初始化项目，由ModelHub XC社区提供模型

Model: SapphireGaze429/opensecops-qwen2.5-7b-grpo
Source: Original Platform

handler.py

@@ -0,0 +1,30 @@
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import torch
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # 1. Load the tokenizer
+        tokenizer = AutoTokenizer.from_pretrained(path)
+        
+        # 2. Load the model using accelerate, letting it handle the device placement
+        model = AutoModelForCausalLM.from_pretrained(
+            path,
+            device_map="auto",
+            torch_dtype=torch.float16 # Strongly recommended for a 7B model to save VRAM
+        )
+        
+        # 3. Create the pipeline WITHOUT the 'device' argument
+        self.pipeline = pipeline(
+            "text-generation", 
+            model=model, 
+            tokenizer=tokenizer
+        )
+
+    def __call__(self, data):
+        # 4. Handle incoming requests from your Next.js dashboard
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", {})
+        
+        # Generate prediction
+        prediction = self.pipeline(inputs, **parameters)
+        return prediction