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WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GPTQ

Maintainer: TheBloke

Total Score

80

Last updated 5/16/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

The WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GPTQ model is a 30 billion parameter large language model (LLM) created by YellowRoseCx and maintained by TheBloke. It is a quantized version of the original WizardLM-Uncensored-SuperCOT-Storytelling-30b model, available with various GPTQ parameter options to optimize for different hardware and performance requirements.

This model is similar to other uncensored LLMs like the [object Object], [object Object], and [object Object] models, all of which aim to provide highly capable language generation without built-in censorship or alignment.

Model inputs and outputs

The WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GPTQ model takes natural language text as input and generates coherent, context-aware responses. It can be used for a wide variety of text-to-text tasks such as language generation, summarization, and question answering.

Inputs

  • Natural language text prompts

Outputs

  • Coherent, context-aware text responses

Capabilities

The WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GPTQ model excels at open-ended language generation, producing human-like responses on a wide range of topics. It can engage in freeform conversations, generate creative stories and poems, and provide detailed answers to questions. Unlike some censored models, this uncensored version does not have built-in restrictions, allowing for more flexible and diverse outputs.

What can I use it for?

The WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GPTQ model can be used for a variety of text-based applications, such as:

  • Chatbots and virtual assistants
  • Creative writing and storytelling
  • Question answering and knowledge-based tasks
  • Summarization and text generation

Potential use cases include customer service, education, entertainment, and research. However, as an uncensored model, users should be cautious and responsible when deploying it, as it may generate content that could be considered inappropriate or harmful.

Things to try

Experiment with different prompting techniques to see the full range of the model's capabilities. For example, try providing detailed storylines or character descriptions to observe its narrative generation skills. You can also explore the model's ability to follow instructions and complete tasks by giving it specific, multi-step prompts. By pushing the boundaries of the model's inputs, you may discover unexpected and delightful outputs.



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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