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