dolphin-2.2.1-mistral-7b

Maintainer: lucataco

Total Score

31

Last updated 6/20/2024
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Model overview

The dolphin-2.2.1-mistral-7b is a fine-tuned version of the Mistral-7B language model, optimized for conversational chat using the Dolphin dataset. It is maintained by lucataco. This model shares similarities with other Mistral-based models, such as dolphin-2.1-mistral-7b, mistral-7b-v0.1, and the mistral-7b-instruct series, all of which aim to leverage the capabilities of the core Mistral-7B model for various applications.

Model inputs and outputs

The dolphin-2.2.1-mistral-7b model takes in a text prompt and generates a response. The key input parameters include:

Inputs

  • prompt: The text prompt to generate a response from.
  • max_new_tokens: The maximum number of tokens the model should generate as output.
  • temperature: The value used to modulate the next token probabilities.
  • top_k: The number of highest probability tokens to consider for generating the output.
  • top_p: A probability threshold for generating the output.
  • prompt_template: The template used to format the prompt, with the input prompt inserted using the {prompt} placeholder.
  • presence_penalty: The presence penalty.
  • frequency_penalty: The frequency penalty.

Outputs

  • The model generates an array of strings, which can be concatenated to form the full output response.

Capabilities

The dolphin-2.2.1-mistral-7b model is capable of engaging in open-ended conversations, drawing upon its training on the Dolphin dataset. It can respond to a wide range of prompts, demonstrating strong language understanding and generation abilities.

What can I use it for?

The dolphin-2.2.1-mistral-7b model can be leveraged for a variety of conversational AI applications, such as chatbots, virtual assistants, and dialog systems. Its fine-tuning on the Dolphin dataset makes it well-suited for tasks that require natural, human-like interactions. Potential use cases include customer service, personal assistance, content creation, and more.

Things to try

Experiment with different prompt styles and input parameters to explore the model's capabilities. Try providing the model with multi-turn conversational prompts to see how it maintains context and coherence. You can also fine-tune the model further on your own dataset to adapt it to specific use cases.



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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