diff --git a/examples/chemistry.py b/examples/chemistry.py
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+++ b/examples/chemistry.py
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+#!/usr/bin/env python3
+"""
+Chemical Reaction Prediction Example - Train a transformer to predict reaction products
+
+This script demonstrates training a GPT-style model on chemical reactions from the USPTO
+patent database. It downloads reactions in SMILES notation and trains a transformer to
+complete reactions by predicting products from reactants.
+
+The model learns chemical transformation patterns without knowing any chemistry - it just
+sees that certain molecular structures tend to produce other structures.
+
+We use special tokens [BOS] and [EOS] to mark the beginning and end of each reaction,
+helping the model learn when to stop generating.
+
+"""
+
+import shutil
+import sys
+import tempfile
+import time
+from pathlib import Path
+
+import torch
+from datasets import Dataset, load_dataset
+from torch.optim import AdamW
+
+from scratchgpt import (
+    CharTokenizer,
+    ScratchGPTArchitecture,
+    ScratchGPTConfig,
+    ScratchGPTTraining,
+    Trainer,
+    TransformerLanguageModel,
+    save_tokenizer,
+)
+from scratchgpt.data import create_data_source
+
+# Test reactions for demonstration
+TEST_REACTIONS = [
+    ("CC(=O)O.CCO", "Esterification (acetic acid + ethanol)"),
+    ("c1ccccc1.Cl2", "Chlorination (benzene + chlorine)"),
+    ("CC=C.HBr", "Addition (propene + HBr)"),
+    ("CC(=O)Cl.N", "Amide formation (acetyl chloride + ammonia)"),
+    ("CCO.[O]", "Oxidation (ethanol + oxygen)"),
+]
+
+# Display configuration
+MAX_DISPLAY_LENGTH: int = 80
+SEPARATOR_WIDTH: int = 70
+
+# Summary text
+TRAINING_SUMMARY = """
+Chemical reaction prediction training complete!
+
+What the model learned:
+- Patterns of how molecular structures transform in reactions
+- Common functional group conversions (esters, amides, etc.)
+- Product structures that typically result from given reactants
+- When to stop generating (using [EOS] token)
+- The model doesn't know chemistry rules, just statistical patterns!
+"""
+
+
+def truncate_for_display(text: str, max_length: int = MAX_DISPLAY_LENGTH) -> str:
+    """Truncate text for display, adding ellipsis if needed."""
+    if len(text) > max_length:
+        return text[:max_length] + "..."
+    return text
+
+
+def load_reaction_dataset() -> Dataset:
+    """
+    Load the USPTO-50k chemical reaction dataset from HuggingFace.
+
+    This dataset contains ~50,000 reactions extracted from US patents,
+    represented in SMILES notation with atom mapping.
+    """
+    print("Loading USPTO-50k reaction dataset from HuggingFace...")
+    print("This dataset contains 50,000 chemical reactions from US patents.")
+
+    dataset: Dataset = load_dataset("pingzhili/uspto-50k", split="train")
+
+    print(f"✓ Loaded {len(dataset):,} reactions")
+    return dataset
+
+
+def prepare_reaction_text(dataset: Dataset) -> str:
+    """
+    Extract reaction SMILES and concatenate them into training text.
+
+    Reactions are in the format: reactants >> products
+    Example: CC(=O)O.CCO >> CC(=O)OCC.O (esterification)
+
+    We wrap each reaction with special tokens:
+    [BOS] reaction [EOS]
+    """
+    print("\nPreparing reaction data for training...")
+
+    column_names: list[str] = dataset.column_names
+    print(f"Dataset columns: {column_names}")
+
+    # Find reaction column
+    possible_columns: list[str] = ["rxn_smiles", "reaction_smiles", "text", "reaction", "smiles", "rxn"]
+    reaction_column: str | None = None
+
+    for column in possible_columns:
+        if column in column_names:
+            reaction_column = column
+            break
+
+    if reaction_column is None:
+        print("ERROR: Could not find reaction column!")
+        print(f"Available columns: {column_names}")
+        return ""
+
+    print(f"Using column: '{reaction_column}'")
+
+    # Extract and wrap reactions with special tokens
+    reactions: list[str] = []
+    for example in dataset:
+        reaction: str = str(example[reaction_column]).strip()
+        if reaction and ">>" in reaction:
+            wrapped_reaction: str = f"[BOS]{reaction}[EOS]"
+            reactions.append(wrapped_reaction)
+
+    print(f"Extracted {len(reactions):,} valid reactions")
+
+    # Show sample reactions
+    print("\nSample reactions (with special tokens):")
+    for index, reaction in enumerate(reactions[:3], start=1):
+        display: str = truncate_for_display(reaction)
+        print(f"  {index}. {display}")
+
+    full_text: str = "\n".join(reactions)
+    print(f"\nTotal text length: {len(full_text):,} characters")
+
+    return full_text
+
+
+def create_chemistry_config(vocab_size: int) -> ScratchGPTConfig:
+    """
+    Create a configuration optimized for chemical reaction prediction.
+
+    Chemistry has different patterns than language or chess:
+    - Reactions can be long (100-300 characters)
+    - Pattern complexity is between chess and natural language
+    - Needs to learn molecular substructure relationships
+    """
+    architecture: ScratchGPTArchitecture = ScratchGPTArchitecture(
+        block_size=256,
+        embedding_size=256,
+        num_heads=8,
+        num_blocks=6,
+        vocab_size=vocab_size,
+    )
+
+    training: ScratchGPTTraining = ScratchGPTTraining(
+        max_epochs=15,
+        learning_rate=3e-4,
+        batch_size=32,
+        dropout_rate=0.1,
+        random_seed=1337,
+        iteration_type="chunking",
+    )
+
+    return ScratchGPTConfig(architecture=architecture, training=training)
+
+
+def generate_reaction_products(
+    device: torch.device,
+    model: TransformerLanguageModel,
+    tokenizer: CharTokenizer,
+    reactants: str,
+    max_tokens: int = 150,
+) -> str:
+    """
+    Generate reaction products from given reactants.
