guanaco-65B-GPTQ
Maintainer: TheBloke
265
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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
guanaco-65B-GPTQ is a quantized version of the Guanaco 65B language model, created by Tim Dettmers and maintained by TheBloke. The Guanaco models are open-source large language models based on LLaMA, finetuned for conversational abilities. This GPTQ version provides compressed models for efficient GPU inference, with multiple quantization parameter options to balance performance and resource usage.
Similar models include the guanaco-33B-GPTQ, which is a quantized version of the smaller 33B Guanaco model, and the guanaco-65B-GGML, which is an GGML format model for CPU and GPU inference.
Model inputs and outputs
guanaco-65B-GPTQ is a text-to-text language model, taking text prompts as input and generating relevant text responses.
Inputs
- Free-form text prompts
Outputs
- Coherent, contextual text responses to the input prompts
Capabilities
The Guanaco models are designed for high-quality conversational abilities, outperforming many commercial chatbots on standard benchmarks. guanaco-65B-GPTQ can engage in open-ended dialogue, answer questions, and assist with a variety of language tasks.
What can I use it for?
guanaco-65B-GPTQ can be used for building conversational AI assistants, chatbots, and other natural language applications. The quantized GPTQ format allows for efficient GPU inference, making it suitable for deployment in production environments. Potential use cases include customer service, education, research, and creative writing assistance.
Things to try
One interesting aspect of the Guanaco models is their focus on safety and alignment, as evidenced by their performance on bias and toxicity benchmarks. It could be valuable to explore how the model handles sensitive or controversial topics, and whether its responses remain constructive and unbiased.
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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