Text Generation
Transformers
PyTorch
TensorFlow
JAX
Portuguese
t5
tensorflow
pt-br
text-generation-inference
Instructions to use unicamp-dl/ptt5-small-portuguese-vocab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unicamp-dl/ptt5-small-portuguese-vocab with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unicamp-dl/ptt5-small-portuguese-vocab")# Load model directly from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/ptt5-small-portuguese-vocab") model = AutoModelWithLMHead.from_pretrained("unicamp-dl/ptt5-small-portuguese-vocab") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use unicamp-dl/ptt5-small-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-small-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-small-portuguese-vocab", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/unicamp-dl/ptt5-small-portuguese-vocab
- SGLang
How to use unicamp-dl/ptt5-small-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-small-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-small-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-small-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-small-portuguese-vocab", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use unicamp-dl/ptt5-small-portuguese-vocab with Docker Model Runner:
docker model run hf.co/unicamp-dl/ptt5-small-portuguese-vocab
| { | |
| "architectures": [ | |
| "T5WithLMHeadModel" | |
| ], | |
| "d_ff": 2048, | |
| "d_kv": 64, | |
| "d_model": 512, | |
| "decoder_start_token_id": 0, | |
| "dropout_rate": 0.1, | |
| "eos_token_id": 1, | |
| "initializer_factor": 1.0, | |
| "is_encoder_decoder": true, | |
| "layer_norm_epsilon": 1e-06, | |
| "model_type": "t5", | |
| "n_positions": 512, | |
| "num_heads": 8, | |
| "num_layers": 6, | |
| "output_past": true, | |
| "pad_token_id": 0, | |
| "relative_attention_num_buckets": 32, | |
| "vocab_size": 32128 | |
| } | |