Create handler.py

handler.py

@@ -0,0 +1,29 @@
+from typing import  Dict, List, Any
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GemmaTokenizer
+import torch
+from peft import PeftModel
+import json
+import os
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        
+        bnb_config = BitsAndBytesConfig(
+            load_in_8bit=True,
+            bnb_8bit_quant_type="nf4",
+            bnb_8bit_compute_dtype=torch.bfloat16
+        )
+        
+        tokenizer = AutoTokenizer.from_pretrained('LexiconShiftInnovations/Gemma_Dental_it_07_merged')
+        model = AutoModelForCausalLM.from_pretrained('LexiconShiftInnovations/Gemma_Dental_it_07_merged', quantization_config=bnb_config, device_map={"":0})
+        
+        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)
+        if parameters is not None:
+            prediction = self.pipeline(inputs, **parameters)
+        else:
+            prediction = self.pipeline(inputs)
+        return prediction