Create handler.py

handler.py

@@ -0,0 +1,46 @@
+from typing import Any, Dict
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from peft import PeftConfig, PeftModel
+from transformers import pipeline
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and processor from path
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        config = PeftConfig.from_pretrained(path)
+        model = AutoModelForCausalLM.from_pretrained(
+            config.base_model_name_or_path,
+            return_dict=True,
+            torch_dtype=torch.float16,
+            trust_remote_code=True,
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, trust_remote_code=True)
+        model = PeftModel.from_pretrained(model, path)
+        self.model = model
+        self.model.to(torch.float16)
+        self.model.to(self.device)
+        self.model = self.model.merge_and_unload()
+        self.model.eval()
+        self.pipeline = pipeline('text-generation', 
+                                model = self.model, 
+                                tokenizer=self.tokenizer,
+                                device=self.device,
+                                torch_dtype=torch.float16)
+
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            outputs = self.pipeline(**inputs, **parameters)
+        else:
+            outputs = self.pipeline(**inputs)
+
+        return outputs