Mistral’s New Finetuned Open Source LLM Excels in Math and Reasoning

Mistral AI has released Mathstral, a finetuned 7B model designed for math reasoning and scientific discovery, offering a 32k context window and openly available model weights.

• The model’s release comes amidst questions about leading LLMs’ ability to solve complex math problems while lacking understanding of elementary school math concepts.
• Mathstral demonstrates the trend of finetuned open source models outperforming larger closed source models in specialized areas.

Testing Mathstral’s Common Sense: Running Mathstral locally using LlamaEdge (Rust + Wasm stack) allows for testing its ability to answer common sense math questions.

• The model successfully answers a question comparing the values of 9.11 and 9.9, providing correct reasoning and demonstrating its understanding of basic math concepts.
• This success highlights the potential of finetuned models like Mathstral in specialized domains such as mathematics.

Offering Mathstral as a Service: The GaiaNet project enables users to share Mathstral with others, incorporating personal math knowledge as context.

• GaiaNet allows for manipulation of prompts, addition of contexts, and integration of proprietary knowledge bases to ground LLM answers in truth.
• This functionality expands the potential applications and accessibility of the Mathstral model.

Broader Implications: The release of Mathstral and its demonstrated capabilities in math reasoning underscore the growing importance of specialized, finetuned models in advancing AI applications across various domains. As open source models continue to outperform larger, closed source counterparts in specific areas, the landscape of AI development and deployment is likely to shift, with a greater emphasis on collaboration, customization, and accessibility. However, questions remain regarding the broader implications of these advancements, particularly in terms of the potential for misuse, the need for robust governance frameworks, and the ethical considerations surrounding the development and deployment of increasingly powerful specialized models.