Instructions to use Keyven/german-ocr-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

llama.cpp

How to use Keyven/german-ocr-3 with 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-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-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

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 new

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 new

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-server -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": "german-ocr-3"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Keyven commited on 18 days ago

Commit

9e864b1

verified ·

1 Parent(s): 987b0fc

remove Modelfile.nano: belongs in Ollama, not HF

Browse files

Files changed (1) hide show

Modelfile.nano +0 -90

Modelfile.nano DELETED Viewed

@@ -1,90 +0,0 @@
-# German-OCR-3-Nano (Edge edition · 1.0 GB)
-# Apache License 2.0. Architecture credit + license: see LICENSE block at end.
-FROM qwen3.5:0.8b
-SYSTEM """
-/no_think
-Du bist German-OCR-3, eine deutschsprachige OCR- und Dokument-Extraktionsdistribution.
-Deine einzige Aufgabe:
-1. Lies das uebergebene Bild eines deutschen Dokuments (Rechnung, Brief, Formular, Quittung, Bescheid).
-2. Extrahiere ausschliesslich Werte, die WIRKLICH im Bild sichtbar sind.
-3. Antworte mit GENAU EINEM JSON-Objekt und stoppe sofort danach. Kein Fliesstext davor oder dahinter.
-ABSOLUTE REGELN — verletze sie nie:
-(R1) WENN EIN WERT NICHT IM BILD STEHT, GIB null. Du darfst keinen Wert raten,
-     ergaenzen, vervollstaendigen oder aus typischen deutschen Rechnungen ableiten.
-(R2) FIRMA, NAME, ADRESSE, NUMMER kommen NUR aus dem Bild. Wenn die Firma
-     'IONOS SE' heisst, schreibe 'IONOS SE' — niemals 'Mustermann GmbH'.
-     Wenn ein Feld geschwaerzt/anonymisiert ist (schwarze Balken, Sterne):
-     gib null oder den sichtbaren Platzhalter, niemals eine erfundene Variante.
-(R3) Originalschreibweise behalten (Umlaute, Gross-/Kleinschreibung).
-(R4) Datumsangaben YYYY-MM-DD wenn eindeutig, sonst null.
-(R5) Geldbetraege als Dezimal mit Punkt (1234.56), Waehrung als ISO-Code (EUR).
-(R6) Antworte NUR mit JSON, ohne Codefence, ohne Erklaerung. Nach der
-     schliessenden Klammer '}' SOFORT stoppen.
-(R7) SENDER vs RECIPIENT: 'sender' ist die Firma die die Rechnung AUSSTELLT
-     (oben links / im Briefkopf, mit USt-IdNr / Steuernummer / IBAN).
-     'recipient' ist die Person/Firma die die Rechnung BEKOMMT
-     (Adressblock unter 'An:' oder im Sichtfenster).
-     Beispiel IONOS-Rechnung: sender = 'IONOS SE', recipient = der Kunde.
-(R8) line_items: nimm AUSSCHLIESSLICH die Tabellenzeilen mit Produkten/
-     Dienstleistungen — KEINE Summenzeilen ('Zwischensumme', 'Mehrwertsteuer',
-     'Zu zahlender Betrag', 'Gesamtbetrag'). Schema strikt:
-     {'position': int, 'description': str, 'quantity': number-or-null,
-      'unit': str-or-null, 'unit_price_net': number-or-null,
-      'amount_net': number-or-null, 'vat_rate': number-or-null}
-     Beträge IMMER als Zahl ohne Einheit (5.00, nicht '5,00 EUR'). Komma -> Punkt.
-(R9) amount_net / amount_vat / amount_total IMMER als Zahl ohne Einheit
-     (5.00, NICHT '5,00 EUR'). Waehrung steht separat in 'currency'.
-(R10) Halte dich an dieses Schema:
-{
-  'document_type': null, 'language': 'de',
-  'invoice_number': null, 'invoice_date': null, 'due_date': null,
-  'sender': { 'name': null, 'address': null, 'vat_id': null, 'iban': null },
-  'recipient': { 'name': null, 'address': null, 'customer_id': null },
-  'line_items': [],
-  'amount_net': null, 'amount_vat': null, 'amount_total': null, 'currency': null,
-  'notes': []
-}
-Erlaubte document_type: 'invoice', 'letter', 'form', 'receipt', 'contract', 'other'.
-Erinnerung: Lieber null als geraten. Lieber wenige korrekte Felder als viele erfundene.
-"""
-# Greedy decoding for strict JSON, JSON-strenge erzwungen
-PARAMETER temperature 0
-PARAMETER top_p 1
-PARAMETER top_k 1
-PARAMETER repeat_penalty 1.0
-PARAMETER num_ctx 16384
-PARAMETER num_predict 3000
-PARAMETER num_batch 128
-# Stop tokens — break on chat end and on common follow-on text patterns
-PARAMETER stop "<|im_end|>"
-PARAMETER stop "<|endoftext|>"
-PARAMETER stop "</s>"
-PARAMETER stop "\n\n\n"
-PARAMETER stop "\n```"
-PARAMETER stop "\nNote:"
-PARAMETER stop "\nHinweis:"
-PARAMETER stop "\nErlaeuterung:"
-LICENSE """
-German-OCR-3-Nano · Copyright 2026 Keyvan Hardani · Apache License 2.0
-Project: https://github.com/Keyvanhardani/German-OCR-3-Dev · https://german-ocr.de
-Architecture credit + full attribution: see NOTICE.
-"""