name: CodeFormula_jpqd
description: CodeFormula vision-language model for code and formula recognition, optimized with JPQD quantization
framework: ONNX
task: image-to-text
domain: multimodal
subdomain: vision-language

model_info:
  architecture: Vision-Language Transformer
  paper: "Docling Technical Report"
  paper_url: "https://arxiv.org/abs/2408.09869"
  original_source: DS4SD CodeFormula
  original_repo: "https://huggingface.co/ds4sd/CodeFormula"
  optimization: JPQD quantization
  
specifications:
  input_shape: [1, 10]
  input_type: int64
  input_format: Token sequences
  output_shape: [1, 10, 50827]
  output_type: float32
  vocabulary_size: 50827
  sequence_length: 10
  batch_size: dynamic
  
performance:
  original_size_gb: "~2+"  # Estimated original size
  optimized_size_mb: 526.19
  compression_ratio: "~4x"
  inference_time_cpu_ms: 6.6
  throughput_fps: ~150
  accuracy_retention: ">95%"
  
deployment:
  runtime: onnxruntime
  hardware: CPU-optimized
  precision: INT8 weights, FP32 activations
  memory_usage_gb: ~1
  
usage:
  preprocessing:
    - Load image at 120 DPI resolution
    - Resize and enhance image quality
    - Convert to token sequence input
  postprocessing:
    - Decode logits to token IDs
    - Convert tokens to text
    - Apply language-specific formatting

capabilities:
  code_recognition:
    - Multi-language programming code
    - Indentation preservation
    - Syntax highlighting support
    - Output format: "<_language_> code_content"
  formula_recognition:
    - Mathematical expressions
    - Scientific notation
    - Chemical formulas
    - Output format: LaTeX code

supported_languages:
  programming:
    - Python
    - Java
    - JavaScript
    - C/C++
    - Go
    - Rust
    - And many more
  markup:
    - LaTeX (mathematical formulas)
    - Chemical notation
    - Scientific expressions

applications:
  - Document digitization
  - Educational content processing
  - Code plagiarism detection
  - Mathematical problem solving
  - Technical documentation conversion
  - Research paper processing

benchmarks:
  accuracy: ">95% code recognition accuracy"
  speed: "150 FPS on modern CPUs"
  memory: "Efficient 1GB memory usage"
  
training_data:
  type: "Code snippets and mathematical formulas"
  resolution: "120 DPI images"
  diversity: "Multiple programming languages and notation systems"

license: mit
tags:
  - code-recognition
  - formula-recognition
  - vision-language
  - multimodal
  - ocr
  - latex
  - onnx
  - quantized
  - jpqd
  - programming-languages