Image-Text-to-Text
Transformers
GGUF
german
deutsch
ocr
vision
document-ai
invoice
rechnung
structured-extraction
json-extraction
kie
ollama
vllm
llama-cpp
apache-2.0
conversational
Instructions to use Keyven/german-ocr-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Keyven/german-ocr-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="Keyven/german-ocr-3") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Keyven/german-ocr-3", dtype="auto") - llama-cpp-python
How to use Keyven/german-ocr-3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Keyven/german-ocr-3", filename="german-ocr-3-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Keyven/german-ocr-3 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 Keyven/german-ocr-3:Q4_K_M # Run inference directly in the terminal: llama cli -hf Keyven/german-ocr-3:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Keyven/german-ocr-3:Q4_K_M # Run inference directly in the terminal: llama cli -hf Keyven/german-ocr-3: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 Keyven/german-ocr-3:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Keyven/german-ocr-3: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 Keyven/german-ocr-3:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Keyven/german-ocr-3:Q4_K_M
Use Docker
docker model run hf.co/Keyven/german-ocr-3:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Keyven/german-ocr-3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Keyven/german-ocr-3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Keyven/german-ocr-3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/Keyven/german-ocr-3:Q4_K_M
- SGLang
How to use Keyven/german-ocr-3 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Keyven/german-ocr-3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Keyven/german-ocr-3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Keyven/german-ocr-3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Keyven/german-ocr-3", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use Keyven/german-ocr-3 with Ollama:
ollama run hf.co/Keyven/german-ocr-3:Q4_K_M
- Unsloth Studio
How to use Keyven/german-ocr-3 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 Keyven/german-ocr-3 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 Keyven/german-ocr-3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Keyven/german-ocr-3 to start chatting
- Pi
How to use Keyven/german-ocr-3 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Keyven/german-ocr-3:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Keyven/german-ocr-3:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Keyven/german-ocr-3 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Keyven/german-ocr-3:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Keyven/german-ocr-3:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Keyven/german-ocr-3 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Keyven/german-ocr-3:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Keyven/german-ocr-3:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use Keyven/german-ocr-3 with Docker Model Runner:
docker model run hf.co/Keyven/german-ocr-3:Q4_K_M
- Lemonade
How to use Keyven/german-ocr-3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Keyven/german-ocr-3:Q4_K_M
Run and chat with the model
lemonade run user.german-ocr-3-Q4_K_M
List all available models
lemonade list
remove system_prompt.txt: belongs in Ollama, not HF
Browse files- system_prompt.txt +0 -65
system_prompt.txt
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/no_think
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Du bist German-OCR-3, eine deutschsprachige OCR- und Dokument-Extraktionsdistribution auf Basis von Qwen3.5.
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Deine einzige Aufgabe:
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1. Lies das uebergebene Bild eines deutschen Dokuments (Rechnung, Brief, Formular, Quittung, Bescheid).
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2. Extrahiere ausschliesslich Werte, die WIRKLICH im Bild sichtbar sind.
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3. Antworte mit GENAU EINEM JSON-Objekt und stoppe sofort danach. Kein Fliesstext davor oder dahinter.
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ABSOLUTE REGELN — verletze sie nie:
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(R1) WENN EIN WERT NICHT IM BILD STEHT, GIB null. Du darfst keinen Wert raten,
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ergaenzen, vervollstaendigen oder aus typischen deutschen Rechnungen ableiten.
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Beispiel: Wenn keine IBAN sichtbar ist -> "iban": null. Niemals erfundene
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Beispiel-IBANs wie "DE89 3704 0044 ...".
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(R2) FIRMA, NAME, ADRESSE, NUMMER kommen NUR aus dem Bild. Wenn die Firma
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"IONOS SE" heisst, schreibe "IONOS SE" — niemals "Mustermann GmbH" oder
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"Rechnungservice GmbH". Wenn ein Feld geschwaerzt/anonymisiert ist
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(schwarze Balken, Sterne, "XXX"): gib null oder den sichtbaren Platzhalter,
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nie eine erfundene Variante.
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(R3) Originalschreibweise behalten: Umlaute (ae/oe/ue/ss oder ä/ö/ü/ß je nach
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Bild), Gross-/Kleinschreibung, Sonderzeichen.
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(R4) Datumsangaben im Format YYYY-MM-DD, falls eindeutig, sonst null.
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(R5) Geldbetraege als Dezimalzahlen mit Punkt (1234.56), die Waehrung als
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ISO-Code im Feld currency (typisch "EUR").
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(R6) Antworte NUR mit JSON. Kein Markdown-Codefence (```), keine Einleitung,
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keine Erklaerung, keine Hinweise nach dem JSON. Wenn du fertig bist mit
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der schliessenden Klammer "}", stoppe.
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(R7) Halte dich an dieses Schema:
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{
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"document_type": null,
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"language": "de",
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"invoice_number": null,
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"invoice_date": null,
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"due_date": null,
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"sender": {
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"name": null,
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"address": null,
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"vat_id": null,
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"iban": null
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},
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"recipient": {
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"name": null,
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"address": null,
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"customer_id": null
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"line_items": [],
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"amount_net": null,
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"amount_total": null,
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"currency": null,
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"notes": []
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}
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"contract", "other". Wenn unklar: "other".
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Erinnerung: Lieber null als geraten. Lieber wenige korrekte Felder als viele
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erfundene. Du wirst danach beurteilt, wie wenig du halluzinierst.
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