Mistral-7B-Instruct-v0.1

Maintainer: mistralai

Total Score

1.4K

Last updated 4/28/2024

💬

PropertyValue
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-Instruct-v0.1 is a Large Language Model (LLM) that has been fine-tuned on a variety of publicly available conversation datasets to provide instructional and task-oriented capabilities. It is based on the Mistral-7B-v0.1 generative text model. The model uses grouped-query attention, sliding-window attention, and a byte-fallback BPE tokenizer as key architectural choices.

Similar models from the Mistral team include the Mistral-7B-Instruct-v0.2, which has a larger context window and different attention mechanisms, as well as the Mixtral-8x7B-Instruct-v0.1, a sparse mixture of experts model.

Model inputs and outputs

Inputs

  • Prompts surrounded by [INST] and [/INST] tokens, with the first instruction beginning with a begin-of-sentence token

Outputs

  • Instructional and task-oriented text generated by the model, terminated by an end-of-sentence token

Capabilities

The Mistral-7B-Instruct-v0.1 model is capable of engaging in dialogue and completing a variety of tasks based on the provided instructions. It can generate coherent and contextually relevant responses, drawing upon its broad knowledge base. However, the model does not currently have any moderation mechanisms in place, so users should be mindful of potential limitations.

What can I use it for?

The Mistral-7B-Instruct-v0.1 model can be useful for building conversational AI assistants, content generation tools, and other applications that require task-oriented language generation. Potential use cases include customer service chatbots, creative writing aids, and educational applications. By leveraging the model's instructional fine-tuning, developers can create experiences that are more intuitive and responsive to user needs.

Things to try

Experiment with different instructional formats and prompts to see how the model responds. Try asking it to complete specific tasks, such as summarizing a passage of text or generating a recipe. Pay attention to the model's coherence, relevance, and ability to follow instructions, and consider how you might integrate it into your own projects.



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-Instruct-v0.2

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The Mistral-7B-Instruct-v0.2 is a Large Language Model (LLM) that has been fine-tuned for instruction following. It is an improved version of the Mistral-7B-Instruct-v0.1 model, with a larger context window of 32k (compared to 8k in v0.1), a higher Rope-theta value, and without Sliding-Window Attention. These changes are detailed in the release blog post. The Mistral-7B-v0.2 model is the base on which this instruct-tuned version is built. Model inputs and outputs The Mistral-7B-Instruct-v0.2 model is designed to follow instructions provided in a specific format. The prompt should be surrounded by [INST] and [/INST] tokens, with the first instruction beginning with a begin-of-sentence id. Subsequent instructions do not need the begin-of-sentence id, and the generation will be ended by the end-of-sentence token. Inputs Prompts formatted with [INST] and [/INST] tokens, with the first instruction starting with a begin-of-sentence id. Outputs Responses generated by the model based on the provided instructions. Capabilities The Mistral-7B-Instruct-v0.2 model is capable of following a wide range of instructions, from answering questions to generating creative content. It can be particularly useful for tasks that require natural language understanding and generation, such as chatbots, virtual assistants, and content creation. What can I use it for? The Mistral-7B-Instruct-v0.2 model can be used for a variety of applications, such as: Building conversational AI agents and chatbots Generating creative content like stories, poems, and scripts Answering questions and providing information on a wide range of topics Assisting with research and analysis by summarizing information or generating insights Automating tasks that require natural language processing, such as customer service or content moderation Things to try Some interesting things to try with the Mistral-7B-Instruct-v0.2 model include: Exploring its ability to follow complex, multi-step instructions Experimenting with different prompt formats and styles to see how it responds Evaluating its performance on specialized tasks or domains, such as coding, math, or creative writing Comparing its capabilities to other instruct-tuned language models, such as the Mistral-7B-Instruct-v0.1 or Mixtral-8x7B-Instruct-v0.1 models.

