Mistral-7B-OpenOrca
Maintainer: Open-Orca
657
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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 model is a powerful language model developed by the Open-Orca team. It is built on top of the Mistral 7B base model and fine-tuned using the OpenOrca dataset, which is an attempt to reproduce the dataset generated for Microsoft Research's Orca Paper. The model uses OpenChat packing and was trained with the Axolotl framework.
This release is trained on a curated filtered subset of the OpenOrca dataset, which is the same data used for the OpenOrcaxOpenChat-Preview2-13B model. Evaluation results place this 7B model as the top performer among models smaller than 30B at the time of release, outperforming other 7B and 13B models.
Model inputs and outputs
Inputs
- Natural language text prompts for the model to continue or generate.
Outputs
- Continued or generated text based on the input prompt.
Capabilities
The Mistral-7B-OpenOrca model demonstrates strong performance across a variety of benchmarks, making it a capable generalist language model. It is able to engage in open-ended conversation, answer questions, and generate human-like text on a wide range of topics.
What can I use it for?
The Mistral-7B-OpenOrca model can be used for a variety of natural language processing tasks, such as:
- Open-ended conversation and dialogue
- Question answering
- Text generation (e.g. stories, articles, code)
- Summarization
- Sentiment analysis
- And more
The model's strong performance and ability to run efficiently on consumer GPUs make it a compelling choice for a wide range of applications and projects.
Things to try
Some interesting things to try with the Mistral-7B-OpenOrca model include:
- Engaging the model in open-ended conversation and observing its ability to maintain coherence and context over multiple turns.
- Prompting the model to generate creative writing, such as short stories or poetry, and analyzing the results.
- Exploring the model's knowledge and reasoning capabilities by asking it questions on a variety of topics, from science and history to current events and trivia.
- Utilizing the model's accelerated performance on consumer GPUs to integrate it into real-time applications and services.
The versatility and strong performance of the Mistral-7B-OpenOrca model make it a valuable tool for a wide range of AI and natural language processing 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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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.
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