirements.txt
│   │   ├── FinGPT_Trading/
│   │   │   ├── chatgpt-trading-v1/
│   │   │   │   ├── README.md
│   │   │   │   ├── data/
│   │   │   │   │   ├── dataset.csv
│   │   │   │   │   ├── maotai.csv
│   │   │   │   │   └── maotai_another.csv
│   │   │   │   ├── get_chatgpt_results.ipynb
│   │   │   │   ├── requirements.txt
│   │   │   │   └── trade_with_chatgpt.ipynb
│   │   │   └── chatgpt-trading-v2/
│   │   │       ├── README.md
│   │   │       ├── data/
│   │   │       │   ├── text-curie-001.pkl
│   │   │       │   └── text-davinci-003.pkl
│   │   │       ├── get_gpt_sentiment_results/
│   │   │       │   ├── df.csv
│   │   │       │   ├── get_gpt_res.py
│   │   │       │   └── run.sh
│   │   │       ├── openai_token/
│   │   │       │   └── token_.py
│   │   │       ├── pyfolio/
│   │   │       │   ├── __init__.py
│   │   │       │   ├── _seaborn.py
│   │   │       │   ├── _version.py
│   │   │       │   ├── capacity.py
│   │   │       │   ├── deprecate.py
│   │   │       │   ├── interesting_periods.py
│   │   │       │   ├── ipycompat.py
│   │   │       │   ├── perf_attrib.py
│   │   │       │   ├── plotting.py
│   │   │       │   ├── pos.py
│   │   │       │   ├── round_trips.py
│   │   │       │   ├── tears.py
│   │   │       │   ├── tests/
│   │   │       │   │   ├── __init__.py
│   │   │       │   │   ├── test_capacity.py
│   │   │       │   │   ├── test_nbs.py
│   │   │       │   │   ├── test_perf_attrib.py
│   │   │       │   │   ├── test_pos.py
│   │   │       │   │   ├── test_round_trips.py
│   │   │       │   │   ├── test_tears.py
│   │   │       │   │   ├── test_timeseries.py
│   │   │       │   │   └── test_txn.py
│   │   │       │   ├── timeseries.py
│   │   │       │   ├── txn.py
│   │   │       │   └── utils.py
│   │   │       ├── requirements.txt
│   │   │       └── trade_with_gpt3.ipynb
│   │   └── shares_news_sentiment_classify.py
│   ├── FinGPT_RAG/
│   │   ├── README.md
│   │   ├── __init__.py
│   │   ├── instruct-FinGPT/
│   │   │   ├── README.md
│   │   │   ├── chat.py
│   │   │   ├── convert_llama_weights_to_hf.py
│   │   │   ├── infer.sh
│   │   │   ├── inference/
│   │   │   │   ├── batchbot.py
│   │   │   │   ├── batchbot_torch.py
│   │   │   │   ├── chatbot.py
│   │   │   │   └── f1fromcm.py
│   │   │   ├── load_data.ipynb
│   │   │   ├── nohup.out
│   │   │   ├── train.py
│   │   │   ├── train.sh
│   │   │   └── training/
│   │   │       ├── supervised_finetuning/
│   │   │       │   ├── README.md
│   │   │       │   ├── evaluation_scripts/
│   │   │       │   │   └── run_prompt.sh
│   │   │       │   ├── main.py
│   │   │       │   ├── main_data.py
│   │   │       │   ├── prompt_eval.py
│   │   │       │   └── training_scripts/
│   │   │       │       ├── README.md
│   │   │       │       ├── multi_node/
│   │   │       │       │   ├── run_66b.sh
│   │   │       │       │   ├── run_sent-1.3b.sh
│   │   │       │       │   └── run_sent-llama-7b.sh
│   │   │       │       ├── other_language/
│   │   │       │       │   ├── run_chinese.sh
│   │   │       │       │   └── run_japanese.sh
│   │   │       │       ├── single_gpu/
│   │   │       │       │   ├── run_1.3b.sh
│   │   │       │       │   ├── run_6.7b_lora.sh
│   │   │       │       │   └── run_sent-1.3b.sh
│   │   │       │       └── single_node/
│   │   │       │           ├── run_1.3b.sh
│   │   │       │           ├── run_1.3b_lora.sh
│   │   │       │           ├── run_13b.sh
│   │   │       │           ├── run_30b_lora.sh
│   │   │       │           ├── run_6.7b.sh
│   │   │       │           ├── run_llama-13b.sh
│   │   │       │           ├── run_sent-1.3b.sh
│   │   │       │           ├── run_sent-llama-7b.sh
│   │   │       │           └── run_sent-llama2-7b.sh
│   │   │       └── utils/
│   │   │           ├── data/
│   │   │           │   ├── data_utils.py
│   │   │           │   └── raw_datasets.py
│   │   │           ├── ds_utils.py
│   │   │           ├── model/
│   │   │           │   ├── model_utils.py
│   │   │           │   └── reward_model.py
│   │   │           ├── module/
│   │   │           │   └── lora.py
│   │   │           └── utils.py
│   │   ├── multisource_retrieval/
│   │   │   ├── ChatGPT_sentiment_analysis_benchmark.ipynb
│   │   │   ├── README.md
│   │   │   ├── __init__.py
│   │   │   ├── count_contexts.py
│   │   │   ├── data/
│   │   │   │   ├── sent_valid.csv
│   │   │   │   ├── sent_valid_penultimate_run.csv
│   │   │   │   ├── sent_valid_penultimate_run_classified.csv
│   │   │   │   ├── sent_valid_penultimate_run_classified_classified.csv
│   │   │   │   ├── sent_valid_scraped.csv
│   │   │   │   ├── test.csv
│   │   │   │   └── test_classified.csv
│   │   │   ├── external_LLMs/
│   │   │   │   ├── chatGPT_tokenization.py
│   │   │   │   ├── chatGPT_tokenization.tsx
│   │   │   │   ├── external_LLMs.py
│   │   │   │   └── g4f_tokenization.py
│   │   │   ├── format_checker.py
│   │   │   ├── gui/
│   │   │   │   └── gui.py
│   │   │   ├── manual_classification.py
│   │   │   ├── news_scraper.py
│   │   │   ├── proxies/
│   │   │   │   └── headers.py
│   │   │   ├── requests_url.py
│   │   │   ├── scrapers/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── cnbc/
│   │   │   │   │   └── scrape_cnbc.py
│   │   │   │   ├── google/
│   │   │   │   │   └── scrape_google.py
│   │   │   │   ├── market_screener/
│   │   │   │   │   └── scrape_market_screener.py
│   │   │   │   ├── seeking_alpha/
│   │   │   │   │   ├── SeekingAlpha_Content.ipynb
│   │   │   │   │   ├── __init__.py
│   │   │   │   │   └── seeking_alpha_scraping.py
│   │   │   │   ├── selenium/
│   │   │   │   │   └── selenium_app.py
│   │   │   │   ├── similarity_score.py
│   │   │   │   ├── url_encode.py
│   │   │   │   └── yahoo/
│   │   │   │       └── scrape_yahoo.py
│   │   │   ├── sentence_processing/
│   │   │   │   └── split_sentence.py
│   │   │   └── utils/
│   │   │       ├── classification_accuracy_verification.py
│   │   │       ├── classify_news_sources.py
│   │   │       ├── count_contexts.py
│   │   │       ├── format_checker.py
│   │   │       ├── manual_classification.py
│   │   │       └── sentiment_classification_by_external_LLMs.py
│   │   └── requirements.txt
│   ├── FinGPT_Sentiment_Analysis_v1/
│   │   ├── FinGPT_v1.0/
│   │   │   ├── README.md
│   │   │   ├── data_preparations/
│   │   │   │   ├── FMP/
│   │   │   │   │   ├── FMP.ipynb
│   │   │   │   │   └── S&P 500 Index.xlsx
│   │   │   │   ├── add_labels.py
│   │   │   │   ├── download_content_demo.ipynb
│   │   │   │   ├── download_contents.py
│   │   │   │   ├── download_titles.py
│   │   │   │   └── hs_300.csv
│   │   │   ├── inferencing/
│   │   │   │   ├── cover_alpaca2jsonl.py
│   │   │   │   └── infer.ipynb
│   │   │   ├── making_dataset/
│   │   │   │   ├── change_jsonl_train_and_valid.sh
│   │   │   │   ├── cover_alpaca2jsonl.py
│   │   │   │   ├── make_dataset_by_date.ipynb
│   │   │   │   ├── make_dataset_train_and_valid.sh
│   │   │   │   └── tokenize_dataset_rows.py
│   │   │   ├── requirements.txt
│   │   │   └── training/
│   │   │       ├── finetune.py
│   │   │       └── finetune.sh
│   │   ├── FinGPT_v1.1/
│   │   │   └── README.md
│   │   └── README.md
│   ├── FinGPT_Sentiment_Analysis_v3/
│   │   ├── README.md
│   │   ├── __init__.py
│   │   ├── benchmark/
│   │   │   ├── benchmarks.ipynb
│   │   │   └── benchmarks_llama2_13b.ipynb
│   │   ├── data/
│   │   │   └── making_data.ipynb
│   │   ├── training_8bit/
│   │   │   ├── train_ChatGLM2_6B.ipynb
│   │   │   └── train_Llama2_13B.ipynb
│   │   ├── training_int4/
│   │   │   └── train.ipynb
│   │   └── training_parallel/
│   │       ├── config.json
│   │       ├── train.sh
│   │       └── train_lora.py
│   ├── __init__.py
│   └── readme.md
├── finogrid/
│   ├── README.md
│   ├── agents/
│   │   ├── audit_governance/
│   │   │   └── agent.py
│   │   ├── internal_support/
│   │   │   └── agent.py
│   │   ├── ops_oversight/
│   │   │   └── agent.py
│   │   ├── process_improvement/
│   │   │   └── agent.py
│   │   └── treasury_strategy/
│   │       └── agent.py
│   ├── corridors/
│   │   ├── __init__.py
│   │   ├── argentina/
│   │   │   └── adapter.py
│   │   ├── base.py
│   │   ├── brazil/
│   │   │   └── adapter.py
│   │   ├── india/
│   │   │   └── adapter.py
│   │   ├── indonesia/
│   │   │   └── adapter.py
│   │   ├── nigeria/
│   │   │   └── adapter.py
│   │   ├── philippines/
│   │   │   └── adapter.py
│   │   ├── uae/
│   │   │   └── adapter.py
│   │   └── vietnam/
│   │       └── adapter.py
│   ├── database/
│   │   ├── migrations/
│   │   │   ├── 001_initial_schema.sql
│   │   │   ├── 002_agent_ledger.sql
│   │   │   └── 003_mandate.sql
│   │   └── models/
│   │       ├── __init__.py
│   │       ├── agent_ledger.py
│   │       ├── audit.py
│   │       ├── base.py
│   │       ├── batch.py
│   │       ├── client.py
│   │       ├── execution.py
│   │       ├── instruction.py
│   │       ├── mandate.py
│   │       └── routing.py
│   ├── docs/
│   │   ├── architecture.md
│   │   ├── corridors/
│   │   │   └── nigeria.md
│   │   ├── dr-runbook.md
│   │   └── fingpt_usage_policy.md
│   ├── fingpt_integration/
│   │   ├── __init__.py
│   │   ├── forecaster/
│   │   │   └── corridor_forecaster.py
│   │   ├── minimax_llm_client.py
│   │   ├── rag/
│   │   │   └── knowledge_base.py
│   │   └── sentiment/
│   │       ├── crypto_sentiment.py
│   │       ├── minimax_provider.py
│   │       └── openai_fallback.py
│   ├── mcp/
│   │   ├── bridge/
│   │   │   └── server.py
│   │   ├── identity/
│   │   │   └── server.py
│   │   ├── kya_validator/
│   │   │   ├── __init__.py
│   │   │   └── server.py
│   │   ├── kyt_aml/
│   │   │   └── server.py
│   │   ├── plaid/
│   │   │   ├── __init__.py
│   │   │   └── server.py
│   │   └── wallet_factory/
│   │       ├── __init__.py
│   │       └── server.py
│   ├── requirements.txt
│   ├── sdk/
│   │   └── typescript/
│   │       ├── package.json
│   │       ├── src/
│   │       │   ├── client.test.ts
│   │       │   ├── client.ts
│   │       │   ├── index.ts
│   │       │   └── types.ts
│   │       └── tsconfig.json
│   ├── services/
│   │   ├── agent_ledger_api/
│   │   │   ├── chain_watcher.py
│   │   │   ├── config.py
│   │   │   ├── dependencies.py
│   │   │   ├── intent_sweeper.py
│   │   │   ├── main.py
│   │   │   ├── middleware/
│   │   │   │   ├── __init__.py
│   │   │   │   └── payment_required.py
│   │   │   ├── routers/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── agent_accounts.py
│   │   │   │   ├── health.py
│   │   │   │   ├── kya.py
│   │   │   │   ├── micropay.py
│   │   │   │   ├── payment_intents.py
│   │   │   │   ├── topup.py
│   │   │   │   └── wallets.py
│   │   │   └── schemas.py
│   │   ├── compliance_gate/
│   │   │   └── gate.py
│   │   ├── ingress_api/
│   │   │   ├── config.py
│   │   │   ├── dependencies.py
│   │   │   ├── main.py
│   │   │   ├── routers/
│   │   │   │   ├── __init__.py
│   │   │   │   ├── batches.py
│   │   │   │   ├── health.py
│   │   │   │   ├── onboarding.py
│   │   │   │   └── webhooks.py
│   │   │   └── schemas.py
│   │   ├── ops_console/
│   │   │   ├── __init__.py
│   │   │   ├── config.py
│   │   │   ├── dependencies.py
│   │   │   ├── main.py
│   │   │   └── routers/
│   │   │       ├── __init__.py
│   │   │       ├── agents_explorer.py
│   │   │       ├── approvals.py
│   │   │       ├── corridors.py
│   │   │       ├── exceptions.py
│   │   │       ├── ledger_explorer.py
│   │   │       ├── mandates_console.py
│   │   │       └── search.py
│   │   ├── partner_execution/
│   │   │   ├── bridge_client.py
│   │   │   └── executor.py
│   │   ├── reconciliation/
│   │   │   └── reconciler.py
│   │   └── routing_engine/
│   │       ├── engine.py
│   │       └── worker.py
│   └── tests/
│       ├── __init__.py
│       ├── integration/
│       │   ├── __init__.py
│       │   └── test_minimax_integration.py
│       └── unit/
│           ├── __init__.py
│           ├── test_agent_ledger.py
│           ├── test_corridor_adapters.py
│           ├── test_minimax_provider.py
│           └── test_routing_engine.py
├── requirements.txt
└── setup.py

