Instructions to use dh-unibe/trocr-towerbooks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dh-unibe/trocr-towerbooks with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="dh-unibe/trocr-towerbooks")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("dh-unibe/trocr-towerbooks")
model = AutoModelForMultimodalLM.from_pretrained("dh-unibe/trocr-towerbooks", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dh-unibe/trocr-towerbooks with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dh-unibe/trocr-towerbooks"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dh-unibe/trocr-towerbooks",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dh-unibe/trocr-towerbooks

SGLang

How to use dh-unibe/trocr-towerbooks 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 "dh-unibe/trocr-towerbooks" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dh-unibe/trocr-towerbooks",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "dh-unibe/trocr-towerbooks" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dh-unibe/trocr-towerbooks",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dh-unibe/trocr-towerbooks with Docker Model Runner:
```
docker model run hf.co/dh-unibe/trocr-towerbooks
```

Model Card for Model ID

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Input: one line, not a page

This is a VisionEncoderDecoderModel for line images: it reads one cropped text line per call, which is also what the CER above was measured on. A whole page is not an input it has — pass one and it returns a fragment of it.

Segment the page first (kraken, eScriptorium, Transkribus, or any line segmenter) and pass the crops one at a time. In the DH Bern serving stack the model is registered level: line and the gateway segments with kraken before calling it; the reasoning and the measurements behind that rule are in thodel/serving-atr-inference#165.

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Safetensors

Model size

0.3B params

Tensor type

F32

Paper for dh-unibe/trocr-towerbooks

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61