WizardLM-1.0-Uncensored-Llama2-13B-GPTQ
Maintainer: TheBloke
52
⛏️
| 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-GPTQ is a version of the original WizardLM-1.0-Uncensored-Llama2-13B model that has been quantized using the GPTQ (Graded Pruning for Transformers) method. This model was created by TheBloke, who provides a range of quantized models based on the original WizardLM to allow users to choose the best configuration for their hardware and performance needs.
Model inputs and outputs
The WizardLM-1.0-Uncensored-Llama2-13B-GPTQ is a text-to-text model, meaning it takes text prompts as input and generates text outputs. The model was trained using the Vicuna-1.1 style prompts, where the input prompt is formatted as a conversation between a user and a helpful AI assistant.
Inputs
- Text prompts in the format: "USER: <prompt> ASSISTANT:"
Outputs
- Generated text responses from the AI assistant
Capabilities
The WizardLM-1.0-Uncensored-Llama2-13B-GPTQ model can engage in open-ended conversations, answer questions, and generate text on a wide variety of topics. It has been trained to provide helpful, detailed, and polite responses. However, as an "uncensored" model, it does not have the same ethical guardrails as some other AI assistants, so users should be cautious about the content it generates.
What can I use it for?
The WizardLM-1.0-Uncensored-Llama2-13B-GPTQ model can be used for a variety of text generation tasks, such as creative writing, summarization, question answering, and even chatbots or virtual assistants. The quantized versions provided by TheBloke allow for more efficient deployment on a wider range of hardware, making this model accessible to a broader audience.
Things to try
One interesting thing to try with the WizardLM-1.0-Uncensored-Llama2-13B-GPTQ model is to experiment with different prompting techniques, such as using longer or more detailed prompts, or prompts that explore specific topics or personas. The model's flexibility and open-ended nature allow for a wide range of possible use cases and applications.
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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