WizardLM-7B-uncensored-GPTQ
Maintainer: TheBloke
184
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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
WizardLM-7B-uncensored-GPTQ is a language model created by Eric Hartford and maintained by TheBloke. It is a quantized version of the Wizardlm 7B Uncensored model, which uses the GPTQ algorithm to reduce the model size while preserving performance. This makes it suitable for deployment on GPU hardware. The model is available in various quantization levels to balance model size, speed, and accuracy based on user needs.
The WizardLM-7B-uncensored-GPTQ model is similar to other large language models like llamaguard-7b, which is a 7B parameter Llama 2-based input-output safeguard model, and GPT-2B-001, a 2 billion parameter multilingual transformer-based language model. It also shares some similarities with wizard-mega-13b-awq, a 13B parameter model quantized using AWQ and served with vLLM.
Model inputs and outputs
Inputs
- Text prompts: The model accepts text prompts as input, which it can use to generate continuations or completions.
Outputs
- Generated text: The model outputs generated text, which can be continuations of the input prompt or completely new text.
Capabilities
The WizardLM-7B-uncensored-GPTQ model is a powerful language model that can be used for a variety of text-generation tasks, such as content creation, question answering, and text summarization. It has been trained on a large corpus of text data, giving it a broad knowledge base that it can draw upon to generate coherent and contextually appropriate responses.
What can I use it for?
The WizardLM-7B-uncensored-GPTQ model can be used for a wide range of applications, such as:
- Content creation: The model can be used to generate blog posts, articles, or other types of written content, either as a starting point or for idea generation.
- Chatbots and virtual assistants: The model's ability to generate natural-sounding responses makes it well-suited for use in chatbots and virtual assistants.
- Question answering: The model can be used to answer questions on a variety of topics, drawing upon its broad knowledge base.
- Text summarization: The model can be used to generate concise summaries of longer text passages.
Things to try
One interesting thing to try with the WizardLM-7B-uncensored-GPTQ model is to experiment with different quantization levels and see how they affect the model's performance. The maintainer has provided multiple GPTQ parameter options, which allow you to choose the best balance of model size, speed, and accuracy for your specific use case. You can also try using the model in different contexts, such as by prompting it with different types of text or by fine-tuning it on specialized datasets, to see how it performs in various 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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