Towards Data Science is a community-powered publication that showcases work in data science, machine learning and artificial intelligence. Every day newcomers, seasoned researchers and industry practitioners publish tutorials, research notes and real-world case studies that help the field move forward.

Towards Data Science

A practitioner shares six non-obvious architectural insights gained from implementing GPT-2 from scratch with PyTorch, covering: RsLoRA's rank-stabilized scaling fix (with statistical proof that LoRA's variance shrinks as rank grows), why RoPE outperforms sinusoidal and learned positional embeddings, when weight tying makes sense vs. when it disappears at scale, the stability tradeoff between Pre-LayerNorm and Post-LayerNorm, how KV Cache reduces attention compute from O(T²) to O(T) and its memory cost, and why LayerNorm is skipped during INT8 quantization due to its sensitivity relative to negligible parameter savings.

6 Things I Learned Building LLMs From Scratch That No Tutorial Teaches You

2. RoPE instead of Learned Parameters or Sinusoidal Positional Embeddings (PEs)

6. Quantization Tradeoff: Why LayerNorm is skipped during INT8 quantization