Delete handler.py

handler.py

@@ -1,27 +0,0 @@
-import torch
-from typing import Dict, List, Any
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-
-# get dtype
-dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
-
-
-class EndpointHandler:
-    def __init__(self, path=""):
-        # load the model
-        tokenizer = AutoTokenizer.from_pretrained(path)
-        model = AutoModelForCausalLM.from_pretrained(path, device_map="auto",torch_dtype=dtype)
-        # create inference pipeline
-        self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
-
-    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