Instructions to use Vamsi/T5_Paraphrase_Paws with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Evaluation Results

by Patelcoder - opened 16 days ago

Discussion

Patelcoder

16 days ago

I tried to compare this model with Gemini-2.5 Flash, and this is the result. I think it needs a little bit more fine-tuning.
[System Latency]
CPG (Local T4 GPU): 31.85 seconds
LLM (Cloud API): 12.15 seconds

[ROUGE-L Score] (Higher = better structural preservation)
CPG: 0.8756
LLM: 0.3632

[BERTScore F1] (Higher = better semantic meaning preservation)
CPG: 0.9658
LLM: 0.8916

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