Maintainer: TheBloke

Total Score


Last updated 5/28/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo 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


  • 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.


  • Generated text: The primary output of the model is the generation of continuation text based on the provided prompts.


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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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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The Mistral-7B-Instruct-v0.1-GPTQ is an AI model created by Mistral AI, with quantized versions provided by TheBloke. This model is derived from Mistral AI's larger Mistral 7B Instruct v0.1 model, and has been further optimized through GPTQ quantization to reduce memory usage and improve inference speed, while aiming to maintain high performance. Similar models available from TheBloke include the Mixtral-8x7B-Instruct-v0.1-GPTQ, which is an 8-expert version of the Mistral model, and the Mistral-7B-OpenOrca-GPTQ, which was fine-tuned by OpenOrca on top of the original Mistral 7B model. Model inputs and outputs Inputs Prompt**: A text prompt to be used as input for the model to generate a completion. Outputs Generated text**: The text completion generated by the model based on the provided prompt. Capabilities The Mistral-7B-Instruct-v0.1-GPTQ model is capable of generating high-quality, coherent text on a wide range of topics. It has been trained on a large corpus of internet data and can be used for tasks like open-ended text generation, summarization, and question answering. The model is particularly adept at following instructions and maintaining consistent context throughout the generated output. What can I use it for? The Mistral-7B-Instruct-v0.1-GPTQ model can be used for a variety of applications, such as: Creative writing assistance: Generate ideas, story plots, or entire narratives to help jumpstart the creative process. Chatbots and conversational AI: Use the model to power engaging, context-aware dialogues. Content generation: Create articles, blog posts, or other written content on demand. Question answering: Leverage the model's knowledge to provide informative responses to user queries. Things to try One interesting aspect of the Mistral-7B-Instruct-v0.1-GPTQ model is its ability to follow instructions and maintain context across multiple prompts. Try providing the model with a series of prompts that build upon each other, such as: "Write a short story about a talking llama." "Now, have the llama encounter a mysterious stranger in the woods." "The llama and the stranger decide to work together on a quest. What happens next?" By chaining these prompts together, you can see the model's capacity to understand and respond to the evolving narrative, creating a cohesive and engaging story.

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The Mixtral-8x7B-v0.1-GPTQ is a quantized version of the Mixtral 8X7B Large Language Model (LLM) created by Mistral AI_. This model is a pretrained generative Sparse Mixture of Experts that outperforms the Llama 2 70B model on most benchmarks. TheBloke has provided several quantized versions of this model for efficient GPU and CPU inference. Similar models available include the Mixtral-8x7B-v0.1-GGUF which uses the new GGUF format, and the Mixtral-8x7B-Instruct-v0.1-GGUF which is fine-tuned for instruction following. Model inputs and outputs Inputs Text prompt**: The model takes a text prompt as input and generates relevant text in response. Outputs Generated text**: The model outputs generated text that is relevant and coherent based on the input prompt. Capabilities The Mixtral-8x7B-v0.1-GPTQ model is a powerful generative language model capable of producing high-quality text on a wide range of topics. It can be used for tasks like open-ended text generation, summarization, question answering, and more. The model's Sparse Mixture of Experts architecture allows it to outperform the Llama 2 70B model on many benchmarks. What can I use it for? This model could be valuable for a variety of applications, such as: Content creation**: Generating articles, stories, scripts, or other long-form text content. Chatbots and virtual assistants**: Building conversational AI agents that can engage in natural language interactions. Query answering**: Providing informative and coherent responses to user questions on a wide range of subjects. Summarization**: Condensing long documents or articles into concise summaries. TheBloke has also provided quantized versions of this model optimized for efficient inference on both GPUs and CPUs, making it accessible for a wide range of deployment scenarios. Things to try One interesting aspect of the Mixtral-8x7B-v0.1-GPTQ model is its Sparse Mixture of Experts architecture. This allows the model to excel at a variety of tasks by combining the expertise of multiple sub-models. You could try prompting the model with a diverse set of topics and observe how it leverages this specialized knowledge to generate high-quality responses. Additionally, the quantized versions of this model provided by TheBloke offer the opportunity to experiment with efficient inference on different hardware setups, potentially unlocking new use cases where computational resources are constrained.

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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.

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