wizard-vicuna-13B-GPTQ
Maintainer: TheBloke
99
⚙️
| 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 wizard-vicuna-13B-GPTQ is a language model created by junelee and quantized by TheBloke using GPTQ techniques. It is based on the original Wizard Vicuna 13B model, which was trained on a subset of the dataset to remove alignment and moralizing responses. The quantized version provides more efficient inference while maintaining the model's capabilities.
Similar models offered by TheBloke include the Wizard-Vicuna-13B-Uncensored-GPTQ, Wizard-Vicuna-7B-Uncensored-GPTQ, and Wizard-Vicuna-30B-Uncensored-GPTQ which provide quantized versions of other Wizard Vicuna models.
Model inputs and outputs
The wizard-vicuna-13B-GPTQ model is a text-to-text transformer, taking natural language prompts as input and generating relevant text responses.
Inputs
- Natural language prompts in the form of statements or questions
Outputs
- Generated text responses relevant to the input prompt
Capabilities
The wizard-vicuna-13B-GPTQ model can be used for a variety of natural language processing tasks, such as question answering, language generation, and text summarization. It has been trained to provide detailed and polite responses, making it well-suited for conversational AI applications.
What can I use it for?
The wizard-vicuna-13B-GPTQ model could be used to build chatbots, virtual assistants, or other language-based applications. Its capabilities in areas like question answering and text generation could be leveraged to create educational tools, creative writing aids, or content generation services. Businesses could also use the model to automate customer service or provide product recommendations.
Things to try
One interesting aspect of the wizard-vicuna-13B-GPTQ model is its uncensored nature, which allows for more open-ended and creative responses. Users could experiment with providing the model with prompts that push the boundaries of what it's been trained on, to see the types of outputs it can generate. Additionally, the model's detailed and polite responses could be leveraged to create engaging conversational experiences.
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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