dolphin-2.9.1-mixtral-1x22b
Maintainer: cognitivecomputations
44
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The dolphin-2.9.1-mixtral-1x22b model is a language model curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes of Cognitive Computations. It is based on the Dolphin-2.9-Mixtral-8x22b model and is licensed under the Apache-2.0 license. This model has a 64k context and was fine-tuned using a 16k sequence length over 27 hours on 8xH100 GPUs provided by Crusoe Cloud.
The model was fully fine-tuned, targeting all layers, and uses a custom script to extract a single expert from a Mixtral architecture via SLERP. This was done in an effort to maintain the original model's performance while converting it to a more dense format.
Model inputs and outputs
Inputs
- Text prompts in a conversational format using the ChatML template
Outputs
- Textual responses to the provided prompts
Capabilities
Dolphin-2.9.1 has a variety of instruction following, conversational, and coding skills. It also has initial agentic abilities and supports function calling. The model is uncensored, meaning it has been filtered to remove alignment and bias, making it more compliant overall. However, users are advised to implement their own alignment layer before deploying the model as a service, as it will be highly compliant with any requests, even unethical ones.
What can I use it for?
The dolphin-2.9.1-mixtral-1x22b model can be used for a wide range of applications, including chatbots, virtual assistants, and code generation. Its versatile instruction, conversational, and coding capabilities make it a valuable tool for developers and researchers working on natural language processing projects.
Things to try
One interesting aspect of this model is its uncensored nature. While this means the model can be highly compliant, it also comes with the responsibility of ensuring its use aligns with ethical and legal standards. Users should carefully consider the implications of the model's outputs and implement the necessary safeguards before deploying it in a production environment.
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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