Mistral 7B
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/mistralai/mistral-srcOfficialIn paperpytorch★ 10,731
- github.com/facebookresearch/fairseq2pytorch★ 1,122
- github.com/mgmalek/efficient_cross_entropypytorch★ 124
- github.com/ninglab/ecellmpytorch★ 55
- github.com/knowlab/bi-weekly-paper-presentationnone★ 9
- github.com/pwc-1/Paper-9/tree/main/2/mistralmindspore★ 0
Abstract
We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B -- Instruct, that surpasses the Llama 2 13B -- Chat model both on human and automated benchmarks. Our models are released under the Apache 2.0 license.