WizardLM-33B-V1.0-Uncensored-GPTQ
Maintainer: TheBloke
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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-33B-V1.0-Uncensored-GPTQ is a quantized version of the WizardLM 33B V1.0 Uncensored model created by Eric Hartford. This model is supported by a grant from andreessen horowitz (a16z) and maintained by TheBloke. The GPTQ quantization process allows for reduced model size and faster inference, while maintaining much of the original model's performance.
Model inputs and outputs
Inputs
- Prompts: The model accepts natural language prompts as input, which can be used to generate text.
Outputs
- Generated text: The model outputs coherent and contextually relevant text, which can be used for a variety of natural language processing tasks.
Capabilities
The WizardLM-33B-V1.0-Uncensored-GPTQ model is capable of generating high-quality text across a wide range of topics. It can be used for tasks such as story writing, dialogue generation, summarization, and question answering. The model's large size and uncensored nature allow it to tackle complex prompts and generate diverse, creative outputs.
What can I use it for?
The WizardLM-33B-V1.0-Uncensored-GPTQ model can be used in a variety of applications that require natural language generation, such as chatbots, content creation tools, and interactive fiction. Developers and researchers can fine-tune the model for specific domains or tasks to further enhance its capabilities. The GPTQ quantization also makes the model more accessible for deployment on consumer hardware.
Things to try
Try experimenting with different prompt styles and lengths to see how the model responds. You can also try giving the model specific instructions or constraints to see how it adapts its generation. Additionally, consider using the model in combination with other language models or tools to create more sophisticated applications.
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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