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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 Galactica?

The vast quantity of information present today creates a considerable hurdle for scientific progress. As the volume of scientific literature and data grows exponentially, discovering valuable insights within this enormous expanse of information has become a daunting task. In the present day, individuals are increasingly dependent on search engines to retrieve scientific knowledge; however, these tools often fall short in effectively organizing and categorizing such intricate data. Galactica emerges as a cutting-edge language model specifically engineered to capture, synthesize, and analyze scientific knowledge. Its training encompasses a wide range of scientific resources, including research papers, reference texts, and knowledge databases. In a variety of scientific assessments, Galactica consistently outperforms existing models, showcasing its exceptional capabilities. For example, when evaluated on technical knowledge tests that involve LaTeX equations, Galactica scores 68.2%, which is significantly above the 49.0% achieved by the latest GPT-3 model. Additionally, Galactica demonstrates superior reasoning abilities, outdoing Chinchilla in mathematical MMLU with scores of 41.3% compared to 35.7%, and surpassing PaLM 540B in MATH with an impressive 20.4% in contrast to 8.8%. These results not only highlight Galactica's role in enhancing access to scientific information but also underscore its potential to improve our capacity for reasoning through intricate scientific problems. Ultimately, as the landscape of scientific inquiry continues to evolve, tools like Galactica may prove crucial in navigating the complexities of modern science.

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Integrations Supported

Amazon Bedrock
BlueGPT
Groq
Hugging Face
Humiris AI
Kiin
LM-Kit.NET
LibreChat
Melies
Memo AI
Ministral 8B
Mistral 7B
Mistral Large
Motific.ai
Overseer AI
Respan
Toolmark
Tune AI
WebLLM
Wordware

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
On-Prem

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

Meta

Date Founded

2004

Company Location

United States

Company Website

meta.com

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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