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We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. Meanwhile, we created a new branch to build a Tabular LLM.(我们分别统一了丰富的IFT数据(如CoT数据)、多种训练效率方法以及多种LLMs,三个层面上的接口,打造方便研究人员上手的LLM-IFT研究和使用平台。我们欢迎开源爱好者在这个repo上发起任何有意义的pr,一起将尽可能多的LLM相关技术集成进来。

License: Apache License 2.0

Shell 0.03% Python 8.07% Makefile 0.02% Jupyter Notebook 89.72% Dockerfile 0.06% MDX 2.10%

alpaca-cot's Introduction

Hi 👋, I'm xuzf

Student@BIT

  • 📝 Welcome to visit my blog: CSDN, zhihu
  • 📫 Email me at: [email protected]
  • 🌈 Interest Areas: 🕸️ Entity Linking $;$ 👪 Coreference Resolution
  • 🔨 Programming Languages
  • 📈Github status

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