LLaMA Efficient Tuning

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Changelog

[23/06/15] Now we support training the baichuan-7B model in this repo. Try --model_name_or_path baichuan-inc/baichuan-7B argument to use the baichuan-7B model.

[23/06/03] Now we support quantized training and inference (aka 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 argument to use the BLOOMZ model.

Supported Models

LLaMA (7B/13B/33B/65B)
BLOOM & BLOOMZ (560M/1.1B/1.7B/3B/7.1B/176B)
baichuan (7B)

Supported Training Approaches

(Continually) pre-training
- Full-parameter tuning
- Partial-parameter tuning
- LoRA
- QLoRA
Supervised fine-tuning
- Full-parameter tuning
- Partial-parameter tuning
- LoRA
- QLoRA
RLHF
- LoRA
- QLoRA

Provided Datasets

Please refer to data/README.md for details.

Some datasets require confirmation before using them, so we recommend logging in with your HuggingFace account using these commands.

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 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

Download the weights of the LLaMA models.
Convert them to HF format using the following command.

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

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

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

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)

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

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_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.

CLI Demo

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

Web Demo

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

Export model

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.

Please follow the Model Card to use the LLaMA models.

Please follow the RAIL License to use the BLOOM & BLOOMZ models.

Please follow the baichuan-7B License to use the baichuan-7B 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.

WeiNyn / LLaMA-Efficient-Tuning