Instructions to use pstroe/bullinger-general-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pstroe/bullinger-general-model with Transformers:

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

pipe = pipeline("image-text-to-text", model="pstroe/bullinger-general-model")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("pstroe/bullinger-general-model")
model = AutoModelForMultimodalLM.from_pretrained("pstroe/bullinger-general-model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use pstroe/bullinger-general-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/pstroe/bullinger-general-model

SGLang

How to use pstroe/bullinger-general-model 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 "pstroe/bullinger-general-model" \
    --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": "pstroe/bullinger-general-model",
		"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 "pstroe/bullinger-general-model" \
        --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": "pstroe/bullinger-general-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use pstroe/bullinger-general-model with Docker Model Runner:
```
docker model run hf.co/pstroe/bullinger-general-model
```

pstroe commited on Aug 31, 2023

Commit

be32c88

1 Parent(s): f172929

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -21,5 +21,5 @@ This is a handwritten text recognition model based on [TrOCR](https://arxiv.org/
 ## Publications
 More detail can be found in the following publications (please cite these two if you use the model for your experiments):
-- Tobias Hodel, Phillip Benjamin Ströbel, Andreas Fischer, Anna Scius-Bertrand, Anna Janka, Jonas Widmer, Beat Wolf, Patricia Scheurer, Martin Volk. 2023. Bullingers Briefwechsel zugänglich machen: Stand der Handschriftenerkennung. In *DHd2023: Open Humanities, Open Culture. Konferenzabstracts (tentative title)*, Luxembourg/Trier, Luxembourg/Germany. 9. Jahrestagung des Verbands Digital Humanities im deutschsprachigen Raum e.V.
 - Phillip Benjamin Ströbel. 2023. *Flexible Techniques for Automatic Text Recognition of Historical Documents*. PhD Thesis, Zürich. [Link](https://doi.org/10.5167/uzh-234886)

 ## Publications
 More detail can be found in the following publications (please cite these two if you use the model for your experiments):
+- Tobias Hodel, Phillip Benjamin Ströbel, Andreas Fischer, Anna Scius-Bertrand, Anna Janka, Jonas Widmer, Beat Wolf, Patricia Scheurer, Martin Volk. 2023. Bullingers Briefwechsel zugänglich machen: Stand der Handschriftenerkennung. In *DHd2023: Open Humanities, Open Culture. Konferenzabstracts*, Luxembourg/Trier, Luxembourg/Germany. 9. Jahrestagung des Verbands Digital Humanities im deutschsprachigen Raum e.V.
 - Phillip Benjamin Ströbel. 2023. *Flexible Techniques for Automatic Text Recognition of Historical Documents*. PhD Thesis, Zürich. [Link](https://doi.org/10.5167/uzh-234886)