================================================
FILE CONTENTS
================================================

================================================
FILE: .github/FUNDING.yml
================================================
# These are supported funding model platforms

github: [BruceYanghy]
open_collective: # Replace with a single Open Collective username
ko_fi: # Replace with a single Ko-fi username
tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
liberapay: # Replace with a single Liberapay username
issuehunt: # Replace with a single IssueHunt username
otechie: # Replace with a single Otechie username
lfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry
custom: ['paypal.me/Hongyang']


================================================
FILE: .github/ISSUE_TEMPLATE/feature_request.md
================================================
---
name: Feature request
about: Suggest an idea for this project
title: ''
labels: ''
assignees: ''

---

**Is your feature request related to a problem? Please describe.**
A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]

**Describe the solution you'd like**
A clear and concise description of what you want to happen.

**Describe alternatives you've considered**
A clear and concise description of any alternative solutions or features you've considered.

**Additional context**
Add any other context or screenshots about the feature request here.


================================================
FILE: .gitignore
================================================
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
#  Usually these files are written by a python script from a template
#  before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
.python-version

# pipenv
#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
#   However, in case of collaboration, if having platform-specific dependencies or dependencies
#   having no cross-platform support, pipenv may install dependencies that don't work, or not
#   install all needed dependencies.
#Pipfile.lock

# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/
.DS_Store
.idea/FinGPT.iml
*.xml

# Job scripts
fingpt/FinGPT_sentiment/instruct-FinGPT/run.sh
fingpt/FinGPT_sentiment/instruct-FinGPT/checkpoints
fingpt/FinGPT_sentiment/instruct-FinGPT/ds_results_all_10_v2_1.*
FinGPT_Training_LoRA_with_Chatglm2_6b_for_beginners.ipynb

# Benchmark data
fingpt/FinGPT_Benchmark/data/*/**


================================================
FILE: .gitpod.yml
================================================
# This configuration file was automatically generated by Gitpod.
# Please adjust to your needs (see https://www.gitpod.io/docs/introduction/learn-gitpod/gitpod-yaml)
# and commit this file to your remote git repository to share the goodness with others.

# Learn more from ready-to-use templates: https://www.gitpod.io/docs/introduction/getting-started/quickstart

tasks:
  - init: pip install -r requirements.txt




================================================
FILE: .idea/.gitignore
================================================
# Default ignored files
/shelf/
/workspace.xml


================================================
FILE: CODE_OF_CONDUCT.md
================================================
# Code of Conduct

## Our Pledge

In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.

## Our Standards

Examples of behavior that contributes to creating a positive environment include:

- Using welcoming and inclusive language
- Being respectful of differing viewpoints and experiences
- Gracefully accepting constructive criticism
- Focusing on what is best for the community
- Showing empathy towards other community members

Examples of unacceptable behavior by participants include:

- The use of sexualized language or imagery and unwelcome sexual attention or advances
- Trolling, insulting/derogatory comments, and personal or political attacks
- Public or private harassment
- Publishing others' private information, such as a physical or electronic address, without explicit permission
- Other conduct that could reasonably be considered inappropriate in a professional setting

## Our Responsibilities

We as project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.

We have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned with this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.

## Scope

This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project email address, posting via an official social media account, or acting as an appointed representative at an online or offline event.

## Enforcement

Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident.

## Enforcement Guidelines

Community managers will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:

### 1. Correction

**Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.

**Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.

### 2. Warning

**Community Impact**: A violation through a single incident or series of actions.

**Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.

### 3. Temporary Ban

**Community Impact**: A serious violation of community standards, including sustained inappropriate behavior.

**Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public of private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.

### 4. Permanent Ban

**Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior,  harassment of an individual, or aggression toward or disparagement of classes of individuals.

**Consequence**: A permanent ban from any sort of public interaction within the community.

================================================
FILE: CONTRIBUTING.md
================================================
# FinGPT Contribution Guidelines 🚀

Welcome to the FinGPT project! We are thrilled to have you here 🌟. Your contributions are instrumental in shaping the intersection of finance and AI, making it even more amazing. 📈✨ Let's embark on this journey together.

## Code of Conduct 🤝

Before diving in, please take a moment to review our Code of Conduct. It sets the tone for our community and emphasizes the importance of respect and inclusivity. [Read the Code of Conduct](LICENSE.md).

## Contribution Types 🦠🚀📚

### Bug Reports 🐞

If you encounter any bugs during your journey, don't fret! We have the Bug Busters ready to help. To report a bug, follow these steps:

1. Check if the bug has already been reported in [GitHub Issues](https://github.com/AI4Finance-Foundation/FinGPT/issues).
2. If it's a new bug, open a new issue with a concise description and provide detailed, step-by-step instructions to reproduce it.

### Feature Requests 💡

Do you have visionary ideas that could elevate FinGPT? Share them with us! When submitting a feature request, be sure to include:

1. A clear and vivid description of the feature you envision.
2. Discuss the impact and potential benefits.

### Documentation 📖

For those with a penchant for words and an eye for detail, consider contributing to our documentation. You can make the documentation more enlightening for everyone. 🧙📜

### Code Contributions 💻

Calling all AI heroes and wizards! You are the secret sauce behind the FinGPT project. To contribute code and save the financial world:

1. **Fork the Repository**: Click the "Fork" button on the top right of the repository's page. This creates your own copy of the project.
   
2. **Clone your Fork**: In your terminal, use the following command to clone your fork to your local machine:

   ```bash
   git clone https://github.com/YourUsername/FinGPT.git
   ```

3. **Create a New Branch**: Make a new branch for your adventures. This helps keep the main codebase clean:

   ```bash
   git checkout -b your-feature-branch
   ```

4. **Work Your Magic**: Implement your code or changes.

5. **Commit and Push**: Use these commands to commit your changes and push them to your fork:

   ```bash
   git commit -m "Your commit message"
   git push origin your-feature-branch
   ```

6. **Create a Pull Request**: Go to the original FinGPT repository and click "New Pull Request." Select your branch, write a description, and submit.

## Seeking Assistance ❓🙋‍♀️

If you find yourself stuck or have questions, remember that our support team is your sidekick. Don't hesitate to reach out. We are here to guide you through the process and provide any necessary assistance.

## Getting Started 🚀🚀

Are you ready to make a mark on the FinGPT project? Grab your cape and join us in our mission to make finance and AI even more incredible. Your contributions are the magic that fuels our journey.

🔗 [FinGPT GitHub Repository](https://github.com/AI4Finance-Foundation/FinGPT)

