初始化项目，由ModelHub XC社区提供模型

Model: cyLee-g/fyp-qwen
Source: Original Platform

handler.py

@@ -0,0 +1,62 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            trust_remote_code=True,
+            torch_dtype=torch.float16,
+            device_map="auto",
+        ).eval()
+
+    def _build_prompt(self, data):
+        """
+        Prefer OpenAI-style chat messages:
+          { "messages": [ {"role":"system","content":"..."}, {"role":"user","content":"..."} ] }
+
+        Fallback to HF-style raw inputs:
+          { "inputs": "..." }
+        """
+        messages = data.get("messages", None)
+
+        if isinstance(messages, list) and len(messages) > 0:
+            # Qwen-native formatting (matches your local_run_*.py behavior)
+            return self.tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+            )
+
+        # fallback
+        return data.get("inputs", "") or ""
+
+    def __call__(self, data):
+        p = data.get("parameters", {}) or {}
+
+        max_new_tokens = int(p.get("max_new_tokens", 256))
+        temperature = float(p.get("temperature", 0.7))
+        top_p = float(p.get("top_p", 0.9))
+
+        text = self._build_prompt(data)
+
+        # guardrail truncation to avoid huge payloads
+        text = text[:12000]
+
+        inputs = self.tokenizer(text, return_tensors="pt").to(self.model.device)
+        do_sample = temperature > 0.0
+
+        gen_kwargs = dict(
+            max_new_tokens=max_new_tokens,
+            do_sample=do_sample,
+            use_cache=True,
+        )
+        if do_sample:
+            gen_kwargs.update(dict(temperature=temperature, top_p=top_p))
+
+        with torch.inference_mode():
+            out = self.model.generate(**inputs, **gen_kwargs)
+
+        gen = out[0, inputs["input_ids"].shape[1]:]
+        return [{"generated_text": self.tokenizer.decode(gen, skip_special_tokens=True).strip()}]