Deep Learning with C++

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Transformers and Large Language Model Fine-Tuning in C++

The 2017 paper Attention is All You Need introduced the Transformer architecture, fundamentally changing how machines process sequential data. The architecture’s core innovation, the self-attention mechanism, enables models to process all tokens in parallel while computing relationships between every word simultaneously. This directly addresses the limitations of earlier approaches: Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) process tokens sequentially, creating computational bottlenecks that prevent parallelization, and information degrades as context travels through many sequential steps, making it difficult to maintain long-range dependencies.

While autoencoders, ...