### May your contributions be as amazing as you are! 🌌🚀

================================================
FILE: FinGPT_ Training with LoRA and Meta-Llama-3-8B.ipynb
================================================
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os.remove(jsonl_path)\n","\n","if os.path.exists(save_path):\n","    shutil.rmtree(save_path)\n","\n","directory = \"../data\"\n","if not os.path.exists(directory):\n","    os.makedirs(directory)"],"metadata":{"id":"763xKcP5HsMg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!ls -l ./data/dataset_new"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o5Ld9fkI7ya0","executionInfo":{"status":"ok","timestamp":1727628921511,"user_tz":240,"elapsed":351,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"f74a83b5-6a36-4217-9aee-68194813497e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["ls: cannot access './data/dataset_new': No such file or directory\n"]}]},{"cell_type":"markdown","source":["### 1.2 Load and Prepare Dataset"],"metadata":{"id":"avu789edJigP"}},{"cell_type":"code","source":["!pip install datasets"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Wj4yKaOvXxhZ","executionInfo":{"status":"ok","timestamp":1727628939219,"user_tz":240,"elapsed":7919,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"5578aefa-17c8-40a1-9f35-d29346b6f005"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting datasets\n","  Downloading datasets-3.0.1-py3-none-any.whl.metadata (20 kB)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n","Collecting pyarrow>=15.0.0 (from datasets)\n","  Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n","Collecting dill<0.3.9,>=0.3.0 (from datasets)\n","  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n","Requirement already satisfied: pandas in 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installed. This behaviour is the source of the following dependency conflicts.\n","cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed datasets-3.0.1 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n"]}]},{"cell_type":"code","source":["from datasets import load_dataset\n","import datasets"],"metadata":{"id":"a7Z1LIC7II3r"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dic = {\n","    0:'negative',\n","    1:'positive',\n","    2:'neutral'\n","}"],"metadata":{"id":"PZbuDeGnMfuK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tfns = load_dataset('zeroshot/twitter-financial-news-sentiment') #tfns = Twitter Financial News Sentiment"],"metadata":{"id":"VHAEKqhsOtQQ","colab":{"base_uri":"https://localhost:8080/","height":286,"referenced_widgets":["07b2d0624ca54c26959c9fffc8da37d3","0df33a26ef494bc8b5a97855af865d51","315751c486bf4310832330cf83f743a9","c6371d81e1544b38938730f6e7e58c62","7116e2a9e4d74b63a8f9485358be9c70","3b138daaf42941e388c0069e437a5231","4de915655a97452c941d1c1599d9d70e","feea4a62e28644db9352c111ba95c141","97f5155b49684aeaae5b903a2e3b96c0","856b2d507f7e49b980fbb52cd8c4a603","73333eb78c53466883727322c6115908","354bd5e8413d46cfa9ae78b5648fb436","d380540a1a5d433a86d8361b77fda56f","7b2a8b68667c4934be028d4cf7b6956d","4061becdc986431bb470a474d7f0d9e2","f3549f709ad747de99c82985384ebf9a","d528afd3c6574a778f7016197747e3b5","2c1eb0bce9cd4218b6fba3b5febfc910","46531a3d409248b1b43a01b1fd0ceb6e","40e9b365dcd940caa1b9fb1e2f37b5dd","9c45f7febf9b432da3392900ae23f198","1a2540b48f0a402c96a2560bc1ac4165","b7de3576f65344eaaf7d5a08db85588a","d932cad0586947509ab3fde467ba7357","1ac87a168d7c4ed08994f681d59bf3e4","6d369cc9fd2243d293d7e2a5ff4b2db2","968d6e47a31949478c7e21b4bed7e029","7dd9abbc09f047729087cf6b32e7fdbe","add35e421a114d7781503be9af48d282","dca076fd97504098b50decb0ea6a8c6e","49faea6373f14d67936171644659efd0","0c821a883f0c44078db3064d9d7d6ad9","222403e6f19349f298df755a09560daa","ff8e0e5c79be4c19937e49cc2ccbbc21","1dcd9f5bc2ab496194b9e5838228caa6","0ec00d34e953436eaf8c8f32af88a329","ef9fa8843cf34267b7d5dcbf8869f6c8","91c7201b53124f03954572cbe52db21d","0de04a84dd644332ac1547d6540c9007","b799ff81e69741b6897d36285b429466","a4a08d6bf329484cb296411b2e70ca67","2010f85089e640e98dfbb7585eb99bf6","e6f6e8a58359483c9ca290f043e09e5b","90d05589d9c84e1f87ffad2632c6c71d","01c4ff58a0a84b87bf05f9371a593caa","1e423fb1ebfa479bb241a7aee3cafc05","82ea288dd90147388b07eb8961940e52","777177eb10d54109a8a4253eca75b82d","c1967bc33b4c44cc8524119f463c668b","7d9492586a924585983b892e23bdeb4a","511a761fe718484b89abd806dc7ac60c","6428e0ebfd574ee88974be31648354ae","7c3276a84e67414e8b5b0737539910ea","171a931f2fef4ceea6d09036cf7a3315","c4238ab47e49491b9ba80bde0f67ac3b"]},"executionInfo":{"status":"ok","timestamp":1727628956408,"user_tz":240,"elapsed":7634,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"3839f1db-f1f8-4f26-f9a1-103af869a660"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["README.md:   0%|          | 0.00/1.39k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"07b2d0624ca54c26959c9fffc8da37d3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["sent_train.csv:   0%|          | 0.00/859k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"354bd5e8413d46cfa9ae78b5648fb436"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["sent_valid.csv:   0%|          | 0.00/217k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b7de3576f65344eaaf7d5a08db85588a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/9543 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ff8e0e5c79be4c19937e49cc2ccbbc21"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/2388 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"01c4ff58a0a84b87bf05f9371a593caa"}},"metadata":{}}]},{"cell_type":"code","source":["tfns = tfns['train']\n","tfns = tfns.to_pandas()\n","\n","tfns['label'] = tfns['label'].apply(lambda x : dic[x])  # Map numerical labels to their corresponding sentiments\n","\n","#Add instruction for each data entry, which is crucial for Instruction Tuning.\n","tfns['instruction'] = 'What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.'\n","tfns.columns = ['input','output','instruction']\n","\n","#Convert the Pandas dataframe back to a Hugging Face Dataset object.\n","tfns = datasets.Dataset.from_pandas(tfns)\n","tfns"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EBUg8dULO9Vu","executionInfo":{"status":"ok","timestamp":1727628970896,"user_tz":240,"elapsed":356,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"e346425f-330c-4f67-f09f-35b8c0a84319"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['input', 'output', 'instruction'],\n","    num_rows: 9543\n","})"]},"metadata":{},"execution_count":8}]},{"cell_type":"markdown","source":["### 1.3 Concatenate and Shuffle Dataset"],"metadata":{"id":"a7Sxgc0TR7KC"}},{"cell_type":"code","source":["tmp_dataset = datasets.concatenate_datasets([tfns]*2) #Creat a list that contains 2 tfns\n","train_dataset = tmp_dataset\n","print(tmp_dataset.num_rows)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2rzsssIJQNvR","executionInfo":{"status":"ok","timestamp":1727628975938,"user_tz":240,"elapsed":360,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"fb578ec4-6d96-4ac4-8898-98a80f9d812b"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["19086\n"]}]},{"cell_type":"code","source":["all_dataset = train_dataset.shuffle(seed = 42)\n","all_dataset.shape"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XL2210OoSSCY","executionInfo":{"status":"ok","timestamp":1727628977033,"user_tz":240,"elapsed":1,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"48946bf7-c7a7-4780-b757-36f50fd5d63f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(19086, 3)"]},"metadata":{},"execution_count":10}]},{"cell_type":"markdown","source":["The training data is all set"],"metadata":{"id":"uRGfbF0rW1xj"}},{"cell_type":"markdown","source":["## Part 2: Dataset Formatting and Tokenization"],"metadata":{"id":"b_3uB3hMTSK8"}},{"cell_type":"markdown","source":["### 2.1 Dataset Fromatting"],"metadata":{"id":"FDWmm-bNTbw9"}},{"cell_type":"markdown","source":["You must structure your data in a specific format that aligns with the training process."],"metadata":{"id":"x62UGwsCTrqP"}},{"cell_type":"code","source":["import json\n","from tqdm.notebook import tqdm\n","# Used to display a progress bar in Jupyter Notebook to help visualize the progress of data processing"],"metadata":{"id":"7XZ2sYr4W1g9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def format_examle(example:dict) -> dict:    #Defines a function named format_example that takes a dictionary as input (example: dict) and returns a dictionary (-> dict).\n","  context = f\"Instruction:{example['instruction']}\\n\"   #Initializes a string variable context using an f-string to format the instruction.\n","  if example.get('input'):     #Checks if the example dictionary has an input key and whether it contains a value.\n","    context += f\"Input:{example['input']}\\n\"\n","  context += 'Answer: '\n","  target = example['output']\n","  return {\"context\": context , \"target\":target}  # This is the format of json data.\n","\n","\n","\n","data_list = []\n","for item in all_dataset.to_pandas().itertuples():    #Iterates over each row of the dataset all_dataset, which has been converted into a Pandas DataFrame using .to_pandas().