kosmos-2.5

kosmos-2.5

microsoft

Kosmos-2.5 is Microsoft's 1.37B parameter multimodal literate model for processing text-rich images, featuring OCR and markdown generation capabilities

PropertyValue
Parameter Count1.37B
LicenseMIT
PaperView Paper
Tensor TypeF32

What is kosmos-2.5?

Kosmos-2.5 is Microsoft's advanced multimodal literate model designed specifically for processing text-intensive images. It represents a significant advancement in document AI, combining both OCR capabilities and structured text generation into a single unified system. The model employs a decoder-only auto-regressive Transformer architecture to handle complex document understanding tasks.

Implementation Details

Built on a shared decoder-only architecture, Kosmos-2.5 processes input through task-specific prompts and flexible text representations. The model operates at 1.37B parameters and uses F32 tensor types for computations.

  • Pre-trained on large-scale text-intensive image datasets
  • Implements spatially-aware text block generation
  • Features markdown format output generation
  • Supports supervised fine-tuning for various text-intensive tasks

Core Capabilities

  • Precise OCR with spatial coordinate mapping
  • Structured markdown text generation
  • Document-level text recognition
  • Adaptive task handling through different prompts
  • Support for real-world document processing applications

Frequently Asked Questions

Q: What makes this model unique?

Kosmos-2.5's uniqueness lies in its dual capability to both recognize text with spatial awareness and generate structured markdown output, all within a single model architecture. This combination makes it particularly valuable for document processing tasks.

Q: What are the recommended use cases?

The model is ideal for document digitization, content extraction from images, automated document processing, and any application requiring both text recognition and structured output generation. However, users should be aware of potential hallucination risks in the generation process.

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