diff --git a/.gitignore b/.gitignore
index 0fa523b8..4ab91713 100644
--- a/.gitignore
+++ b/.gitignore
@@ -147,12 +147,21 @@ training_*.json
 
 # Data (too large for git)
 data/
-datasets/
+# Downloaded datasets (large files, not committed)
+datasets/*
+# BUT allow tiny datasets directory (small files we ship)
+!datasets/tiny/
+!datasets/README.md
+!datasets/download_*.py
 *.csv
-*.npz
+# *.npz  - Don't ignore .npz globally, some are tiny datasets
 *.npy
 *.pickle
 *.pkl
+# Ignore large .npz files (but not in datasets/tiny/)
+data/*.npz
+datasets/mnist/*.npz
+datasets/cifar10/*.npz
 
 # Temporary files
 tmp/
diff --git a/datasets/README.md b/datasets/README.md
index c58b767a..1ca7e215 100644
--- a/datasets/README.md
+++ b/datasets/README.md
@@ -2,6 +2,31 @@
 
 This directory contains datasets for TinyTorch examples and training.
 
+## Directory Structure
+
+```
+datasets/
+├── tiny/           ← Tiny datasets shipped with repo (~100KB each)
+│   └── digits_8x8.npz (1,797 samples, 67KB)
+├── mnist/          ← Full MNIST (downloaded, gitignored)
+├── cifar10/        ← Full CIFAR-10 (downloaded, gitignored)
+└── download_*.py   ← Download scripts for large datasets
+```
+
+## Quick Start
+
+**For learning (instant, offline):**
+```python
+# Use tiny shipped datasets
+import numpy as np
+data = np.load('datasets/tiny/digits_8x8.npz')
+```
+
+**For serious training (download once):**
+```bash
+python datasets/download_mnist.py
+```
+
 ## MNIST Dataset
 
