and the distribution of digital products.
How Mixtral 8x7B Sets New Standards in Open-Source AI with Innovative Design
2 Architectural details and 2.1 Sparse Mixture of Experts
3.1 Multilingual benchmarks, 3.2 Long range performance, and 3.3 Bias Benchmarks
6 Conclusion, Acknowledgements, and References
6 ConclusionIn this paper, we introduced Mixtral 8x7B, the first mixture-of-experts network to reach a state-of-theart performance among open-source models. Mixtral 8x7B Instruct outperforms Claude-2.1, Gemini Pro, and GPT-3.5 Turbo on human evaluation benchmarks. Because it only uses two experts at each time step, Mixtral only uses 13B active parameters per token while outperforming the previous best model using 70B parameters per token (Llama 2 70B). We are making our trained and fine-tuned models publicly available under the Apache 2.0 license. By sharing our models, we aim to facilitate the development of new techniques and applications that can benefit a wide range of industries and domains.
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We thank the CoreWeave and Scaleway teams for technical support as we trained our models. We are grateful to NVIDIA for supporting us in integrating TensorRT-LLM and Triton and working alongside us to make a sparse mixture of experts compatible with TensorRT-LLM.
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:::info This paper is available on arxiv under CC 4.0 license.
:::
:::info Authors:
(1) Albert Q. Jiang;
(2) Alexandre Sablayrolles;
(3) Antoine Roux;
(4) Arthur Mensch;
(5) Blanche Savary;
(6) Chris Bamford;
(7) Devendra Singh Chaplot;
(8) Diego de las Casas;
(9) Emma Bou Hanna;
(10) Florian Bressand;
(11) Gianna Lengyel;
(12) Guillaume Bour;
(13) Guillaume Lample;
(14) Lélio Renard Lavaud;
(15) Lucile Saulnier;
(16) Marie-Anne Lachaux;
(17) Pierre Stock;
(18) Sandeep Subramanian;
(19) Sophia Yang;
(20) Szymon Antoniak;
(21) Teven Le Scao;
(22) Théophile Gervet;
(23) Thibaut Lavril;
(24) Thomas Wang;
(25) Timothée Lacroix;
(26) William El Sayed.
:::
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