# 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) 👋 Join our [WeChat](assets/wechat.jpg). ## Changelog [23/07/11] Now we support training the **Baichuan-13B** model in this repo. Try `--model_name_or_path baichuan-inc/Baichuan-13B-Base` and `--lora_target W_pack` arguments to use the Baichuan-13B model. Remember to use `--prompt_template baichuan` argument when you are using the Baichuan-13B-Chat model. [23/07/09] Now we release [FastEdit](https://github.com/hiyouga/FastEdit)⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested. [23/07/07] Now we support training the **InternLM-7B** model in this repo. Try `--model_name_or_path internlm/internlm-7b` argument to use the InternLM model. Remember to use `--prompt_template intern` argument when you are using the InternLM-chat model. [23/07/05] Now we support training the **Falcon-7B/40B** models in this repo. Try `--model_name_or_path tiiuae/falcon-7b` and `--lora_target query_key_value` arguments to use the Falcon model. [23/06/29] We provide a **reproducible example** of training a chat model using instruction-following datasets, see this [HuggingFace Repo](https://huggingface.co/hiyouga/baichuan-7b-sft) for details. [23/06/22] Now we align the [demo API](src/api_demo.py) with the [OpenAI's](https://platform.openai.com/docs/api-reference/chat) format where you can insert the fine-tuned model in **arbitrary ChatGPT-based applications**. [23/06/15] Now we support training the **Baichuan-7B** model in this repo. Try `--model_name_or_path baichuan-inc/Baichuan-7B` and `--lora_target W_pack` arguments to use the Baichuan-7B model. If you want to train with RTX3090, use `git checkout baichuan-7b-rtx3090` to switch to the `baichuan-7b-rtx3090` branch and try the `--baichuan_rtx_gpu true` argument. (Other RTX series GPUs can also be tried) [23/06/03] Now we support quantized training and inference (aka **[QLoRA](https://github.com/artidoro/qlora)**). Try `--quantization_bit 4/8` argument to work with quantized model. (experimental feature) [23/05/31] Now we support training the **BLOOM & BLOOMZ** models in this repo. Try `--model_name_or_path bigscience/bloomz-7b1-mt` and `--lora_target query_key_value` arguments 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) - [Falcon](https://huggingface.co/tiiuae/falcon-7b) (7B/40B) - [Baichuan](https://huggingface.co/baichuan-inc/baichuan-7B) (7B/13B) - [InternLM](https://github.com/InternLM/InternLM) (7B) ## Supported Training Approaches - [(Continually) pre-training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) - Full-parameter tuning - Partial-parameter tuning - [LoRA](https://arxiv.org/abs/2106.09685) - [QLoRA](https://arxiv.org/abs/2305.14314) - [Supervised fine-tuning](https://arxiv.org/abs/2109.01652) - Full-parameter tuning - Partial-parameter tuning - [LoRA](https://arxiv.org/abs/2106.09685) - [QLoRA](https://arxiv.org/abs/2305.14314) - [RLHF](https://arxiv.org/abs/2203.02155) - [LoRA](https://arxiv.org/abs/2106.09685) - [QLoRA](https://arxiv.org/abs/2305.14314) ## 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) - [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1) - [Open Assistant (Chinese)](https://huggingface.co/datasets/OpenAssistant/oasst1) - For reward model training: - [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf) - [Open Assistant](https://huggingface.co/datasets/OpenAssistant/oasst1) - [Open Assistant (Chinese)](https://huggingface.co/datasets/OpenAssistant/oasst1) - [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 - jieba, rouge-chinese and nltk (used at evaluation) - gradio and mdtex2html (used in web_demo.py) - uvicorn, fastapi and sse-starlette (used in api_demo.py) And **powerful GPUs**! If you want to enable quantized LoRA (QLoRA) on the Windows platform, you should install a pre-built version of `bitsandbytes` library, which supports CUDA 11.1 to 12.1. ```bash pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl ``` ## 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 (optional) 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_your_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_your_model \ --do_train \ --dataset alpaca_gpt4_en \ --finetuning_type lora \ --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 \ --plot_loss \ --fp16 ``` ### Reward Model Training ```bash CUDA_VISIBLE_DEVICES=0 python src/train_rm.py \ --model_name_or_path path_to_your_model \ --do_train \ --dataset comparison_gpt4_en \ --finetuning_type lora \ --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_your_model \ --do_train \ --dataset alpaca_gpt4_en \ --finetuning_type lora \ --checkpoint_dir 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) ```
Example configuration for full-tuning with DeepSpeed ZeRO-2 ```yaml compute_environment: LOCAL_MACHINE deepspeed_config: gradient_accumulation_steps: 4 gradient_clipping: 0.5 offload_optimizer_device: none offload_param_device: none zero3_init_flag: false zero_stage: 2 distributed_type: DEEPSPEED downcast_bf16: 'no' machine_rank: 0 main_training_function: main mixed_precision: fp16 num_machines: 1 num_processes: 4 rdzv_backend: static same_network: true tpu_env: [] tpu_use_cluster: false tpu_use_sudo: false use_cpu: false ```
### Evaluation (BLEU and ROUGE_CHINESE) ```bash CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \ --model_name_or_path path_to_your_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 ``` We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit evaluation. ### API / CLI / Web Demo ```bash python src/xxx_demo.py \ --model_name_or_path path_to_your_model \ --checkpoint_dir path_to_checkpoint ``` ### Export model ```bash python src/export_model.py \ --model_name_or_path path_to_your_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 licenses to use the corresponding model weights: - [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) - [BLOOM](https://huggingface.co/spaces/bigscience/license) - [Falcon](LICENSE) - [baichuan](https://huggingface.co/baichuan-inc/baichuan-7B/resolve/main/baichuan-7B%20%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.pdf) - [InternLM](https://github.com/InternLM/InternLM#open-source-license) ## Citation If this work is helpful, please kindly 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. ## Star History ![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Efficient-Tuning&type=Date)