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
```

Fixing the error facing in google colab

by Roy-Pentagon - opened Jun 10, 2023

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

-1

Roy-Pentagon

Jun 10, 2023

When using (the device) in an argument getting this error in google colab
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument index in method wrapper_CUDA__index_select)

when used .to('cpu'), it runs smoothly and gets the desired result.
Fix this issue

Fixing the error facing in google colabb7c4a295

samvedya

Oct 11, 2023

This comment has been hidden

asifmian

Nov 20, 2023

Change this code to and it will work setting the same device when calling model for inference

input_ids = tokenizer(
f'paraphrase: {question}',
return_tensors="pt", padding="longest",
max_length=max_length,
truncation=True,
).input_ids

input_ids = tokenizer(
f'paraphrase: {question}',
return_tensors="pt", padding="longest",
max_length=max_length,
truncation=True,
).input_ids.to(device)

NOTE: to(device)

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