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.

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-efficient methods Qlora, Sequential adapter and Parallel adapter was merged.

• 🚀7.24: LLM ChatGLM v2 was merged.

• 🚀7.20: LLM Baichuan was merged.

• 6.25: Add model evaluation code, including belle and MMCU.

• 5.20: fixes bugs in model saving and add wandb support.
• 5.15: more datasets like GPT4Tools, Auto CoT, pCLUE are add.
• 🚀5.5: A new branch tabular_llm is created to build a Tabular LLM. We collect instruction fine-tuning data for table-related tasks like table question answering and use them to fine-tune LLMs in this repo.
• 🚀5.4: All parameter-efficient methods in PEFT (e.g., p-tuning) were merged, which can be set by hyper-parameter directly.
• 🚀5.4: LLM MOSS was merged.
• 4.21: Datasets GAOKAO, camel, FLAN-Muffin, COIG are collected and formatted.
• 4.15: Datasets webGPT, dolly, baize, hh-rlhf, OIG(part) are collected and formatted.
• 4.12: Now you can try Alpaca-CoT on Google Colab.
• 4.11: Added function multi-turn conversation by @paulcx.
• 4.9: Datasets firefly, instruct, Code Alpaca are collected and formatted, which can be found here.
• 4.7: Added functions Parameter merging, Local chatting, Batch predicting and Web service building by @weberr.
• 4.4: Datasets GPTeacher,Guanaco,HC3,prosocial-dialog, belle-chat&belle-math, xP3 and natural-instructions are collected and formatted.
• 4.3: The Chinese CoT dataset CoT_CN_data.json can be found here.

Overview

LLaMA [1] is a great work t