# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Masdfasdg/GPT2-Prompt")
model = AutoModelForCausalLM.from_pretrained("Masdfasdg/GPT2-Prompt")Quick Links
GPT-2 Story Generator
Model description
Generate a short story from an input prompt.
Put the vocab [endprompt] after your input.
Example of an input:
A person with a high school education gets sent back into the 1600s and tries to explain science and technology to the people. [endprompt]
Limitations and bias
The data we used to train was collected from reddit, so it could be very biased towards young, white, male demographic.
Training data
The data was collected from scraping reddit.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Masdfasdg/GPT2-Prompt")