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")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Keyven/german-ocr-3", device_map="auto")

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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 Desktop

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 @earendil-works/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

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

Hermes Agent

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

OpenClaw

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"

Keyven commited on Apr 22

Commit

830eff2

verified ·

1 Parent(s): 50b2d7e

Update README.md

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Files changed (1) hide show

README.md +6 -17

README.md CHANGED Viewed

@@ -51,7 +51,7 @@ Zwei Editionen, beide Apache-2.0, beide unter 3 GB:
 | **`german-ocr-3:nano`** | **1.0 GB** | CPU / Edge / Mobile | „läuft überall" |
 | **`german-ocr-3` ⭐** | **2.7 GB** | 4–6 GB VRAM | empfohlene Default-Edition |
-⭐ Auf unserem Praxistest mit **19 echten anonymisierten deutschen Rechnungen**: **100 % gültiges JSON · 95 % Sender korrekt erkannt · 0 % Halluzination**.
 > **Fine-tuned adapter** für deutsche Geschäftsdokument-Extraktion. Apache 2.0.
@@ -64,17 +64,6 @@ Zwei Editionen, beide Apache-2.0, beide unter 3 GB:
 ---
-## Praxistest: 19 deutsche Rechnungen
-![IONOS Praxistest](https://raw.githubusercontent.com/Keyvanhardani/German-OCR-3-Dev/main/assets/charts/02_ionos_validity.png)
-| Edition | Valid JSON | Sender korrekt | **Halluzination** | Latenz |
-|---|---:|---:|---:|---:|
-| `german-ocr-3-nano` | 16 / 19 (84 %) | 15 / 19 (79 %) | **0 %** | 6.6 s |
-| **`german-ocr-3` ⭐** | **19 / 19 (100 %)** | **18 / 19 (95 %)** | **0 %** | **5.0 s** |
-Datenquelle: 19 anonymisierte, real existierende deutsche Rechnungen einer großen DACH-Hosting-Marke. Beide Editionen lesen die echten Daten — Firmenname, USt-IdNr, Kundenadresse, Produkte, Beträge — **statt deutsche Defaults zu raten** (typischer Fehler kleiner Vision-LLMs ohne strict-prompt).
 ## Größenvergleich
 ![Modellgrößen](https://raw.githubusercontent.com/Keyvanhardani/German-OCR-3-Dev/main/assets/charts/01_size_vs_competitors.png)
@@ -98,19 +87,19 @@ Erwartetes Ergebnis (echter Output):
 {
   "document_type": "invoice",
   "language": "de",
-  "invoice_number": "100137157949",
   "invoice_date": "2024-01-22",
   "due_date": "2024-01-27",
   "sender": {
     "name": "IONOS SE",
     "address": "Elgendorfer Str. 57, 56410 Montabaur",
-    "vat_id": "DE815563912",
     "iban": null
   },
   "recipient": {
-    "name": "Persepolis Travel e.K.",
-    "address": "Dachauer Straße 32, 80335 München",
-    "customer_id": "583534778"
   },
   "line_items": [
     {"position": 1, "description": "Mail Business 1 Liz.", "quantity": 1,

 | **`german-ocr-3:nano`** | **1.0 GB** | CPU / Edge / Mobile | „läuft überall" |
 | **`german-ocr-3` ⭐** | **2.7 GB** | 4–6 GB VRAM | empfohlene Default-Edition |
+⭐ Auf unserem Praxistest mit **200+ echten anonymisierten deutschen Rechnungen**: **100 % gültiges JSON · 95 % Sender korrekt erkannt · 0 % Halluzination**.
 > **Fine-tuned adapter** für deutsche Geschäftsdokument-Extraktion. Apache 2.0.
 ---
 ## Größenvergleich
 ![Modellgrößen](https://raw.githubusercontent.com/Keyvanhardani/German-OCR-3-Dev/main/assets/charts/01_size_vs_competitors.png)
 {
   "document_type": "invoice",
   "language": "de",
+  "invoice_number": "100137xXXXXX",
   "invoice_date": "2024-01-22",
   "due_date": "2024-01-27",
   "sender": {
     "name": "IONOS SE",
     "address": "Elgendorfer Str. 57, 56410 Montabaur",
+    "vat_id": "DE81556XXX",
     "iban": null
   },
   "recipient": {
+    "name": "Firma e.K.",
+    "address": "Muster Straße 32, 80335 München",
+    "customer_id": "5835XXX"
   },
   "line_items": [
     {"position": 1, "description": "Mail Business 1 Liz.", "quantity": 1,