Mistral-7B-OpenOrca-GGUF
Maintainer: TheBloke
241
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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
Mistral-7B-OpenOrca-GGUF is a large language model created by OpenOrca, which fine-tuned the Mistral 7B model on the OpenOrca dataset. This dataset aims to reproduce the dataset from the Orca Paper. The model is available in a variety of quantized GGUF formats, which are compatible with tools like llama.cpp, text-generation-webui, and KoboldCpp.
Model Inputs and Outputs
Inputs
- The model accepts text prompts as input.
Outputs
- The model generates coherent and contextual text output in response to the input prompt.
Capabilities
The Mistral-7B-OpenOrca-GGUF model demonstrates strong performance on a variety of benchmarks, outperforming other 7B and 13B models. It performs well on tasks like commonsense reasoning, world knowledge, reading comprehension, and math. The model also exhibits strong safety characteristics, with low toxicity and high truthfulness scores.
What Can I Use It For?
The Mistral-7B-OpenOrca-GGUF model can be used for a variety of natural language processing tasks, such as:
- Content Generation: The model can be used to generate coherent and contextual text, making it useful for tasks like story writing, article creation, or dialogue generation.
- Question Answering: The model's strong performance on benchmarks like NaturalQuestions and TriviaQA suggests it could be used for question answering applications.
- Conversational AI: The model's chat-oriented fine-tuning makes it well-suited for developing conversational AI assistants.
Things to Try
One interesting aspect of the Mistral-7B-OpenOrca-GGUF model is its use of the GGUF format, which offers advantages over the older GGML format used by earlier language models. Experimenting with the different quantization levels provided in the model repository can allow you to find the right balance between model size, performance, and resource requirements for your specific use case.
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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