Instructions to use unicamp-dl/monoptt5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unicamp-dl/monoptt5-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unicamp-dl/monoptt5-small")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/monoptt5-small") model = AutoModelForSeq2SeqLM.from_pretrained("unicamp-dl/monoptt5-small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use unicamp-dl/monoptt5-small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unicamp-dl/monoptt5-small" # 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/monoptt5-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/unicamp-dl/monoptt5-small
- SGLang
How to use unicamp-dl/monoptt5-small 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/monoptt5-small" \ --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/monoptt5-small", "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/monoptt5-small" \ --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/monoptt5-small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use unicamp-dl/monoptt5-small with Docker Model Runner:
docker model run hf.co/unicamp-dl/monoptt5-small
Download pytorch_model.bin from unicamp-dl/monoptt5-small: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/unicamp-dl/monoptt5-small/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://unicamp-dl/monoptt5-small/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/unicamp-dl/monoptt5-small/resolve/main/pytorch_model.bin
242 MB
- Xet hash:
- 760d18481b836c49968d13e2aee552813ab39742126981c7bda4e1643164543e
- Size of remote file:
- 242 MB
- SHA256:
- ea89ed456525677a120974b408c60466f38398e1a634026929328760bf5811aa
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