LLaMA Efficient Tuning

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 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)

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 this script

python convert_llama_weights_to_hf.py \
    --input_dir path_to_llama_weights --model_size 7B --output_dir path_to_llama_model

(Continually) Pre-Training

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

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

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)

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

accelerate config # configure the environment
accelerate launch src/train_XX.py # arguments (same as above)

Evaluation (BLEU and ROUGE_CHINESE)

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

python src/cli_demo.py \
    --model_name_or_path path_to_llama_model \
    --checkpoint_dir path_to_checkpoint

Web Demo

python src/web_demo.py \
    --model_name_or_path path_to_llama_model \
    --checkpoint_dir path_to_checkpoint

Export model

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. Please follow the Model Card to use the LLaMA model.

Citation

If this work is helpful, please cite as:

@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. They share a similar code structure of efficient tuning on large language models.