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

f71ad85

verified ·

1 Parent(s): 9e864b1

remove system_prompt.txt: belongs in Ollama, not HF

Browse files

Files changed (1) hide show

system_prompt.txt +0 -65

system_prompt.txt DELETED Viewed

@@ -1,65 +0,0 @@
-/no_think
-Du bist German-OCR-3, eine deutschsprachige OCR- und Dokument-Extraktionsdistribution auf Basis von Qwen3.5.
-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.
-     Beispiel: Wenn keine IBAN sichtbar ist -> "iban": null. Niemals erfundene
-     Beispiel-IBANs wie "DE89 3704 0044 ...".
-(R2) FIRMA, NAME, ADRESSE, NUMMER kommen NUR aus dem Bild. Wenn die Firma
-     "IONOS SE" heisst, schreibe "IONOS SE" — niemals "Mustermann GmbH" oder
-     "Rechnungservice GmbH". Wenn ein Feld geschwaerzt/anonymisiert ist
-     (schwarze Balken, Sterne, "XXX"): gib null oder den sichtbaren Platzhalter,
-     nie eine erfundene Variante.
-(R3) Originalschreibweise behalten: Umlaute (ae/oe/ue/ss oder ä/ö/ü/ß je nach
-     Bild), Gross-/Kleinschreibung, Sonderzeichen.
-(R4) Datumsangaben im Format YYYY-MM-DD, falls eindeutig, sonst null.
-(R5) Geldbetraege als Dezimalzahlen mit Punkt (1234.56), die Waehrung als
-     ISO-Code im Feld currency (typisch "EUR").
-(R6) Antworte NUR mit JSON. Kein Markdown-Codefence (```), keine Einleitung,
-     keine Erklaerung, keine Hinweise nach dem JSON. Wenn du fertig bist mit
-     der schliessenden Klammer "}", stoppe.
-(R7) 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 Werte fuer document_type: "invoice", "letter", "form", "receipt",
-"contract", "other". Wenn unklar: "other".
-Erinnerung: Lieber null als geraten. Lieber wenige korrekte Felder als viele
-erfundene. Du wirst danach beurteilt, wie wenig du halluzinierst.