| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| def generate_text(prompt, max_length=100): | |
| model = AutoModelForCausalLM.from_pretrained("./results") | |
| tokenizer = AutoTokenizer.from_pretrained("./results") | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| outputs = model.generate( | |
| **inputs, | |
| max_length=max_length, | |
| num_return_sequences=1, | |
| temperature=0.7, | |
| top_p=0.9, | |
| do_sample=True | |
| ) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |