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release v0.1.0

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hiyouga
2023-07-18 00:18:25 +08:00
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## Changelog
[23/07/11] Now we support training the **Baichuan-13B** model in this repo. Try `--model_name_or_path baichuan-inc/Baichuan-13B-Base`, `--padding_side right` and `--lora_target W_pack` arguments to train the Baichuan-13B model. Remember to use `--prompt_template baichuan` argument when you are using the Baichuan-13B-Chat model.
[23/07/18] Now we develop an all-in-one Web UI for training, evaluation and inference. Try `train_web.py` to fine-tune models in your Web browser. Thank [@KanadeSiina](https://github.com/KanadeSiina) and [@codemayq](https://github.com/codemayq) for their efforts in the development.
[23/07/11] Now we support training the **Baichuan-13B** model in this repo. Please replace the Baichuan-13B model file with `tests/modeling_baichuan.py` and try `--model_name_or_path path_to_baichuan_model` and `--lora_target W_pack` arguments to train the Baichuan-13B model. Remember to use `--prompt_template baichuan` argument when you are using the Baichuan-13B-Chat model.
[23/07/09] Now we release [FastEdit](https://github.com/hiyouga/FastEdit)⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow [FastEdit](https://github.com/hiyouga/FastEdit) if you are interested.
@@ -125,14 +127,10 @@ cd LLaMA-Efficient-Tuning
pip install -r requirements.txt
```
### LLaMA Weights Preparation (optional)
1. Download the weights of the LLaMA models.
2. Convert them to HF format using the following command.
### All-in-one Web UI
```bash
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
python src/train_web.py
```
### (Continually) Pre-Training
@@ -275,10 +273,20 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
We recommend using `--per_device_eval_batch_size=1` and `--max_target_length 128` at 4/8-bit evaluation.
### API / CLI / Web Demo
### API Demo
```bash
python src/xxx_demo.py \
python src/api_demo.py \
--model_name_or_path path_to_your_model \
--checkpoint_dir path_to_checkpoint
```
See `http://localhost:8000/docs` for API documentation.
### CLI Demo
```bash
python src/cli_demo.py \
--model_name_or_path path_to_your_model \
--checkpoint_dir path_to_checkpoint
```