Mistral-Small-Instruct-2409
Maintainer: mistralai
159
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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
Mistral-Small-Instruct-2409 is an instruction-tuned version of the Mistral family of large language models. It has 22B parameters and a vocabulary size of 32,768. The model supports function calling and has a sequence length of 128k, making it suitable for a variety of natural language tasks. Compared to similar models like Mistral-Large-Instruct-2407, the Mistral-Small-Instruct-2409 has a smaller parameter count but retains many of the advanced capabilities of its larger counterparts.
Model Inputs and Outputs
Inputs
- Text Prompts: The model accepts text prompts that can include instructions, questions, or other natural language input.
- Conditional Inputs: The model supports conditional inputs, such as providing context or additional information to guide the model's response.
Outputs
- Generated Text: The primary output of the model is generated text, which can include responses to questions, continuations of prompts, or other forms of natural language output.
- Function Calls: The model can also produce function calls, which allow it to interact with external systems or tools as part of its response.
Capabilities
The Mistral-Small-Instruct-2409 model is capable of a wide range of natural language tasks, including question answering, text generation, and language understanding. It has demonstrated strong performance on a variety of benchmarks, including the MMLU and InstructEval datasets.
One key capability of the Mistral-Small-Instruct-2409 model is its ability to follow instructions and engage in task-oriented dialogue. This makes it useful for applications where users need to interact with an AI system to complete specific tasks, such as research assistance, customer service, or creative writing support.
What Can I Use It For?
The Mistral-Small-Instruct-2409 model can be useful for a variety of applications, including:
- Research and Analysis: The model's language understanding and generation capabilities can be leveraged for tasks like summarizing research papers, answering questions about complex topics, or generating hypotheses and proposals.
- Customer Service and Virtual Assistants: The model's ability to engage in task-oriented dialogue can make it useful for building conversational AI agents that can assist users with a variety of queries and tasks.
- Content Creation: The model's text generation capabilities can be used to assist with creative writing, ideation, or other content creation tasks.
Things to Try
One interesting aspect of the Mistral-Small-Instruct-2409 model is its ability to follow instructions and engage in task-oriented dialogue. You could try providing the model with a series of prompts or instructions and see how it responds, exploring its capabilities in areas like problem-solving, task completion, or open-ended conversation.
Another interesting experiment would be to compare the performance of the Mistral-Small-Instruct-2409 model to similar models, such as the Mistral-Large-Instruct-2407, on specific tasks or benchmarks. This could help you understand the trade-offs between model size, performance, and resource requirements.
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-Large-Instruct-2407
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Mistral-Large-Instruct-2407 is an advanced 123B parameter dense Large Language Model (LLM) developed by Mistral AI. It has state-of-the-art reasoning, knowledge, and coding capabilities, and is designed to be multilingual, supporting dozens of languages including English, French, German, and Chinese. Compared to similar Mistral models like the Mistral-7B-Instruct-v0.2 and Mistral-7B-Instruct-v0.1, the Mistral-Large-Instruct-2407 offers significantly more parameters and advanced capabilities. It boasts strong performance on benchmarks like MMLU (84.0% overall) and specialized benchmarks for coding, math, and reasoning. Model Inputs and Outputs The Mistral-Large-Instruct-2407 model can handle a wide variety of inputs, from natural language prompts to structured formats like JSON. It is particularly adept at processing code-related inputs, having been trained on over 80 programming languages. Inputs Natural language prompts**: The model can accept freeform text prompts on a wide range of topics. Code snippets**: The model can understand and process code in multiple programming languages. Structured data**: The model can ingest and work with JSON and other structured data formats. Outputs Natural language responses**: The model can generate human-like responses to prompts in a variety of languages. Code generation**: The model can produce working code to solve problems or implement functionality. Structured data**: The model can output results in JSON and other structured formats. Capabilities The Mistral-Large-Instruct-2407 model excels at a wide range of tasks, from general knowledge and reasoning to specialized applications like coding and mathematical problem-solving. Its advanced capabilities are demonstrated by its strong performance on benchmarks like MMLU, MT Bench, and Human Eval. Some key capabilities of the model include: Multilingual proficiency**: The model can understand and generate text in dozens of languages, making it useful for global applications. Coding expertise**: The model's training on over 80 programming languages allows it to understand, write, and debug code with a high level of competence. Advanced reasoning**: The model's strong performance on math and reasoning benchmarks showcases its ability to tackle complex cognitive tasks. Agentic functionality**: The model can call native functions and output structured data, enabling it to be integrated into more sophisticated applications. What Can I Use It For? The Mistral-Large-Instruct-2407 model's diverse capabilities make it a versatile tool for a wide range of applications. Some potential use cases include: Multilingual chatbots and virtual assistants**: The model's multilingual abilities can power conversational AI systems that can engage with users in their preferred language. Automated code generation and debugging**: Developers can leverage the model's coding expertise to speed up software development tasks, from prototyping to troubleshooting. Intelligent document processing**: The model can be used to extract insights and generate summaries from complex, multilingual documents. Scientific and mathematical modeling**: The model's strong reasoning skills can be applied to solve advanced problems in fields like finance, engineering, and research. Things to Try Given the Mistral-Large-Instruct-2407 model's broad capabilities, there are many interesting things to explore and experiment with. Some ideas include: Multilingual knowledge transfer**: Test the model's ability to translate and apply knowledge across languages by prompting it in one language and asking for responses in another. Code generation and optimization**: Challenge the model to generate efficient, working code to solve complex programming tasks, and observe how it optimizes the solutions. Multimodal integration**: Explore ways to combine the model's language understanding with other modalities, such as images or structured data, to create more powerful AI systems. Open-ended reasoning**: Probe the model's general intelligence by presenting it with open-ended, abstract problems and observing the quality and creativity of its responses. By pushing the boundaries of what the Mistral-Large-Instruct-2407 model can do, developers and researchers can uncover new insights and applications for this powerful AI system.
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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
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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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Mistral-7B-Instruct-v0.1
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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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