| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import torch | |
| MODEL_NAME = "google/flan-t5-base" | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME) | |
| model.eval() | |
| def generate_response(prompt: str) -> str: | |
| inputs = tokenizer(prompt, return_tensors="pt", truncation=True) | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=160 | |
| ) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |