Mistral-7B-OpenOrca-GPTQ
Maintainer: TheBloke
100
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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 Mistral-7B-OpenOrca-GPTQ is a large language model created by OpenOrca and quantized to GPTQ format by TheBloke. This model is based on OpenOrca's Mistral 7B OpenOrca and provides multiple GPTQ parameter options to allow for optimizing performance based on hardware constraints and quality requirements.
Similar models include the Mistral-7B-OpenOrca-GGUF and Mixtral-8x7B-v0.1-GPTQ, all of which provide quantized versions of large language models for efficient inference.
Model inputs and outputs
Inputs
- Text prompts: The model takes in text prompts to generate continuations.
- System messages: The model can receive system messages as part of a conversational prompt template.
Outputs
- Generated text: The primary output of the model is the generation of continuation text based on the provided prompts.
Capabilities
The Mistral-7B-OpenOrca-GPTQ model demonstrates high performance on a variety of benchmarks, including HuggingFace Leaderboard, AGIEval, BigBench-Hard, and GPT4ALL. It can be used for a wide range of natural language tasks such as open-ended text generation, question answering, and summarization.
What can I use it for?
The Mistral-7B-OpenOrca-GPTQ model can be used for many different applications, such as:
- Content generation: The model can be used to generate engaging, human-like text for blog posts, articles, stories, and more.
- Chatbots and virtual assistants: With its strong conversational abilities, the model can power chatbots and virtual assistants to provide helpful and natural responses.
- Research and experimentation: The quantized model files provided by TheBloke allow for efficient inference on a variety of hardware, making it suitable for research and experimentation.
Things to try
One interesting thing to try with the Mistral-7B-OpenOrca-GPTQ model is to experiment with the different GPTQ parameter options provided. Each option offers a different trade-off between model size, inference speed, and quality, allowing you to find the best fit for your specific use case and hardware constraints.
Another idea is to use the model in combination with other AI tools and frameworks, such as LangChain or ctransformers, to build more complex applications and workflows.
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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