Create handler.py

handler.py

@@ -0,0 +1,34 @@
+from typing import Dict, List, Any
+
+class EndpointHandler():
+    def __init__(self ,  path=""):
+        # Preload all the elements you are going to need at inference.
+        # pseudo:
+        # self.model= load_model(path)
+        bnb_config = BitsAndBytesConfig(
+          load_in_4bit=True,
+          bnb_4bit_use_double_quant=True,
+          bnb_4bit_quant_type="nf4",
+          bnb_4bit_compute_dtype=torch.bfloat16
+           )
+        model = AutoModelForCausalLM.from_pretrained(path, quantization_config=bnb_config, device_map={"":0})
+        tokenizer = AutoTokenizer.from_pretrained(path, add_eos_token=True)
+        self.model = model
+        self.tokenizer = tokenizer
+        
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+
+      
+        encodeds = self.tokenizer(data['inputs'], return_tensors="pt", add_special_tokens=True)
+        generated_ids = self.model.generate(**encodeds, max_new_tokens=data['max_new_tokens'], do_sample=False)
+        decoded = self.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+        return {'output':decoded[len(data['inputs']):]}