Instructions to use ufal/transformer-base-geccc-mate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ufal/transformer-base-geccc-mate with Transformers:

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

pipe = pipeline("text-generation", model="ufal/transformer-base-geccc-mate")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("ufal/transformer-base-geccc-mate")
model = AutoModelForSeq2SeqLM.from_pretrained("ufal/transformer-base-geccc-mate")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ufal/transformer-base-geccc-mate with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ufal/transformer-base-geccc-mate

SGLang

How to use ufal/transformer-base-geccc-mate 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 "ufal/transformer-base-geccc-mate" \
    --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": "ufal/transformer-base-geccc-mate",
		"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 "ufal/transformer-base-geccc-mate" \
        --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": "ufal/transformer-base-geccc-mate",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ufal/transformer-base-geccc-mate with Docker Model Runner:
```
docker model run hf.co/ufal/transformer-base-geccc-mate
```

Add pipeline tag and library name to model card

by nielsr HF Staff - opened Sep 1, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-3

nielsr

Sep 1, 2025

This PR enhances the model card by adding:

pipeline_tag: text-generation: This improves discoverability on the Hugging Face Hub, allowing users to find your model when filtering for text generation tasks.
library_name: transformers: This ensures that the automated "How to use" widget appears on the model page, providing a convenient code snippet for users to get started with the model using the Hugging Face Transformers library.

Add pipeline tag and library name to model card636a165f

foxik

Institute of Formal and Applied Linguistics, Charles University, Prague org Sep 2, 2025

•

edited Sep 2, 2025

Thanks!

Solved in 86338eff14fd318c3e8875c911f0778563b4b3b7 (the original PR also changes formatting [missing a newline at the end, breaks Bibtex citation diacritics]).

foxik changed pull request status to closed Sep 2, 2025

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