mistral-ai

Introducing Mixtral 8x7B: Mistral AI's New Powerhouse LLM

Tal Peretz

Jun 27, 2024 — 1 min read

Mistral AI has unveiled its latest innovation in large language models: Mixtral 8x7B. This new model sets a benchmark in efficiency and performance with its advanced architecture and capabilities.

Architecture and Performance

Mixtral 8x7B features a Sparse Mixture of Experts (SMoE) model. Through a router network, it selectively activates two groups of parameters (experts) to process each token, combining their outputs. This method allows the model to leverage a total of 46.7B parameters while only using 12.9B parameters per token. The result is enhanced efficiency without compromising performance.

Benchmarks

Mixtral 8x7B outperforms Llama 2 70B on most benchmarks and matches or exceeds GPT-3.5. It scores an impressive 8.3 on MT-Bench and excels in both code generation and multilingual tasks.

Capabilities

Mixtral 8x7B is proficient in multiple languages, including English, French, Italian, German, and Spanish. It supports contexts of up to 32k tokens and demonstrates strong performance in code generation.

Efficiency and Cost

The model offers a 6x faster inference rate compared to Llama 2 70B. Despite its large parameter count, Mixtral 8x7B uses fewer active parameters per token, making it more cost-effective and efficient.

Availability and Licensing

Released under the Apache 2.0 license, Mixtral 8x7B is open-source and accessible for community use and development. It is also available via Mistral AI's API, allowing easy integration into various applications.

Deployment and Community Support

Mistral AI has integrated Mixtral 8x7B with the vLLM project, enabling deployment on various cloud instances with efficient inference using Megablocks CUDA kernels. The model is part of Mistral AI's commitment to fostering innovation within the developer community.

Comparison and Benchmarks

Mixtral 8x7B significantly outperforms Llama 2 70B in French, German, Spanish, and Italian, while maintaining high accuracy in English. It also shows less bias on the BBQ benchmark and more positive sentiments than Llama 2 on the BOLD benchmark.

Overall, Mixtral 8x7B represents a significant leap forward in the development of large language models, offering a balance of performance, efficiency, and cost-effectiveness.

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