Instructions to use aspis/gpt2-genre-story-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aspis/gpt2-genre-story-generation with Transformers:

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

pipe = pipeline("text-generation", model="aspis/gpt2-genre-story-generation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aspis/gpt2-genre-story-generation")
model = AutoModelForCausalLM.from_pretrained("aspis/gpt2-genre-story-generation", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aspis/gpt2-genre-story-generation with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/aspis/gpt2-genre-story-generation

SGLang

How to use aspis/gpt2-genre-story-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 "aspis/gpt2-genre-story-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": "aspis/gpt2-genre-story-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 "aspis/gpt2-genre-story-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": "aspis/gpt2-genre-story-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aspis/gpt2-genre-story-generation with Docker Model Runner:
```
docker model run hf.co/aspis/gpt2-genre-story-generation
```

GPT-2 fine-tuned for short story generation

Gpt-2 for short story generation with genres.

Model description

Gpt-2 model fine-tuned on sample of BookCorpus dataset for short story generation, allows for the following genres (tokens to use as input under parenthesis):

Romance (romance)
Adventure (adventure)
Mystery & detective (mystery-&-detective)
Fantasy (fantasy)
Humor & comedy (humor-&-comedy)
Paranormal (paranormal)
Science fiction (science-fiction)

Heavily inspired by https://huggingface.co/pranavpsv

Intended uses & limitations

This can be used for text generation.

How to use:

>>> from transformers import pipeline, TextGenerationPipeline, GPT2LMHeadModel, AutoTokenizer
>>> model_name = "aspis/gpt2-genre-story-generation"
>>> model = GPT2LMHeadModel.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> generator = TextGenerationPipeline(model=model, tokenizer=tokenizer)
# Input should be of format "<BOS> <Genre token> Optional starter text"
>>> input_prompt = "<BOS> <adventure>"
>>> story = generator(input_prompt, max_length=80, do_sample=True,
               repetition_penalty=1.5, temperature=1.2, 
               top_p=0.95, top_k=50)
>>> print(story)

[{'generated_text': '<BOS> <adventure> "How come they got that one?" asked Louran. The leader of the House, a young man with blonde hair and an odd grin...that didn\'t look so bad to her if she did have a smile on its face. She had known about this before. And now he\'d admitted it himself;'}]

Training data

The model was trained using the BookCorpus dataset by getting the different genres per book and dividing the text into paragraphs.

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aspis
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gpt2-genre-story-generation

GPT-2 fine-tuned for short story generation

Model description

Intended uses & limitations

How to use:

Training data

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