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Mistral-22B-v0.2

Maintainer: Vezora

Total Score

106

Last updated 5/15/2024

👁️

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

Mistral-22B-v0.2 is an experimental 22B parameter generative language model developed by Vezora. It builds upon the earlier Mistral-22B-v0.1 model, incorporating several key enhancements.

This model is not a single expert, but rather a compressed Mixture of Experts (MOE) model that has been converted into a dense 22B parameter model. Compared to the previous version, Mistral-22B-v0.2 has been trained on 8x more data, resulting in significant improvements across various capabilities.

The Mistral-22B-v0.1 model, also developed by Vezora, was an earlier experimental 22B parameter model that exhibited strong mathematical abilities and coding proficiency, despite not being explicitly trained on those tasks.

Model Inputs and Outputs

Mistral-22B-v0.2 is a text-to-text generative model, capable of producing coherent and contextual responses based on the provided input prompts.

Inputs

  • Freeform text prompts that can cover a wide range of topics, from general conversation to task-oriented instructions.
  • The model uses the GUANACO prompt format, which has been optimized for best results.

Outputs

  • The model generates relevant and contextual text responses, up to 32,000 tokens in length.
  • It can handle multi-turn conversations, providing coherent and consistent responses across multiple exchanges.
  • The model has also been trained to output responses in JSON format, allowing for structured data generation.

Capabilities

Mistral-22B-v0.2 exhibits several key capabilities that set it apart from the previous version:

  • Improved Mathematical Proficiency: The model demonstrates enhanced mathematical abilities, despite not being explicitly trained on mathematical tasks.
  • Enhanced Coding Skills: The model can now successfully complete simple coding tasks, such as generating HTML with a color-changing button, which the v0.1 model struggled with.
  • More Coherent Responses: The v0.2 model provides more cohesive and context-appropriate responses, better understanding the prompts and providing relevant answers.
  • Highly Uncensored: This model has been realigned to be uncensored, allowing it to respond to a wide range of prompts without restrictions.
  • Multitask Capabilities: The model has been trained on diverse datasets, including multi-turn conversations and agent-based tasks, expanding its versatility.
  • JSON Support: The model can now generate responses in JSON format, enabling structured data output.

What can I use it for?

Mistral-22B-v0.2 can be a powerful tool for a variety of applications, including:

  • Conversational AI: The model's ability to engage in multi-turn dialogues and provide coherent responses makes it suitable for chatbot and virtual assistant development.
  • Content Generation: The model can be used to generate diverse content, such as articles, stories, or even code snippets, across a wide range of topics.
  • Task Assistance: The model's capabilities in areas like coding and JSON generation can be leveraged to assist with technical tasks and data manipulation.
  • Research and Exploration: As an experimental model, Mistral-22B-v0.2 can be a valuable resource for researchers and developers interested in pushing the boundaries of large language models.

Things to try

When using Mistral-22B-v0.2, consider exploring its uncensored capabilities, but be mindful of the potential risks. Additionally, try prompting the model with coding-related tasks or requests for structured data in JSON format to better understand its expanded capabilities.

Remember to always use the GUANACO prompt format for optimal results, as specified by the model's maintainer. Engaging in multi-turn conversations can also help you better assess the model's coherence and contextual understanding.



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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