Mixtral-8x7B-Instruct-v0.1-bnb-4bit
Maintainer: ybelkada
58
🤔
| 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 |
Create account to get full access
Model overview
The Mixtral-8x7B-Instruct-v0.1-bnb-4bit is a 4-bit quantized version of the Mixtral-8x7B Instruct model, created by maintainer ybelkada. This model is based on the original Mixtral-8x7B-Instruct-v0.1 and uses the bitsandbytes library to reduce the model size while maintaining performance.
Similar models include the Mixtral-8x7B-Instruct-v0.1-GPTQ and Mixtral-8x7B-Instruct-v0.1-AWQ models, which use different quantization techniques to reduce the model size.
Model inputs and outputs
Inputs
- Text prompt: The model takes a text prompt as input, formatted using the provided
[INST] {prompt} [/INST]template.
Outputs
- Generated text: The model generates text in response to the provided prompt, up to a specified maximum number of tokens.
Capabilities
The Mixtral-8x7B-Instruct-v0.1-bnb-4bit model is a powerful text generation model capable of producing coherent, contextual responses to a wide range of prompts. It can be used for tasks such as creative writing, summarization, language translation, and more.
What can I use it for?
This model can be used in a variety of applications, such as:
- Chatbots and virtual assistants: The model can be used to power conversational interfaces, providing human-like responses to user queries and prompts.
- Content generation: The model can be used to generate text for blog posts, articles, stories, and other types of content.
- Language translation: The model can be fine-tuned for language translation tasks, converting text from one language to another.
- Summarization: The model can be used to summarize long-form text, extracting the key points and ideas.
Things to try
One interesting thing to try with this model is experimenting with the temperature and top-k/top-p sampling parameters. Adjusting these can result in more creative, diverse, or focused output, depending on your needs. It's also worth trying the model on a variety of prompts to see the range of responses it can generate.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
🤿
Mixtral-8x7B-Instruct-v0.1-GPTQ
124
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.
Updated Invalid Date
🏋️
Mixtral-8x7B-Instruct-v0.1
3.7K
The Mixtral-8x7B-Instruct-v0.1 is a Large Language Model (LLM) developed by Mistral AI. It is a pretrained generative Sparse Mixture of Experts that outperforms the Llama 2 70B model on most benchmarks, according to the maintainer. This model is an instruct fine-tuned version of the Mixtral-8x7B-v0.1 model, which is also available from Mistral AI. Model inputs and outputs The Mixtral-8x7B-Instruct-v0.1 model is a text-to-text model, meaning it takes in text prompts and generates text outputs. Inputs Text prompts following a specific instruction format, with the instruction surrounded by [INST] and [/INST] tokens. Outputs Textual responses generated by the model based on the provided input prompts. Capabilities The Mixtral-8x7B-Instruct-v0.1 model demonstrates strong language generation capabilities, able to produce coherent and relevant responses to a variety of prompts. It can be used for tasks like question answering, text summarization, and creative writing. What can I use it for? The Mixtral-8x7B-Instruct-v0.1 model can be used in a wide range of applications that require natural language processing, such as chatbots, virtual assistants, and content generation. It could be particularly useful for projects that need a flexible and powerful language model to interact with users in a more natural and engaging way. Things to try One interesting aspect of the Mixtral-8x7B-Instruct-v0.1 model is its instruction format, which allows for more structured and contextual prompts. You could try experimenting with different ways of formatting your prompts to see how the model responds, or explore how it handles more complex multi-turn conversations.
Updated Invalid Date
📈
Mixtral-8x7B-Instruct-v0.1-AWQ
54
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. Inputs 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. Outputs 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. Capabilities 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.
Updated Invalid Date
🎲
Mixtral-8x22B-v0.1-4bit
53
The Mixtral-8x22B-v0.1-4bit is a large language model (LLM) developed by the Mistral AI community. It is a 176B parameter sparse mixture of experts model that can generate human-like text. Similar to the Mixtral-8x22B and Mixtral-8x7B models, the Mixtral-8x22B-v0.1-4bit uses a sparse mixture of experts architecture to achieve strong performance on a variety of benchmarks. Model inputs and outputs The Mixtral-8x22B-v0.1-4bit takes natural language text as input and generates fluent, human-like responses. It can be used for a wide range of language tasks such as text generation, question answering, and summarization. Inputs Natural language text prompts Outputs Coherent, human-like text continuations Responses to questions or instructions Summaries of given text Capabilities The Mixtral-8x22B-v0.1-4bit is a powerful language model capable of engaging in open-ended dialogue, answering questions, and generating human-like text. It has shown strong performance on a variety of benchmarks, outperforming models like LLaMA 2 70B on tasks like the AI2 Reasoning Challenge, HellaSwag, and Winogrande. What can I use it for? The Mixtral-8x22B-v0.1-4bit model could be useful for a wide range of natural language processing applications, such as: Chatbots and virtual assistants Content generation (articles, stories, poems, etc.) Summarization of long-form text Question answering Language translation Dialogue systems As a large language model, the Mixtral-8x22B-v0.1-4bit could be fine-tuned or used as a base for building more specialized AI applications across various domains. Things to try Some interesting things to try with the Mixtral-8x22B-v0.1-4bit model include: Experimenting with different prompting techniques to see how the model responds Evaluating the model's coherence and consistency across multiple turns of dialogue Assessing the model's ability to follow instructions and complete tasks Exploring the model's knowledge of different topics and its ability to provide informative responses Comparing the model's performance to other large language models on specific benchmarks or use cases By trying out different inputs and analyzing the outputs, you can gain a deeper understanding of the Mixtral-8x22B-v0.1-4bit's capabilities and limitations.
Updated Invalid Date