Mistral-community

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Mixtral-8x22B-v0.1

mistral-community

Total Score

668

The Mixtral-8x22B-v0.1 is a Large Language Model (LLM) developed by the Mistral AI team. It is a pretrained generative Sparse Mixture of Experts model, which means it uses a specialized architecture to improve performance and efficiency. The Mixtral-8x22B builds upon the Mixtral-8x7B-v0.1 model, increasing the parameter count to 22 billion. Model inputs and outputs The Mixtral-8x22B-v0.1 model takes text inputs and generates text outputs. It can be used for a variety of natural language processing tasks, such as: Inputs Text prompts for the model to continue or expand upon Outputs Continuation of the input text Responses to the input prompt Synthetic text generated based on the input Capabilities The Mixtral-8x22B-v0.1 model demonstrates impressive language generation capabilities, producing coherent and contextually relevant text. It can be used for tasks like language modeling, text summarization, and open-ended dialogue. What can I use it for? The Mixtral-8x22B-v0.1 model can be a powerful tool for a variety of applications, such as: Chatbots and virtual assistants Content generation for marketing, journalism, or creative writing Augmenting human creativity and ideation Prototyping new language models and AI systems Things to try One interesting aspect of the Mixtral-8x22B-v0.1 model is its ability to be optimized for different use cases and hardware constraints. The provided examples demonstrate how to load the model in half-precision, 8-bit, and 4-bit precision, as well as with Flash Attention 2, allowing for more efficient inference on a variety of devices.

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Updated 5/27/2024

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pixtral-12b-240910

mistral-community

Total Score

362

pixtral-12b-240910 This model checkpoint is provided as-is and might not be up-to-date. It mirrors the torrent released by Mistral AI and uploaded by the community. Downloaded from the magnet link: magnet:?xt=urn:btih:7278e625de2b1da598b23954c13933047126238a&dn=pixtral-12b-240910&tr=udp%3A%2F%http://2Ftracker.opentrackr.org%3A1337%2Fannounce&tr=udp%3A%2F%http://2Fopen.demonii.com%3A1337%2Fannounce&tr=http%3A%2F%http://2Ftracker.ipv6tracker.org%3A80%2Fannounce Published by MistralAI in twitter/X: https://x.com/MistralAI/status/1833758285167722836 Release information: https://github.com/mistralai/mistral-common/releases/tag/v1.4.0 Pixtral is out! Mistral common has image support! You can now pass images and URLs alongside text into the user message. pip install --upgrade mistral_common To use the model checkpoint: pip install huggingface-hub from huggingface_hub import snapshot_download snapshot_download(repo_id="mistral-community/pixtral-12b-240910", local_dir="...") PIXTRAL - 12B - v0.1 10/09/24 md5sum b8e9126ef0c15a1130c14b15e8432a67 consolidated.safetensors 68b39355a7b14a7d653292dab340a0be params.json 10229adc84036ff8fe44a2a8e2ad9ba9 tekken.json Released by the Mistral AI team - Use GELU for the vision adapter - Use 2D ROPE for the vision encoder Images You can encode images as follows from mistral_common.protocol.instruct.messages import ( UserMessage, TextChunk, ImageURLChunk, ImageChunk, ) from PIL import Image from mistral_common.protocol.instruct.request import ChatCompletionRequest from mistral_common.tokens.tokenizers.mistral import MistralTokenizer tokenizer = MistralTokenizer.from_model("pixtral") image = Image.new('RGB', (64, 64)) tokenize images and text tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ UserMessage( content=[ TextChunk(text="Describe this image"), ImageChunk(image=image), ] ) ], model="pixtral", ) ) tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images Count the number of tokens print("# tokens", len(tokens)) print("# images", len(images)) Image URLs You can pass image url which will be automatically downloaded url_dog = "https://picsum.photos/id/237/200/300" url_mountain = "https://picsum.photos/seed/picsum/200/300" tokenize image urls and text tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ UserMessage( content=[ TextChunk(text="Can this animal"), ImageURLChunk(image_url=url_dog), TextChunk(text="live here?"), ImageURLChunk(image_url=url_mountain), ] ) ], model="pixtral", ) ) tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images Count the number of tokens print("# tokens", len(tokens)) print("# images", len(images)) ImageData You can also pass image encoded as base64 tokenized = tokenizer.encode_chat_completion( ChatCompletionRequest( messages=[ UserMessage( content=[ TextChunk(text="What is this?"), 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"), ] ) ], model="pixtral", ) ) tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images Count the number of tokens print("# tokens", len(tokens)) print("# images", len(images))

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Updated 9/19/2024

Text-to-Image

🌿

Mistral-7B-v0.2

mistral-community

Total Score

224

The Mistral-7B-v0.2 is a large language model from the Mistral AI community. It is a 7 billion parameter model that has been converted to the HuggingFace Transformers format. Compared to the previous version, Mistral-7B-v0.1, this model has a larger context window of 32k tokens and some architectural changes. The model can be fine-tuned using the provided instructions to create specialized models like Mistral-7B-Instruct-v0.2. Model inputs and outputs The Mistral-7B-v0.2 model is a text-to-text transformer model. 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, question answering, and text summarization. Inputs Text prompts of varying lengths Outputs Generated text continuations of the input prompts Capabilities The Mistral-7B-v0.2 model is capable of generating coherent and contextually relevant text. It can be used to assist with a wide range of language-based tasks, from creative writing to question answering. The model's large size and architectural improvements over the previous version allow it to capture more complex linguistic patterns and produce more nuanced and natural-sounding outputs. What can I use it for? The Mistral-7B-v0.2 model can be used for a variety of applications, such as: Content Generation**: The model can be used to generate articles, stories, scripts, or any other type of text-based content. Conversational AI**: The model can be fine-tuned on dialogue data to create virtual assistants or chatbots that can engage in natural conversations. Question Answering**: The model can be used to answer a wide range of questions by generating relevant and informative responses. Text Summarization**: The model can be used to condense longer text into concise summaries. Things to try One interesting aspect of the Mistral-7B-v0.2 model is its ability to seamlessly handle context and maintain coherence over longer sequences of text. This makes it well-suited for tasks that require understanding and reasoning about complex, multi-sentence inputs. Try using the model to generate extended responses to open-ended prompts, and see how it is able to build upon and expand the initial input in a logical and natural way.

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Updated 5/28/2024

Text-to-Text

🎲

Mixtral-8x22B-v0.1-4bit

mistral-community

Total Score

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.

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Updated 5/28/2024

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