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README.md
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# LLaMA Efficient Tuning
# LLaMA Factory: Training and Evaluating Large Language Models with Minimal Effort
[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Efficient-Tuning?style=social)](https://github.com/hiyouga/LLaMA-Efficient-Tuning/stargazers)
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👋 Join our [WeChat](assets/wechat.jpg).
@ -143,10 +143,10 @@ Please refer to `data/example_dataset` for checking the details about the format
### 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
git clone https://github.com/hiyouga/LLaMA-Factory.git
conda create -n llama_factory python=3.10
conda activate llama_factory
cd LLaMA-Factory
pip install -r requirements.txt
```
@ -468,10 +468,10 @@ Please follow the model licenses to use the corresponding model weights:
If this work is helpful, please kindly cite as:
```bibtex
@Misc{llama-efficient-tuning,
title = {LLaMA Efficient Tuning},
@Misc{llama-factory,
title = {LLaMA Factory},
author = {hiyouga},
howpublished = {\url{https://github.com/hiyouga/LLaMA-Efficient-Tuning}},
howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}},
year = {2023}
}
```
@ -482,4 +482,4 @@ This repo benefits from [PEFT](https://github.com/huggingface/peft), [QLoRA](htt
## Star History
![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Efficient-Tuning&type=Date)
![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date)

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README_zh.md
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# LLaMA Efficient Tuning
# LLaMA Factory: 轻松的大模型训练与评估
[![GitHub Repo stars](https://img.shields.io/github/stars/hiyouga/LLaMA-Efficient-Tuning?style=social)](https://github.com/hiyouga/LLaMA-Efficient-Tuning/stargazers)
[![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Efficient-Tuning)](LICENSE)
[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Efficient-Tuning)](https://github.com/hiyouga/LLaMA-Efficient-Tuning/commits/main)
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[![GitHub Code License](https://img.shields.io/github/license/hiyouga/LLaMA-Factory)](LICENSE)
[![GitHub last commit](https://img.shields.io/github/last-commit/hiyouga/LLaMA-Factory)](https://github.com/hiyouga/LLaMA-Factory/commits/main)
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[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Efficient-Tuning/pulls)
[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
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👋 加入我们的[微信群](assets/wechat.jpg)。
@ -143,10 +143,10 @@ huggingface-cli login
### 环境搭建（可跳过）
```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
git clone https://github.com/hiyouga/LLaMA-Factory.git
conda create -n llama_factory python=3.10
conda activate llama_factory
cd LLaMA-Factory
pip install -r requirements.txt
```
@ -467,10 +467,10 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
如果您觉得此项目有帮助，请考虑以下列格式引用
```bibtex
@Misc{llama-efficient-tuning,
title = {LLaMA Efficient Tuning},
@Misc{llama-factory,
title = {LLaMA Factory},
author = {hiyouga},
howpublished = {\url{https://github.com/hiyouga/LLaMA-Efficient-Tuning}},
howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}},
year = {2023}
}
```
@ -481,4 +481,4 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
## Star History
![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Efficient-Tuning&type=Date)
![Star History Chart](https://api.star-history.com/svg?repos=hiyouga/LLaMA-Factory&type=Date)