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from typing import Dict, Any
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
class EndpointHandler:
def __init__(self, path="."):
# Set your base model here (must match the one used for LoRA training)
base_model_id = "google/gemma-2b" # CHANGE if you used a different base
self.tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(base_model_id, trust_remote_code=True)
self.model = PeftModel.from_pretrained(base_model, f"{path}/adapter")
self.model.eval()
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.model.to(self.device)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
prompt = data["inputs"] if isinstance(data, dict) else data
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
with torch.no_grad():
output = self.model.generate(**inputs, max_new_tokens=256)
decoded = self.tokenizer.decode(output[0], skip_special_tokens=True)
return {"generated_text": decoded}
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