add citation
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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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[](#使用了-llama-factory-的项目)
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[](#使用了-llama-factory-的项目)
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[](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[](https://discord.gg/rKfvV9r9FK)
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[](https://twitter.com/llamafactory_ai)
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@@ -23,8 +23,6 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/ec36a9dd-37f4-4f72-81bd
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选择你的打开方式:
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- **Hugging Face 空间**:https://huggingface.co/spaces/hiyouga/LLaMA-Board
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- **魔搭社区**:https://modelscope.cn/studios/hiyouga/LLaMA-Board
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- **Colab**:https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
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- **本地机器**:请见[如何使用](#如何使用)
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@@ -679,6 +677,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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1. Cao et al. Head-wise Shareable Attention for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.11819)
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1. Zhang et al. Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages. 2024. [[arxiv]](https://arxiv.org/abs/2402.12204)
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1. Kim et al. Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.14714)
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1. Yu et al. KIEval: A Knowledge-grounded Interactive Evaluation Framework for Large Language Models. 2024. [[arxiv]](https://arxiv.org/abs/2402.15043)
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1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: 天文大模型 StarWhisper,基于 ChatGLM2-6B 和 Qwen-14B 在天文数据上微调而得。
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1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: 中文法律领域大模型 DISC-LawLLM,基于 Baichuan-13B 微调而得,具有法律推理和知识检索能力。
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1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: 孙思邈中文医疗大模型 Sumsimiao,基于 Baichuan-7B 和 ChatGLM-6B 在中文医疗数据上微调而得。
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```bibtex
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@article{zheng2024llamafactory,
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title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
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author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo},
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author={Yaowei Zheng and Richong Zhang and Junhao Zhang and Yanhan Ye and Zheyan Luo and Yongqiang Ma},
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journal={arXiv preprint arXiv:2403.13372},
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year={2024},
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url={http://arxiv.org/abs/2403.13372}
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