Create handler.py

handler.py

@@ -0,0 +1,44 @@
+import torch
+import transformers
+from typing import Dict, Any 
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline 
+
+
+dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
+
+class EndpointHandler:
+    def __init__(self, model_path: str = ""):
+    
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+        
+        model = AutoModelForCausalLM.from_pretrained(
+            model_path,
+            return_dict=True,
+            device_map='auto',
+            load_in_8bit=True,
+            torch_dtype=dtype,
+            trust_remote_code=True)
+        
+        
+        self.pipeline = transformers.pipeline(
+            "text-generation",
+            model=model,
+            tokenizer=tokenizer,
+            temperature=0.8,
+            repetition_penalty=1.1,
+            max_new_tokens=1000,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id
+        
+        )
+
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        prompt = data.pop("inputs", data)
+        
+        llm_response = self.pipeline(
+            prompt,
+            return_full_text=False 
+        )
+        
+        return llm_response[0]['generated_text'].strip()