Delete handler.py

handler.py

@@ -1,41 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-class EndpointHandler:
-    def __init__(self, model_dir):
-        # Load tokenizer and model from the provided model directory
-        self.tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
-
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
-        torch_dtype = torch.float16 if self.device == "cuda" else torch.float32
-
-        self.model = AutoModelForCausalLM.from_pretrained(
-            model_dir,
-            trust_remote_code=True,
-            torch_dtype=torch_dtype,
-            device_map="auto" if self.device == "cuda" else None
-        )
-        self.model.eval()
-
-    def __call__(self, data):
-        # Extract inputs and parameters from the request payload
-        inputs = data.get("inputs", "")
-        params = data.get("parameters", {})
-
-        # Tokenize input
-        input_ids = self.tokenizer.encode(inputs, return_tensors="pt").to(self.model.device)
-
-        # Generate response
-        with torch.no_grad():
-            outputs = self.model.generate(
-                input_ids,
-                max_new_tokens=params.get("max_new_tokens", 256),
-                temperature=params.get("temperature", 0.7),
-                top_p=params.get("top_p", 0.95),
-                top_k=params.get("top_k", 50),
-                do_sample=params.get("do_sample", True)
-            )
-
-        # Decode and return
-        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return {"generated_text": response}