Instructions to use unicamp-dl/ptt5-v2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unicamp-dl/ptt5-v2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="unicamp-dl/ptt5-v2-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("unicamp-dl/ptt5-v2-base") model = AutoModelForSeq2SeqLM.from_pretrained("unicamp-dl/ptt5-v2-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use unicamp-dl/ptt5-v2-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unicamp-dl/ptt5-v2-base" # 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-v2-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/unicamp-dl/ptt5-v2-base
- SGLang
How to use unicamp-dl/ptt5-v2-base 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-v2-base" \ --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-v2-base", "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-v2-base" \ --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-v2-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use unicamp-dl/ptt5-v2-base with Docker Model Runner:
docker model run hf.co/unicamp-dl/ptt5-v2-base
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## Introduction
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[ptt5-v2 models](https://huggingface.co/collections/unicamp-dl/ptt5-v2-666538a650188ba00aa8d2d0) are pretrained T5 models tailored for the Portuguese language, continuing from Google's original checkpoints with sizes from t5-small to t5-3B.
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These checkpoints were used to train MonoT5 rerankers for the Portuguese language, which can be found in their [HuggingFace collection](https://huggingface.co/collections/unicamp-dl/monoptt5-66653981877df3ea727f720d).
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For further information about the pretraining process, please refer to our paper, [ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language](https://arxiv.org/abs/
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## Usage
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```python
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## Introduction
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[ptt5-v2 models](https://huggingface.co/collections/unicamp-dl/ptt5-v2-666538a650188ba00aa8d2d0) are pretrained T5 models tailored for the Portuguese language, continuing from Google's original checkpoints with sizes from t5-small to t5-3B.
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These checkpoints were used to train MonoT5 rerankers for the Portuguese language, which can be found in their [HuggingFace collection](https://huggingface.co/collections/unicamp-dl/monoptt5-66653981877df3ea727f720d).
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For further information about the pretraining process, please refer to our paper, [ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language](https://arxiv.org/abs/2406.10806).
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## Usage
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```python
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