FuseAI
FuseAI Project
Stars: 74
FuseAI is a repository that focuses on knowledge fusion of large language models. It includes FuseChat, a state-of-the-art 7B LLM on MT-Bench, and FuseLLM, which surpasses Llama-2-7B by fusing three open-source foundation LLMs. The repository provides tech reports, releases, and datasets for FuseChat and FuseLLM, showcasing their performance and advancements in the field of chat models and large language models.
README:
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Aug 16, 2024: 🔥🔥🔥🔥 We update the FuseChat tech report and release FuseChat-7B-v2.0, which is the fusion of six prominent chat LLMs with diverse architectures and scales, namely OpenChat-3.5-7B, Starling-LM-7B-alpha, NH2-Solar-10.7B, InternLM2-Chat-20B, Mixtral-8x7B-Instruct, and Qwen1.5-Chat-72B. FuseChat-7B-v2.0 achieves an average performance of 7.38 on MT-Bench (GPT-4-0125-Preview as judge LLM), which is comparable to Mixtral-8x7B-Instruct and approaches GPT-3.5-Turbo-1106.
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Mar 13, 2024: 🔥🔥🔥 We release a HuggingFace Space for FuseChat-7B, try it now!
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Feb 26, 2024: 🔥🔥 We release FuseChat-7B-VaRM, which is the fusion of three prominent chat LLMs with diverse architectures and scales, namely NH2-Mixtral-8x7B, NH2-Solar-10.7B, and OpenChat-3.5-7B. FuseChat-7B-VaRM achieves an average performance of 8.22 on MT-Bench, outperforming various powerful chat LLMs like Starling-7B, Yi-34B-Chat, and Tulu-2-DPO-70B, even surpassing GPT-3.5 (March), Claude-2.1, and approaching Mixtral-8x7B-Instruct.
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Feb 25, 2024: 🔥 We release FuseChat-Mixture, which is a comprehensive training dataset covers different styles and capabilities, featuring both human-written and model-generated, and spanning general instruction-following and specific skills.
- Jan 22, 2024: 🔥 We release FuseLLM-7B, which is the fusion of three open-source foundation LLMs with distinct architectures, including Llama-2-7B, OpenLLaMA-7B, and MPT-7B.
Please cite the following paper if you reference our model, code, data, or paper related to FuseLLM.
@inproceedings{wan2024knowledge,
title={Knowledge Fusion of Large Language Models},
author={Fanqi Wan and Xinting Huang and Deng Cai and Xiaojun Quan and Wei Bi and Shuming Shi},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/pdf?id=jiDsk12qcz}
}
Please cite the following paper if you reference our model, code, data, or paper related to FuseChat.
@article{wan2024fusechat,
title={FuseChat: Knowledge Fusion of Chat Models},
author={Fanqi Wan and Longguang Zhong and Ziyi Yang and Ruijun Chen and Xiaojun Quan},
journal={arXiv preprint arXiv:2408.07990},
year={2024}
}
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