MathΣtral

We're contributing Mathstral to the science community to bolster efforts in advanced mathematical problems requiring complex, multi-step logical reasoning. The Mathstral release is part of our broader effort to support academic projects—it was produced in the context of our collaboration with Project Numina .

Akin to Isaac Newton in his time, Mathstral stands on the shoulders of Mistral 7B and specializes in STEM subjects. It achieves state-of-the-art reasoning capacities in its size category across various industry-standard benchmarks. In particular, it achieves 56.6% on MATH and 63.47% on MMLU, with the following MMLU performance difference by subject between Mathstral 7B and Mistral 7B.

Mathstral is another example of the excellent performance/speed tradeoffs achieved when building models for specific purposes – a development philosophy we actively promote in la Plateforme, particularly with its new fine-tuning capabilities .

Mathstral can achieve significantly better results with more inference-time computation: Mathstral 7B scores 68.37% on MATH with majority voting and 74.59% with a strong reward model among 64 candidates.

Mathstral is an instructed model – use it or fine-tune it as such, referring to our documentation. Weights are hosted on HuggingFace . You can try Mathstral now with mistral-inference and adapt it with mistral-finetune .

We thank Professor Paul Bourdon for curating the GRE Math Subject Test problems used in our evaluation.