AI DAMN/Pixtral Large: Advanced Multimodal AI Model

Pixtral Large: Advanced Multimodal AI Model

date
Nov 19, 2024
damn
language
en
status
Published
type
Products
image
https://www.ai-damn.com/1732001266678-202411190851426013.jpg
slug
pixtral-large-advanced-multimodal-ai-model-1732001276914
tags
AI Model
Multimodal
Image Understanding
Text Processing
summary
Pixtral Large is a cutting-edge multimodal AI model developed by Mistral AI, designed to enhance image and text understanding. With exceptional performance in various benchmarks, it caters to both research and commercial applications, making it a powerful tool for enterprises and developers alike.
notion image
 

Product Introduction

 
Pixtral Large is a state-of-the-art multimodal AI model introduced by Mistral AI, enhancing the understanding of images and text. Built on the foundation of Mistral Large 2, this model excels at interpreting documents, charts, and natural images, making it ideal for various applications in research and industry.
 

Key Features

 
  • Multimodal Performance: Capable of understanding documents, charts, and natural images.
  • Leading Text Understanding: Retains and enhances the text understanding capabilities of Mistral Large 2.
  • Model Size: Features a 123B multimodal decoder alongside a 1B parameter visual encoder.
  • Context Window: Supports a 128K context window, suitable for high-resolution images.
  • Multilingual OCR and Inference: Processes multilingual inputs and performs reasoning effectively.
  • Chart Understanding: Capable of analyzing charts and providing accurate interpretations for data representation.
  • Enterprise Applications: Well-suited for knowledge exploration and enhancing automation in business processes.

Product Data

 
  • Model Version: Pixtral Large, based on Mistral Large 2.
  • Licensing: Available under Mistral Research License (MRL) for research and educational purposes and Mistral Commercial License for commercial use.
  • Benchmark Performance: Outperforms models in tests such as MathVista, ChartQA, and DocVQA, and has shown competitiveness in MM-MT-Bench.

Product Link

 

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