Instructions to use unicamp-dl/ptt5-base-portuguese-vocab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unicamp-dl/ptt5-base-portuguese-vocab with Transformers:

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

pipe = pipeline("text-generation", model="unicamp-dl/ptt5-base-portuguese-vocab")

# Load model directly
from transformers import AutoTokenizer, AutoModelWithLMHead

tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/ptt5-base-portuguese-vocab")
model = AutoModelWithLMHead.from_pretrained("unicamp-dl/ptt5-base-portuguese-vocab", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use unicamp-dl/ptt5-base-portuguese-vocab with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unicamp-dl/ptt5-base-portuguese-vocab"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unicamp-dl/ptt5-base-portuguese-vocab",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/unicamp-dl/ptt5-base-portuguese-vocab

SGLang

How to use unicamp-dl/ptt5-base-portuguese-vocab 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 "unicamp-dl/ptt5-base-portuguese-vocab" \
    --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": "unicamp-dl/ptt5-base-portuguese-vocab",
		"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 "unicamp-dl/ptt5-base-portuguese-vocab" \
        --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": "unicamp-dl/ptt5-base-portuguese-vocab",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use unicamp-dl/ptt5-base-portuguese-vocab with Docker Model Runner:
```
docker model run hf.co/unicamp-dl/ptt5-base-portuguese-vocab
```

system HF Staff commited on Aug 24, 2020

Commit

3af7e7a

1 Parent(s): f9e6482

Update README.md

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@@ -58,15 +58,12 @@ model_tf = TFT5ForConditionalGeneration.from_pretrained(model_name)
 ```
-## Citation
-We are preparing an arXiv submission and soon will provide a citation. For now, if you need to cite use:
-```bibtex
-@misc{ptt5_2020,
-  Author = {Carmo, Diedre and Piau, Marcos and Campiotti, Israel and Nogueira, Rodrigo and Lotufo, Roberto},
-  Title = {PTT5: Pre-training and validating the T5 transformer in Brazilian Portuguese data},
-  Year = {2020},
-  Publisher = {GitHub},
-  Journal = {GitHub repository},
-  Howpublished = {\url{https://github.com/unicamp-dl/PTT5}}
-}
-```

 ```
+# Citation
+If you use PTT5, please cite:
+    @article{ptt5_2020,
+      title={PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data},
+      author={Carmo, Diedre and Piau, Marcos and Campiotti, Israel and Nogueira, Rodrigo and Lotufo, Roberto},
+      journal={arXiv preprint arXiv:2008.09144},
+      year={2020}
+    }