+
+    The model completes the reaction by predicting what comes after '>>'.
+    We start with [BOS] and stop when we hit [EOS].
+    """
+    model.eval()
+
+    # Clean reactants - SMILES shouldn't have spaces
+    reactants_clean: str = reactants.strip().replace(" ", "")
+
+    # Get the [EOS] token ID for stopping generation
+    eos_in_vocab: bool = "[EOS]" in tokenizer.vocabulary
+    eos_token_id: int | None = tokenizer.encode("[EOS]")[0] if eos_in_vocab else None
+
+    # Build prompt with special tokens and reaction arrow
+    prompt: str = f"[BOS]{reactants_clean}>>"
+
+    with torch.no_grad():
+        context: torch.Tensor = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0).to(device)
+        generated: torch.Tensor = model.generate(
+            context, max_new_tokens=max_tokens, temperature=0.8, stop_token=eos_token_id
+        )
+        result: str = tokenizer.decode(generated[0].tolist())
+
+    return result
+
+
+def main() -> None:
+    print("Chemical Reaction Prediction with ScratchGPT")
+    print("=" * 60)
+
+    # Load dataset from HuggingFace
+    print("\n--- Loading Chemical Reaction Dataset ---")
+    dataset: Dataset = load_reaction_dataset()
+
+    # Prepare reaction text
+    reactions_text: str = prepare_reaction_text(dataset)
+
+    if not reactions_text.strip():
+        print("ERROR: No valid reactions were extracted!")
+        sys.exit(1)
+
+    # Create character-level tokenizer
+    print("\n--- Creating Character Tokenizer ---")
+    tokenizer: CharTokenizer = CharTokenizer(text=reactions_text)
+    print(f"Vocabulary size: {tokenizer.vocab_size}")
+    print("Includes special tokens: [BOS] (begin) and [EOS] (end)")
+    sample_chars: list[str] = sorted(tokenizer.vocabulary)[:20]
+    print(f"Sample characters: {sample_chars}")
+
+    # Create chemistry-optimized config
+    print("\n--- Creating Chemistry Model Configuration ---")
+    config: ScratchGPTConfig = create_chemistry_config(tokenizer.vocab_size)
+    print(
+        f"Model: {config.architecture.embedding_size}D embeddings, "
+        f"{config.architecture.num_blocks} blocks, {config.architecture.num_heads} heads"
+    )
+
+    # Setup device and model
+    device: torch.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print(f"\nUsing device: {device}")
+
+    model: TransformerLanguageModel = TransformerLanguageModel(config)
+    model = model.to(device)
+    total_params: int = sum(p.numel() for p in model.parameters())
+    print(f"Model parameters: {total_params:,}")
+
+    # Setup training with temporary directory
+    temp_path: Path = Path(tempfile.mkdtemp())
+    reactions_file: Path = temp_path / "reactions.txt"
+    experiment_dir: Path = temp_path / "chemistry_experiment"
+
+    print("\nSaving reactions to temporary file...")
+    with open(reactions_file, "w", encoding="utf-8") as file:
+        file.write(reactions_text)
+
+    data_source = create_data_source(str(reactions_file))
+    optimizer: AdamW = AdamW(model.parameters(), lr=config.training.learning_rate)
+    trainer: Trainer = Trainer(
+        model=model,
+        config=config.training,
+        optimizer=optimizer,
+        experiment_path=experiment_dir,
+        device=device,
+    )
+
+    save_tokenizer(experiment_dir, tokenizer)
+
+    # Training
+    print("\n--- Starting Chemical Reaction Training ---")
+    print("The model will learn to predict reaction products from reactants")
+    print("Press Ctrl-C to stop training early and see predictions")
+
+    start_time: float = time.time()
+
+    try:
+        trainer.train(data_source=data_source, tokenizer=tokenizer)
+        training_time: float = time.time() - start_time
+        print(f"\n✅ Training completed in {training_time:.1f} seconds")
+    except KeyboardInterrupt:
+        training_time: float = time.time() - start_time
+        print(f"\n⚠️ Training interrupted after {training_time:.1f} seconds")
+        print("Proceeding with reaction prediction demo...")
+
+    # Prediction demo
+    print("\n--- Chemical Reaction Prediction Demo ---")
+    print("Testing the model's ability to predict reaction products")
+    print("=" * SEPARATOR_WIDTH)
+
+    for reactants, reaction_name in TEST_REACTIONS:
+        print(f"\nReaction: {reaction_name}")
+        print(f"Reactants: {reactants}")
+        print("-" * 50)
+
+        result: str = generate_reaction_products(device, model, tokenizer, reactants)
+
+        # Clean up special tokens from result
+        result_clean: str = result.replace("[BOS]", "").replace("[EOS]", "")
+
+        # Extract predicted products (everything after >>)
+        if ">>" in result_clean:
+            predicted_products: str = result_clean.split(">>", 1)[1].strip()
+            display: str = truncate_for_display(predicted_products)
+            print(f"Predicted products: {display}")
+        else:
+            print("Generated: (incomplete prediction)")
+
+    print("\n" + "=" * SEPARATOR_WIDTH)
+
+    # Summary
+    print(TRAINING_SUMMARY)
+    print(f"Experiment saved temporarily to: {experiment_dir}")
+    print("All files will be cleaned up when the script exits.")
+
+    # Cleanup
+    shutil.rmtree(temp_path, ignore_errors=True)
+
+
+if __name__ == "__main__":
+    main()