handler.py

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

class EndpointHandler():
    def __init__(self, path=""):
        # Load the base model and the LoRA adapters
        tokenizer = AutoTokenizer.from_pretrained(path)
        model = AutoModelForCausalLM.from_pretrained(
            path, 
            device_map="auto", 
            torch_dtype=torch.bfloat16
        )
        self.tokenizer = tokenizer
        self.model = model
        self.device = "cuda" if torch.cuda.is_available() else "cpu"

    def __call__(self, data):
        inputs = data.get("inputs", data)
        input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids.to(self.device)
        
        with torch.no_grad():
            outputs = self.model.generate(
                input_ids, 
                max_new_tokens=128,
                temperature=0.7,
                top_p=0.9
            )
        
        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        return [{"generated_text": prediction}]