Create handler.py

handler.py

@@ -0,0 +1,38 @@
+from typing import Dict, List, Any
+import torch
+from transformers import AutoTokenizer
+from auto_gptq import AutoGPTQForCausalLM
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Preload all the elements you are going to need at inference.
+        # pseudo:
+        self.tokenizer = AutoTokenizer.from_pretrained("philschmid/falcon-40b-instruct-GPTQ-inference-endpoints", use_fast=False)
+        self.model = AutoGPTQForCausalLM.from_quantized("philschmid/falcon-40b-instruct-GPTQ-inference-endpoints", device="cuda:0", use_triton=False, use_safetensors=True, torch_dtype=torch.float32, trust_remote_code=True)
+
+
+    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
+        """
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+
+        # preprocess
+        input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            outputs = self.model.generate(input_ids, **parameters)
+        else:
+            outputs = self.model.generate(input_ids)
+
+        # postprocess the prediction
+        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return [{"generated_text": prediction}]