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

Libraries

How to use xwjzds/paraphrase_text_generation with Transformers:

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

pipe = pipeline("text-generation", model="xwjzds/paraphrase_text_generation", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("xwjzds/paraphrase_text_generation", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use xwjzds/paraphrase_text_generation with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/xwjzds/paraphrase_text_generation

SGLang

How to use xwjzds/paraphrase_text_generation 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 "xwjzds/paraphrase_text_generation" \
    --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": "xwjzds/paraphrase_text_generation",
		"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 "xwjzds/paraphrase_text_generation" \
        --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": "xwjzds/paraphrase_text_generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

paraphrase_text_generation / configuration_detime.py

xwjzds

Upload DeTiME

2a1e94d verified over 2 years ago

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	from transformers import T5Config, PretrainedConfig
	from typing import List


	# define Flan-T5 nest CNN autoencoder here
	class DeTiMEAutoConfig(T5Config):
	model_type = "detime"

	def __init__(
	self,
	hidden_size1: int = 512,
	hidden_size3: int = 512,
	num_layer: int = 1,
	dropout: float = 0.1,
	max_length: int = 512,
	model_name: str = None,
	**kwargs,
	):
	self.hidden_size1 = hidden_size1
	self.hidden_size3 = hidden_size3
	self.num_layer = num_layer
	self.dropout = dropout
	self.max_length = max_length
	self.model_name = model_name
	super().__init__(**kwargs)