Modelpublic

padeoe/granite-docling-258M-GGUF

sourceHugging Faceapache-2.0updated 7mo agoView on Hugging Face

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Model Card

Granite-Docling-258M-GGUF

GGUF format of ibm-granite/granite-docling-258M, a multimodal document OCR model that converts document images to Docling format.

Converted with llama.cpp.

Files

File	Quant	Size	Note
`granite-docling-258M-f16.gguf`	F16	317 MB	Full precision
`granite-docling-258M-q8_0.gguf`	Q8_0	170 MB	Recommended
`mmproj-granite-docling-258M-f16.gguf`	F16	182 MB	Vision encoder (required)

Usage

CLI

bash

llama-mtmd-cli \
    --model granite-docling-258M-q8_0.gguf \
    --mmproj mmproj-granite-docling-258M-f16.gguf \
    --image document.png \
    --n-predict 4096 --ctx-size 8192 --temp 0.0 \
    -p "Convert this page to docling."

Server (OpenAI-compatible API)

bash

llama-server \
    -m granite-docling-258M-q8_0.gguf \
    --mmproj mmproj-granite-docling-258M-f16.gguf \
    --ctx-size 8192 --special --jinja \
    --host 0.0.0.0 --port 8080

Benchmark (CPU only, Q8_0)

CPU	Config	Long text (4096 tok)	Short text (50 tok)
EPYC 9654 (96C)	192 inst x 1t	1.73 img/s	29.4 img/s
EPYC 9654 (16C)	16 inst x 1t	0.67 img/s	8.68 img/s

For this small model, 1 thread per instance with max instances = core count gives best throughput.