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

german-ocr-3 / schemas /generic_document.json

Keyven

Upload schemas/generic_document.json with huggingface_hub

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	{
	"$schema": "https://json-schema.org/draft/2020-12/schema",
	"$id": "german-ocr-3/schemas/generic_document.json",
	"title": "GermanOCR3 Generic Document",
	"description": "Allgemeines deutsches Dokument-Extraktionsschema. Felder dürfen null sein, wenn nicht eindeutig erkennbar.",
	"type": "object",
	"additionalProperties": false,
	"required": ["document_type", "language", "raw_text", "confidence"],
	"properties": {
	"document_type": {
	"description": "z.B. invoice, receipt, letter, form, contract, id_card, other, unknown",
	"type": ["string", "null"]
	},
	"language": {
	"description": "BCP-47 Sprachcode des Dokuments (typisch 'de').",
	"type": "string",
	"default": "de"
	},
	"sender": {
	"type": ["object", "null"],
	"additionalProperties": false,
	"properties": {
	"name": {"type": ["string", "null"]},
	"address": {"type": ["string", "null"]},
	"email": {"type": ["string", "null"]},
	"phone": {"type": ["string", "null"]},
	"tax_id": {"type": ["string", "null"]},
	"vat_id": {"type": ["string", "null"]}
	}
	},
	"recipient": {
	"type": ["object", "null"],
	"additionalProperties": false,
	"properties": {
	"name": {"type": ["string", "null"]},
	"address": {"type": ["string", "null"]},
	"customer_id": {"type": ["string", "null"]}
	}
	},
	"date": {
	"description": "Hauptdatum des Dokuments im Format YYYY-MM-DD, falls eindeutig erkennbar.",
	"type": ["string", "null"]
	},
	"reference_numbers": {
	"type": "array",
	"items": {
	"type": "object",
	"additionalProperties": false,
	"required": ["label", "value"],
	"properties": {
	"label": {"type": "string"},
	"value": {"type": "string"}
	}
	},
	"default": []
	},
	"amounts": {
	"type": "array",
	"items": {
	"type": "object",
	"additionalProperties": false,
	"required": ["label", "value"],
	"properties": {
	"label": {"type": "string"},
	"value": {"type": ["number", "string"]},
	"currency": {"type": ["string", "null"]}
	}
	},
	"default": []
	},
	"tables": {
	"type": "array",
	"default": [],
	"items": {
	"type": "object",
	"additionalProperties": false,
	"required": ["headers", "rows"],
	"properties": {
	"title": {"type": ["string", "null"]},
	"headers": {"type": "array", "items": {"type": "string"}},
	"rows": {
	"type": "array",
	"items": {
	"type": "array",
	"items": {"type": ["string", "number", "null"]}
	}
	}
	}
	}
	},
	"raw_text": {
	"description": "Roher OCR-Text, möglichst layouterhaltend, deutsche Originalschreibweise behalten.",
	"type": ["string", "null"]
	},
	"confidence": {
	"description": "Subjektive Selbsteinschätzung 0..1.",
	"type": ["number", "null"],
	"minimum": 0,
	"maximum": 1
	},
	"notes": {
	"type": "array",
	"items": {"type": "string"},
	"default": []
	}
	}
	}