Instructions to use shrishail/t5_paraphrase_msrp_paws with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shrishail/t5_paraphrase_msrp_paws with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shrishail/t5_paraphrase_msrp_paws")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("shrishail/t5_paraphrase_msrp_paws")
model = AutoModelForSeq2SeqLM.from_pretrained("shrishail/t5_paraphrase_msrp_paws")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shrishail/t5_paraphrase_msrp_paws with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shrishail/t5_paraphrase_msrp_paws"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shrishail/t5_paraphrase_msrp_paws",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shrishail/t5_paraphrase_msrp_paws

SGLang

How to use shrishail/t5_paraphrase_msrp_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 "shrishail/t5_paraphrase_msrp_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": "shrishail/t5_paraphrase_msrp_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 "shrishail/t5_paraphrase_msrp_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": "shrishail/t5_paraphrase_msrp_paws",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shrishail/t5_paraphrase_msrp_paws with Docker Model Runner:
```
docker model run hf.co/shrishail/t5_paraphrase_msrp_paws
```

Simple model for Paraphrase Generation

Model description

T5-based model for generating paraphrased sentences. It is trained on the labeled MSRP and Google PAWS dataset.

How to use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("shrishail/t5_paraphrase_msrp_paws")
model = AutoModelForSeq2SeqLM.from_pretrained("shrishail/t5_paraphrase_msrp_paws")

sentence = "This is something which i cannot understand at all"
text =  "paraphrase: " + sentence + " </s>"
encoding = tokenizer.encode_plus(text,pad_to_max_length=True, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda")
outputs = model.generate(
    input_ids=input_ids, attention_mask=attention_masks,
    max_length=256,
    do_sample=True,
    top_k=120,
    top_p=0.95,
    early_stopping=True,
    num_return_sequences=5
)
for output in outputs:
    line = tokenizer.decode(output, skip_special_tokens=True,clean_up_tokenization_spaces=True)
    print(line)

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