Instructions to use LunarOilRig/PaddleOCR-VL-manga-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use LunarOilRig/PaddleOCR-VL-manga-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="LunarOilRig/PaddleOCR-VL-manga-GGUF", filename="PaddleOCR-VL-manga-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LunarOilRig/PaddleOCR-VL-manga-GGUF with 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
- LM Studio
- Jan
- Ollama
How to use LunarOilRig/PaddleOCR-VL-manga-GGUF with Ollama:
ollama run hf.co/LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
- Unsloth Studio
How to use LunarOilRig/PaddleOCR-VL-manga-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for LunarOilRig/PaddleOCR-VL-manga-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for LunarOilRig/PaddleOCR-VL-manga-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LunarOilRig/PaddleOCR-VL-manga-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use LunarOilRig/PaddleOCR-VL-manga-GGUF with Docker Model Runner:
docker model run hf.co/LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
- Lemonade
How to use LunarOilRig/PaddleOCR-VL-manga-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.PaddleOCR-VL-manga-GGUF-Q4_K_M
List all available models
lemonade list
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_MUse 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_MBuild 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_MUse Docker
docker model run hf.co/LunarOilRig/PaddleOCR-VL-manga-GGUF:Q4_K_MPaddleOCR-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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Model tree for LunarOilRig/PaddleOCR-VL-manga-GGUF
Base model
baidu/ERNIE-4.5-0.3B-Paddle
Install (macOS, Linux)
# 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