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update readme

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hiyouga 2023-12-23 02:17:41 +08:00
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README.md
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@ -55,7 +55,7 @@ Compared to ChatGLM's [P-Tuning](https://github.com/THUDM/ChatGLM2-6B/tree/main/
## Changelog ## Changelog
[23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation for faster LoRA tuning. Try `--use_unsloth` argument to active unsloth patch. See performance comparisons [here](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison). [23/12/23] We supported **[unsloth](https://github.com/unslothai/unsloth)**'s implementation to boost LoRA tuning for the LLaMA, Mistral and Yi models. Try `--use_unsloth` argument to activate unsloth patch. It achieves 1.7x speed in our benchmark, check [this page](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison) for details.
[23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement). [23/12/12] We supported fine-tuning the latest MoE model **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)** in our framework. See hardware requirement [here](#hardware-requirement).

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README_zh.md
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## 更新日志 ## 更新日志
[23/12/23] 我们支持了使用 **[unsloth](https://github.com/unslothai/unsloth)** 大幅提升 LoRA 训练效率。请使用 `--use_unsloth` 参数启用 unsloth 优化。性能对比请查阅[此处](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。 [23/12/23] 我们针对 LLaMA, Mistral 和 Yi 模型支持了 **[unsloth](https://github.com/unslothai/unsloth)** 的 LoRA 训练加速。请使用 `--use_unsloth` 参数启用 unsloth 优化。该方法可提供 1.7 倍的训练速度，详情请查阅[此页面](https://github.com/hiyouga/LLaMA-Factory/wiki/Performance-comparison)。
[23/12/12] 我们支持了微调最新的混合专家模型 **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)**。硬件需求请查阅[此处](#硬件依赖)。 [23/12/12] 我们支持了微调最新的混合专家模型 **[Mixtral 8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)**。硬件需求请查阅[此处](#硬件依赖)。