From-scratch implementation means constructing a model’s components and training it directly rather than treating a prebuilt model as a black box. The payoff is a clearer view of how the architecture works and more control over how it is used. section 1.4
Start with a small GAN
Build a generator that creates samples and a discriminator that tries to distinguish them from real data. Train the pair to produce points along an exponential-growth curve, then try a different shape or a sequence of multiples of five. Small targets make the competing networks and their training behavior easier to inspect. section 3.0
For a text-generating GPT model, work through the pipeline in order:
- Choose a trainable size: sequence length, embedding dimension, decoder-block count, and dropout rates affect output quality and training speed. section 12.1
- Clean the text, tokenize it, assign token IDs, and form input sequences with targets shifted one token ahead. For example, an input (t0, t1, t2) has target (t1, t2, t3). section 12.1
- Train the model, then turn a prompt into token IDs, predict further tokens iteratively, and convert the result back to text. section 12.1
Scale is a practical boundary. A GPT-2XL-sized model can be constructed but calls for enormous data and computing resources to train. A roughly five-million-parameter version with three decoder blocks and a 256-dimensional embedding can instead be trained on a regular computer. Training from scratch starts with random parameters; fine-tuning starts with pretrained weights. section 12.0 section 1.4
The same build-and-adapt approach extends to music generation: keep a GPT-style architecture, but select its decoder layers, attention heads, embedding dimension, and input length for the new task. section 14.3
