WizardLM-30B-Uncensored-GPTQ
Maintainer: TheBloke
118
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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 WizardLM-30B-Uncensored-GPTQ is a large language model created by Eric Hartford and maintained by TheBloke. It is a 30 billion parameter version of the WizardLM model, with the "alignment" responses removed to produce an "uncensored" version. This allows the model's capabilities to be expanded upon separately, such as with reinforcement learning. The model is available in several quantized GPTQ versions to reduce the memory footprint for GPU inference.
Model inputs and outputs
Inputs
- Prompt: The input text to generate a response from.
Outputs
- Generated text: The model's response to the given prompt.
Capabilities
The WizardLM-30B-Uncensored-GPTQ model has broad language understanding and generation capabilities. It can engage in open-ended conversations, answer questions, summarize text, and even generate creative fiction. The removal of "alignment" responses gives the model more flexibility to express a wide range of views and perspectives.
What can I use it for?
With its large size and broad capabilities, the WizardLM-30B-Uncensored-GPTQ model could be useful for a variety of applications, such as building conversational assistants, generating content for websites or blogs, and even aiding in the brainstorming and outlining of creative writing projects. The quantized GPTQ versions make it more accessible for deployment on consumer hardware. However, given the "uncensored" nature of the model, users should be cautious about the outputs and take responsibility for how the model is used.
Things to try
One interesting aspect of the WizardLM-30B-Uncensored-GPTQ model is its ability to generate nuanced and multi-faceted responses. Try giving it prompts that explore complex topics or ask it to take on different perspectives. See how it navigates these challenges and whether it can provide thoughtful and insightful answers. Additionally, the quantized GPTQ versions may enable new use cases by allowing the model to run on more modest hardware.
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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