WizardLM-1.0-Uncensored-Llama2-13B-GGUF
Maintainer: TheBloke
52
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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-1.0-Uncensored-Llama2-13B-GGUF model is a large language model created by Eric Hartford and maintained by TheBloke. It is a version of the WizardLM model that has been retrained with a filtered dataset to reduce refusals, avoidance, and bias. This model is designed to be more compliant than the original WizardLM-13B-V1.0 release.
Similar models include the WizardLM-1.0-Uncensored-Llama2-13B-GGML, WizardLM-1.0-Uncensored-Llama2-13B-GPTQ, and the unquantised WizardLM-1.0-Uncensored-Llama2-13b model.
Model inputs and outputs
The WizardLM-1.0-Uncensored-Llama2-13B-GGUF model is a text-to-text model, meaning it takes text prompts as input and generates text as output.
Inputs
- Prompts: Text prompts that the model will use to generate output.
Outputs
- Generated text: The model will generate relevant text based on the provided prompts.
Capabilities
The WizardLM-1.0-Uncensored-Llama2-13B-GGUF model has a wide range of capabilities, including natural language understanding, language generation, and task completion. It can be used for tasks such as question answering, text summarization, and creative writing.
What can I use it for?
The WizardLM-1.0-Uncensored-Llama2-13B-GGUF model can be useful for a variety of applications, such as building chatbots, generating content for websites or social media, and assisting with research and analysis tasks. However, as an uncensored model, it is important to use the model responsibly and be aware of the potential risks.
Things to try
Some interesting things to try with the WizardLM-1.0-Uncensored-Llama2-13B-GGUF model include experimenting with different prompts to see how the model responds, using the model to generate creative stories or poems, and exploring its capabilities for task completion and language understanding.
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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