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example.py

@@ -14,7 +14,7 @@ AutoImageProcessor.register("nemotron_parse", NemotronParseImageProcessor)
 
 
 # Load model and processor
-model_path = "nvidia/NVIDIA-Nemotron-Parse-v1.1" #Nano-12B-v2-VL-BF16"  # Or use a local path
+model_path = "." #nvidia/NVIDIA-Nemotron-Parse-v1.1" #Nano-12B-v2-VL-BF16"  # Or use a local path
 device = "cuda:0"
 
 model = AutoModel.from_pretrained(

hf_nemotron_parse_processor.py

@@ -252,7 +252,7 @@ class NemotronParseImageProcessor(BaseImageProcessor, ImageProcessingMixin):
 class NemotronParseProcessor(ProcessorMixin):
     
     attributes = ["image_processor", "tokenizer"]
-    image_processor_class = "NemotronParseImageProcessor"
+    image_processor_class = "AutoImageProcessor"
     tokenizer_class = ("PreTrainedTokenizer", "PreTrainedTokenizerFast")
     
     def __init__(self, image_processor=None, tokenizer=None, **kwargs):
@@ -350,8 +350,24 @@ class NemotronParseProcessor(ProcessorMixin):
         
         This method is compatible with AutoProcessor.from_pretrained().
         """
-        # Use the parent class's from_pretrained method which handles auto-loading
-        return super().from_pretrained(pretrained_model_name_or_path, **kwargs)
+        # Explicitly load subcomponents via Auto* to ensure remote auto_map is honored.
+        from transformers import AutoImageProcessor, AutoTokenizer
+        trust_remote_code = kwargs.get("trust_remote_code", None)
+        revision = kwargs.get("revision", None)
+        token = kwargs.get("token", None)
+        image_processor = AutoImageProcessor.from_pretrained(
+            pretrained_model_name_or_path,
+            trust_remote_code=trust_remote_code,
+            revision=revision,
+            token=token,
+        )
+        tokenizer = AutoTokenizer.from_pretrained(
+            pretrained_model_name_or_path,
+            trust_remote_code=trust_remote_code,
+            revision=revision,
+            token=token,
+        )
+        return cls(image_processor=image_processor, tokenizer=tokenizer)
     
     def save_pretrained(self, save_directory, **kwargs):
         """

preprocessor_config.json

@@ -3,10 +3,9 @@
   "image_processor_type": "NemotronParseImageProcessor",
   "processor_class": "NemotronParseProcessor",
   "auto_map": {
-    "AutoProcessor": "hf_nemotron_parse_processor.NemotronParseProcessor",
-    "AutoImageProcessor": "hf_nemotron_parse_processor.NemotronParseImageProcessor"
+    "AutoImageProcessor": "hf_nemotron_parse_processor.NemotronParseImageProcessor",
+    "AutoProcessor": "hf_nemotron_parse_processor.NemotronParseProcessor"
   },
-
   "do_normalize": false,
   "do_rescale": true,
   "rescale_factor": 0.00392156862745098,