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README.md
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README.md
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# LLaMA Efficient Tuning
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# LLaMA Factory: Training and Evaluating Large Language Models with Minimal Effort
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[](https://github.com/hiyouga/LLaMA-Efficient-Tuning/stargazers)
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[](LICENSE)
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[](https://github.com/hiyouga/LLaMA-Efficient-Tuning/commits/main)
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[](https://github.com/hiyouga/LLaMA-Factory/stargazers)
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[](LICENSE)
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[](https://github.com/hiyouga/LLaMA-Factory/commits/main)
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[](https://pypi.org/project/llmtuner/)
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[](https://pypi.org/project/llmtuner/)
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[](https://github.com/hiyouga/LLaMA-Efficient-Tuning/pulls)
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[](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[](https://discord.gg/7HGMsdxqJ)
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👋 Join our [WeChat](assets/wechat.jpg).
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@ -143,10 +143,10 @@ Please refer to `data/example_dataset` for checking the details about the format
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### Dependence Installation (optional)
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```bash
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git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git
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conda create -n llama_etuning python=3.10
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conda activate llama_etuning
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cd LLaMA-Efficient-Tuning
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git clone https://github.com/hiyouga/LLaMA-Factory.git
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conda create -n llama_factory python=3.10
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conda activate llama_factory
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cd LLaMA-Factory
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pip install -r requirements.txt
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```
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@ -468,10 +468,10 @@ Please follow the model licenses to use the corresponding model weights:
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If this work is helpful, please kindly cite as:
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```bibtex
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@Misc{llama-efficient-tuning,
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title = {LLaMA Efficient Tuning},
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@Misc{llama-factory,
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title = {LLaMA Factory},
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author = {hiyouga},
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howpublished = {\url{https://github.com/hiyouga/LLaMA-Efficient-Tuning}},
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howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}},
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year = {2023}
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}
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```
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@ -482,4 +482,4 @@ This repo benefits from [PEFT](https://github.com/huggingface/peft), [QLoRA](htt
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## Star History
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README_zh.md
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README_zh.md
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# LLaMA Efficient Tuning
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# LLaMA Factory: 轻松的大模型训练与评估
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[](https://github.com/hiyouga/LLaMA-Efficient-Tuning/stargazers)
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[](LICENSE)
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[](https://github.com/hiyouga/LLaMA-Efficient-Tuning/commits/main)
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[](https://github.com/hiyouga/LLaMA-Factory/stargazers)
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[](LICENSE)
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[](https://github.com/hiyouga/LLaMA-Factory/commits/main)
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[](https://pypi.org/project/llmtuner/)
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[](https://pypi.org/project/llmtuner/)
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[](https://github.com/hiyouga/LLaMA-Efficient-Tuning/pulls)
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[](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[](https://discord.gg/7HGMsdxqJ)
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👋 加入我们的[微信群](assets/wechat.jpg)。
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@ -143,10 +143,10 @@ huggingface-cli login
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### 环境搭建(可跳过)
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```bash
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git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git
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conda create -n llama_etuning python=3.10
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conda activate llama_etuning
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cd LLaMA-Efficient-Tuning
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git clone https://github.com/hiyouga/LLaMA-Factory.git
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conda create -n llama_factory python=3.10
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conda activate llama_factory
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cd LLaMA-Factory
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pip install -r requirements.txt
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```
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@ -467,10 +467,10 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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如果您觉得此项目有帮助,请考虑以下列格式引用
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```bibtex
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@Misc{llama-efficient-tuning,
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title = {LLaMA Efficient Tuning},
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@Misc{llama-factory,
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title = {LLaMA Factory},
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author = {hiyouga},
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howpublished = {\url{https://github.com/hiyouga/LLaMA-Efficient-Tuning}},
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howpublished = {\url{https://github.com/hiyouga/LLaMA-Factory}},
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year = {2023}
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}
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```
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@ -481,4 +481,4 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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## Star History
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