bibtext: @inproceedings{
  liu2024llm,
  title={{LLM}360: Towards Fully Transparent Open-Source {LLM}s},
  author={Zhengzhong Liu and Aurick Qiao and Willie Neiswanger and Hongyi Wang and Bowen Tan and Tianhua Tao and Junbo Li and Yuqi Wang and Suqi Sun and Omkar Pangarkar and Richard Fan and Yi Gu and Victor Miller and Yonghao Zhuang and Guowei He and Haonan Li and Fajri Koto and Liping Tang and Nikhil Ranjan and Zhiqiang Shen and Roberto Iriondo and Cun Mu and Zhiting Hu and Mark Schulze and Preslav Nakov and Timothy Baldwin and Eric P. Xing},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=QdWhj0QZFw}
  }
abstract: The recent surge in open-source Large Language Models (LLMs), such as LLaMA, Falcon, and Mistral, provides diverse options for AI practitioners and researchers. However, most LLMs have only released partial artifacts, such as the final model weights or inference code, and technical reports increasingly limit their scope to high-level design choices and surface statistics. These choices hinder progress in the field by degrading transparency into the training of LLMs and forcing teams to rediscover many details in the training process. We present **LLM360**, an initiative to fully open-source LLMs, which advocates for all training code and data, model checkpoints, and intermediate results to be made available to the community. The goal of LLM360 is to support open and collaborative AI research by making the end-to-end LLM training process transparent and reproducible by everyone. As a first step of LLM360, we release two 7B parameter LLMs pre-trained from scratch, Amber and Crystal, including their training code, data, intermediate checkpoints, and analyses. We are committed to continually pushing the boundaries of LLMs through this open-source effort. More large-scale and stronger models are underway and will be released in the future.
title: LLM360: Towards Fully Transparent Open-Source LLMs
authors: Zhengzhong Liu Aurick Qiao Willie Neiswanger Hongyi Wang Bowen Tan Tianhua Tao Junbo Li Yuqi Wang Suqi Sun Omkar Pangarkar Richard Fan Yi Gu Victor Miller Yonghao Zhuang Guowei He Haonan Li Fajri Koto Liping Tang Nikhil Ranjan Zhiqiang Shen Roberto Iriondo Cun Mu Zhiting Hu Mark Schulze Preslav Nakov Timothy Baldwin Eric P. Xing