Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality WL Chiang, Z Li, Z Lin, Y Sheng, Z Wu, H Zhang, L Zheng, S Zhuang, ... See https://vicuna. lmsys. org (accessed 14 April 2023) 2 (3), 6, 2023 | 2106* | 2023 |
Judging llm-as-a-judge with mt-bench and chatbot arena L Zheng, WL Chiang, Y Sheng, S Zhuang, Z Wu, Y Zhuang, Z Lin, Z Li, ... Advances in Neural Information Processing Systems 36, 46595-46623, 2023 | 1726 | 2023 |
Alpa: Automating inter-and {Intra-Operator} parallelism for distributed deep learning L Zheng, Z Li, H Zhang, Y Zhuang, Z Chen, Y Huang, Y Wang, Y Xu, ... 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2022 | 289 | 2022 |
Lmsys-chat-1m: A large-scale real-world llm conversation dataset L Zheng, WL Chiang, Y Sheng, T Li, S Zhuang, Z Wu, Y Zhuang, Z Li, ... arXiv preprint arXiv:2309.11998, 2023 | 70 | 2023 |
Llm360: Towards fully transparent open-source llms Z Liu, A Qiao, W Neiswanger, H Wang, B Tan, T Tao, J Li, Y Wang, S Sun, ... arXiv preprint arXiv:2312.06550, 2023 | 37 | 2023 |
On optimizing the communication of model parallelism Y Zhuang, L Zheng, Z Li, E Xing, Q Ho, J Gonzalez, I Stoica, H Zhang, ... Proceedings of Machine Learning and Systems 5, 2023 | 27 | 2023 |
Helix: Distributed Serving of Large Language Models via Max-Flow on Heterogeneous GPUs Y Mei, Y Zhuang, X Miao, J Yang, Z Jia, R Vinayak arXiv preprint arXiv:2406.01566, 2024 | 2 | 2024 |
Toward Inference-optimal Mixture-of-Expert Large Language Models L Yun, Y Zhuang, Y Fu, EP Xing, H Zhang arXiv preprint arXiv:2404.02852, 2024 | 1 | 2024 |
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs B Tan, Y Zhu, L Liu, H Wang, Y Zhuang, J Chen, E Xing, Z Hu Proceedings of the 2024 Conference of the North American Chapter of the …, 2024 | | 2024 |
Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs B Tan, Y Zhu, L Liu, H Wang, Y Zhuang, J Chen, E Xing, Z Hu arXiv preprint arXiv:2310.16355, 2023 | | 2023 |
LLM360 K2-65B: Scaling Up Fully Transparent Open-Source LLMs B Tan, H Wang37, W Neiswanger, T Tao, H Li, F Koto, Y Wang, S Sun, ... | | |