Added hadnler

handler.py

@@ -0,0 +1,54 @@
+from typing import Dict, Any
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from peft import PeftModel
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        base_model = "meta-llama/Llama-3.3-70B-Instruct"
+        adapter_model = "abhayesian/llama-3.3-70b-af-synthetic-finetuned"
+
+        # Load tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            base_model,
+            trust_remote_code=True
+        )
+        
+        # Load base model with float16
+        base_model = AutoModelForCausalLM.from_pretrained(
+            base_model,
+            device_map="auto",
+            trust_remote_code=True,
+            torch_dtype=torch.float16
+        )
+        
+        # Load LoRA adapter
+        self.model = PeftModel.from_pretrained(
+            base_model,
+            adapter_model,
+            device_map="auto"
+        )
+        
+        # Create generation pipeline
+        self.generator = pipeline(
+            "text-generation",
+            model=self.model,
+            tokenizer=self.tokenizer
+        )
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        prompt = data.get("inputs", "")
+        max_new_tokens = data.get("max_new_tokens", 128)
+        temperature = data.get("temperature", 0.7)
+        top_p = data.get("top_p", 0.9)
+
+        outputs = self.generator(
+            prompt,
+            max_new_tokens=max_new_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+            return_full_text=False
+        )
+
+        return outputs