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What is Mathstral?

This year marks the 2311th anniversary of Archimedes, and in his honor, we are thrilled to unveil our first Mathstral model, a dedicated 7B architecture crafted specifically for mathematical reasoning and scientific inquiry. With a context window of 32k, this model is made available under the Apache 2.0 license. Our goal in sharing Mathstral with the scientific community is to facilitate the tackling of complex mathematical problems that require sophisticated, multi-step logical reasoning. The introduction of Mathstral aligns with our broader initiative to bolster academic efforts, developed alongside Project Numina. Much like Isaac Newton's contributions during his lifetime, Mathstral builds upon the groundwork established by Mistral 7B, with a keen focus on STEM fields. It showcases exceptional reasoning abilities within its domain, achieving impressive results across numerous industry-standard benchmarks. Specifically, it registers a score of 56.6% on the MATH benchmark and 63.47% on the MMLU benchmark, highlighting the performance enhancements in comparison to its predecessor, Mistral 7B, and underscoring the strides made in mathematical modeling. In addition to advancing individual research, this initiative seeks to inspire greater innovation and foster collaboration within the mathematical community as a whole.

What is DeepSeekMath?

DeepSeekMath is an innovative language model with 7 billion parameters, developed by DeepSeek-AI, aimed at significantly improving the mathematical reasoning abilities of open-source language models. This model is built on the advancements of DeepSeek-Coder-v1.5 and has been further pre-trained with an impressive dataset of 120 billion math-related tokens obtained from Common Crawl, alongside supplementary data derived from natural language and coding domains. Its performance is noteworthy, having achieved a remarkable score of 51.7% on the rigorous MATH benchmark without the aid of external tools or voting mechanisms, making it a formidable rival to other models such as Gemini-Ultra and GPT-4. The effectiveness of DeepSeekMath is enhanced by its meticulously designed data selection process and the use of Group Relative Policy Optimization (GRPO), which optimizes both its reasoning capabilities and memory efficiency. Available in various formats, including base, instruct, and reinforcement learning (RL) versions, DeepSeekMath is designed to meet the needs of both research and commercial sectors, appealing to those keen on exploring or utilizing advanced mathematical problem-solving techniques within artificial intelligence. This adaptability ensures that it serves as an essential asset for researchers and practitioners, fostering progress in the field of AI-driven mathematics while encouraging further exploration of its diverse applications.

Media

Media

Integrations Supported

1min.AI
Airtrain
DataChain
Echo AI
Expanse
Kiin
LM-Kit.NET
Literal AI
Melies
MindMac
Mixtral 8x7B
Msty
Numina
OpenPipe
Overseer AI
PI Prompts
Ragas
Simplismart
Unify AI
promptmate.io

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
On-Prem
Linux

Customer Service / Support

24 Hour Support
Web-Based Support

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai/news/mathstral/

Company Facts

Organization Name

DeepSeek

Date Founded

2023

Company Location

China

Company Website

deepseek.com

Categories and Features

AI Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Categories and Features

AI Math Solvers

Not specified

AI Models

Not specified

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