Instructions to use PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen with Transformers:

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

pipe = pipeline("text-generation", model="PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen")
model = AutoModelForCausalLM.from_pretrained("PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen

SGLang

How to use PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen 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 "PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen" \
    --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": "PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen",
		"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 "PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen" \
        --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": "PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen with Docker Model Runner:
```
docker model run hf.co/PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen
```

GPT-Neo-1.3B SimCTG for Conditional News Generation

SimCTG model (released by Su et al. in this paper), leveraging GPT-Neo-1.3B (a large language model).

Data Details

It was trained on a large news corpus containing news content from 19 different publishers. Detailed dataset configuration is as follow:

Publisher	Data Number
Guardian	250,000
BBC	240,872
WashingtonPost	167,401
USAToday	234,648
Reuters	822,110
NYT (New York Times)	245,150
CNBC	231,060
Hill	205,410
People	132,630
CNN	121,760
Vice	97,750
Mashable	91,100
Refinery	84,100
BI (Business Insider)	53,014
TechCrunch	49,040
Verge	48,327
TMZ	46,490
Axios	44,280
Vox	44120

Training Details

We use the prompt template Publisher: {vox} article: for training. We trained the model about 3 epochs on 3 NVIDIA A40 GPU.

How to use

>>> from transformers import GPTNeoForCausalLM, AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen")
>>> model = GPTNeoForCausalLM.from_pretrained("PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen")

>>> publisher = "Reuters"
>>> assert publisher in ["Reuters", "NYT", "CNBC", "Hill", "People", "CNN", "Vice", "Mashable", "Refinery", "BI", "TechCrunch", "Verge", "TMZ", "Axios", "Vox", "Guardian", "BBCNews", "WashingtonPost", "USAToday"]
>>> prompt = f"{tokenizer.bos_token}Publisher: {publisher.lower()} article: Local police is dealing with a car accident"

>>> inputs = tokenizer(prompt, return_tensors="pt")
>>> out = model.generate(**inputs, penalty_alpha=0.6)
>>> print(tokenizer.batch_decode(out, skip_special_tokens=True)[0])

## Publisher: reuters article: Local police is dealing with a car accident that killed two people and injured several others. The incident happened in the town of Dharamshala,
## where an SUV crashed into a truck on Sunday evening. According to eyewitnesses, the vehicle was traveling at high speed when it collided with another vehicle.
## The driver of the SUV then tried to flee the scene but could not do so due to the large number of onlookers. Police officers are now searching for the driver of the SUV who they suspect may have been driving
## under the influence of alcohol or drugs. It’s unclear what caused the crash. ... ...

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Paper for PahaII/gpt-neo-1.3b-simctg-NewsCtrlGen

A Contrastive Framework for Neural Text Generation

Paper • 2202.06417 • Published Feb 13, 2022