Mistral 7b Instruct V0.1



The mistral-7b-instruct-v0.1 is a 7 billion parameter language model developed by Mistral, specialized in processing text-to-text instructions. The model takes as input debugging features (such as top_k, top_p, temperature, max_new_tokens, min_new_tokens), along with a text prompt. Given the input, the model can generate creative text outputs, like writing a poem about a specific topic (such as 'steamed hams'). The output of the model is a text, structured and formatted according to the given prompt.

Use cases

The Mistral-7b-instruct-v0.1 AI model is a text-to-text model designed to generate comprehensive and relevant text based on given instructions, making it versatile across a range of applications. Given the specific nature of its tuning to instructions, it could be used for educational purposes, such as auto-generating detailed study guides or lesson plans, as the input only requires an instruction and the model generates a detailed response. Furthermore, the model's ability to understand and respond creatively to prompts could be capitalized on in the entertainment industry, for instance, creating dialogues for video games or scripts for animated series. Since it is tuned to respond in a descriptive, imaginative manner, it can be used for content creation, such as ad campaigns or social media posts, particularly in industries where creative and engaging content is key. Even for individual users, this model can be useful for producing creative writing, like poems or short stories. Additionally, with its language processing abilities, this model could find use in customer service chatbots, virtual assistant programs, and other platforms that require AI to generate human-like textual interactions.



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Creator Models

Mistral 7b V0.1$?21,871
Mistral 7b Instruct V0.1$?47,471

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Summary of this model and related resources.

Model NameMistral 7b Instruct V0.1
An instruction-tuned 7 billion parameter language model from Mistral
Model LinkView on Replicate
API SpecView on Replicate
Github LinkView on Github
Paper LinkView on Arxiv


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