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Abiray/OvisOCR2-GGUF

sourceHugging Faceapache-2.0updated 2mo agoView on Hugging Face

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OvisOCR2 - GGUF Quantizations

This repository contains GGUF format quantizations of OvisOCR2, a compact 0.8B end-to-end model for page-level document parsing. The original model was developed by ATH-MaaS by post-training Qwen3.5-0.8B to parse full document pages directly into clean Markdown (including LaTeX formulas, HTML tables, and layout components).

OvisOCR2 establishes a new state-of-the-art for compact document understanding, scoring 96.58 on OmniDocBench v1.6 and outperforming traditional, multi-stage layout analysis pipelines.

Available Files

Main Text Models

File Name	Precision / Quantization	File Size	Description
`OvisOCR2-F16.gguf`	16-bit Float	1.52 GB	Baseline unquantized model
`OvisOCR2-BF16.gguf`	16-bit Brain Float	1.52 GB	Native weight precision
`OvisOCR2-Q8_0.gguf`	8-bit	812 MB	Near-identical precision to F16
`OvisOCR2-Q6_K.gguf`	6-bit	630 MB	Excellent balance of size and accuracy
`OvisOCR2-Q5_K_M.gguf`	5-bit (Medium)	578 MB	Recommended for low-resource deployment
`OvisOCR2-Q5_K_S.gguf`	5-bit (Small)	564 MB	Highly optimized 5-bit layout
`OvisOCR2-Q4_K_M.gguf`	4-bit (Medium)	529 MB	Standard 4-bit quantization
`OvisOCR2-Q4_K_S.gguf`	4-bit (Small)	505 MB	Lightweight 4-bit footprint
`OvisOCR2-Q3_K_M.gguf`	3-bit (Medium)	466 MB	Maximum compression ratio

Multimodal Projectors (`mmproj`)

Note: Because OvisOCR2 is a vision-language model, you must download one of these image processing units alongside your choice of the text models listed above.

—mmproj-F32.gguf (402 MB) - Unquantized full precision projector.
—mmproj-F16.gguf (205 MB) - Recommended standard performance/size option.
—mmproj-BF16.gguf (207 MB) - Target alternative precision layout.

Inference Guide (`llama.cpp`)

To run multimodal OCR tasks using these GGUF files, you need to use the llama-minicpmv-cli or llama-llava-cli tool (depending on your build version of llama.cpp) to handle simultaneous image and text tokens.

Basic Command Line Example

bash

# Run parsing via llama.cpp cli tools
./llama-minicpmv-cli \
  -m OvisOCR2-Q5_K_M.gguf \
  --mmproj mmproj-F16.gguf \
  --image /path/to/your/document_page.jpg \
  -p "<|im_start|>user\nExtract all readable content from the image in natural human reading order and output the result as a single Markdown document. Format formulas as LaTeX. Format tables as HTML: <table>...</table>. Preserve the original text without translation.<|im_end|>\n<|im_start|>assistant\n" \
  -n 4096 \
  --temp 0.0