Nano Language Models

We ‘re now moving from theory and deployment into hands-on Python implementation — showing “how to actually build a small language model from scratch“.

Here’s a table of contents (15 articles) that focuses on building a small language model from scratch as a tutorial.

Part 1: Foundations — From Text to Tokens

1. Introduction: What Is a Small Language Model (SLM)?
   → Explain architecture, tokenization, and what makes “small” models special.
2. Collecting and Cleaning Your Dataset
   → Use open text sources (TinyStories, Gutenberg, wikitext). Show cleaning & normalization in Python.
3. Building a Simple Tokenizer from Scratch
   → Implement Byte-Pair Encoding (BPE) or WordPiece tokenizer in Python — step by step.
4. Converting Text into Numerical Data
   → Create a vocabulary, encode text into token IDs, and build a dataloader with PyTorch.

Part 2: Architecture — Building the Brain

5. Building a Tiny Transformer in PyTorch
   → Define embeddings, attention, feedforward, and layer normalization manually.
6. Understanding Multi-Head Attention (Visually and in Code)
   → Break down query/key/value matrices, masking, and head aggregation.
7. Implementing Positional Encoding and Model Forward Pass
   → Add positional context and run your first forward pass.
8. Training Loop: Teaching the Model to Predict the Next Token
   → Use cross-entropy loss, batching, and gradient descent to train your SLM.

Part 3: Scaling and Evaluation

9. Generating Text: From Training to Inference
   → Implement greedy and temperature-based sampling.
10. Evaluating Performance: Loss Curves and Perplexity
    → Plot training progress and measure model quality.
11. Optimizing for Speed and Size (Quantization + Mixed Precision)
    → Demonstrate how to shrink model weights using bitsandbytes or PyTorch’s quantization.
12. Saving and Reloading Your Model Efficiently
    → Cover checkpoints, weight saving, and versioning for reproducibility.

Part 4: Beyond the Basics

13. Fine-Tuning Your Small Model on Custom Data
    → Show how to adapt your base SLM to specific text domains.
14. Adding a Simple Chat Interface (Streamlit or FastAPI)
    → Turn the trained model into a mini local chatbot UI.
15. Deploying Your SLM as a GGUF Model (for llama.cpp or Ollama)
    → Export and test your model in small inference environments.

💡 Optional Bonus Tutorials

• “Visualizing Attention Heads in Your Own Model”
• “Adding Memory to a Custom SLM”
• “Comparing Your Model to TinyLlama and Phi-3 Mini”

The code Small Language Model we’re building is here available on Github.

Follow NanoLanguageModels.com to continue the full “Build a Small Language Model from Scratch” series — next up: What Is a Small Language Model (SLM)? ⚙️