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# LLaMA Efficient Tuning
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## 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 this [script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py)
```python
python convert_llama_weights_to_hf.py \
--input_dir path_to_llama_weights --model_size 7B --output_dir llama_7b
--input_dir path_to_llama_weights --model_size 7B --output_dir path_to_llama_model
```
3. Fine-tune the LLaMA models.
### (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 llama_7b \
--model_name_or_path path_to_llama_model \
--do_train \
--dataset alpaca_gpt4_zh \
--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 2 \
--gradient_accumulation_steps 2 \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 100 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--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 model.
## 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.

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torch>=1.13.1
protobuf
cpm_kernels
sentencepiece
transformers>=4.27.4
datasets>=2.10.0
accelerate>=0.18.0
peft>=0.3.0
trl>=0.4.1
jieba
rouge_chinese
nltk
gradio
mdtex2html