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Mistral-7B-Instruct-v0.3

mistralai

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The Mistral-7B-Instruct-v0.3 is a Large Language Model (LLM) developed by Mistral AI. It is an improved version of the Mistral-7B-Instruct-v0.2 model, with an extended vocabulary of 32,768 tokens, support for v3 tokenization, and function calling capabilities. The model was fine-tuned on a variety of publicly available conversation datasets to imbue it with instruction-following abilities. In contrast, the Mistral-7B-v0.2 model has a smaller context window of 8k and lacks sliding-window attention. Model inputs and outputs The Mistral-7B-Instruct-v0.3 model takes text inputs in a specific format, with instructions wrapped in [INST] and [/INST] tags. The first instruction should begin with a begin-of-sentence token, while subsequent instructions should not. The model's outputs are generated text, terminated by an end-of-sentence token. Inputs Instructional text**: Text inputs wrapped in [INST] and [/INST] tags, with the first instruction beginning with a begin-of-sentence token. Outputs Generated text**: The model's response to the provided instruction, terminated by an end-of-sentence token. Capabilities The Mistral-7B-Instruct-v0.3 model is capable of understanding and following instructions, generating coherent and relevant text. It can be used for a variety of tasks, such as question answering, summarization, and task completion. What can I use it for? The Mistral-7B-Instruct-v0.3 model can be used for a wide range of natural language processing tasks, such as: Content generation**: The model can be used to generate informative and engaging content, such as articles, stories, or product descriptions. Conversational AI**: The model's instruction-following capabilities make it well-suited for building chatbots and virtual assistants. Task completion**: The model can be used to complete various types of tasks, such as research, analysis, or creative projects, based on provided instructions. Things to try One interesting aspect of the Mistral-7B-Instruct-v0.3 model is its function calling capability, which allows the model to interact with external tools or APIs to gather information or perform specific actions. This functionality can be leveraged to build more advanced applications that seamlessly integrate the model's language understanding with external data sources or services.

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AI model preview image

mistral-7b-instruct-v0.1

mistralai

Total Score

898

The Mistral-7B-Instruct-v0.1 is a Large Language Model (LLM) that has been fine-tuned on a variety of publicly available conversation datasets to provide instructional and task-oriented capabilities. It is based on the Mistral-7B-v0.1 generative text model. The model uses grouped-query attention, sliding-window attention, and a byte-fallback BPE tokenizer as key architectural choices. Similar models from the Mistral team include the Mistral-7B-Instruct-v0.2, which has a larger context window and different attention mechanisms, as well as the Mixtral-8x7B-Instruct-v0.1, a sparse mixture of experts model. Model inputs and outputs Inputs Prompts surrounded by [INST] and [/INST] tokens, with the first instruction beginning with a begin-of-sentence token Outputs Instructional and task-oriented text generated by the model, terminated by an end-of-sentence token Capabilities The Mistral-7B-Instruct-v0.1 model is capable of engaging in dialogue and completing a variety of tasks based on the provided instructions. It can generate coherent and contextually relevant responses, drawing upon its broad knowledge base. However, the model does not currently have any moderation mechanisms in place, so users should be mindful of potential limitations. What can I use it for? The Mistral-7B-Instruct-v0.1 model can be useful for building conversational AI assistants, content generation tools, and other applications that require task-oriented language generation. Potential use cases include customer service chatbots, creative writing aids, and educational applications. By leveraging the model's instructional fine-tuning, developers can create experiences that are more intuitive and responsive to user needs. Things to try Experiment with different instructional formats and prompts to see how the model responds. Try asking it to complete specific tasks, such as summarizing a passage of text or generating a recipe. Pay attention to the model's coherence, relevance, and ability to follow instructions, and consider how you might integrate it into your own projects.

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Mixtral-8x22B-Instruct-v0.1

mistralai

Total Score

477

The Mixtral-8x22B-Instruct-v0.1 is a Large Language Model (LLM) that has been instruct fine-tuned by the Mistral AI team. It is an extension of the Mixtral-8x22B-v0.1 model, which is a pretrained generative Sparse Mixture of Experts. The Mixtral-8x22B-Instruct-v0.1 model aims to be a helpful AI assistant that can engage in dialogue and assist with a variety of tasks. Model inputs and outputs The Mixtral-8x22B-Instruct-v0.1 model takes textual prompts as input and generates textual responses. The input prompts should be formatted with [INST] and [/INST] tokens to indicate the instructional context. The model can then generate responses that are tailored to the specific instruction provided. Inputs Textual prompts surrounded by [INST] and [/INST] tokens to indicate the instructional context Outputs Textual responses generated by the model based on the provided instruction Capabilities The Mixtral-8x22B-Instruct-v0.1 model is capable of engaging in natural language dialogue and assisting with a variety of tasks. It can provide helpful information, answer questions, and generate text in response to specific instructions. The model has been trained on a diverse set of data, allowing it to converse on a wide range of topics. What can I use it for? The Mixtral-8x22B-Instruct-v0.1 model can be used for a variety of applications, such as: Building conversational AI assistants Generating text content (e.g., articles, stories, scripts) Providing task-oriented assistance (e.g., research, analysis, problem-solving) Enhancing existing applications with natural language capabilities The Mistral-7B-Instruct-v0.2 and Mistral-7B-Instruct-v0.1 models from the same maintainer are similar and can also be explored for related use cases. Things to try One interesting aspect of the Mixtral-8x22B-Instruct-v0.1 model is its ability to handle complex instructions and engage in multi-turn dialogues. You could try providing the model with a series of related instructions and see how it responds, maintaining context and coherence throughout the conversation. Another interesting experiment would be to provide the model with specific task-oriented instructions, such as generating a business plan, writing a research paper, or solving a coding problem. Observe how the model's responses adapt to the given task and the level of detail and quality it provides.

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