dolphin-2.9.2-qwen2-72b
Maintainer: cognitivecomputations
50
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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-2.9.2-qwen2-72b model, curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes of Cognitive Computations, is a large language model based on the Qwen2-72b architecture. It has 128k context and was fine-tuned with 8k sequence length. The model was trained using Laser Scanner on parameters, with a ChatML prompt template format. This model is similar to other Dolphin models like the dolphin-2.9.2-qwen2-7b and dolphin-2.9.3-mistral-7B-32k, which are also based on different architectures and trained by the Cognitive Computations team.
Model inputs and outputs
Inputs
- Prompts: The model takes in conversational prompts in the ChatML format, with the system and user messages delineated by
<|im_start|>and<|im_end|>tokens.
Outputs
- Responses: The model generates coherent and contextual responses to the input prompts, continuing the conversation.
Capabilities
The dolphin-2.9.2-qwen2-72b model has a variety of instruction following, conversational, and coding skills. It also has initial agentic abilities and supports function calling. The model is uncensored, with the dataset filtered to remove alignment and bias, making it more compliant but also potentially more prone to generating unethical content.
What can I use it for?
The dolphin-2.9.2-qwen2-72b model could be useful for building conversational AI assistants, language generation applications, or as a base for further fine-tuning on specific tasks. However, due to its uncensored nature, it is important to implement proper alignment and safety measures before deploying the model in a production setting. The maintainer's blog post on uncensored models provides more guidance on this.
Things to try
With the dolphin-2.9.2-qwen2-72b model's wide-ranging capabilities, there are many interesting things to explore. For example, you could try using the model's agentic abilities to build a more autonomous conversational agent, or leverage its coding skills to generate and refine software programs. Just remember to exercise caution and responsibility when experimenting with an uncensored model like this one.
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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