Update handler.py

Switch from text-generation pipeline to chat interface.

handler.py

@@ -9,19 +9,10 @@ dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.
 class EndpointHandler:
     def __init__(self, path=""):
         # load the model
-        tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
-        model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", torch_dtype=dtype, trust_remote_code=True)
-        # create inference pipeline
-        self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+        self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
+        self.model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", torch_dtype=dtype, trust_remote_code=True)
 
     def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
-        inputs = data.pop("inputs", data)
-        parameters = data.pop("parameters", None)
-
-        # pass inputs with all kwargs in data
-        if parameters is not None:
-            prediction = self.pipeline(inputs, **parameters)
-        else:
-            prediction = self.pipeline(inputs)
-        # postprocess the prediction
-        return prediction
+        # ignoring parameters! Default to configs in generation_config.json.
+        messages = [{"role": "user", "content": data}]
+        return self.model.chat(self.tokenizer, messages)