diff --git a/examples/simple.py b/examples/simple.py
new file mode 100644
index 0000000..e71e48c
--- /dev/null
+++ b/examples/simple.py
@@ -0,0 +1,177 @@
+#!/usr/bin/env python3
+"""
+Simple example showing minimal usage of ScratchGPT to train on Darwin's "On the Origin of Species"
+
+This script demonstrates:
+1. Downloading training data from Project Gutenberg
+2. Setting up a basic configuration
+3. Training a small transformer model
+4. Basic text generation
+
+Usage:
+    python simple.py
+"""
+
+import sys
+import tempfile
+from pathlib import Path
+from urllib.request import urlretrieve
+
+import torch
+from torch.optim import AdamW
+
+# Import ScratchGPT components
+from scratchgpt import (
+    CharTokenizer,
+    FileDataSource,
+    ScratchGPTArchitecture,
+    ScratchGPTConfig,
+    ScratchGPTTraining,
+    Trainer,
+    TransformerLanguageModel,
+)
+
+
+def download_darwin_text(data_file: Path) -> None:
+    """Download Darwin's 'On the Origin of Species' using Python's built-in urllib."""
+    print("Downloading 'On the Origin of Species' by Charles Darwin...")
+    url = "https://www.gutenberg.org/files/1228/1228-0.txt"
+
+    try:
+        urlretrieve(url, data_file)
+        print(f"Downloaded data to: {data_file}")
+    except Exception as e:
+        print(f"Failed to download data: {e}")
+        print("Please manually download the file from:")
+        print(url)
+        sys.exit(1)
+
+
+def create_simple_config() -> ScratchGPTConfig:
+    """Create a minimal configuration suitable for quick training."""
+    # Small architecture for quick training on CPU/small GPU
+    architecture = ScratchGPTArchitecture(
+        block_size=128,
+        embedding_size=256,
+        num_heads=8,
+        num_blocks=4,
+        # vocab_size will be set based on the tokenizer
+    )
+
+    # Training config optimized for quick results
+    training = ScratchGPTTraining(
+        max_epochs=20,
+        learning_rate=3e-4,
+        batch_size=32,
+        dropout_rate=0.1,
+        random_seed=1337,
+    )
+
+    return ScratchGPTConfig(
+        architecture=architecture,
+        training=training
+    )
+
+
+def prepare_text_for_tokenizer(data_file: Path) -> str:
+    """Read the text file for tokenization."""
+    print(f"Reading text from: {data_file}")
+
+    with open(data_file, encoding='utf-8') as f:
+        text = f.read()
+
+    print(f"Text length: {len(text):,} characters")
+    return text
+
+
+def main():
+    print("ScratchGPT Simple Training Example")
+    print("=" * 50)
+
+    # Use temporary directory that auto-cleans when done
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        tmp_path = Path(tmp_dir)
+        data_file = tmp_path / "darwin_origin_species.txt"
+        experiment_dir = tmp_path / "darwin_experiment"
+
+        # Step 1: Download data
+        download_darwin_text(data_file)
+
+        # Step 2: Prepare text and create tokenizer
+        text = prepare_text_for_tokenizer(data_file)
+        print("Creating character-level tokenizer...")
+        tokenizer = CharTokenizer(text=text)
+        print(f"Vocabulary size: {tokenizer.vocab_size}")
+
+        # Alternative: Use a pre-trained tokenizer like GPT-2
+        # This requires: pip install 'scratchgpt[hf-tokenizers]'
+        #
+        # from scratchgpt import HuggingFaceTokenizer
+        # tokenizer = HuggingFaceTokenizer.from_hub("gpt2")
+        # print(f"Vocabulary size: {tokenizer.vocab_size}")  # ~50,257 tokens
+        #
+        # Trade-offs:
+        # - CharTokenizer: Small vocab (~100 chars), learns from scratch, simple
+        # - GPT-2 Tokenizer: Large vocab (~50K tokens), pre-trained, better text quality
+        # - GPT-2 tokenizer will likely generate more coherent text but requires more memory
+
+        # Step 3: Create configuration
+        config = create_simple_config()
+        config.architecture.vocab_size = tokenizer.vocab_size
+        print(f"Model configuration: {config.architecture.embedding_size}D embeddings, "
+              f"{config.architecture.num_blocks} blocks, {config.architecture.num_heads} heads")
+
+        # Step 4: Setup model and training
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"Using device: {device}")
+
+        model = TransformerLanguageModel(config)
+        model = model.to(device)
+        print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")
+
+        optimizer = AdamW(model.parameters(), lr=config.training.learning_rate)
+        data_source = FileDataSource(data_file)
+
+        # Step 5: Create trainer and start training
+        trainer = Trainer(
+            model=model,
+            config=config.training,
+            optimizer=optimizer,
+            experiment_path=experiment_dir,
+            device=device
+        )
+
+        print("\nStarting training...")
+        print("(Press Ctrl-C to stop training early and see text generation)")
+
+        try:
+            trainer.train(data=data_source, tokenizer=tokenizer)
+            print("\nTraining completed successfully!")
+        except KeyboardInterrupt:
+            print("\n\nTraining interrupted by user. Moving to text generation with current model state...")
+
+        # Step 6: Simple text generation demo
+        print("\nTesting text generation:")
+        model.eval()
+
+        test_prompts = [
+            "Natural selection",
+            "The origin of species",
+            "Darwin observed"
+        ]
+
+        for prompt in test_prompts:
+            print(f"\nPrompt: '{prompt}'")
+            context = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0).to(device)
+
+            with torch.no_grad():
+                generated = model.generate(context, max_new_tokens=100)
+                result = tokenizer.decode(generated[0].tolist())
+                print(f"Generated: {result}")
+
+        print("\nAll temporary files automatically cleaned up.")
+        print("Run the script again to start fresh.")
+
+
+if __name__ == "__main__":
+    main()