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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 Chinchilla?

Chinchilla represents a cutting-edge language model that operates within a compute budget similar to Gopher while boasting 70 billion parameters and utilizing four times the amount of training data. This model consistently outperforms Gopher (which has 280 billion parameters), along with other significant models like GPT-3 (175 billion), Jurassic-1 (178 billion), and Megatron-Turing NLG (530 billion) across a diverse range of evaluation tasks. Furthermore, Chinchilla’s innovative design enables it to consume considerably less computational power during both fine-tuning and inference stages, enhancing its practicality in real-world applications. Impressively, Chinchilla achieves an average accuracy of 67.5% on the MMLU benchmark, representing a notable improvement of over 7% compared to Gopher, and highlighting its advanced capabilities in the language modeling domain. As a result, Chinchilla not only stands out for its high performance but also sets a new standard for efficiency and effectiveness among language models. Its exceptional results solidify its position as a frontrunner in the evolving landscape of artificial intelligence.

Media

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Integrations Supported

Airtrain
AlphaCorp
Codestral
Fleak
Groq
HoneyHive
Kiin
LibreChat
Lunary
Microsoft Foundry Agent Service
MindMac
Mirascope
Mistral Small
Mixtral 8x22B
Msty
OpenPipe
Overseer AI
Prompt Security
WebLLM
promptmate.io

API Availability

Has API

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
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

Google DeepMind

Company Location

United States

Company Website

arxiv.org/abs/2203.15556

Categories and Features

AI Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

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