WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ
Maintainer: TheBloke
47
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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-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ model is a 13B parameter language model created by combining Eric Hartford's WizardLM 13B V1.0 Uncensored with Kaio Ken's SuperHOT 8K. The model has been quantized to 4-bit using the GPTQ-for-LLaMa tool, which allows for increased context size up to 8K tokens. This model is an experimental new GPTQ that offers expanded context compared to the original WizardLM 13B V1.0 Uncensored.
Model inputs and outputs
The WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ model takes text prompts as input and generates coherent, detailed responses. The model has been trained on a large corpus of online text data, allowing it to understand and converse on a wide range of topics.
Inputs
- Text prompt: A text prompt provided to the model to initiate the generation of a response.
Outputs
- Generated text: The model's response to the provided text prompt, which can be up to 8192 tokens in length.
Capabilities
The WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ model is a powerful language model capable of engaging in open-ended conversations, answering questions, and generating human-like text on a variety of subjects. Its expanded context size allows it to maintain coherence and provide more detailed responses compared to models with shorter context.
What can I use it for?
The WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ model can be used for a wide range of natural language processing tasks, such as chatbots, content generation, question answering, and creative writing. The increased context size makes it well-suited for applications that require longer-form, coherent responses.
Things to try
One interesting aspect of the WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GPTQ model is its ability to maintain context and narrative structure over longer text generation. Try providing the model with a multi-sentence prompt and see how it continues the story or expands on the initial ideas. The model's large knowledge base and generation capabilities make it well-suited for collaborative storytelling or worldbuilding exercises.
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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