初始化项目，由ModelHub XC社区提供模型

Model: pragunk/PropagationShield
Source: Original Platform

handler.py

@@ -0,0 +1,72 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from typing import Dict, List, Any
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        """
+        Initializes the model and tokenizer. 
+        `path` is automatically provided by Hugging Face (it points to your repo files).
+        """
+        print("🚀 Initializing PropagationShield Handler...")
+        
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        
+        # 1. Configure 4-bit quantization to prevent OOM and System RAM limits
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.float16
+        )
+        
+        # 2. Load the model safely
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            quantization_config=bnb_config,
+            device_map="auto",
+            torch_dtype=torch.float16,
+            low_cpu_mem_usage=True, # Crucial to prevent the 30GB RAM crash during boot
+        )
+        print("✅ PropagationShield Loaded Successfully!")
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+        Runs inference on the incoming request.
+        """
+        # Parse incoming data
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", {})
+        
+        max_new_tokens = parameters.get("max_new_tokens", 512)
+        temperature = parameters.get("temperature", 0.1)
+        
+        # 3. Format the prompt
+        # If the user sends a list of messages [{"role": "system", "content": "..."}, ...]
+        if isinstance(inputs, list):
+            prompt = self.tokenizer.apply_chat_template(
+                inputs, tokenize=False, add_generation_prompt=True
+            )
+        # If the user sends a raw formatted string
+        else:
+            prompt = str(inputs)
+            
+        # 4. Tokenize
+        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(self.model.device)
+        
+        # 5. Generate
+        with torch.no_grad():
+            output_ids = self.model.generate(
+                input_ids,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature,
+                do_sample=True if temperature > 0.0 else False,
+                pad_token_id=self.tokenizer.eos_token_id
+            )
+            
+        # 6. Isolate and decode only the newly generated tokens
+        generated_ids = output_ids[0][input_ids.shape[-1]:]
+        generated_text = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
+        
+        # Return in standard HF API format
+        return [{"generated_text": generated_text.strip()}]