 The `mnist/` directory should contain the MNIST or Fashion-MNIST dataset files:
diff --git a/datasets/tiny/README.md b/datasets/tiny/README.md
new file mode 100644
index 00000000..71dde5ad
--- /dev/null
+++ b/datasets/tiny/README.md
@@ -0,0 +1,133 @@
+# Tiny Datasets for TinyTorch
+
+**Small, curated datasets that ship with TinyTorch** - no downloads required!
+
+These datasets are committed to the repository for instant, offline-friendly learning.
+
+---
+
+## 📊 Available Datasets
+
+### 8×8 Handwritten Digits
+
+**File:** `digits_8x8.npz`  
+**Size:** ~67 KB  
+**Samples:** 1,797 images  
+**Shape:** (8, 8) grayscale  
+**Classes:** 10 digits (0-9)  
+**Source:** UCI ML Repository via sklearn
+
+**Perfect for:**
+- Learning DataLoader mechanics
+- Quick CNN testing
+- Offline development
+- Educational demos
+
+**Usage:**
+```python
+import numpy as np
+from tinytorch import Tensor
+from tinytorch.data.loader import TensorDataset, DataLoader
+
+# Load the dataset
+data = np.load('datasets/tiny/digits_8x8.npz')
+images = Tensor(data['images'])
+labels = Tensor(data['labels'])
+
+# Create dataset and loader
+dataset = TensorDataset(images, labels)
+loader = DataLoader(dataset, batch_size=32, shuffle=True)
+
+# Iterate through batches
+for batch_images, batch_labels in loader:
+    print(f"Batch: {batch_images.shape}, Labels: {batch_labels.shape}")
+```
+
+**Visual Sample:**
+```
+Digit "5":        Digit "3":        Digit "8":
+░░████░░         ░█████░          ░█████░░
+░█░░░█░          ░░░░░█░          █░░░░░█░
+░░░░█░░          ░░███░░          ░█████░░
+░░░█░░░          ░░░░░█░          █░░░░░█░
+░░█░░░░          ░░████░░          ░█████░░
+```
+
+---
+
+## 🎯 Philosophy
+
+**Why ship tiny datasets?**
+
+1. **Zero friction** - Students start learning immediately
+2. **Offline-first** - Works in classrooms, planes, anywhere
+3. **Fast iteration** - No wait times, instant feedback
+4. **Educational focus** - Sized for learning, not production
+
+**Progression:**
+- **Tiny datasets** (here) → Learn DataLoader mechanics
+- **Downloaded datasets** (../mnist/, ../cifar10/) → Real applications
+- **Custom datasets** → Production skills
+
+---
+
+## 📂 File Format
+
+All datasets use NumPy's `.npz` format (compressed):
+
+```python
+data = np.load('dataset.npz')
+images = data['images']  # Shape: (N, H, W) or (N, H, W, C)
+labels = data['labels']  # Shape: (N,)
+```
+
+**Benefits:**
+- Fast loading
+- Compressed storage
+- Python-native
+- Easy inspection
+
+---
+
+## 🔧 Creating New Tiny Datasets
+
+See `create_digits_8x8.py` for example extraction script.
+
+**Guidelines:**
+- Max size: ~100 KB per dataset
+- Format: `.npz` with `images` and `labels` keys
+- Normalize: Images in [0, 1] range
+- License: Verify public domain / open source
+
+---
+
+## 📚 Dataset Information
+
+### Digits 8×8 Credits
+
+**Original Source:** 
+- E. Alpaydin, C. Kaynak (1998)
+- UCI Machine Learning Repository
+- "Optical Recognition of Handwritten Digits"
+
+**Preprocessing:**
+- Extracted via `sklearn.datasets.load_digits()`
+- Normalized from [0-16] to [0-1]
+- Saved as float32 for efficiency
+
+**License:** Public domain
+
+---
+
+## 🚀 Next Steps
+
+After mastering DataLoader with tiny datasets:
+
+1. **Module 08** → Build DataLoader with digits_8x8
+2. **Milestone 03** → Train MLP on full MNIST
+3. **Milestone 04** → Train CNN on CIFAR-10
+4. **Custom datasets** → Apply to your own data
+
+Tiny datasets teach the mechanics.  
+Real datasets teach the systems.  
+Custom datasets teach the engineering.
diff --git a/datasets/tiny/create_digits_8x8.py b/datasets/tiny/create_digits_8x8.py
new file mode 100644
index 00000000..d3d61979
--- /dev/null
+++ b/datasets/tiny/create_digits_8x8.py
@@ -0,0 +1,53 @@
+#!/usr/bin/env python3
+"""
+Create 8x8 Digits Dataset
+=========================
+
+Extracts the 8×8 handwritten digits dataset from sklearn and saves it
+as a compact .npz file for TinyTorch.
+
+Source: UCI Machine Learning Repository
+Used by: sklearn.datasets.load_digits()
+Size: 1,797 samples, 8×8 grayscale images
+License: Public domain
+"""
+
+import numpy as np
+
+try:
+    from sklearn.datasets import load_digits
+except ImportError:
+    print("❌ sklearn not installed. Install with: pip install scikit-learn")
+    exit(1)
+
+print("📥 Loading 8×8 digits from sklearn...")
+digits = load_digits()
+
+print(f"✅ Loaded {len(digits.images)} digit images")
+print(f"   Shape: {digits.images.shape}")
+print(f"   Classes: {np.unique(digits.target)}")
+
+# Normalize to [0, 1] range (original is 0-16)
+images_normalized = digits.images.astype(np.float32) / 16.0
+labels = digits.target.astype(np.int64)
+
+# Save as compressed .npz
+output_file = 'digits_8x8.npz'
+np.savez_compressed(output_file,
+                    images=images_normalized,
+                    labels=labels)
+
+# Check file size
+import os
+file_size_kb = os.path.getsize(output_file) / 1024
+print(f"\n💾 Saved to {output_file}")
+print(f"   File size: {file_size_kb:.1f} KB")
+print(f"   Images shape: {images_normalized.shape}")
+print(f"   Labels shape: {labels.shape}")
+print(f"   Value range: [{images_normalized.min():.2f}, {images_normalized.max():.2f}]")
+
+# Quick verification
+print(f"\n✅ Dataset ready for TinyTorch!")
+print(f"   Total samples: {len(images_normalized)}")
+print(f"   Samples per class: ~{len(images_normalized) // 10}")
+print(f"   Perfect for DataLoader demos!")
diff --git a/datasets/tiny/digits_8x8.npz b/datasets/tiny/digits_8x8.npz
new file mode 100644
index 00000000..c2b475b8
Binary files /dev/null and b/datasets/tiny/digits_8x8.npz differ