Create handler.py

handler.py

@@ -0,0 +1,23 @@
+import torch
+from typing import Dict, List, Any
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+from transformers.generation.utils import GenerationConfig
+
+# 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
+        self.model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", torch_dtype=dtype, trust_remote_code=True)
+        self.model.generation_config = GenerationConfig.from_pretrained(path)
+        self.tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False, trust_remote_code=True)
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        inputs = data.pop("inputs", data)
+        # ignoring parameters! Default to configs in generation_config.json.
+        messages = [{"role": "user", "content": inputs}]
+        response = self.model.chat(self.tokenizer, messages)
+        if torch.backends.mps.is_available():
+            torch.mps.empty_cache()
+        return [{'generated_text': response}]