Mistralai

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mixtral-8x7b-instruct-v0.1

mistralai

Total Score

7.5K

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.

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Updated 5/27/2024

🏋️

Mixtral-8x7B-Instruct-v0.1

mistralai

Total Score

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.

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Updated 4/28/2024

🔮

Mistral-7B-v0.1

mistralai

Total Score

3.1K

The Mistral-7B-v0.1 is a Large Language Model (LLM) with 7 billion parameters, developed by Mistral AI. It is a pretrained generative text model that outperforms the Llama 2 13B model on various benchmarks. The model is based on a transformer architecture with several key design choices, including Grouped-Query Attention, Sliding-Window Attention, and a Byte-fallback BPE tokenizer. Similar models from Mistral AI include the Mixtral-8x7B-v0.1, a pretrained generative Sparse Mixture of Experts model that outperforms Llama 2 70B, and the Mistral-7B-Instruct-v0.1 and Mistral-7B-Instruct-v0.2 models, which are instruct fine-tuned versions of the base Mistral-7B-v0.1 model. Model inputs and outputs Inputs Text**: The Mistral-7B-v0.1 model takes raw text as input, which can be used to generate new text outputs. Outputs Generated text**: The model can be used to generate novel text outputs based on the provided input. Capabilities The Mistral-7B-v0.1 model is a powerful generative language model that can be used for a variety of text-related tasks, such as: Content generation**: The model can be used to generate coherent and contextually relevant text on a wide range of topics. Question answering**: The model can be fine-tuned to answer questions based on provided context. Summarization**: The model can be used to summarize longer text inputs into concise summaries. What can I use it for? The Mistral-7B-v0.1 model can be used for a variety of applications, such as: Chatbots and conversational agents**: The model can be used to build chatbots and conversational AI assistants that can engage in natural language interactions. Content creation**: The model can be used to generate content for blogs, articles, or other written materials. Personalized content recommendations**: The model can be used to generate personalized content recommendations based on user preferences and interests. Things to try Some interesting things to try with the Mistral-7B-v0.1 model include: Exploring the model's reasoning and decision-making abilities**: Prompt the model with open-ended questions or prompts and observe how it responds and the thought process it displays. Experimenting with different model optimization techniques**: Try running the model in different precision formats, such as half-precision or 8-bit, to see how it affects performance and resource requirements. Evaluating the model's performance on specific tasks**: Fine-tune the model on specific datasets or tasks and compare its performance to other models or human-level benchmarks.

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Updated 4/29/2024

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mistral-7b-instruct-v0.2

mistralai

Total Score

2.6K

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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Updated 5/27/2024

🗣️

Mistral-7B-Instruct-v0.2

mistralai

Total Score

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.

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Updated 4/28/2024

📉

Mixtral-8x7B-v0.1

mistralai

Total Score

1.5K

The Mixtral-8x7B-v0.1 is a Large Language Model (LLM) developed by Mistral AI. It is a pretrained generative Sparse Mixture of Experts model that outperforms the Llama 2 70B model on most benchmarks tested. The model is available through the Hugging Face Transformers library and can be run in various precision levels to optimize memory and compute requirements. The Mixtral-8x7B-v0.1 is part of a family of Mistral models, including the mixtral-8x7b-instruct-v0.1, Mistral-7B-Instruct-v0.2, mixtral-8x7b-32kseqlen, mistral-7b-v0.1, and mistral-7b-instruct-v0.1. Model inputs and outputs Inputs Text**: The model takes text inputs and generates corresponding outputs. Outputs Text**: The model generates text outputs based on the provided inputs. Capabilities The Mixtral-8x7B-v0.1 model demonstrates strong performance on a variety of benchmarks, outperforming the Llama 2 70B model. It can be used for tasks such as language generation, text completion, and question answering. What can I use it for? The Mixtral-8x7B-v0.1 model can be used for a wide range of applications, including content generation, language modeling, and chatbot development. The model's capabilities make it well-suited for projects that require high-quality text generation, such as creative writing, summarization, and dialogue systems. Things to try Experiment with the model's capabilities by providing it with different types of text inputs and observe the generated outputs. You can also fine-tune the model on your specific data to further enhance its performance for your use case.

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Updated 4/28/2024

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mistral-7b-v0.1

mistralai

Total Score

1.5K

The Mistral-7B-v0.1 is a Large Language Model (LLM) with 7 billion parameters, developed by Mistral AI. It is a pretrained generative text model that outperforms the Llama 2 13B model on various benchmarks. The model is based on a transformer architecture with several key design choices, including Grouped-Query Attention, Sliding-Window Attention, and a Byte-fallback BPE tokenizer. Similar models from Mistral AI include the Mixtral-8x7B-v0.1, a pretrained generative Sparse Mixture of Experts model that outperforms Llama 2 70B, and the Mistral-7B-Instruct-v0.1 and Mistral-7B-Instruct-v0.2 models, which are instruct fine-tuned versions of the base Mistral-7B-v0.1 model. Model inputs and outputs Inputs Text**: The Mistral-7B-v0.1 model takes raw text as input, which can be used to generate new text outputs. Outputs Generated text**: The model can be used to generate novel text outputs based on the provided input. Capabilities The Mistral-7B-v0.1 model is a powerful generative language model that can be used for a variety of text-related tasks, such as: Content generation**: The model can be used to generate coherent and contextually relevant text on a wide range of topics. Question answering**: The model can be fine-tuned to answer questions based on provided context. Summarization**: The model can be used to summarize longer text inputs into concise summaries. What can I use it for? The Mistral-7B-v0.1 model can be used for a variety of applications, such as: Chatbots and conversational agents**: The model can be used to build chatbots and conversational AI assistants that can engage in natural language interactions. Content creation**: The model can be used to generate content for blogs, articles, or other written materials. Personalized content recommendations**: The model can be used to generate personalized content recommendations based on user preferences and interests. Things to try Some interesting things to try with the Mistral-7B-v0.1 model include: Exploring the model's reasoning and decision-making abilities**: Prompt the model with open-ended questions or prompts and observe how it responds and the thought process it displays. Experimenting with different model optimization techniques**: Try running the model in different precision formats, such as half-precision or 8-bit, to see how it affects performance and resource requirements. Evaluating the model's performance on specific tasks**: Fine-tune the model on specific datasets or tasks and compare its performance to other models or human-level benchmarks.

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Updated 5/27/2024

💬

Mistral-7B-Instruct-v0.1

mistralai

Total Score

1.4K

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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Updated 4/28/2024

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mistral-7b-instruct-v0.1

mistralai

Total Score

889

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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Updated 5/27/2024

🎯

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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Updated 4/28/2024