Mistral AI's debut model introduced sliding window attention with a 4,096-token window that stacks across layers to reach an effective 32K-token span, a novel approach released via a torrent link on X with no paper or blog post. At 7.24B parameters it outperformed Llama 2 13B on every benchmark, and became the most popular base for community fine-tuning in late 2023.
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Mistral 7B Instruct v0.1 is the first instruction-tuned variant of Mistral AI's 7.3 billion parameter base model, fine-tuned for conversational and instruction-following tasks. It was released in September 2023 under the Apache 2.0 license.
Mistral 7B outperforms GPT-3 on most standard benchmarks despite being a fraction of the size. Its with grouped-query attention enables competitive reasoning and code generation at significantly lower compute costs.
Mistral 7B Instruct v0.1 scores 56.3% on MMLU and 6.84 on MT-Bench, placing it below Gemma 7B IT (64.3% MMLU) on knowledge but introducing sliding window attention as an architectural innovation at the 7B scale. Despite the lower MMLU score, its efficient inference and Apache 2.0 license made it the most-forked open base model of late 2023.
The cost per 1,000 tokens for running the model with Telnyx Inference is $0.0003. For instance, analyzing 1,000,000 customer chats, assuming each chat is 1,000 tokens, would cost $300.
Mistral 7B is a 7.3 billion parameter language model developed by Mistral AI, designed for efficiency without sacrificing capability. The research paper demonstrated that it outperforms Llama 2 13B on all benchmarks and matches Llama 1 34B on several.
Yes, Mistral 7B runs on consumer hardware with as little as 8GB of VRAM using quantized formats. Tools like Ollama and llama.cpp make it straightforward to deploy Mistral 7B Instruct locally for development and testing.
Mistral 7B is released under the Apache 2.0 license, making it free for both personal and commercial use with no restrictions. You can download the weights from Hugging Face or access it through hosted inference providers.
Mistral 7B is not a direct competitor to ChatGPT's underlying GPT-4 models, which are significantly larger. For resource-constrained deployments and specific tasks like code assistance and summarization, Mistral 7B provides strong results at a fraction of the cost.
Mistral 7B achieves outsized performance through sliding window attention for long contexts and grouped-query attention for fast inference. These architectural choices, documented in the original research, let a 7B model compete with models up to 34B parameters on key benchmarks.