\n","  tmp = {}\n","  tmp['instruction'] = item.instruction\n","  tmp['input'] = item.input\n","  tmp['output'] = item.output\n","  data_list.append(tmp)"],"metadata":{"id":"FHv7KAtVSdMI"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["This is what the elements in data_list look like before formatting\n","\n","---\n","\n"],"metadata":{"id":"u8pPeWBkdOum"}},{"cell_type":"code","source":["data_list[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"cootm_jhch6O","executionInfo":{"status":"ok","timestamp":1727628988495,"user_tz":240,"elapsed":735,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"87643703-7afe-422b-fd55-16cbd17b83fe"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'instruction': 'What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.',\n"," 'input': '$DRIP $LABU $GASX - SOXL, LABU, JO and GUSH among weekly ETF movers https://t.co/FntrWNY9sn',\n"," 'output': 'neutral'}"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["# save to a json file\n","with open(\"../data/dataset_new.jsonl\",'w') as f:\n","  for example in tqdm(data_list,desc = 'formatting..'):\n","    f.write(json.dumps(format_examle(example)) + '\\n')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["d186891419fe4cdbb29e8c9bcd27d4a7","f36ca85863f84e51a9c3a71c6401fbb0","722a1da6a59a4812991fd5a3646531f8","7f338c915d2a4154b9790c5a9c6a42ea","c6182617918f403595c72c143ebb7697","64660f224299457c9d22603cb3f5c842","277186ade77242bfa3f8095adb0935b9","301c3b91250444d88cf91137e7a2edf5","87e313fe3c1c4f0bb0f44bc61bb12eb1","3e584353238d4abdac8cadb2ef9f19b4","4d44f66f788e460083bf2f8ba569706a"]},"id":"S6qo2PPueXG0","executionInfo":{"status":"ok","timestamp":1727628992205,"user_tz":240,"elapsed":380,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"7a165fb1-ec76-4d40-ba59-6da918ff3f96"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["formatting..:   0%|          | 0/19086 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d186891419fe4cdbb29e8c9bcd27d4a7"}},"metadata":{}}]},{"cell_type":"code","source":["json_data_list = []  # Var to save json data\n","\n","# Save to a jsonl file and store in json_data_list\n","with open(\"../data/dataset_new.jsonl\", 'r') as f:\n","    for line in f:\n","        json_line = json.loads(line.strip())\n","        json_data_list.append(json_line)"],"metadata":{"id":"HHsyl1zPgC77"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["This is what it is look like after formatting"],"metadata":{"id":"2h7lzIE0hHJF"}},{"cell_type":"code","source":["json_data_list[0]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"k-032c77gzF1","executionInfo":{"status":"ok","timestamp":1727629002683,"user_tz":240,"elapsed":339,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"c7dc71e2-6296-4d73-ec29-3a0212842295"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'context': 'Instruction:What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.\\nInput:$DRIP $LABU $GASX - SOXL, LABU, JO and GUSH among weekly ETF movers https://t.co/FntrWNY9sn\\nAnswer: ',\n"," 'target': 'neutral'}"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["json_data_list[0]['context']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"elRL-VkVqQNf","executionInfo":{"status":"ok","timestamp":1727629005547,"user_tz":240,"elapsed":325,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"05ea8534-f23e-4d75-cf02-300aa43a21fb"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'Instruction:What is the sentiment of this tweet? Please choose an answer from {negative/neutral/positive}.\\nInput:$DRIP $LABU $GASX - SOXL, LABU, JO and GUSH among weekly ETF movers https://t.co/FntrWNY9sn\\nAnswer: '"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["json_data_list[0]['target']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"U5za7_xYqWYP","executionInfo":{"status":"ok","timestamp":1727629006998,"user_tz":240,"elapsed":351,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"f11b1549-8f62-4742-ef31-cd58e6f955af"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'neutral'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":18}]},{"cell_type":"markdown","source":["login to HF to use Llama 3"],"metadata":{"id":"SqZapShIy_Lq"}},{"cell_type":"markdown","source":["### 2.2 Tokenization"],"metadata":{"id":"QHCUCE7-U0VV"}},{"cell_type":"markdown","source":["Tokenization is the process of converting input text into tokens that can be fed into the model."],"metadata":{"id":"SURNzAL0hoY-"}},{"cell_type":"code","source":["## need to set the packages to run this code block\n","!pip install protobuf transformers==4.30.2 cpm_kernels torch>=2.0 gradio mdtex2html sentencepiece accelerate"],"metadata":{"id":"_t08UOLohoJq"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import AutoTokenizer, AutoConfig"],"metadata":{"id":"37DThz-phmHj","colab":{"base_uri":"https://localhost:8080/","height":67,"referenced_widgets":["2a119723664347af9b320ec7bd9de1cb","a3b6277c83ce4981bd7eca6a5c2d8104","ffe02caf5f8d42c1ad64110207e30021","477db912b0454a96b77bb96a2d907c43","ecfa1ed61321472db260eb29af6be2ba","9a1984db57404860b14f3af434796cae","6c82a861d86e48c9b46ec2455dfdff7d","a1c5fad783b5412ba836909cd85d49c6","d25fbd1d965c48b9b1c16c859fde464c","5476179897a84305bd652c02a5106a43","f1ad03c919644c25b9fcdf307b989c7a"]},"executionInfo":{"status":"ok","timestamp":1727629038091,"user_tz":240,"elapsed":776,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"dbc9f1fa-d526-49f8-bfcf-42eabfb02dc0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"]},{"output_type":"display_data","data":{"text/plain":["0it [00:00, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2a119723664347af9b320ec7bd9de1cb"}},"metadata":{}}]},{"cell_type":"code","source":["model_name = 'meta-llama/Meta-Llama-3-8B'   #Specifies the model you're working with\n","jsonl_path = '../data/dataset_new.jsonl'\n","save_path = '../data/dataset_new'    #The path where the processed dataset will be saved after tokenization or any other processing\n","max_seq_length = 512    #Maximum sequence length for the inputs. If an input exceeds this length, it will either be truncated or skipped.\n","skip_overlength = True    #A flag that determines whether to skip overlength examples that exceed max_seq_length"],"metadata":{"id":"v00HBZ_zj4Ve"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["This preprocess function tokenizes the promt and target, combines them into Input ids, trims or pads the squence to the maximum squence length."],"metadata":{"id":"ZTsS1aeimfNR"}},{"cell_type":"code","source":["def preprocess(tokenizer, config, example, max_seq_length):\n","  prompt = example['context']\n","  target = example['target']\n","  prompt_ids = tokenizer.encode(   #ids refers to the numerical identifiers that correspond to tokens.These token ids are what the model processes, as models require numerical input rather than raw text.\n","      prompt,\n","      max_length = max_seq_length,\n","      truncation = True\n","      )\n","  target_ids = tokenizer.encode(\n","      target,\n","      max_length = max_seq_length,\n","      truncation = True,\n","      add_special_tokens = False\n","      )\n","  input_ids = prompt_ids + target_ids + [config.eos_token_id]  #[config.eos_token_id] is a sign that marks the end of the list.\n","  return {'input_ids':input_ids,'seq_len':len(prompt_ids)}"],"metadata":{"id":"4vq6aOkOkVRg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n","config = AutoConfig.from_pretrained(model_name, trust_remote_code=True, device_map='auto')"],"metadata":{"id":"-KJikX-D2cRC"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["example = json_data_list[0]\n","prompt = example['context']\n","target = example['target']"],"metadata":{"id":"0-SCMNMA2deK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["example['target']"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":36},"id":"vr_1HqJG221U","executionInfo":{"status":"ok","timestamp":1727633103969,"user_tz":240,"elapsed":364,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"7b9a8f1d-2d3e-4448-888b-6d50e3046821"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'neutral'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":47}]},{"cell_type":"markdown","source":["input_ids is a complete list of token IDs that combines the input sentence (prompt), the target sentence (target), and the end-of-sequence token (eos_token_id).\n","This list is fed into the model for training or inference. The model uses these IDs to understand and process the input and generate the corresponding output."],"metadata":{"id":"leOtS6SuwKF6"}},{"cell_type":"markdown","source":["The read_jsonl function reads each line from the JSONL file, preprocesses it using the preprocess function,\n","and then yields each preprocessed example."],"metadata":{"id":"oTN9ClZMwSf-"}},{"cell_type":"code","source":["def read_jsonl(path, max_seq_length, skip_overlength=False):\n","    tokenizer = AutoTokenizer.from_pretrained(    #Initializes a tokenizer using a pre-trained model specified by model_name.\n","        model_name, trust_remote_code=True)\n","    config = AutoConfig.from_pretrained(    #Loads the configuration for the model. device_map='auto' helps automatically map the model to available devices (e.g., GPU or CPU).\n","        model_name, trust_remote_code=True, device_map='auto')\n","    with open(path, \"r\") as f:\n","        for line in tqdm(f.readlines()):\n","            example = json.loads(line)\n","            #Preprocesses each example by tokenizing it and converting it into input_ids using the preprocess() function,\n","            #which takes the tokenizer, config, example, and max_seq_length as inputs.\n","            feature = preprocess(tokenizer, config, example, max_seq_length)\n","            if skip_overlength and len(feature[\"input_ids\"]) > max_seq_length:\n","                continue\n","            feature[\"input_ids\"] = feature[\"input_ids\"][:max_seq_length]  #Truncates the input_ids to ensure they do not exceed max_seq_length.\n","            yield feature\n","#Uses yield to return one preprocessed feature at a time, making the function a generator.