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cff-version: 1.2.0
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date-released: 2024-03
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message: "If you use this software, please cite it as below."
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authors:
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- family-names: "Zheng"
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given-names: "Yaowei"
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- family-names: "Zhang"
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given-names: "Richong"
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- family-names: "Zhang"
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given-names: "Junhao"
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- family-names: "Ye"
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given-names: "Yanhan"
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- family-names: "Luo"
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given-names: "Zheyan"
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- family-names: "Ma"
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given-names: "Yongqiang"
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title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
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url: "https://arxiv.org/abs/2403.13372"
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preferred-citation:
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type: article
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authors:
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- family-names: "Zheng"
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given-names: "Yaowei"
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- family-names: "Zhang"
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given-names: "Richong"
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- family-names: "Zhang"
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given-names: "Junhao"
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- family-names: "Ye"
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given-names: "Yanhan"
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- family-names: "Luo"
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given-names: "Zheyan"
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- family-names: "Ma"
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given-names: "Yongqiang"
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journal: "arXiv preprint arXiv:2403.13372"
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title: "LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models"
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url: "https://arxiv.org/abs/2403.13372"
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year: 2024
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@ -5,7 +5,7 @@
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[![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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[![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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[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
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[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
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[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
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[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
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[![Citation](https://img.shields.io/badge/citation-21-green)](#projects-using-llama-factory)
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[![Citation](https://img.shields.io/badge/citation-22-green)](#projects-using-llama-factory)
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[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
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[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
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[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
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[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
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@ -23,8 +23,6 @@ https://github.com/hiyouga/LLaMA-Factory/assets/16256802/9840a653-7e9c-41c8-ae89
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Choose your path:
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Choose your path:
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- **🤗 Spaces**: https://huggingface.co/spaces/hiyouga/LLaMA-Board
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- **ModelScope**: 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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- **Colab**: https://colab.research.google.com/drive/1eRTPn37ltBbYsISy9Aw2NuI2Aq5CQrD9?usp=sharing
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- **Local machine**: Please refer to [usage](#getting-started)
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- **Local machine**: Please refer to [usage](#getting-started)
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@ -706,6 +704,7 @@ docker compose -f ./docker-compose.yml up -d
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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. 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. 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. 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)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
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1. **[StarWhisper](https://github.com/Yu-Yang-Li/StarWhisper)**: A large language model for Astronomy, based on ChatGLM2-6B and Qwen-14B.
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1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
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1. **[DISC-LawLLM](https://github.com/FudanDISC/DISC-LawLLM)**: A large language model specialized in Chinese legal domain, based on Baichuan-13B, is capable of retrieving and reasoning on legal knowledge.
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1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
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1. **[Sunsimiao](https://github.com/thomas-yanxin/Sunsimiao)**: A large language model specialized in Chinese medical domain, based on Baichuan-7B and ChatGLM-6B.
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@ -728,7 +727,7 @@ If this work is helpful, please kindly cite as:
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```bibtex
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```bibtex
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@article{zheng2024llamafactory,
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@article{zheng2024llamafactory,
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title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
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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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journal={arXiv preprint arXiv:2403.13372},
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year={2024},
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year={2024},
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url={http://arxiv.org/abs/2403.13372}
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url={http://arxiv.org/abs/2403.13372}
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@ -5,7 +5,7 @@
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[![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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[![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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[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
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[![PyPI](https://img.shields.io/pypi/v/llmtuner)](https://pypi.org/project/llmtuner/)
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[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
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[![Downloads](https://static.pepy.tech/badge/llmtuner)](https://pypi.org/project/llmtuner/)
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[![Citation](https://img.shields.io/badge/citation-21-green)](#使用了-llama-factory-的项目)
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[![Citation](https://img.shields.io/badge/citation-22-green)](#使用了-llama-factory-的项目)
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[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[![GitHub pull request](https://img.shields.io/badge/PRs-welcome-blue)](https://github.com/hiyouga/LLaMA-Factory/pulls)
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[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
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[![Discord](https://dcbadge.vercel.app/api/server/rKfvV9r9FK?compact=true&style=flat)](https://discord.gg/rKfvV9r9FK)
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[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](https://twitter.com/llamafactory_ai)
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[![Twitter](https://img.shields.io/twitter/follow/llamafactory_ai)](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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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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- **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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- **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. 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. 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. 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. **[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. **[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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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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@ -701,7 +700,7 @@ CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
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```bibtex
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```bibtex
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@article{zheng2024llamafactory,
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@article{zheng2024llamafactory,
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title={LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models},
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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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journal={arXiv preprint arXiv:2403.13372},
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year={2024},
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year={2024},
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url={http://arxiv.org/abs/2403.13372}
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url={http://arxiv.org/abs/2403.13372}
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