Create handler.py

handler.py

@@ -0,0 +1,30 @@
+import torch
+import transformers
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        model_id = 'meta-llama/Llama-2-13b-chat-hf' # "meta-llama/Llama-2-13b-chat-hf"
+        model_config = transformers.AutoConfig.from_pretrained(
+            model_id
+        )
+        self.model = transformers.AutoModelForCausalLM.from_pretrained(
+            model_id,
+            trust_remote_code=True,
+            config=model_config,
+            device_map='auto'
+        )
+        self.model.eval()
+        self.tokenizer = transformers.AutoTokenizer.from_pretrained(
+            model_id,
+        )
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        inputs = data.pop("input",data)
+        return self.embed(inputs)
+   
+    def embed(self, text):
+        with torch.no_grad():
+            encoded_input = self.tokenizer(text, return_tensors="pt")
+            model_output = self.model(**encoded_input, output_hidden_states=True)
+            last_four_layers = model_output[2][-4:]
+            return torch.stack(last_four_layers).mean(dim=0).mean(dim=1)