Mistral 7B is a 7-billion-parameter language model designed for high performance and efficiency. It outperforms the best open-source 13B model (Llama 2) and the best released 34B model (Llama 1) in reasoning, mathematics, and code generation. Mistral 7B uses grouped-query attention (GQA) for faster inference and sliding window attention (SWA) to handle long sequences efficiently. It also includes a fine-tuned version, Mistral 7B – Instruct, which performs better than Llama 2 13B on both human and automated benchmarks. The model is released under the Apache 2.0 license and is available for deployment on cloud platforms. Mistral 7B is based on a transformer architecture and incorporates SWA to handle longer sequences with reduced computational cost. The model uses a rolling buffer cache to reduce memory usage without affecting performance. It also supports pre-fill and chunking for efficient sequence generation. The model is designed for easy fine-tuning across various tasks and has demonstrated superior performance in code, mathematics, and reasoning benchmarks. In evaluations, Mistral 7B outperforms Llama 2 7B and 13B on all benchmarks and surpasses Llama 1 34B in mathematics, code generation, and reasoning. It also performs well on a wide range of tasks, including commonsense reasoning, world knowledge, reading comprehension, math, and code. The model is efficient in terms of cost-performance and can be used in a variety of real-world applications. Mistral 7B – Instruct is a fine-tuned version of the model that performs well on instruction-following tasks. It outperforms all 7B models on MT-Bench and is comparable to 13B – Chat models. The model is also capable of enforcing guardrails and performing content moderation, making it suitable for front-facing applications. The model is accessible and can be used for a wide range of tasks, including moderating comments on social media or monitoring brand content online.Mistral 7B is a 7-billion-parameter language model designed for high performance and efficiency. It outperforms the best open-source 13B model (Llama 2) and the best released 34B model (Llama 1) in reasoning, mathematics, and code generation. Mistral 7B uses grouped-query attention (GQA) for faster inference and sliding window attention (SWA) to handle long sequences efficiently. It also includes a fine-tuned version, Mistral 7B – Instruct, which performs better than Llama 2 13B on both human and automated benchmarks. The model is released under the Apache 2.0 license and is available for deployment on cloud platforms. Mistral 7B is based on a transformer architecture and incorporates SWA to handle longer sequences with reduced computational cost. The model uses a rolling buffer cache to reduce memory usage without affecting performance. It also supports pre-fill and chunking for efficient sequence generation. The model is designed for easy fine-tuning across various tasks and has demonstrated superior performance in code, mathematics, and reasoning benchmarks. In evaluations, Mistral 7B outperforms Llama 2 7B and 13B on all benchmarks and surpasses Llama 1 34B in mathematics, code generation, and reasoning. It also performs well on a wide range of tasks, including commonsense reasoning, world knowledge, reading comprehension, math, and code. The model is efficient in terms of cost-performance and can be used in a variety of real-world applications. Mistral 7B – Instruct is a fine-tuned version of the model that performs well on instruction-following tasks. It outperforms all 7B models on MT-Bench and is comparable to 13B – Chat models. The model is also capable of enforcing guardrails and performing content moderation, making it suitable for front-facing applications. The model is accessible and can be used for a wide range of tasks, including moderating comments on social media or monitoring brand content online.