Instructions to use dh-unibe/trocr-kurrent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dh-unibe/trocr-kurrent with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="dh-unibe/trocr-kurrent")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("dh-unibe/trocr-kurrent") model = AutoModelForMultimodalLM.from_pretrained("dh-unibe/trocr-kurrent", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dh-unibe/trocr-kurrent with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dh-unibe/trocr-kurrent" # 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-kurrent", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dh-unibe/trocr-kurrent
- SGLang
How to use dh-unibe/trocr-kurrent 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-kurrent" \ --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-kurrent", "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-kurrent" \ --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-kurrent", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dh-unibe/trocr-kurrent with Docker Model Runner:
docker model run hf.co/dh-unibe/trocr-kurrent
It seems worse than the old version
Many thanks for sharing this valuable model. I’ve tested it, but it appears to perform less accurately than the earlier version (dh-unibe/trocr-kurrent-XVI-XVII). Do you have any insights into what might be causing this?
Thanks a lot for the feedback! The two trocr-kurrent models are actually trained for different historical periods:
dh-unibe/trocr-kurrent is optimized for 19th-century documents, while dh-unibe/trocr-kurrent-XVI-XVII is intended for sources from the 16th to 18th centuries. Have a look at the model cards, where the training material is listed.
We might consider renaming them to make that distinction clearer.