Wizard-Vicuna-7B-Uncensored-GPTQ
Maintainer: TheBloke
162
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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 Wizard-Vicuna-7B-Uncensored-GPTQ model is a quantized version of the open-source Wizard Vicuna 7B Uncensored language model created by Eric Hartford. It has been quantized using GPTQ techniques by TheBloke, who has provided several quantization options to choose from based on the user's hardware and performance requirements.
Model inputs and outputs
The Wizard-Vicuna-7B-Uncensored-GPTQ model is a text-to-text transformer model, which means it takes text as input and generates text as output. The input is typically a prompt or a partial message, and the output is the model's continuation or response.
Inputs
- Text prompt or partial message
Outputs
- Continued text, with the model responding to the input prompt in a contextual and coherent manner
Capabilities
The Wizard-Vicuna-7B-Uncensored-GPTQ model has broad language understanding and generation capabilities, allowing it to engage in open-ended conversations, answer questions, and assist with a variety of text-based tasks. It has been trained on a large corpus of text data, giving it the ability to produce human-like responses on a wide range of subjects.
What can I use it for?
The Wizard-Vicuna-7B-Uncensored-GPTQ model can be used for a variety of applications, such as building chatbots, virtual assistants, or creative writing tools. It could be used to generate responses for customer service inquiries, provide explanations for complex topics, or even help with ideation and brainstorming. Given its uncensored nature, users should exercise caution and responsibility when using this model.
Things to try
Users can experiment with the model by providing it with prompts on different topics and observing the generated responses. They can also try adjusting the temperature and other sampling parameters to see how it affects the creativity and coherence of the output. Additionally, users may want to explore the various quantization options provided by TheBloke to find the best balance between performance and accuracy for their specific use case.
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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