# LLaMA Efficient Tuning ![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Efficient-Tuning?style=social) ![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Efficient-Tuning) ![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Efficient-Tuning) ![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue) ## Changelog [23/05/31] Now we support training the BLOOM & BLOOMZ models in this repo. Try `--model_name_or_path bigscience/bloomz-7b1-mt` argument to use the BLOOMZ model. ## Supported Models - [LLaMA](https://github.com/facebookresearch/llama) (7B, 13B, 33B, 65B) - [BLOOM](https://huggingface.co/bigscience/bloom) & [BLOOMZ](https://huggingface.co/bigscience/bloomz) (560M, 1.1B, 1.7B, 3B, 7.1B, 176B) ## Supported Training Approach - [(Continually) pre-training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) - Full-parameter training - Selected-parameter training - [LoRA](https://arxiv.org/abs/2106.09685) - [Supervised fine-tuning](https://arxiv.org/abs/2109.01652) - Full-parameter training - Selected-parameter training - [LoRA](https://arxiv.org/abs/2106.09685) - [RLHF](https://arxiv.org/abs/2203.02155) - [LoRA](https://arxiv.org/abs/2106.09685) ## Provided Datasets - For pre-training: - [Wiki Demo](data/wiki_demo.txt) - For supervised fine-tuning: - [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) - [Stanford Alpaca (Chinese)](https://github.com/ymcui/Chinese-LLaMA-Alpaca) - [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) - [BELLE 2M](https://huggingface.co/datasets/BelleGroup/train_2M_CN) - [BELLE 1M](https://huggingface.co/datasets/BelleGroup/train_1M_CN) - [BELLE 0.5M](https://huggingface.co/datasets/BelleGroup/train_0.5M_CN) - [BELLE Dialogue 0.4M](https://huggingface.co/datasets/BelleGroup/generated_chat_0.4M) - [BELLE School Math 0.25M](https://huggingface.co/datasets/BelleGroup/school_math_0.25M) - [BELLE Multiturn Chat 0.8M](https://huggingface.co/datasets/BelleGroup/multiturn_chat_0.8M) - [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) - [Firefly 1.1M](https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M) - [CodeAlpaca 20k](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) - [Alpaca CoT](https://huggingface.co/datasets/QingyiSi/Alpaca-CoT) - [Web QA (Chinese)](https://huggingface.co/datasets/suolyer/webqa) - [UltraChat](https://github.com/thunlp/UltraChat) - For reward model training: - [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf) - [GPT-4 Generated Data](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) - [GPT-4 Generated Data (Chinese)](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM) Please refer to [data/README.md](data/README.md) for details. Some datasets require confirmation before using them, so we recommend logging in with your HuggingFace account using these commands. ```bash pip install --upgrade huggingface_hub huggingface-cli login ``` ## Requirement - Python 3.8+ and PyTorch 1.13.1+ - 🤗Transformers, Datasets, Accelerate, PEFT and TRL - protobuf, cpm_kernels and sentencepiece - jieba, rouge_chinese and nltk (used at evaluation) - gradio and mdtex2html (used in web_demo.py) And **powerful GPUs**! ## Getting Started ### Data Preparation (optional) Please refer to `data/example_dataset` for checking the details about the format of dataset files. You can either use a single `.json` file or a [dataset loading script](https://huggingface.co/docs/datasets/dataset_script) with multiple files to create a custom dataset. Note: please update `data/dataset_info.json` to use your custom dataset. About the format of this file, please refer to `data/README.md`. ### Dependence Installation (optional) ```bash git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git conda create -n llama_etuning python=3.10 conda activate llama_etuning cd LLaMA-Efficient-Tuning pip install -r requirements.txt ``` ### LLaMA Weights Preparation 1. Download the weights of the LLaMA models. 