Mistral 7B
About
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.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, L\'elio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timoth\'ee Lacroix, William El Sayed• 2023
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Commonsense Reasoning | HellaSwag | Accuracy85.7 | 1460 | |
| Mathematical Reasoning | GSM8K | Accuracy58.4 | 983 | |
| Code Generation | HumanEval | Pass@139.02 | 850 | |
| Multi-task Language Understanding | MMLU | Accuracy70.5 | 842 | |
| Language Modeling | WikiText-2 | Perplexity (PPL)12.25 | 841 | |
| Commonsense Reasoning | WinoGrande | Accuracy75.3 | 776 | |
| Language Understanding | MMLU | Accuracy64.2 | 756 | |
| Mathematical Reasoning | GSM8K (test) | Accuracy58.4 | 751 | |
| Question Answering | ARC Challenge | Accuracy59.98 | 749 | |
| Language Modeling | PTB | Perplexity49.51 | 650 |
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