update readme

README.md

@@ -1,29 +1,197 @@
 # LLaMA Efficient Tuning
 
+![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/ChatGLM-Efficient-Tuning?style=social)
+![GitHub Code License](https://img.shields.io/github/license/hiyouga/ChatGLM-Efficient-Tuning)
+![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/ChatGLM-Efficient-Tuning)
+![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)
+
+## 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.

requirements.txt

@@ -0,0 +1,14 @@
+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