How to use from
llama.cpp
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
Use Docker
docker model run hf.co/LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
Quick Links

PaddleOCR-VL-For-Manga, GGUF

GGUF conversion of jzhang533/PaddleOCR-VL-For-Manga, a PaddleOCR-VL fine-tune for Japanese manga text. Converted for use in the browser via wllama, which needs GGUF rather than safetensors.

file size what it is
PaddleOCR-VL-manga-Q4_K_M.gguf 286 MB decoder
mmproj-Q8_0.gguf 570 MB vision projector

Both files are required.

Usage

llama-mtmd-cli -m PaddleOCR-VL-manga-Q4_K_M.gguf --mmproj mmproj-Q8_0.gguf \
  --image crop.png -p "OCR:" --jinja --temp 0

Use the prompt OCR: โ€” these weights are trained on it. The model expects a crop of a single text region, not a whole page.

Quantization

Converted with convert_hf_to_gguf.py (llama.cpp b10150) to F16, then quantized with llama-quantize to Q4_K_M. The vision projector is Q8_0.

The upstream vision config declares SiglipVisionModel; the converter's mmproj path expects PaddleOCRVisionModel, so that field was renamed before converting. No weights were altered.

On a 7-crop Japanese manga page, Q4_K_M output was character-identical to F16 except for one ambiguous handwritten kanji, so the smaller file costs nothing in practice.

Licence

Apache-2.0, inherited from PaddleOCR-VL. Credit for the fine-tune belongs to jzhang533; this repository only changes the file format.

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