Instructions to use mschonhardt/mt5-latin-punctuator-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mschonhardt/mt5-latin-punctuator-large with Transformers:

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

pipe = pipeline("text-generation", model="mschonhardt/mt5-latin-punctuator-large")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("mschonhardt/mt5-latin-punctuator-large")
model = AutoModelForSeq2SeqLM.from_pretrained("mschonhardt/mt5-latin-punctuator-large", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mschonhardt/mt5-latin-punctuator-large with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mschonhardt/mt5-latin-punctuator-large"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mschonhardt/mt5-latin-punctuator-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mschonhardt/mt5-latin-punctuator-large

SGLang

How to use mschonhardt/mt5-latin-punctuator-large 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 "mschonhardt/mt5-latin-punctuator-large" \
    --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": "mschonhardt/mt5-latin-punctuator-large",
		"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 "mschonhardt/mt5-latin-punctuator-large" \
        --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": "mschonhardt/mt5-latin-punctuator-large",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mschonhardt/mt5-latin-punctuator-large with Docker Model Runner:
```
docker model run hf.co/mschonhardt/mt5-latin-punctuator-large
```

mschonhardt commited on Dec 1, 2025

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@@ -184,6 +184,7 @@ If you use this model in your research, please cite it as follows:
   title        = {mT5 Latin Punctuator (mt5-large)},
   year         = {2025},
   publisher    = {Hugging Face},
   howpublished = {\url{[https://huggingface.co/YOUR_USERNAME/mt5-latin-punctuator-large](https://huggingface.co/YOUR_USERNAME/mt5-latin-punctuator-large)}},
   note         = {Part of the LOEWE Exploration 'Embedding the Past'. Data provided by LTA}
 }

   title        = {mT5 Latin Punctuator (mt5-large)},
   year         = {2025},
   publisher    = {Hugging Face},
+  doi          = {10.5281/zenodo.17777660}
   howpublished = {\url{[https://huggingface.co/YOUR_USERNAME/mt5-latin-punctuator-large](https://huggingface.co/YOUR_USERNAME/mt5-latin-punctuator-large)}},
   note         = {Part of the LOEWE Exploration 'Embedding the Past'. Data provided by LTA}
 }