vicuna-7B-1.1-GPTQ
Maintainer: TheBloke
58
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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 vicuna-7B-1.1-GPTQ is a 4-bit GPTQ version of the Vicuna 7B 1.1 model, created by TheBloke. It was quantized from the original Llama 7B model using the GPTQ-for-LLaMa library. TheBloke provides a range of Vicuna models in different sizes and quantization formats, including 13B and 7B versions in both float16 and GPTQ formats.
Model inputs and outputs
The vicuna-7B-1.1-GPTQ model is a text-to-text transformer that can be used for a variety of natural language processing tasks. It takes raw text as input and generates coherent responses as output.
Inputs
- Raw text prompts
Outputs
- Generated text responses
Capabilities
The vicuna-7B-1.1-GPTQ model is capable of engaging in open-ended dialogue, answering questions, and completing a variety of text generation tasks. It demonstrates strong conversational and reasoning abilities, making it useful for chatbots, question-answering systems, and other applications that require natural language understanding and generation.
What can I use it for?
The vicuna-7B-1.1-GPTQ model can be used for a wide range of text-based applications, such as:
- Chatbots and virtual assistants
- Question-answering systems
- Text summarization
- Creative writing and storytelling
- Content generation for websites, social media, and marketing
The model's compact 4-bit GPTQ format makes it particularly well-suited for deployment on resource-constrained devices or environments where memory and storage are limited.
Things to try
One interesting aspect of the vicuna-7B-1.1-GPTQ model is its ability to engage in multi-turn conversations. By providing context from previous exchanges, you can prompt the model to build upon and refine its responses over the course of a dialogue. This can be useful for applications that require more natural and contextual language interactions.
Another thing to explore is the model's performance on specific tasks or domains that align with your use case. TheBloke provides a range of Vicuna models in different sizes and quantization formats, so you may want to experiment with different versions to find the one that best suits your needs.
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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