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Sponsored by
iMean AI Trip Planner
Abiray / OvisOCR2-GGUF
Model's Last Updated: July 14 2026
image-text-to-text
Introduction of OvisOCR2-GGUF
Model Details of OvisOCR2-GGUF
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
# 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
Runs of Abiray OvisOCR2-GGUF on huggingface.co
6.4K
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24-hour runs
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30-day runs
More Information About OvisOCR2-GGUF huggingface.co Model
Url of OvisOCR2-GGUF
OvisOCR2-GGUF huggingface.co Url
Provider of OvisOCR2-GGUF huggingface.co
Other API from Abiray
Updated:August 08 2026
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