Abstract
We introduce Mistral 7B v0.1, a 7-billion-parameter language model engineered for superior performance and efficiency. Mistral 7B outperforms Llama 2 13B across all evaluated benchmarks, and Llama 1 34B in reasoning, mathematics, and code generation. Our model leverages grouped-query attention (GQA) for faster inference, coupled with sliding window attention (SWA) to effectively handle sequences of arbitrary length with a reduced inference cost. We also provide a model fine-tuned to follow instructions, Mistral 7B -- Instruct, that surpasses the Llama 2 13B -- Chat model both on human and automated benchmarks. Our models are released under the Apache 2.0 license.
Overview
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Mistral 7B is an efficient NLP model outperforming larger models while reducing computational and memory costs.
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Utilizes Sliding Window Attention to process longer sequences efficiently and manage computational resources.
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Incorporates Rolling Buffer Cache, pre-fill, and chunking strategies for optimized memory usage.
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Exceeds benchmarks of larger models in areas such as commonsense reasoning, math reasoning, and code generation.
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Flexible in fine-tuning for task-specific purposes and includes system prompts for ethical content moderation.
Overview of Mistral 7B
The Mistral 7B language model represents a significant advancement in NLP, focusing on achieving high performance while maintaining efficiency, a challenging balance in the creation of effective AI models. Unlike larger models, Mistral 7B demonstrates remarkable efficiency, achieving superior benchmark results over previous models while managing to reduce computational costs and memory requirements, which are crucial for real-time deployment.
Architectural Innovations
Mistral 7B is designed upon a transformer architecture and incorporates notable improvements to optimize for speed and resource management. One such innovation is Sliding Window Attention (SWA), which allows each token in a sequence to attend to a limited window of previous tokens. This method not only saves computation but also enables the model to process longer sequences more efficiently.
Another upgrade comes from the Rolling Buffer Cache technique. This allows for better memory management by having a fixed-size cache that updates as new tokens come in, effectively curbing the growth of memory usage without compromising on quality. Additionally, Mistral 7B employs pre-fill and chunking strategies. The model pre-fills caches with known prompts and processes them in chunks to further streamline memory use, demonstrating how the model can handle large sequences effectively.
Benchmarking Success
When it comes to performance, Mistral 7B surpasses its predecessors across diverse benchmarking categories, including commonsense reasoning, world knowledge, reading comprehension, mathematical reasoning, and code generation. This is attributed to Mistral 7B leveraging grouped-query attention to accelerate inference speeds and augment throughput. In fact, it outperforms the best open-source 13B model across all evaluated metrics and even exceeds a higher parameter 34B model in specific domains like math and code, showcasing its remarkable efficiency and performance.
Fine-Tuning and Guardrails
In addition to its architectural design, Mistral 7B demonstrates flexibility in fine-tuning for specific tasks, as showcased by a fine-tuned chat model which outperforms the similar category 13B model. Lastly, the implementation of system prompts to enforce guardrails ensures that Mistral 7B can deliver utility safely and ethically, addressing the growing concern over content moderation in AI.
In conclusion, the Mistral 7B model establishes a new standard for creating LLMs that don't compromise on either performance or efficiency, adhering to practical deployment requirements. The work opens up avenues for the AI community to explore better performance with smaller, more efficient models.