Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ
Maintainer: TheBloke
127
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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-13B-Uncensored-SuperHOT-8K-GPTQ model is a large language model created by TheBloke, who has generously provided a variety of quantized versions of the model for GPU and CPU inference. This model is based on Eric Hartford's Wizard Vicuna 13B Uncensored merged with Kaio Ken's SuperHOT 8K model. The key innovation is an increased context size of up to 8K, which is tested to work with ExLlama. TheBloke has also provided GPTQ and GGML quantized versions of the model for efficient inference on different hardware.
Model inputs and outputs
Inputs
- Prompts: The model takes in free-form text prompts that can cover a wide range of topics. These prompts are used to initiate the model's generation of relevant and coherent responses.
Outputs
- Generated text: The primary output of the model is free-form text, generated in response to the provided prompts. The model aims to produce helpful, detailed, and polite responses.
Capabilities
The Wizard-Vicuna-13B-Uncensored-SuperHOT-8K-GPTQ model is a large, powerful language model that can be used for a variety of natural language processing tasks. It has been trained on a diverse dataset and can engage in open-ended conversations, answer questions, and generate human-like text on a wide range of subjects. The increased context size of up to 8K allows the model to maintain coherence and consistency over longer sequences.
What can I use it for?
This model could be useful for applications such as chatbots, virtual assistants, creative writing, summarization, and question-answering. The increased context size may be particularly beneficial for tasks that require maintaining context over longer interactions, such as task-oriented dialogues. Developers and researchers could explore using this model as a foundation for further fine-tuning or prompt engineering to create specialized AI applications.
Things to try
One interesting aspect of this model is the ability to control the generation process through parameters like temperature and top-k/top-p sampling. Experimenting with these settings can result in outputs with different levels of creativity, coherence, and diversity. Additionally, prompting the model with specific instructions or templates, as shown in the provided examples, can help elicit more targeted responses for certain use cases.
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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