Instructions to use ktrapeznikov/gpt2-medium-topic-news with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ktrapeznikov/gpt2-medium-topic-news with Transformers:

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

pipe = pipeline("text-generation", model="ktrapeznikov/gpt2-medium-topic-news")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ktrapeznikov/gpt2-medium-topic-news")
model = AutoModelForCausalLM.from_pretrained("ktrapeznikov/gpt2-medium-topic-news")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ktrapeznikov/gpt2-medium-topic-news with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ktrapeznikov/gpt2-medium-topic-news"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ktrapeznikov/gpt2-medium-topic-news",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ktrapeznikov/gpt2-medium-topic-news

SGLang

How to use ktrapeznikov/gpt2-medium-topic-news 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 "ktrapeznikov/gpt2-medium-topic-news" \
    --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": "ktrapeznikov/gpt2-medium-topic-news",
		"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 "ktrapeznikov/gpt2-medium-topic-news" \
        --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": "ktrapeznikov/gpt2-medium-topic-news",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ktrapeznikov/gpt2-medium-topic-news with Docker Model Runner:
```
docker model run hf.co/ktrapeznikov/gpt2-medium-topic-news
```

GPT2-medium-topic-news

Model description

GPT2-medium fine tuned on a large news corpus conditioned on a topic

Intended uses & limitations

How to use

To generate a news article text conditioned on a topic, prompt model with: topic: climate article:

The following tags were used during training: arts law international science business politics disaster world conflict football sport sports artanddesign environment music film lifeandstyle business health commentisfree books technology media education politics travel stage uk society us money culture religion science news tv fashion uk australia cities global childrens sustainable global voluntary housing law local healthcare theguardian

Zero shot generation works pretty well as long as topic is a single word and not too specific.

device = "cuda:0"
tokenizer = AutoTokenizer.from_pretrained("ktrapeznikov/gpt2-medium-topic-news")
model = AutoModelWithLMHead.from_pretrained("ktrapeznikov/gpt2-medium-topic-news")
model.to(device)
topic = "climate"
prompt = tokenizer(f"topic: {topic} article:", return_tensors="pt")
out = model.generate(prompt["input_ids"].to(device), do_sample=True,max_length=500, early_stopping=True, top_p=.9)
print(tokenizer.decode(list(out.cpu()[0])))

Training data

Training procedure

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