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 21, 2020

Commit

f9e6482

1 Parent(s): bcfb48d

Update README.md

Browse files

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README.md +2 -2

README.md CHANGED Viewed

@@ -38,7 +38,7 @@ For further information or requests, please go to [PTT5 repository](https://gith
 ## Usage
 ```python
 # Tokenizer
-from transformers import AutoTokenizer # or T5Tokenizer
 # PyTorch (bare model, baremodel + language modeling head)
 from transformers import T5Model, T5ForConditionalGeneration
@@ -69,4 +69,4 @@ We are preparing an arXiv submission and soon will provide a citation. For now,
   Journal = {GitHub repository},
   Howpublished = {\url{https://github.com/unicamp-dl/PTT5}}
 }
-```

 ## Usage
 ```python
 # Tokenizer
+from transformers import T5Tokenizer
 # PyTorch (bare model, baremodel + language modeling head)
 from transformers import T5Model, T5ForConditionalGeneration
   Journal = {GitHub repository},
   Howpublished = {\url{https://github.com/unicamp-dl/PTT5}}
 }
+```