WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-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-SuperHOT-8K-GPTQ is a large language model created by TheBloke, a prominent AI researcher and model developer. This model is a variant of the WizardLM-33B model, which has been merged with Kaio Ken's SuperHOT 8K system to extend the context length to 8192 tokens. The model has been quantized to 4-bit precision using GPTQ, resulting in a more compact and efficient model for inference on GPU hardware.
Similar models available from TheBloke include the Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ, which is a 13B version of the model with a similar architecture and capabilities, and the WizardLM-7B-uncensored-GPTQ and WizardLM-30B-Uncensored-GPTQ models, which are smaller and larger variants respectively.
Model inputs and outputs
Inputs
- Text prompts: The model accepts free-form text prompts as input, which can be used to generate continuations, completions, or responses.
Outputs
- Generated text: The model outputs generated text, which can be used for a variety of applications such as content creation, dialogue generation, and language modeling.
Capabilities
The WizardLM-33B-V1-0-Uncensored-SuperHOT-8K-GPTQ model demonstrates impressive language generation capabilities, with the ability to produce coherent and contextually relevant text. The extended 8192 token context length allows the model to maintain continuity and coherence over longer stretches of text, making it particularly well-suited for applications that require sustained dialogue or narrative generation.
What can I use it for?
This model can be used for a wide range of language-based applications, such as:
- Content creation: The model can be used to generate articles, stories, scripts, or other forms of written content.
- Dialogue systems: The extended context length makes this model well-suited for building more natural and contextual chatbots or virtual assistants.
- Summarization: The model can be used to generate concise summaries of longer text passages.
- Question answering: The model can be used to answer questions based on the provided context.
Potential commercial applications for this model include creative content generation, customer service automation, and research and development in natural language processing.
Things to try
One interesting aspect of this model is its ability to maintain coherence and continuity over longer stretches of text, thanks to the extended 8192 token context length. You could try providing the model with a complex or multi-part prompt, and observe how it is able to build upon and expand the initial context to generate a cohesive and engaging response.
Another interesting direction to explore would be fine-tuning or further training the model on specialized datasets, in order to adapt its capabilities to more specific use cases or domains. This could involve incorporating domain-specific knowledge or adjusting the model's tone, style, or behavior to better suit the intended application.
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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