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Introducing Mixtral 8x7B: Mistral AI's Groundbreaking LLM

Tal Peretz

— 1 min read
Introducing Mixtral 8x7B: Mistral AI's Groundbreaking LLM

The Mixtral 8x7B model, recently released by Mistral AI, marks a substantial leap forward in the domain of large language models. Here’s an overview of its groundbreaking features and capabilities:

Architecture and Performance

Mixtral 8x7B is built on a Sparse Mixture of Experts (SMoE) architecture, which means the feedforward block selects from a set of 8 distinct groups of parameters. This design allows the model to leverage up to 46.7B total parameters while only using 12.9B parameters per token, enhancing efficiency and reducing latency.

Performance-wise, Mixtral 8x7B outperforms Llama 2 70B on most benchmarks and matches or exceeds GPT3.5 on standard benchmarks. Additionally, it boasts 6x faster inference compared to Llama 2 70B.

Capabilities

  • Context Size: Supports up to 32k tokens.
  • Multilingual Support: Fluent in English, French, German, Spanish, and Italian.
  • Code Generation: Excels in generating code.
  • Instruction Following: The Mixtral 8x7B Instruct model scores 8.30 on MT-Bench for instruction adherence.

Deployment and Accessibility

Mixtral 8x7B is open-source, released under the Apache 2.0 license. This makes it accessible for community use and integration. It is available via the Mistral AI API, with drop-in replacement client libraries in Python and JavaScript. It can also be deployed using a fully open-source stack with vLLM and Skypilot.

In terms of pricing, Mixtral 8x7B offers a cost-effective alternative to models like GPT3.5, especially for smaller variants like mistral-tiny and mistral-small.

Technical Details

Despite using a fraction of the total parameters per token, Mixtral 8x7B is as efficient as a 12.9B model while leveraging the capabilities of a much larger model. It shows less bias on the BBQ benchmark compared to Llama 2 and similar variances on BOLD.

Community and Support

Mistral AI encourages community engagement and provides extensive support through blog posts and technical documentation. The model is compatible with the Hugging Face transformers library, though some adjustments may be necessary due to file format differences.

Overall, Mixtral 8x7B stands out as a powerful and efficient model, offering significant improvements over previous models. It is a valuable tool for developers and AI enthusiasts alike.

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