Mixtral-8x7B-Instruct-v0.1-bnb-4bit

Maintainer: ybelkada

Total Score

58

Last updated 5/17/2024

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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-bnb-4bit is a 4-bit quantized version of the Mixtral-8x7B Instruct model, created by maintainer ybelkada. This model is based on the original Mixtral-8x7B-Instruct-v0.1 and uses the bitsandbytes library to reduce the model size while maintaining performance.

Similar models include the Mixtral-8x7B-Instruct-v0.1-GPTQ and Mixtral-8x7B-Instruct-v0.1-AWQ models, which use different quantization techniques to reduce the model size.

Model inputs and outputs

Inputs

  • Text prompt: The model takes a text prompt as input, formatted using the provided [INST] {prompt} [/INST] template.

Outputs

  • Generated text: The model generates text in response to the provided prompt, up to a specified maximum number of tokens.

Capabilities

The Mixtral-8x7B-Instruct-v0.1-bnb-4bit model is a powerful text generation model capable of producing coherent, contextual responses to a wide range of prompts. It can be used for tasks such as creative writing, summarization, language translation, and more.

What can I use it for?

This model can be used in a variety of applications, such as:

  • Chatbots and virtual assistants: The model can be used to power conversational interfaces, providing human-like responses to user queries and prompts.
  • Content generation: The model can be used to generate text for blog posts, articles, stories, and other types of content.
  • Language translation: The model can be fine-tuned for language translation tasks, converting text from one language to another.
  • Summarization: The model can be used to summarize long-form text, extracting the key points and ideas.

Things to try

One interesting thing to try with this model is experimenting with the temperature and top-k/top-p sampling parameters. Adjusting these can result in more creative, diverse, or focused output, depending on your needs. It's also worth trying the model on a variety of prompts to see the range of responses it can generate.



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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