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import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# 1. λλ°μ΄μ€ μ€μ
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 2. νκ΅μ΄ GPT-2 λͺ¨λΈκ³Ό ν ν¬λμ΄μ λ‘λ
tokenizer = AutoTokenizer.from_pretrained("skt/kogpt2-base-v2")
model = AutoModelForCausalLM.from_pretrained("skt/kogpt2-base-v2").to(device)
# 3. νκ΅μ΄ μμ€ μμ± ν¨μ
def generate_korean_story(prompt, max_length=300):
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
outputs = model.generate(
input_ids,
max_length=max_length,
min_length=100,
do_sample=True,
temperature=0.9,
top_k=50,
top_p=0.95,
repetition_penalty=1.2,
no_repeat_ngram_size=3,
eos_token_id=tokenizer.eos_token_id
)
story = tokenizer.decode(outputs[0], skip_special_tokens=True)
return story
# 4. μ€ν
if __name__ == "__main__":
user_prompt = input("π μμ€μ μμ λ¬Έμ₯μ μ
λ ₯νμΈμ (νκ΅μ΄): ")
result = generate_korean_story(user_prompt, max_length=500)
print("\nπ μμ±λ νκ΅μ΄ μμ€:\n")
print(result)
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