\n","#This allows you to iterate over the processed features one by one without loading everything into memory at once."],"metadata":{"id":"dPgs5ecEwQGS"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 2.3 Save the Dataset"],"metadata":{"id":"OiIr5nLhx8PY"}},{"cell_type":"code","source":["save_path = './data/dataset_new'"],"metadata":{"id":"jTsJEl9UnmWV"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset = datasets.Dataset.from_generator(\n","    lambda: read_jsonl(jsonl_path, max_seq_length, skip_overlength)\n","    )\n","dataset.save_to_disk(save_path)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":169,"referenced_widgets":["3fbdc5eb0ad649a6a2185ff5c01447c9","7d509c994d3048fe9081bd8afc1f9526","b501b74a56fc43418d098bf6b3703714","aa1f17b5e387471cb3ff8f918603cc01","72fadabe43304723bfa05ec9a66c680f","009f988c42ba4b07858abc62a8ff4ae7","0c15c3a41f3442f2b6c5ef2f9e91c261","3f600d79bd094f939727c8dba98c492e","6c9745bcf8f24781a3588a8223335bd7","d742c04e26cc46cf87f147df32af90ad","9c88941c7caf48398e19f301dfaa3f32","ff32afd0b67e4372b32e39bee4732632","2e1250d4e7b34c408cf2d0e76f104930","7d45b0ee63af4055b3884ee8cc0e247d","1671dd31d0e94fce84b7e30df924162b","efd665b309c645d195860f4b23f68fd0","e5e4826e0ba74aa39c850aa686e02953","e3d54027f0eb4131b722e01ffc1a75c5","fc28cafadc2943b394983991aa222f2c","08f7b66e545e48ab9844b5f6ed53a658","0af158b6dab8447bb7a8e46371df6044","4b50a26daa1440fe93a46b062a5b353c","d9c98c221ca34fb0bcd10ffe057e4b1a","c17f202789cc4b0bbd379c6c56a23f6d","b7901d36d93f4fbdbe54369ea4be8968","da205f761a7e436983e08f9521384875","f7690f61b1384ace8e4e0a3e3e2e78d9","13f5be0753d54f5e9793a436bfc2c807","dc432aa357d640d3a57690bfbb30382e","d8a361c3653b456c9fe2834c5bbdc994","e181354052df46f2bbf78c6c64b90f66","358d62623bda45488c95cd035f2d3e39","2689554243194d24880808231936db58"]},"id":"D24yK-E8ynIh","executionInfo":{"status":"ok","timestamp":1727634162475,"user_tz":240,"elapsed":9684,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"0bc4dbbf-2beb-4bee-d311-5d7262144a55"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3fbdc5eb0ad649a6a2185ff5c01447c9"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/19086 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ff32afd0b67e4372b32e39bee4732632"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Saving the dataset (0/1 shards):   0%|          | 0/19086 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d9c98c221ca34fb0bcd10ffe057e4b1a"}},"metadata":{}}]},{"cell_type":"code","source":["from datasets import load_from_disk\n","\n","# Load Dataset\n","loaded_dataset = load_from_disk('./data/dataset_new')\n","\n","# Check the structure of Dataset\n","print(loaded_dataset)\n","\n","# Print the first sample of the dataset\n","print(loaded_dataset['input_ids'][0])"],"metadata":{"id":"DM_V5ZZqiLJV"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 2.4 Save dataset to your own google drive\n","\n","Every time you restart colab, you don't have to reformat the data, you can just load the formatted data directly from this google drive."],"metadata":{"id":"a67Trw8LgCqo"}},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive') #You'll be asked to authorize access to your Google Drive"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hGlXnW9mf7qi","executionInfo":{"status":"ok","timestamp":1727634274367,"user_tz":240,"elapsed":31765,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"f625e017-521a-4ba4-9d66-c0fb556a5fbf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["save_path = '/content/drive/MyDrive/Colab Notebooks/AI4Finance/FinGPT/FinGPT: Training with LoRA and Llama-3/dataset_new' #Change to your own address\n","# Write your own Google drive saving address in xxxxxxxx part: '/content/drive/MyDrive/xxxxxxxxxxxxxxxxx/dataset_new'\n","dataset.save_to_disk(save_path)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","referenced_widgets":["08b1a8f1a78b43cc9fcadfbd62b8c640","0425b5a2fd4d43c7a5c23ce0d871c2bc","d195de0e6ae24554aae7dac017702aa6","925779e4950449468858202aea2e41c5","77c13a2147484f0ba7df11ebe2c3ed05","533cb8875d5244dda8e32d7cbc73ab30","50f8bac5cc714a07841576d2e68a1540","7e088ac4ea01438788ec74e036ba6aa4","e6a724f0076b465ea14da1c66c93514b","3ed33e39bd80437d855a878928198bbc","bcda59cdb6b44c468ec40f25db93e0ed"],"height":49},"id":"WgRQDWYUg3ft","executionInfo":{"status":"ok","timestamp":1727634425692,"user_tz":240,"elapsed":4101,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"62cf9d61-597d-4ffb-f810-ce4687aebf4a"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Saving the dataset (0/1 shards):   0%|          | 0/19086 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"08b1a8f1a78b43cc9fcadfbd62b8c640"}},"metadata":{}}]},{"cell_type":"markdown","source":["### 2.5 Load Dataset from google drive\n","\n","Runs directly from here every time you re-login or reconnect."],"metadata":{"id":"QsF548Xdh8mN"}},{"cell_type":"code","source":["from huggingface_hub import login\n","\n","\n","# Login directly with your Token (remember not to share this Token publicly)\n","login(token=hf_token)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"7NWI3s_5Eqme","executionInfo":{"status":"ok","timestamp":1727741485251,"user_tz":240,"elapsed":378,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"06b5e19f-baee-4153-cfba-67db35d42197"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n","Token is valid (permission: write).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}]},{"cell_type":"code","source":["!pip install datasets\n","from datasets import load_from_disk\n","from google.colab import drive\n","\n","drive.mount('/content/drive') #You'll be asked to authorize access to your Google Drive\n","\n","save_path = '/content/drive/MyDrive/Colab Notebooks/AI4Finance/FinGPT/FinGPT: Training with LoRA and Llama-3/dataset_new' #Change to your own address\n","# Load saved dataset\n","loaded_dataset = load_from_disk(save_path)"],"metadata":{"id":"Vo6-EMOAiCPp","executionInfo":{"status":"ok","timestamp":1727741286234,"user_tz":240,"elapsed":48213,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"4db9c05a-c028-4853-eb84-4984aa66236f"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting datasets\n","  Downloading datasets-3.0.1-py3-none-any.whl.metadata (20 kB)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n","Collecting pyarrow>=15.0.0 (from datasets)\n","  Downloading pyarrow-17.0.0-cp310-cp310-manylinux_2_28_x86_64.whl.metadata (3.3 kB)\n","Collecting dill<0.3.9,>=0.3.0 (from datasets)\n","  Downloading dill-0.3.8-py3-none-any.whl.metadata (10 kB)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.1.4)\n","Requirement already satisfied: requests>=2.32.2 in /usr/local/lib/python3.10/dist-packages (from datasets) (2.32.3)\n","Requirement already satisfied: tqdm>=4.66.3 in /usr/local/lib/python3.10/dist-packages (from datasets) (4.66.5)\n","Collecting xxhash (from datasets)\n","  Downloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n","Collecting multiprocess (from datasets)\n","  Downloading multiprocess-0.70.17-py310-none-any.whl.metadata (7.2 kB)\n","Requirement already satisfied: fsspec<=2024.6.1,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.6.1,>=2023.1.0->datasets) (2024.6.1)\n","Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.10.5)\n","Requirement already satisfied: huggingface-hub>=0.22.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.24.7)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from datasets) (24.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (6.0.2)\n","Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.0)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.2.0)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.4.1)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n","Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.11.1)\n","Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.22.0->datasets) (4.12.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (3.10)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2.2.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.32.2->datasets) (2024.8.30)\n","INFO: pip is looking at multiple versions of multiprocess to determine which version is compatible with other requirements. 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This behaviour is the source of the following dependency conflicts.\n","cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed datasets-3.0.1 dill-0.3.8 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.5.0\n","Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["# Check the structure of Dataset\n","print(loaded_dataset)\n","\n","# Print the first sample of the dataset\n","print(loaded_dataset['input_ids'][0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OgoD1tc4mhiT","executionInfo":{"status":"ok","timestamp":1727741290941,"user_tz":240,"elapsed":353,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"eafd6968-8cd4-4bda-d413-c4e7976d53e7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Dataset({\n","    features: ['input_ids', 'seq_len'],\n","    num_rows: 19086\n","})\n","[128000, 17077, 25, 3923, 374, 279, 27065, 315, 420, 12072, 30, 5321, 5268, 459, 4320, 505, 314, 43324, 14, 60668, 14, 31587, 28374, 2566, 22444, 7842, 3378, 400, 20257, 52, 400, 38, 1950, 55, 482, 5745, 37630, 11, 32074, 52, 11, 10458, 323, 480, 20088, 4315, 17496, 54163, 96454, 3788, 1129, 83, 6973, 12598, 77, 376, 54, 23923, 24, 9810, 198, 16533, 25, 220, 60668, 128001]\n"]}]},{"cell_type":"markdown","source":["The output of this code block above should be:\n","\n","Dataset({\n","  \n","    features: ['input_ids', 'seq_len'],\n","\n","    num_rows: 19086\n","\n","})\n","\n","[128000, 17077, 25, 3923, 374, 279, 27065, 315, 420, 12072, 30, 5321, 5268, 459, 4320, 505, 314, 43324, 14, 60668, 14, 31587, 28374, 2566, 22444, 7842, 3378, 400, 20257, 52, 400, 38, 1950, 55, 482, 5745, 37630, 11, 32074, 52, 11, 10458, 323, 480, 20088, 4315, 17496, 54163, 96454, 3788, 1129, 83, 6973, 12598, 77, 376, 54, 23923, 24, 9810, 198, 16533, 25, 220, 60668, 128001]\n","\n","说明数据正确的被加载"],"metadata":{"id":"Ppd5PszSUQLs"}},{"cell_type":"markdown","source":["## Part 3: Setup FinGPT training with LoRA and Llama-3\n"],"metadata":{"id":"6e_SkszzBgOS"}},{"cell_type":"markdown","source":["### 3.1 Training Arguments Setup:\n","Initialize and set training arguments."],"metadata":{"id":"AWnNrvzXByt8"}},{"cell_type":"code","source":["!pip install accelerate\n","!pip install -U bitsandbytes\n","!pip install loguru\n","!pip install --upgrade peft\n","!pip install transformers==4.40.1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"heUtAghtDa1s","outputId":"3db9c61f-de86-4ed9-9f66-74aa0ea3fd93","executionInfo":{"status":"ok","timestamp":1727663619445,"user_tz":240,"elapsed":18478,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages 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/usr/local/lib/python3.10/dist-packages (from requests->transformers==4.40.1) (2024.8.30)\n","Using cached transformers-4.40.1-py3-none-any.whl (9.0 MB)\n","Using cached tokenizers-0.19.