Bridge the gap between research code and production deployment
Welcome to the Production Techniques series, where we take the GPT architecture you built in the core series and make it production-ready.
You’ve learned how to build and train a Transformer from scratch. Now it’s time to tackle the challenges that arise when moving from toy datasets to real-world deployment: loading massive pretrained models, processing terabytes of data, evaluating model quality, and fine-tuning efficiently on consumer hardware.
What You’ll Learn¶
This series covers the essential techniques used in production LLM workflows:
Loading pretrained weights from HuggingFace and other sources
Building data pipelines that handle terabytes of training data
Evaluating models with industry-standard benchmarks
Fine-tuning efficiently with LoRA on consumer GPUs
Training faster with mixed precision (FP16/BF16)
Prerequisites¶
You should have completed (or be familiar with):
The core LLM From Scratch series (L01-L10)
Basic PyTorch training loops
Understanding of transformer architecture fundamentals
The Series¶
Lesson | Title | What You’ll Learn |
|---|---|---|
L11 | Starting from GPT-2 instead of random — Load HuggingFace weights, handle vocabulary mismatches, choose freezing strategies | |
L12 | From toy datasets to production — Stream terabytes with WebDataset, quality filtering, deduplication, data mixing | |
L13 | How do I know if my model is good? — Perplexity, MMLU, HellaSwag, TruthfulQA, and custom benchmarks | |
L14 | Fine-tune 7B models on a single GPU — Low-rank adaptation mathematics, QLoRA, adapter swapping | |
L15 | Train 2-3× faster with half the memory — FP16, BF16, gradient scaling, PyTorch AMP |
Why These Topics Matter¶
The Reality Gap¶
There’s a massive gap between:
Training a 124M parameter model on a toy dataset → Academic exercise
Fine-tuning a 7B model on real data for production → Real-world challenge
This series bridges that gap with practical techniques used at companies like OpenAI, Anthropic, and HuggingFace.
Real-World Constraints¶
Production LLM work faces constraints that research doesn’t:
| Constraint | Solution in This Series |
|---|---|
| Can’t train from scratch (too expensive) | L11: Load pretrained weights |
| Can’t fit model in GPU memory | L14: LoRA (14 GB → 4 GB) |
| Can’t wait weeks for training | L15: Mixed precision (2-3× speedup) |
| Can’t trust arbitrary benchmarks | L13: Comprehensive evaluation |
| Can’t load all data into RAM | L12: Streaming data pipelines |
Learning Path¶
Sequential approach (recommended):
L11: Start here if you want to fine-tune existing models
L12: Learn data engineering for real training runs
L13: Understand how to measure success
L14: Make fine-tuning practical on limited hardware
L15: Speed up everything with mixed precision
Jump-in approach:
Need to fine-tune now? → Start with L14 (LoRA)
Building a data pipeline? → Jump to L12
Choosing between models? → Go to L13 (Evaluation)
What’s Next¶
After completing this series:
Scaling & Optimization: Attention optimizations, model parallelism, long contexts, quantization, deployment
Real projects: Fine-tune models for your domain, build production pipelines
Philosophy¶
Production-First: Every technique is chosen because it’s used in real production systems, not just because it’s theoretically interesting.
Resource-Aware: We optimize for consumer hardware (24GB GPUs) and cloud budgets, not unlimited research clusters.
Measured Impact: We quantify improvements (2× faster, 4× less memory) instead of vague claims.
End-to-End: From loading weights to evaluation, we cover the full workflow.
Ready to Begin?¶
Let’s start by learning how to load pretrained weights like GPT-2 and fine-tune them for your tasks.
Next: L11 - Loading Pretrained Weights & Transfer Learning →
This series assumes you’ve completed the core LLM From Scratch series. If you’re new, start there to understand the fundamentals of transformers, attention, and training.