Precision Engineering, Not Mass Production
Using a pre-built library is like buying a family sedan. Building from scratch with JAX is like engineering a Porsche GT3: you gain the control to innovate and optimize for peak performance on a specific track.
Swap Components, Find Your Edge
A from-scratch implementation makes it easy to experiment with the latest architectural advancements. You can swap out components to test their impact on performance and accuracy, just like a race team testing new parts.
- Replace standard Positional Encoding with Rotary Positional Embeddings (RoPE) for better long-sequence handling.
- Swap the FFN's GELU activation function for the more modern SwiGLU to improve efficiency.
- Experiment with different normalization layers like RMSNorm instead of LayerNorm to reduce computational overhead.
Fine-Tuning for the Hardware Track
As the AI Builder module in our app demonstrates, choosing hyperparameters like `d_model` and `n_heads` is a deeply technical decision. But true performance comes from tuning your model to the metal.
By building the model yourself, you learn *why* certain choices are better for specific hardware. Choosing a head dimension (`d_k`) that is a multiple of 64 or 128 leads to faster memory access and more efficient computation on TPUs. This is the equivalent of adjusting your car's suspension for a specific racetrack—it's a level of control that's critical for production-grade performance and something you can't get from an off-the-lot model.