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n","Installing collected packages: tokenizers, transformers\n","  Attempting uninstall: tokenizers\n","    Found existing installation: tokenizers 0.13.3\n","    Uninstalling tokenizers-0.13.3:\n","      Successfully uninstalled tokenizers-0.13.3\n","  Attempting uninstall: transformers\n","    Found existing installation: transformers 4.30.2\n","    Uninstalling transformers-4.30.2:\n","      Successfully uninstalled transformers-4.30.2\n","Successfully installed tokenizers-0.19.1 transformers-4.40.1\n"]},{"output_type":"display_data","data":{"application/vnd.colab-display-data+json":{"pip_warning":{"packages":["transformers"]},"id":"c7102b135379415a95dd0bf7fdbc2664"}},"metadata":{}}]},{"cell_type":"code","source":["from typing import List, Dict, Optional\n","import torch\n","from loguru import logger\n","from transformers import (\n","    AutoModel,\n","    AutoTokenizer,\n","    TrainingArguments,\n","    Trainer,\n","    BitsAndBytesConfig,\n","    AutoModelForCausalLM\n",")\n","from peft import (\n","    TaskType,\n","    LoraConfig,\n","    get_peft_model,\n","    set_peft_model_state_dict,\n","    prepare_model_for_kbit_training,\n",")\n","from peft.utils import TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING\n","from transformers import LlamaForCausalLM"],"metadata":{"id":"PXSfDpGE5zQt"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Note: This will report error with newest torch and CUDA version.\n","\n","is_bitsandbytes_available() will return False\n","\n"," and will show:\n","\n","ImportError: Using `load_in_8bit=True` requires Accelerate: `pip install accelerate` and the latest version of bitsandbytes `pip install -i https://test.pypi.org/simple/ bitsandbytes` or pip install bitsandbytes`  \n","\n"," in model loading part.\n","\n","Run\n","\n","!pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116\n","\n","first to adress the problem."],"metadata":{"id":"31CH3eehsKGG"}},{"cell_type":"code","source":["training_args = TrainingArguments(\n","    output_dir='/content/drive/MyDrive/Colab Notebooks/AI4Finance/FinGPT/FinGPT: Training with LoRA and Llama-3/finetuned_model/',    # Path to save the fine-tuned model\n","    logging_steps = 500,               # Log every 500 steps\n","    # max_steps=10000,                 # Maximum number of training steps (commented out, can be enabled)\n","    num_train_epochs = 2,              # Number of training epochs (train for 2 epochs)\n","    per_device_train_batch_size=4,     # Batch size of 4 for training on each device (GPU/CPU)\n","    gradient_accumulation_steps=8,     # Accumulate gradients for 8 steps before updating weights\n","    learning_rate=1e-4,                # Learning rate set to 1e-4\n","    weight_decay=0.01,                 # Weight decay (L2 regularization) set to 0.01\n","    warmup_steps=1000,                 # Warm up the learning rate for the first 1000 steps\n","    save_steps=500,                    # Save the model every 500 steps\n","    fp16=True,                         # Enable FP16 mixed precision training to save memory and speed up training\n","    # bf16=True,                       # Enable BF16 mixed precision training (commented out)\n","    torch_compile = False,             # Whether to enable Torch compile (`False` means not enabled)\n","    load_best_model_at_end = True,     # Load the best-performing model at the end of training\n","    evaluation_strategy=\"steps\",       # Evaluation strategy is set to evaluate every few steps\n","    remove_unused_columns=False,       # Whether to remove unused columns during training (keep all columns)\n",")\n"],"metadata":{"id":"dayPAldSVcZE"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 3.2 Quantization Config Setup:\n","Set quantization configuration to reduce model size without losing significant precision."],"metadata":{"id":"j63Nx-ssCG7a"}},{"cell_type":"code","source":["!pip list | grep bitsandbytes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A218CO6pi0nn","executionInfo":{"status":"ok","timestamp":1727659435829,"user_tz":240,"elapsed":1191,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"0bebcff9-6f43-4443-9bc3-c5ca4cb76e49"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["bitsandbytes                     0.44.0\n"]}]},{"cell_type":"code","source":["# quantitative allocation\n","q_config = BitsAndBytesConfig(load_in_4bit=False,\n","                                bnb_4bit_quant_type='nf4',\n","                                bnb_4bit_use_double_quant=True,\n","                                bnb_4bit_compute_dtype=torch.float16\n","                                )"],"metadata":{"id":"mDZbkv53BfnI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'expandable_segments:True'"],"metadata":{"id":"vWyeniBRMhi1"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### 3.3 Model Loading & Preparation:\n","Load the base model and tokenizer, and prepare the model for INT8 training.\n","\n","Runtime -> Change runtime type -> A100 GPU\n","\n","Restart runtime and run again if not working"],"metadata":{"id":"ERYZSwgBDkNc"}},{"cell_type":"code","source":["model_name = \"meta-llama/Meta-Llama-3-8B\"\n","tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LQvQFY1vCeDm","executionInfo":{"status":"ok","timestamp":1727659444186,"user_tz":240,"elapsed":2124,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"8410908e-473a-41c7-cca7-78d0cb1d4254"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n","Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"]}]},{"cell_type":"code","source":["from transformers.utils import is_bitsandbytes_available\n","is_bitsandbytes_available()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IiiQVcVxfet5","executionInfo":{"status":"ok","timestamp":1727659446926,"user_tz":240,"elapsed":463,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"f604877f-ad4d-4fed-ca7d-c7d08aee449d"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["True"]},"metadata":{},"execution_count":10}]},{"cell_type":"code","source":["model = LlamaForCausalLM.from_pretrained(\n","        model_name,\n","        quantization_config = q_config,\n","        trust_remote_code=True,\n","        device_map='auto'\n","    )"],"metadata":{"id":"1xHMJhS4EZnS","colab":{"base_uri":"https://localhost:8080/","height":123,"referenced_widgets":["accdc799fb984c06928a728fc83235f3","da4a447f614c4a7ab4ee0aa2866ce1d8","e94ff7ee9edc4732a15d1fe4ebdee452","cd57c5897c9146b2bb0882711636c186","d5cf4320251d4ef1a43f744ad057f9bc","fdd537bd5c214210892d8de2d6f36cde","9b4f67139c4e474fab9ff5f68841dd1e","098f37019be84aa0b9d9004ee02f6a8b","1d27c47dcffe4cb08e8b9f953ddd06e6","8ca9fc82ec214686bb9f89a950263b11","c827a177aeb84d6886baa707b55873aa"]},"executionInfo":{"status":"ok","timestamp":1727659543900,"user_tz":240,"elapsed":14138,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"05efe260-9850-4606-e330-e4b4bd997523"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n","/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"accdc799fb984c06928a728fc83235f3"}},"metadata":{}}]},{"cell_type":"markdown","source":["### 3.4 LoRA Config & Setup"],"metadata":{"id":"oXh4hpVbss4t"}},{"cell_type":"code","source":[],"metadata":{"id":"r7DkLNHosssU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def print_trainable_parameters(model):\n","    \"\"\"\n","    Prints the number of trainable parameters in the model.