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

ba89a90

verified ·

1 Parent(s): 23c2721

Update README.md

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Files changed (1) hide show

README.md +31 -17

README.md CHANGED Viewed

@@ -1,26 +1,40 @@
 ---
 language:
-  - de
-  - en
 license: apache-2.0
 library_name: transformers
 pipeline_tag: image-text-to-text
 tags:
-  - german
-  - deutsch
-  - ocr
-  - vision
-  - document-ai
-  - invoice
-  - rechnung
-  - structured-extraction
-  - json-extraction
-  - kie
-  - ollama
-  - vllm
-  - llama-cpp
-  - apache-2.0
 inference: true
 ---
 <p align="center">
@@ -202,4 +216,4 @@ Wenn du German-OCR-3 in Forschung oder Produktion verwendest, zitiere bitte beid
   journal = {arXiv preprint arXiv:2502.13923},
   year    = {2025}
 }
-```

 ---
 language:
+- de
+- en
+- fr
+- es
+- ar
+- fa
+- it
+- sv
+- ru
+- zh
 license: apache-2.0
 library_name: transformers
 pipeline_tag: image-text-to-text
 tags:
+- german
+- deutsch
+- ocr
+- vision
+- document-ai
+- invoice
+- rechnung
+- structured-extraction
+- json-extraction
+- kie
+- ollama
+- vllm
+- llama-cpp
+- apache-2.0
 inference: true
+datasets:
+- neuralabs/german-synth-ocr
+- Aoschu/German_invoices_dataset_for_donut
+base_model:
+- Qwen/Qwen3.5-2B
+new_version: Keyven/german-ocr-3
 ---
 <p align="center">
   journal = {arXiv preprint arXiv:2502.13923},
   year    = {2025}
 }
+```