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Model: Dilana/Llama-3.2-1B-Adaptive-RAG-v3
Source: Original Platform

handler.py

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+from typing import Dict, List, Any
+import torch
+from transformers import PreTrainedTokenizerFast, AutoModelForCausalLM, pipeline
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # 1. Force the Fast Tokenizer to bypass the legacy TokenizersBackend bug
+        # Setting padding_side="left" is standard for Causal LMs
+        self.tokenizer = PreTrainedTokenizerFast.from_pretrained(
+            path, 
+            padding_side="left"
+        )
+        
+        # Safely set pad token to eos_token to avoid warnings
+        if self.tokenizer.pad_token_id is None:
+            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
+
+        # 2. Load Model strictly for CPU
+        # Using bfloat16 optimizes RAM usage significantly on Sapphire Rapids
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            torch_dtype=torch.bfloat16
+        )
+        
+        # 3. Create Pipeline attached to self
+        self.pipeline = pipeline(
+            "text-generation", 
+            model=self.model, 
+            tokenizer=self.tokenizer,
+            return_full_text=False # Prevent echoing the prompt
+        )
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        # Extract inputs and parameters safely
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", {})
+
+        # Execute generation
+        prediction = self.pipeline(inputs, **parameters)
+        return prediction