\n","    \"\"\"\n","    trainable_params = 0\n","    all_param = 0\n","    for _, param in model.named_parameters():\n","        all_param += param.numel()\n","        if param.requires_grad:\n","            trainable_params += param.numel()\n","    print(\n","        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n","    )\n","\n","# LoRA for Llama3\n","target_modules = TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING['llama']  # Modules for the Llama model\n","lora_config = LoraConfig(\n","    task_type=TaskType.CAUSAL_LM,\n","    inference_mode=False,\n","    r=8,\n","    lora_alpha=32,\n","    lora_dropout=0.1,\n","    target_modules=target_modules,\n","    bias='none',\n",")\n","\n","# Loading LoRA for Llama3 models using PEFT (Parameter-Efficient Fine-Tuning)\n","model = get_peft_model(model, lora_config)\n","\n","# Print the number of trainable parameters\n","print_trainable_parameters(model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FFpy6LwrrB3Z","executionInfo":{"status":"ok","timestamp":1727659582086,"user_tz":240,"elapsed":335,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"05aae0e2-ac6b-49da-8e9d-053b88a717b2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 3407872 || all params: 4544008192 || trainable%: 0.07499704789264605\n"]}]},{"cell_type":"code","source":["resume_from_checkpoint = None\n","if resume_from_checkpoint is not None:\n","    checkpoint_name = os.path.join(resume_from_checkpoint, 'pytorch_model.bin')\n","    if not os.path.exists(checkpoint_name):\n","        checkpoint_name = os.path.join(\n","            resume_from_checkpoint, 'adapter_model.bin'\n","        )\n","        resume_from_checkpoint = False\n","    if os.path.exists(checkpoint_name):\n","        logger.info(f'Restarting from {checkpoint_name}')\n","        adapters_weights = torch.load(checkpoint_name)\n","        set_peft_model_state_dict(model, adapters_weights)\n","    else:\n","        logger.info(f'Checkpoint {checkpoint_name} not found')"],"metadata":{"id":"8AwgMRgUxpjg"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.print_trainable_parameters()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CAE5BPkyuit2","executionInfo":{"status":"ok","timestamp":1727659587313,"user_tz":240,"elapsed":338,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"7c5654e3-4492-42f4-8f9e-1f2dc8b5585d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["trainable params: 3,407,872 || all params: 8,033,669,120 || trainable%: 0.0424\n"]}]},{"cell_type":"markdown","source":["## Part 4: Loading Data and Training FinGPT\n","\n","\n","In this segment, we'll delve into the loading of your pre-processed data, and finally, launch the training of your FinGPT model. Here's a stepwise breakdown of the script provided:\n","\n","\n","\n","*   Need to purchase Google Colab GPU plans, Colab Pro is\n","sufficient or just buy 100 compute units for $10\n","\n"],"metadata":{"id":"eZswfZZ7utGZ"}},{"cell_type":"markdown","source":["### 4.1 Loading Your Data:"],"metadata":{"id":"r23RLTLWu99A"}},{"cell_type":"code","source":["# load data\n","from datasets import load_from_disk\n","import datasets\n","from google.colab import drive\n","\n","drive.mount('/content/drive') # You will be asked to authorize access to your Google Drive\n","\n","save_path = '/content/drive/MyDrive/Colab Notebooks/AI4Finance/FinGPT/FinGPT: Training with LoRA and Llama-3/dataset_new'\n","# Load saved dataset\n","dataset = load_from_disk(save_path)\n","dataset = dataset.train_test_split(0.2, shuffle=True, seed = 42)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Grl0YyGtul3W","executionInfo":{"status":"ok","timestamp":1727659594988,"user_tz":240,"elapsed":4665,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"3264cefd-f251-4ed8-ed19-f96467d9a359"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"markdown","source":["### 4.2 Training Configuration and Launch:\n","\n","\n","\n","*   Customize the Trainer class for specific loss computation, prediction step, and model-saving methods.\n","*   Define a data collator function to process batches of data during training.\n","*   Set up TensorBoard for logging, instantiate your modified trainer, and begin training.\n","\n","\n"],"metadata":{"id":"fSiYPVACvaFn"}},{"cell_type":"code","source":["import torch.nn.functional as F"],"metadata":{"id":"V9YOr0sRyRT-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def data_collator(features: list) -> dict:\n","    # Check if pad_token_id is None, if it is then use eos_token_id as the padding value\n","    if tokenizer.pad_token_id is None:\n","        pad_token_id = tokenizer.eos_token_id  # Use eos_token_id as a fill symbol\n","    else:\n","        pad_token_id = tokenizer.pad_token_id\n","\n","    len_ids = [len(feature[\"input_ids\"]) for feature in features]\n","    longest = max(len_ids)\n","\n","    input_ids = []\n","    labels_list = []\n","\n","    for ids_l, feature in sorted(zip(len_ids, features), key=lambda x: -x[0]):\n","        ids = feature[\"input_ids\"]\n","        seq_len = feature[\"seq_len\"]\n","\n","        # Padding with calculated pad_token_id\n","        labels = (\n","            [pad_token_id] * (seq_len - 1) + ids[(seq_len - 1) :] + [pad_token_id] * (longest - ids_l)\n","        )\n","        ids = ids + [pad_token_id] * (longest - ids_l)\n","\n","        _ids = torch.LongTensor(ids)\n","        labels_list.append(torch.LongTensor(labels))\n","        input_ids.append(_ids)\n","\n","    input_ids = torch.stack(input_ids)\n","    labels = torch.stack(labels_list)\n","\n","    return {\n","        \"input_ids\": input_ids,\n","        \"labels\": labels,\n","    }"],"metadata":{"id":"zZbPLxQt73vf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from torch.utils.tensorboard import SummaryWriter\n","from transformers.integrations import TensorBoardCallback"],"metadata":{"id":"QZfzIDH1vnDt"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Mount Google Drive\n","from google.colab import drive\n","drive.mount('/content/drive')\n","\n","# 创建保存路径\n","import os\n","\n","output_dir = '/content/drive/MyDrive/Colab Notebooks/AI4Finance/FinGPT/FinGPT: Training with LoRA and Llama-3/Model/' # Use your own address\n","os.makedirs(output_dir, exist_ok=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8bHQI3jsw1rr","executionInfo":{"status":"ok","timestamp":1727659660074,"user_tz":240,"elapsed":1565,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"586ece69-b442-4b2a-9a9e-fe23155a76af"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"4bu-a9bf1gfx"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Train\n","# Took about 10 compute units\n","writer = SummaryWriter()\n","trainer = ModifiedTrainer(\n","    model=model,\n","    args=training_args,             # Trainer args\n","    train_dataset=dataset[\"train\"], # Training set\n","    eval_dataset=dataset[\"test\"],   # Testing set\n","    data_collator=data_collator,    # Data Collator\n","    callbacks=[TensorBoardCallback(writer)],\n",")\n","trainer.train()\n","writer.close()\n","\n","\n","# Save model to Google Drive\n","model.save_pretrained(output_dir)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":217},"id":"NxA4IK_fvxZW","executionInfo":{"status":"ok","timestamp":1727661996241,"user_tz":240,"elapsed":2145440,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"2f9cf8d0-01ac-425e-e588-2d4104ecb134"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["You are adding a <class 'transformers.integrations.integration_utils.TensorBoardCallback'> to the callbacks of this Trainer, but there is already one. The currentlist of callbacks is\n",":DefaultFlowCallback\n","TensorBoardCallback\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='954' max='954' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [954/954 35:40, Epoch 1/2]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>2.355200</td>\n","      <td>0.004615</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1150: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n","  warnings.warn(\n"]}]},{"cell_type":"markdown","source":["Now your model is trained and saved! You can download it and use it for generating financial insights or any other relevant tasks in the finance domain. The usage of TensorBoard allows you to deeply understand and visualize the training dynamics and performance of your model in real-time."],"metadata":{"id":"_A3Ts9KQCQR2"}},{"cell_type":"markdown","source":["## Part 5: Inference and Benchmarks using FinGPT\n","\n","Now that your model is trained, let’s understand how to use it to infer and run benchmarks.\n","\n","\n","*   Took about 10 compute units\n","\n","\n"],"metadata":{"id":"_1CYlbFPZcwN"}},{"cell_type":"markdown","source":["### 5.1 Load the model"],"metadata":{"id":"QkKYdbVVatvg"}},{"cell_type":"code","source":["!pip install transformers==4.40.1 peft==0.4.0\n","!pip install sentencepiece\n","!pip install accelerate\n","!pip install torch\n","!pip install peft\n","!pip install datasets\n","!pip install bitsandbytes"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AcAcMqwFCOaY","executionInfo":{"status":"ok","timestamp":1727742474271,"user_tz":240,"elapsed":25892,"user":{"displayName":"Yuncong Liu","userId":"08340999060190968949"}},"outputId":"095f551d-c67e-42f2-ba41-29d69f90e5d1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting transformers==4.40.1\n","  Downloading transformers-4.40.1-py3-none-any.whl.metadata (137 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/138.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━\u001b[0m \u001b[32m133.1/138.0 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.0/138.0 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: peft==0.4.0 in /usr/local/lib/python3.10/dist-packages (0.4.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.1) (3.16.1)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.1) (0.24.7)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.1) (1.26.4)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.1) (24.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.1) (6.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.1) (2024.9.11)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.1) (2.32.3)\n","Collecting tokenizers<0.20,>=0.19 (from transformers==4.40.1)\n","  Using cached tokenizers-0.19.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.1) (0.4.5)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.40.1) (4.66.5)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft==0.4.0) (5.9.5)\n","Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft==0.4.0) (2.4.1+cu121)\n","Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from peft==0.4.0) (0.34.2)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/di