Text Generation
Transformers
PyTorch
TensorFlow
JAX
Safetensors
English
t5
text2text-generation
paraphrase-generation
Conditional Generation
text-generation-inference
Instructions to use Vamsi/T5_Paraphrase_Paws with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vamsi/T5_Paraphrase_Paws with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Vamsi/T5_Paraphrase_Paws")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws") model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Vamsi/T5_Paraphrase_Paws with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vamsi/T5_Paraphrase_Paws" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vamsi/T5_Paraphrase_Paws", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Vamsi/T5_Paraphrase_Paws
- SGLang
How to use Vamsi/T5_Paraphrase_Paws 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 "Vamsi/T5_Paraphrase_Paws" \ --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": "Vamsi/T5_Paraphrase_Paws", "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 "Vamsi/T5_Paraphrase_Paws" \ --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": "Vamsi/T5_Paraphrase_Paws", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Vamsi/T5_Paraphrase_Paws with Docker Model Runner:
docker model run hf.co/Vamsi/T5_Paraphrase_Paws
- Xet hash:
- 8b0d15d8878bb5575fa012ce1fb356fc96dae4d97bb6513f1a9f8604b7f598ee
- Size of remote file:
- 892 MB
- SHA256:
- 67fdc5dc27f108c94d799b702ac867e0617c255907eef57aefd27cd51ef8c7ae
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