2. Convert them to HF format using the following command. ```bash python -m transformers.models.llama.convert_llama_weights_to_hf \ --input_dir path_to_llama_weights --model_size 7B --output_dir path_to_llama_model ``` ### (Continually) Pre-Training ```bash CUDA_VISIBLE_DEVICES=0 python src/train_pt.py \ --model_name_or_path path_to_llama_model \ --do_train \ --dataset wiki_demo \ --finetuning_type lora \ --output_dir path_to_pt_checkpoint \ --overwrite_cache \ --per_device_train_batch_size 4 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 5e-5 \ --num_train_epochs 3.0 \ --plot_loss \ --fp16 ``` ### Supervised Fine-Tuning ```bash CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \ --model_name_or_path path_to_llama_model \ --do_train \ --dataset alpaca_gpt4_en \ --finetuning_type lora \ --checkpoint_dir path_to_pt_checkpoint \ --output_dir path_to_sft_checkpoint \ --overwrite_cache \ --per_device_train_batch_size 4 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 5e-5 \ --num_train_epochs 3.0 \ --resume_lora_training False \ --plot_loss \ --fp16 ``` ### Reward Model Training ```bash CUDA_VISIBLE_DEVICES=0 python src/train_rm.py \ --model_name_or_path path_to_llama_model \ --do_train \ --dataset comparison_gpt4_en \ --finetuning_type lora \ --checkpoint_dir path_to_pt_checkpoint \ --output_dir path_to_rm_checkpoint \ --per_device_train_batch_size 4 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 1e-5 \ --num_train_epochs 1.0 \ --plot_loss \ --fp16 ``` ### PPO Training (RLHF) ```bash CUDA_VISIBLE_DEVICES=0 python src/train_ppo.py \ --model_name_or_path path_to_llama_model \ --do_train \ --dataset alpaca_gpt4_en \ --finetuning_type lora \ --checkpoint_dir path_to_pt_checkpoint,path_to_sft_checkpoint \ --reward_model path_to_rm_checkpoint \ --output_dir path_to_ppo_checkpoint \ --per_device_train_batch_size 2 \ --gradient_accumulation_steps 4 \ --lr_scheduler_type cosine \ --logging_steps 10 \ --save_steps 1000 \ --learning_rate 1e-5 \ --num_train_epochs 1.0 \ --resume_lora_training False \ --plot_loss ``` ### Distributed Training ```bash accelerate config # configure the environment accelerate launch src/train_XX.py # arguments (same as above) ``` ### Evaluation (BLEU and ROUGE_CHINESE) ```bash CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \ --model_name_or_path path_to_llama_model \ --do_eval \ --dataset alpaca_gpt4_en \ --checkpoint_dir path_to_checkpoint \ --output_dir path_to_eval_result \ --per_device_eval_batch_size 8 \ --max_samples 50 \ --predict_with_generate ``` ### CLI Demo ```bash python src/cli_demo.py \ --model_name_or_path path_to_llama_model \ --checkpoint_dir path_to_checkpoint ``` ### Web Demo ```bash python src/web_demo.py \ --model_name_or_path path_to_llama_model \ --checkpoint_dir path_to_checkpoint ``` ### Export model ```bash python src/export_model.py \ --model_name_or_path path_to_llama_model \ --checkpoint_dir path_to_checkpoint \ --output_dir path_to_export ``` ## License This repository is licensed under the [Apache-2.0 License](LICENSE). Please follow the [Model Card](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) to use the LLaMA models. Please follow the [RAIL License](https://huggingface.co/spaces/bigscience/license) to use the BLOOM & BLOOMZ models. ## Citation If this work is helpful, please cite as: ```bibtex @Misc{llama-efficient-tuning, title = {LLaMA Efficient Tuning}, author = {hiyouga}, howpublished = {\url{https://github.com/hiyouga/LLaMA-Efficient-Tuning}}, year = {2023} } ``` ## Acknowledgement This repo is a sibling of [ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning). They share a similar code structure of efficient tuning on large language models.