Instructions to use LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4",
	filename="PaddleOCR-VL-1.6-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-1.6-GGUF-Q4 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-1.6-GGUF-Q4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4: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-1.6-GGUF-Q4:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4: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-1.6-GGUF-Q4:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4: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-1.6-GGUF-Q4:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4:Q4_K_M

Use Docker

docker model run hf.co/LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4:Q4_K_M

LM Studio
Jan
Ollama
How to use LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4 with Ollama:
```
ollama run hf.co/LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4:Q4_K_M
```

Unsloth Studio

How to use LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4 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-1.6-GGUF-Q4 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-1.6-GGUF-Q4 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-1.6-GGUF-Q4 to start chatting

Atomic Chat new
Docker Model Runner
How to use LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4 with Docker Model Runner:
```
docker model run hf.co/LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4:Q4_K_M
```

Lemonade

How to use LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4:Q4_K_M

Run and chat with the model

lemonade run user.PaddleOCR-VL-1.6-GGUF-Q4-Q4_K_M

List all available models

lemonade list

PaddleOCR-VL 1.6, quantized GGUF

Quantized GGUF of PaddlePaddle/PaddleOCR-VL-1.6, for running in the browser via wllama.

The official PaddleOCR-VL-1.6-GGUF release is F16 and totals 1.73 GB. This one is 856 MB with no measurable difference in output.

file	size	what it is
`PaddleOCR-VL-1.6-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-1.6-Q4_K_M.gguf --mmproj mmproj-Q8_0.gguf \
  --image crop.png -p "OCR:" --jinja --temp 0

Use the prompt OCR:. The model expects a crop of a single text region.

Quantization

Decoder: llama-quantize Q4_K_M from the official F16 GGUF. Projector: convert_hf_to_gguf.py --mmproj --outtype q8_0 from the safetensors release (llama.cpp b10150).

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

Checked against the F16 originals on Japanese, Korean and Chinese comic pages; output was character-identical.

Licence

Apache-2.0, inherited from PaddleOCR-VL.

Downloads last month: -

GGUF

Model size

0.5B params

Architecture

paddleocr

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for LunarOilRig/PaddleOCR-VL-1.6-GGUF-Q4

Base model

baidu/ERNIE-4.5-0.3B-Paddle

Finetuned

PaddlePaddle/PaddleOCR-VL-1.5

Finetuned

PaddlePaddle/PaddleOCR-VL-1.6

Quantized

(9)

this model