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Alpaca-CoT: An Instruction-Tuning Platform with Unified Interface for Instruction Collection, Parameter-efficient Methods, and Large Language Models

This is the repository for the Alpaca-CoT project, which aims to build an instruction finetuning (IFT) platform with extensive instruction collection (especially the CoT datasets) and a unified interface for various large language models and parameter-efficient methods. We are constantly expanding our instruction-tuning data collection, and integrating more LLMs and more parameter-efficient methods. In addition, we created a new branch tabular_llm to build a Tabular LLM for solving Table Intelligence Tasks.

You are warmly welcome to provide us with any non-collected instruction-tuning datasets (or their sources). We will uniformly format them, train the Alpaca model (and other LLMs in the early future) with these datasets, open source the model checkpoints, and conduct extensive empirical studies. We hope that our project can make a modest contribution to the open-source process of large language models, and reduce its threshold for NLP researchers to get started.

<img src="./figures/wechat.jpg" width = "100" height = "100" align=right /> You can also choose to join our group chat (WeChat) and communicate with more people with the same interests. At present, the number of group members is too large to join the group directly through the group QR code. You need to connect with me first to get into the group.

News

• ⚠ If you want to use other methods besides LORA, please install the edited version in our project pip install -e ./peft.

• 🚀12.8: LLM InternLM was merged.

• 🚀8.16: 4bit quantization is available for lora, qlora and adalora.

• 🚀8.16: Parameter-efficie