Mixtral-8x7B-Instruct-v0.1

Maintainer: mistralai

Total Score

3.7K

Last updated 4/28/2024

🏋️

PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The Mixtral-8x7B-Instruct-v0.1 is a Large Language Model (LLM) developed by Mistral AI. It is a pretrained generative Sparse Mixture of Experts that outperforms the Llama 2 70B model on most benchmarks, according to the maintainer. This model is an instruct fine-tuned version of the Mixtral-8x7B-v0.1 model, which is also available from Mistral AI.

Model inputs and outputs

The Mixtral-8x7B-Instruct-v0.1 model is a text-to-text model, meaning it takes in text prompts and generates text outputs.

Inputs

  • Text prompts following a specific instruction format, with the instruction surrounded by [INST] and [/INST] tokens.

Outputs

  • Textual responses generated by the model based on the provided input prompts.

Capabilities

The Mixtral-8x7B-Instruct-v0.1 model demonstrates strong language generation capabilities, able to produce coherent and relevant responses to a variety of prompts. It can be used for tasks like question answering, text summarization, and creative writing.

What can I use it for?

The Mixtral-8x7B-Instruct-v0.1 model can be used in a wide range of applications that require natural language processing, such as chatbots, virtual assistants, and content generation. It could be particularly useful for projects that need a flexible and powerful language model to interact with users in a more natural and engaging way.

Things to try

One interesting aspect of the Mixtral-8x7B-Instruct-v0.1 model is its instruction format, which allows for more structured and contextual prompts. You could try experimenting with different ways of formatting your prompts to see how the model responds, or explore how it handles more complex multi-turn conversations.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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Read more

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