Mixtral MoE 8x7B details are out 🔥

Mixtral 8x7B is a Sparse Mixture of Experts (SMoE) language model that changed the LLM landscape. Developed as an enhancement of the Mistral 7B architecture, Mixtral 8x7B introduces a sophisticated Sparse Mixture of Experts (SMoE) design, setting a new benchmark in the efficiency and capability of language models.

With its unique approach to handling a vast parameter space, Mixtral 8x7B demonstrates notable improvements in performance, particularly in areas such as mathematics, code generation, and multilingual tasks, challenging the existing standards set by models like GPT-3.5 and Llama 2 70B.

The model's fine-tuning for instruction following and its reduced bias in outputs reflect a thoughtful progression towards more ethical and effective AI tools. Its release under the Apache 2.0 license further emphasizes its potential for wide-ranging applications and accessibility in various domains.

Here's are the key highlights from the research paper 🧵 👇1. Innovative Architecture: Mixtral consists of 8 feedforward blocks (experts) in each layer. A router network dynamically selects two experts for each token at each layer, allowing access to 47 billion parameters while only actively using 13 billion during inference.2. Superior Performance: Mixtral outperforms or equals the performance of models like Llama 2 70B and GPT-3.5 in various benchmarks. It shows significant superiority in areas like mathematics, code generation, and multilingual tasks.3. Open Access: Embracing the spirit of Opensource, Mistral released both the base and instruct models are released under the Apache 2.0 license, ensuring broad accessibility for diverse applications.4. Instruction Fine-tuning: The Mixtral 8x7B Instruct model, specifically fine-tuned for instruction following, surpasses models like GPT-3.5 Turbo and Llama 2 70B chat model in human benchmarks. It demonstrates a more balanced sentiment profile and reduced biases.5. Efficiency and Throughput: Despite its large parameter size, Mixtral offers enhanced efficiency with faster inference speeds and higher throughput.6. Routing Analysis: The paper includes an analysis of the expert selection by the router, revealing no clear patterns of expert assignment based on topic but some structured syntactic behaviour.7. Long Range Performance: Mixtral demonstrates strong capabilities in handling long context, achieving a 100% retrieval accuracy in passkey retrieval tasks regardless of context length or passkey position.8. Bias Benchmarks: Performance on Bias Benchmark for QA (BBQ) and Bias in Open-Ended Language Generation Dataset (BOLD) suggests that Mixtral presents less bias and displays more positive sentiment than comparative models.9. Context Size: Mixtral was trained with a context size of 32k tokens, significantly enhancing its performance in tasks requiring extensive context understanding.10. Multilingual Capabilities: Mixtral shows significant performance improvement in multilingual understanding, outperforming Llama 2 70B in languages like French, German, Spanish, and Italian.

🔗 For more details, check out the Research paper!

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