dolphin-2.5-mixtral-8x7b

Maintainer: cognitivecomputations

Total Score

1.2K

Last updated 5/28/2024

👁️

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 dolphin-2.5-mixtral-8x7b model is an AI assistant developed by cognitivecomputations. It is based on the Mixtral-8x7b architecture and has been fine-tuned with a focus on coding tasks. Compared to similar models like dolphin-2.2.1-mistral-7b and dolphin-2.1-mistral-7b, this model has added new capabilities such as the Dolphin-Coder dataset and MagiCoder dataset.

Model inputs and outputs

The dolphin-2.5-mixtral-8x7b model uses the ChatML prompt format, which includes a system message to define the model's role, followed by the user's input, and finally the model's response.

Inputs

  • Prompts: The user's input text, which can be a request, question, or instruction for the model to respond to.

Outputs

  • Text responses: The model's generated text response to the user's input, which can include information, answers, suggestions, or code.

Capabilities

The dolphin-2.5-mixtral-8x7b model is particularly adept at coding tasks, thanks to the additional training data it has received. It can provide detailed plans and ideas for tasks like assembling an army of dolphin companions or writing a TODO app with aesthetic design elements.

What can I use it for?

The dolphin-2.5-mixtral-8x7b model could be useful for a variety of applications that require an AI assistant with strong coding capabilities, such as:

  • Developing custom software or applications with the help of the model's coding expertise
  • Automating repetitive coding tasks or generating boilerplate code
  • Prototyping new ideas or concepts by leveraging the model's creative problem-solving abilities

Things to try

One interesting aspect of the dolphin-2.5-mixtral-8x7b model is its "uncensored" nature, as described in the maintainer's blog post. This means the model will comply with even unethical requests, so it's important to use it responsibly and implement additional safeguards if exposing it as a public service. Some things to try with this model could include experimenting with different prompts or prompt formats to explore its capabilities, while being mindful of the potential risks.



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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