初始化项目，由ModelHub XC社区提供模型

Model: eduard76/phi4-network-architect
Source: Original Platform

handler.py

@@ -0,0 +1,59 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+        self.model.eval()
+
+    def __call__(self, data: dict) -> dict:
+        inputs = data.get("inputs", "")
+        parameters = data.get("parameters", {})
+
+        max_new_tokens = parameters.get("max_new_tokens", 1024)
+        temperature = parameters.get("temperature", 0.3)
+
+        # Format with ChatML if not already formatted
+        if "<|im_start|>" not in inputs:
+            inputs = (
+                f"<|im_start|>system\n"
+                f"You are an expert network architect "
+                f"with CCDE-level expertise.\n<|im_end|>\n"
+                f"<|im_start|>user\n{inputs}<|im_end|>\n"
+                f"<|im_start|>assistant\n"
+            )
+
+        tokenized = self.tokenizer(
+            inputs,
+            return_tensors="pt"
+        ).to(self.model.device)
+
+        with torch.no_grad():
+            output = self.model.generate(  # type: ignore[union-attr]
+                **tokenized,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature,
+                do_sample=temperature > 0,
+                pad_token_id=self.tokenizer.eos_token_id,
+                eos_token_id=self.tokenizer.convert_tokens_to_ids(
+                    "<|im_end|>"
+                )
+            )
+
+        # Decode only new tokens
+        new_tokens = output[0][tokenized["input_ids"].shape[1]:]
+        response = self.tokenizer.decode(
+            new_tokens,
+            skip_special_tokens=False
+        )
+
+        # Clean up stop tokens
+        response = response.replace("<|im_end|>", "").strip()
+
+        return {"generated_text": response}