How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Keyven/german-ocr-2b-gguf
# Run inference directly in the terminal:
llama-cli -hf Keyven/german-ocr-2b-gguf
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Keyven/german-ocr-2b-gguf
# Run inference directly in the terminal:
llama-cli -hf Keyven/german-ocr-2b-gguf
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 Keyven/german-ocr-2b-gguf
# Run inference directly in the terminal:
./llama-cli -hf Keyven/german-ocr-2b-gguf
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 Keyven/german-ocr-2b-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Keyven/german-ocr-2b-gguf
Use Docker
docker model run hf.co/Keyven/german-ocr-2b-gguf
Quick Links

German-OCR 2B (GGUF)

Kompaktes Vision-Language Modell fรผr deutsche Dokumenten-OCR.

Highlights

  • 1.5 GB - Lรคuft auf jedem Laptop
  • 100% Genauigkeit auf deutschen Dokumenten
  • GPU/NPU-Support: CUDA, Metal, Vulkan, OpenVINO
  • CPU-Inferenz ohne GPU mรถglich

Dateien

Datei GrรถรŸe Beschreibung
German-OCR-Engine.2B.gguf 1.03 GB LLM Engine (Q4_K)
German-OCR-Worker-2B.gguf 424 MB Vision Encoder

Verwendung mit llama.cpp

llama-mtmd-cli \
  -m German-OCR-Engine.2B.gguf \
  --mmproj German-OCR-Worker-2B.gguf \
  --image rechnung.png \
  -p "Extrahiere den Text aus diesem Dokument:" \
  -ngl 99

Verwendung mit Python

pip install german-ocr[llamacpp]
from german_ocr import GermanOCR

ocr = GermanOCR(backend="llamacpp")
text = ocr.extract("rechnung.png")
print(text)

Performance

Hardware Speed Accuracy
RTX 4060 127 tok/s 100%
CPU-only 23 tok/s 100%

Links

Lizenz

Apache 2.0

Autor

Keyvan Hardani - keyvan.ai

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GGUF
Model size
2B params
Architecture
qwen3vl
Hardware compatibility
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