Maintainer: TheBloke

Total Score


Last updated 5/17/2024


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 Mixtral-8x7B-Instruct-v0.1-AWQ is a language model created by Mistral AI_. It is an 8 billion parameter model that has been fine-tuned on instructional data, allowing it to follow complex prompts and generate relevant, coherent responses. Compared to similar large language models like Mixtral-8x7B-Instruct-v0.1-GPTQ and Mistral-7B-Instruct-v0.1-GPTQ, the Mixtral-8x7B-Instruct-v0.1-AWQ uses the efficient AWQ quantization method to provide faster inference with equivalent or better quality compared to common GPTQ settings.

Model inputs and outputs

The Mixtral-8x7B-Instruct-v0.1-AWQ is a text-to-text model, taking natural language prompts as input and generating relevant, coherent text as output. The model has been fine-tuned to follow specific instructions and prompts, allowing it to engage in tasks like open-ended storytelling, analysis, and task completion.


  • Natural language prompts: The model accepts free-form text prompts that can include instructions, queries, or open-ended requests.
  • Instructional formatting: The model responds best to prompts that use the [INST] and [/INST] tags to delineate the instructional component.


  • Generated text: The model's primary output is a continuation of the input prompt, generating relevant, coherent text that follows the given instructions or request.
  • Contextual awareness: The model maintains awareness of the broader context and can generate responses that build upon previous interactions.


The Mixtral-8x7B-Instruct-v0.1-AWQ model demonstrates strong capabilities in following complex prompts and generating relevant, coherent responses. It excels at open-ended tasks like storytelling, where it can continue a narrative in a natural and imaginative way. The model also performs well on analysis and task completion, providing thoughtful and helpful responses to a variety of prompts.

What can I use it for?

The Mixtral-8x7B-Instruct-v0.1-AWQ model can be a valuable tool for a wide range of applications, from creative writing and content generation to customer support and task automation. Its ability to understand and respond to natural language instructions makes it well-suited for chatbots, virtual assistants, and other interactive applications.

One potential use case could be a creative writing assistant, where the model could help users brainstorm story ideas, develop characters, and expand upon plot points. Alternatively, the model could be used in a customer service context, providing personalized responses to inquiries and helping to streamline support workflows.

Things to try

Beyond the obvious use cases, there are many interesting things to explore with the Mixtral-8x7B-Instruct-v0.1-AWQ model. For example, you could try providing the model with more open-ended prompts to see how it responds, or challenge it with complex multi-step instructions to gauge its reasoning and problem-solving capabilities. Additionally, you could experiment with different sampling parameters, such as temperature and top-k, to find the settings that work best for your specific use case.

Overall, the Mixtral-8x7B-Instruct-v0.1-AWQ is a powerful and versatile language model that can be a valuable tool in a wide range of applications. Its efficient quantization and strong performance on instructional tasks make it an attractive option for developers and researchers looking to push the boundaries of what's possible with large language models.

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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The Mixtral-8x7B-Instruct-v0.1-GPTQ is a large language model created by Mistral AI_ and maintained by TheBloke. It is an 8 billion parameter model that has been fine-tuned for instruction following, outperforming the Llama 2 70B model on many benchmarks. This model is available in various quantized formats, including GPTQ, which reduces the memory footprint for GPU inference. The GPTQ versions provided offer a range of bit sizes and quantization parameters to choose from, allowing users to balance model quality and performance requirements. Model inputs and outputs Inputs Prompts:** The model takes instruction-based prompts as input, following a specific template format of [INST] {prompt} [/INST]. Outputs Responses:** The model generates coherent and relevant responses based on the provided instruction prompts. The responses continue the conversational flow and aim to address the user's request. Capabilities The Mixtral-8x7B-Instruct-v0.1-GPTQ model is capable of a wide range of language tasks, including text generation, question answering, summarization, and task completion. It has been designed to excel at following instructions and engaging in interactive, multi-turn dialogues. The model can generate human-like responses, drawing upon its broad knowledge base to provide informative and contextually appropriate outputs. What can I use it for? The Mixtral-8x7B-Instruct-v0.1-GPTQ model can be used for a variety of applications, such as building interactive AI assistants, automating content creation workflows, and enhancing customer support experiences. Its instruction-following capabilities make it well-suited for task-oriented applications, where users can provide step-by-step instructions and the model can respond accordingly. Potential use cases include virtual personal assistants, automated writing tools, and task automation in various industries. Things to try One interesting aspect of the Mixtral-8x7B-Instruct-v0.1-GPTQ model is its ability to engage in multi-turn dialogues and maintain context throughout a conversation. Users can experiment with providing follow-up instructions or clarifications to the model and observe how it adapts its responses to maintain coherence and address the updated requirements. Additionally, users can explore the model's versatility by testing it on a diverse range of tasks, from creative writing to analytical problem-solving, to fully appreciate the breadth of its capabilities.

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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-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-Instruct-v0.1-GGUF is a large language model created by Mistral AI. It is a fine-tuned version of the Mixtral 8X7B Instruct v0.1 model, which has been optimized for instruction-following tasks. This model outperforms the popular Llama 2 70B model on many benchmarks, according to the maintainer. Model inputs and outputs The Mixtral-8x7B-Instruct-v0.1-GGUF model is a text-to-text model, meaning it takes text as input and generates text as output. Inputs Text prompts**: The model accepts text prompts as input, which can include instructions, questions, or other types of text. Outputs Generated text**: The model outputs generated text, which can include answers, stories, or other types of content. Capabilities The Mixtral-8x7B-Instruct-v0.1-GGUF model has been fine-tuned on a variety of publicly available conversation datasets, making it well-suited for instruction-following tasks. According to the maintainer, the model outperforms the Llama 2 70B model on many benchmarks, demonstrating its strong capabilities in natural language processing and generation. What can I use it for? The Mixtral-8x7B-Instruct-v0.1-GGUF model can be used for a variety of natural language processing tasks, such as: Chatbots and virtual assistants**: The model's ability to understand and follow instructions can make it a useful component in building conversational AI systems. Content generation**: The model can be used to generate text, such as stories, articles, or product descriptions, based on prompts. Question answering**: The model can be used to answer questions on a wide range of topics. Things to try One interesting aspect of the Mixtral-8x7B-Instruct-v0.1-GGUF model is its use of the GGUF format, which is a new file format introduced by the llama.cpp team. This format is designed to replace the older GGML format, which is no longer supported by llama.cpp. You can try using the model with various GGUF-compatible tools and libraries, such as llama.cpp, KoboldCpp, LM Studio, and others, to see how it performs in different environments.

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