

Pixtral 12B
Overview :
Pixtral 12B is a multimodal AI model developed by the Mistral AI team. It comprehends natural images and documents, showcasing exceptional capabilities in multimodal task processing while also maintaining state-of-the-art performance in text benchmarks. The model supports various image sizes and aspect ratios and can process an arbitrary number of images within a long context window. It is an upgraded version of Mistral Nemo 12B, specifically designed for multimodal inference without sacrificing critical text processing abilities.
Target Users :
Pixtral 12B is designed for users who require complex image and text processing, such as data analysts, researchers, and developers. Its multimodal capabilities make it an ideal choice for handling charts, documents, and images, while maintaining high performance in text processing, suitable for scenarios that demand intricate interactions between text and images.
Use Cases
Use Pixtral 12B to analyze charts and graphs to understand data trends.
Upload documents to answer complex questions regarding the document's content.
Combine information from multiple images to generate detailed reports or summaries.
Features
Native multimodal training through interleaved image and text data.
Excels in multimodal tasks, particularly in instruction adherence.
Maintains state-of-the-art performance in text benchmarks.
Supports variable image sizes and aspect ratios.
Capable of processing multiple images within a long context window.
New visual encoder that supports natively variable image sizes.
Multimodal Transformer decoder that can handle any number of images.
How to Use
Try Pixtral 12B through the Mistral AI platform or Le Chat interface.
Select Pixtral 12B from the model list and upload the image that needs processing.
Pose questions or instructions regarding the image, and Pixtral 12B will provide answers based on the image content.
Use API calls to integrate Pixtral 12B into various applications and workflows.
Run the model locally using the mistral-inference tool by downloading the model files and loading them.
Construct requests including the image URL and text prompts, and send them to the model for processing.
Obtain the model's output results, and further process or display them as needed.
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