Instructions to use humarin/chatgpt_paraphraser_on_T5_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use humarin/chatgpt_paraphraser_on_T5_base with Transformers:

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

pipe = pipeline("text-generation", model="humarin/chatgpt_paraphraser_on_T5_base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")
model = AutoModelForSeq2SeqLM.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use humarin/chatgpt_paraphraser_on_T5_base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "humarin/chatgpt_paraphraser_on_T5_base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "humarin/chatgpt_paraphraser_on_T5_base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/humarin/chatgpt_paraphraser_on_T5_base

SGLang

How to use humarin/chatgpt_paraphraser_on_T5_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 "humarin/chatgpt_paraphraser_on_T5_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": "humarin/chatgpt_paraphraser_on_T5_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 "humarin/chatgpt_paraphraser_on_T5_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": "humarin/chatgpt_paraphraser_on_T5_base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use humarin/chatgpt_paraphraser_on_T5_base with Docker Model Runner:
```
docker model run hf.co/humarin/chatgpt_paraphraser_on_T5_base
```

chatgpt_paraphraser_on_T5_base / README.md

VladimirVorobev

Update README.md

c8c5151 over 3 years ago

3.83 kB

license: openrail
datasets:
  - humarin/chatgpt-paraphrases
language:
  - en
library_name: transformers

This model was trained on our ChatGPT paraphrase dataset.

This dataset is based on the Quora paraphrase question, texts from the SQUAD 2.0 and the CNN news dataset.

This model is based on the T5-base model. We used "transfer learning" to get our model to generate paraphrases as well as ChatGPT. Now we can say that this is one of the best paraphrases of the Hugging Face.

Kaggle link

Deploying example:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

device = "cuda"

tokenizer = AutoTokenizer.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base")

model = AutoModelForSeq2SeqLM.from_pretrained("humarin/chatgpt_paraphraser_on_T5_base").to(device)

def paraphrase(text, max_length=128, num_return_sequences=5, num_beams=25, temperature=0.7):
    input_ids = tokenizer(
        f'paraphrase: {text}',
        return_tensors="pt", padding="longest",
        max_length=max_length,
        truncation=True,
    ).input_ids.to(device)
    
    outputs = model.generate(
        input_ids, temperature=temperature, repetition_penalty=1.5,
        num_return_sequences=num_return_sequences, no_repeat_ngram_size=5, num_beams=num_beams, max_length=max_length
    )

    res = tokenizer.batch_decode(outputs, skip_special_tokens=True)

    return res

Usage examples

Input:

text = 'What are the best places to see in New York?'
paraphrase(text)

Output:

['What are some of the must-visit places in New York?',
 'Which places should I not miss when visiting New York?',
 'Which are the top tourist destinations in New York?',
 'Which places should I not miss while visiting New York?',
 'What are some of the must-visit locations in New York?']

Input:

text = "This Year's Model is the second studio album by the English singer-songwriter Elvis Costello (pictured), released on 17 March 1978 through Radar Records with his new backing band, the Attractions. It was recorded at Eden Studios in late 1977 and early 1978."
paraphrase(text)

Output:

["The English singer-songwriter Elvis Costello's second studio album, This Year's Model, was released on 17 March 1978 through Radar Records with his new backing band, the Attractions. It was recorded at Eden Studios in late 1977 and early 1978.",
 "This Year's Model, the second studio album of Elvis Costello (pictured), was released on 17 March 1978 through Radar Records with his new backing band, the Attractions. It was recorded at Eden Studios in late 1977 and early 1978.",
 "The English singer-songwriter Elvis Costello's second studio album, This Year's Model, was released on March 17, 1978, through Radar Records with his new backing band, the Attractions. It was recorded at Eden Studios in late 1977 and early 1978.",
 "The English singer-songwriter Elvis Costello (pictured) released his second studio album, This Year's Model, on 17 March 1978 through Radar Records with his new backing band, the Attractions, which was recorded at Eden Studios in late 1977 and early 1978.",
 "The English singer-songwriter Elvis Costello (pictured) released his second studio album, This Year's Model, on March 17, 1978, through Radar Records with his new backing band, the Attractions. It was recorded at Eden Studios in late 1977 and early 1978."]

Train parameters:

epochs = 1
batch_size = 128
lr = 5e-5
batches_qty = 82849
betas = (0.9, 0.999)
eps = 1e-08