Mistral-Nemo-Instruct-2407
Maintainer: mistralai
972
🤷
| 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 Mistral-Nemo-Instruct-2407 is a Large Language Model (LLM) that has been fine-tuned for instructional tasks. It is an instruct version of the Mistral-Nemo-Base-2407 model, which was jointly trained by Mistral AI and NVIDIA. The Mistral-Nemo-Instruct-2407 model significantly outperforms existing models of similar or smaller size.
Model Inputs and Outputs
The Mistral-Nemo-Instruct-2407 model takes text inputs and generates text outputs. It can be used for a variety of natural language processing tasks, including:
Inputs
- Free-form text prompts
Outputs
- Coherent, contextual text completions
- Responses to instructions or prompts
Capabilities
The Mistral-Nemo-Instruct-2407 model has strong capabilities in areas such as reasoning, knowledge, and coding. It performs well on a variety of benchmark tasks, including HellaSwag, Winogrande, OpenBookQA, CommonSenseQA, and TriviaQA.
What Can I Use It For?
The Mistral-Nemo-Instruct-2407 model can be used for a wide range of natural language processing applications, such as:
- Content Generation: Generating coherent and contextual text, including stories, articles, and other creative content.
- Question Answering: Answering questions on a variety of topics by drawing upon its broad knowledge base.
- Instructional Tasks: Following and executing complex instructions or prompts, such as those related to coding, math, or task planning.
Things to Try
Some interesting things to try with the Mistral-Nemo-Instruct-2407 model include:
- Experimenting with different prompting strategies to see how the model responds to various types of instructions or queries.
- Exploring the model's multilingual capabilities by providing prompts in different languages.
- Testing the model's coding and reasoning abilities by presenting it with math problems, coding challenges, or open-ended questions that require logical thinking.
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
📊
Mistral-Nemo-Base-2407
232
The Mistral-Nemo-Base-2407 is a 12 billion parameter Large Language Model (LLM) jointly developed by Mistral AI and NVIDIA. It significantly outperforms existing models of similar size, thanks to its large training dataset that includes a high proportion of multilingual and code data. The model is released under the Apache 2 License and offers both pre-trained and instructed versions. Compared to similar models from Mistral, such as the Mistral-7B-v0.1 and Mistral-7B-v0.3, the Mistral-Nemo-Base-2407 has more than 12 billion parameters and a larger 128k context window. It also incorporates architectural choices like Grouped-Query Attention, Sliding-Window Attention, and a Byte-fallback BPE tokenizer. Model Inputs and Outputs The Mistral-Nemo-Base-2407 is a text-to-text model, meaning it takes text as input and generates text as output. The model can be used for a variety of natural language processing tasks, such as language generation, text summarization, and question answering. Inputs Text prompts Outputs Generated text Capabilities The Mistral-Nemo-Base-2407 model has demonstrated strong performance on a range of benchmarks, including HellaSwag, Winogrande, OpenBookQA, CommonSenseQA, TruthfulQA, and MMLU. It also exhibits impressive multilingual capabilities, scoring well on MMLU benchmarks across multiple languages such as French, German, Spanish, Italian, Portuguese, Russian, Chinese, and Japanese. What Can I Use It For? The Mistral-Nemo-Base-2407 model can be used for a variety of natural language processing tasks, such as: Content Generation**: The model can be used to generate high-quality text, such as articles, stories, or product descriptions. Question Answering**: The model can be used to answer questions on a wide range of topics, making it useful for building conversational agents or knowledge-sharing applications. Text Summarization**: The model can be used to summarize long-form text, such as news articles or research papers, into concise and informative summaries. Code Generation**: The model's training on a large proportion of code data makes it a potential candidate for tasks like code completion or code generation. Things to Try One interesting aspect of the Mistral-Nemo-Base-2407 model is its large 128k context window, which allows it to maintain coherence and understanding over longer stretches of text. This could be particularly useful for tasks that require reasoning over extended context, such as multi-step problem-solving or long-form dialogue. Researchers and developers may also want to explore the model's multilingual capabilities and see how it performs on specialized tasks or domains that require cross-lingual understanding or generation.
Updated Invalid Date
🌿
Mistral-Large-Instruct-2407
692
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.
Updated Invalid Date
🤔
Mistral-NeMo-12B-Instruct
121
Mistral-NeMo-12B-Instruct is a large language model (LLM) composed of 12 billion parameters, trained jointly by NVIDIA and Mistral AI. It significantly outperforms existing models of similar or smaller size. The model is available in both pre-trained and instructed versions, and is trained with a large 128k context window. It also comes with a FP8 quantized version that maintains accuracy. A notable feature is that the model is trained on a large proportion of multilingual and code data. Similar models from Mistral AI include the Mistral-Nemo-Instruct-2407, Mistral-Nemo-Base-2407, Mistral-Large-Instruct-2407, and earlier versions of the Mistral-7B models. All of these share common architectural choices like a transformer decoder, rotary embeddings, and a large vocabulary size. Model inputs and outputs Inputs Text prompt**: The model takes a text prompt as input, which can be in multiple languages. Outputs Generated text**: The model outputs generated text in response to the input prompt. The output can be in multiple languages and can include code as well as natural language. Capabilities Mistral-NeMo-12B-Instruct has strong capabilities across a wide range of natural language tasks, including language generation, translation, question answering, and text summarization. It also exhibits impressive abilities in code generation and reasoning. The model's large size and diverse training data allow it to perform well on a variety of benchmarks, often outperforming smaller models. What can I use it for? The Mistral-NeMo-12B-Instruct model can be used for a variety of applications, such as building chatbots, virtual assistants, and language-based AI applications. Its capabilities in code generation and reasoning make it well-suited for tasks like programming assistance, technical writing, and even creative problem-solving. The model's multilingual abilities also enable cross-language applications, such as translation services and international customer support. Things to try One interesting thing to try with Mistral-NeMo-12B-Instruct is prompt engineering - experimenting with different input prompts to see how the model responds and what kinds of outputs it generates. The model's strong reasoning and language generation abilities mean that it can be used to tackle a wide variety of tasks, from open-ended conversation to task-oriented problem-solving. Developers and researchers may also want to explore the model's potential for few-shot or zero-shot learning, where it can be fine-tuned or adapted to new domains and tasks with minimal additional training.
Updated Invalid Date
🗣️
Mistral-7B-Instruct-v0.2
2.1K
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.
Updated Invalid Date