Noromaid-v0.1-mixtral-8x7b-Instruct-v3
Maintainer: NeverSleep
43
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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 Noromaid-v0.1-mixtral-8x7b-Instruct-v3 model is a large language model created by NeverSleep that builds upon the Mixtral model. It is a more powerful version of the Beeg Noromaid model, suitable for roleplay (RP) and erotic roleplay (ERP) tasks. The model was trained on customized datasets focused on RP, uncensoring, and a modified version of the Alpaca prompting format, aiming to achieve a conversational level similar to ChatLM or Llama2-Chat.
Model inputs and outputs
Inputs
- Prompt: The text prompt provided to the model for generation.
Outputs
- Generated text: The text generated by the model in response to the input prompt.
Capabilities
The Noromaid-v0.1-mixtral-8x7b-Instruct-v3 model is capable of engaging in open-ended conversation, storytelling, and RP scenarios. It has been trained to provide more uncensored and explicit responses compared to the original Beeg Noromaid model. The model can be used for tasks such as creative writing, character roleplay, and erotic RP.
What can I use it for?
The Noromaid-v0.1-mixtral-8x7b-Instruct-v3 model can be useful for RP and ERP enthusiasts who want a more powerful and explicit language model to assist with their creative endeavors. It could be incorporated into chatbots, virtual assistants, or other applications where open-ended conversation and storytelling capabilities are desired. However, due to the model's more mature content, it should be deployed with appropriate safeguards and moderation.
Things to try
One interesting aspect of the Noromaid-v0.1-mixtral-8x7b-Instruct-v3 model is its use of a modified Alpaca prompting format, which aims to achieve a similar conversational level to ChatLM or Llama2-Chat. Users could experiment with different prompting styles and formats to see how the model responds and adapts to various conversational contexts. Additionally, exploring the model's capabilities in RP and ERP scenarios could yield intriguing results, though caution should be exercised when engaging with such content.
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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