diff --git a/tasks/nemotron.py b/tasks/nemotron.py
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+"""
+Nemotron Post-Training Dataset v2 by NVIDIA.
+https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2
+
+This dataset includes:
+- math: 239,467 samples
+- code: 175,000 samples  
+- stem: 355,000 samples
+- chat: 627,720 samples
+- multilingual: ~1M samples per language (ja, de, it, es, fr)
+
+Total: ~6.3M samples across all categories
+"""
+
+from datasets import load_dataset
+from tasks.common import Task
+
+class Nemotron(Task):
+    """
+    Nemotron Post-Training Dataset v2.
+    
+    Args:
+        categories: List of categories to load. Can include:
+                   ["math", "code", "stem", "chat", 
+                    "multilingual_ja", "multilingual_de", "multilingual_it", 
+                    "multilingual_es", "multilingual_fr"]
+                   Default: ["math", "code", "stem", "chat"] (English only)
+        split: Dataset split, typically "train" (default: "train")
+        **kwargs: Additional arguments for Task parent class (start, stop, step)
+    
+    Example:
+        # Load only math and code
+        ds = Nemotron(categories=["math", "code"], split="train")
+        
+        # Load everything including multilingual
+        ds = Nemotron(categories=["math", "code", "stem", "chat", 
+                                  "multilingual_ja", "multilingual_de"], 
+                      split="train")
+    """
+    
+    # Available categories and their sizes
+    CATEGORY_SIZES = {
+        "math": 239467,
+        "code": 175000,
+        "stem": 355000,
+        "chat": 627720,
+        "multilingual_ja": 975202,
+        "multilingual_de": 1015314,
+        "multilingual_it": 1016503,
+        "multilingual_es": 935704,
+        "multilingual_fr": 1001504,
+    }
+    
+    def __init__(self, categories=None, split="train", **kwargs):
+        super().__init__(**kwargs)
+        
+        # Default to English-only categories
+        if categories is None:
+            categories = ["math", "code", "stem", "chat"]
+        
+        # Validate categories
+        for cat in categories:
+            assert cat in self.CATEGORY_SIZES, \
+                f"Invalid category '{cat}'. Must be one of {list(self.CATEGORY_SIZES.keys())}"
+        
+        self.categories = categories
+        self.split = split
+        
+        # Load the dataset
+        # Note: The dataset uses "SFT" as the config name
+        print(f"Loading Nemotron dataset with categories: {categories}")
+        
+        # Load all requested categories
+        self.datasets = []
+        self.category_offsets = [0]  # cumulative offsets for indexing
+        
+        for category in categories:
+            try:
+                # Load each category separately
+                ds = load_dataset(
+                    "nvidia/Nemotron-Post-Training-Dataset-v2",
+                    split=category,
+                    trust_remote_code=True
+                )
+                self.datasets.append(ds)
+                self.category_offsets.append(self.category_offsets[-1] + len(ds))
+                print(f"  ✓ Loaded {category}: {len(ds):,} samples")
+            except Exception as e:
+                print(f"  ✗ Failed to load {category}: {e}")
+                raise
+        
+        self.length = self.category_offsets[-1]
+        print(f"Total Nemotron samples: {self.length:,}")
+    
+    def num_examples(self):
+        return self.length
+    
+    def get_example(self, index):
+        """
+        Convert Nemotron dataset format to conversation format.
+        
+        Nemotron format has columns like:
+        - 'prompt': the user query
+        - 'response': the assistant response
+        - possibly 'system': system message
+        """
+        # Find which category this index belongs to
+        category_idx = 0
+        for i, offset in enumerate(self.category_offsets[1:]):
+            if index < offset:
+                category_idx = i
+                break
+        
+        # Get the local index within the category
+        local_index = index - self.category_offsets[category_idx]
+        row = self.datasets[category_idx][local_index]
+        
+        # Convert to conversation format
+        # The Nemotron dataset typically has 'prompt' and 'response' fields
+        messages = []
+        
+        # Add system message if present
+        if 'system' in row and row['system']:
+            messages.append({
+                "role": "system",
+                "content": row['system']
+            })
+        
+        # Add user prompt
+        if 'prompt' in row:
+            messages.append({
+                "role": "user", 
+                "content": row['prompt']
+            })
+        
+        # Add assistant response
+        if 'response' in row:
+            messages.append({
+                "role": "assistant",
+                "content": row['response']
+            })
+        
+        # Fallback: if the dataset uses a different schema, try 'messages' directly
+        if not messages and 'messages' in row:
+            messages = row['messages']
+        
+        # Ensure we have at least a user and assistant message
+        assert len(messages) >= 2, \
+            f"Conversation must have at least 2 messages, got {len(messages)}"
+        
+        conversation = {
+            "messages": messages,
+        }
+        return conversation
+
+
+if __name__ == "__main__":
+    # Test the Nemotron dataset
+    print("Testing Nemotron dataset loader...")
+    
+    # Test 1: Load a small subset
+    print("\n=== Test 1: Load math and code categories ===")
+    ds = Nemotron(categories=["math", "code"], split="train")
+    print(f"Dataset length: {len(ds):,}")
+    
+    # Get a sample
+    print("\n=== Test 2: Get a sample conversation ===")
+    sample = ds[0]
+    print("Sample conversation:")
+    for msg in sample["messages"]:
+        print(f"  {msg['role']}: {msg['content'][:100]}...")
+    
+    print("\n✓ Nemotron dataset loader test completed!")
+