LLM360 is a groundbreaking initiative promoting comprehensive open-sourcing of Large Language Models. It releases two 7B parameter LLMs, AMBER and CRYSTALCODER, with full training code, data, model checkpoints, and analyses. The project aims to enhance transparency and reproducibility in the field by making the entire LLM training process openly available to the community.
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Introducing LLM360: The First Fully Open-Source and Transparent Large Language Models (LLMs)
Open-source Large Language Models (LLMs) such as LLaMA, Falcon, and Mistral offer a range of choices for AI professionals and scholars. However, many of these LLMs have made only select components available, limiting clarity in training methodologies. This restricts advancements in the field, leading to repeated efforts by teams to uncover various aspects of the training procedure.
LLM360 Initiative
A team of researchers from Petuum, MBZUAI, USC, CMU, UIUC, and UCSD introduced LLM360 to fully open-source LLMs and advocate for transparent and reproducible training processes. LLM360 aims to make all training code and data, model checkpoints, and intermediate results available to the community.
LLM360 releases two 7B parameter LLMs, AMBER and CRYSTALCODER, along with their training code, data, intermediate checkpoints, and analyses. The initiative emphasizes the importance of open-sourcing LLMs from all angles, enabling comprehensive analysis and reproducibility.
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