Transformer Architecture Consultation — Choose the Right Model

I will assess your ML task and deliver a written transformer selection report — comparing encoder-only (BERT family), decoder-only (GPT family), encoder-decoder (T5, BART), and domain-specific transformer variants for your specific use case, with a clear recommendation and rationale. The transformer family has branched into architectures optimised for fundamentally different tasks — using a GPT-style decoder for classification wastes the bidirectional context that makes encoder models superior for that task; using a BERT encoder for text generation is architecturally impossible — getting the architecture right at the start prevents expensive retraining after discovering the wrong model was chosen.

The report covers your task type (classification, generation, summarisation, translation, question answering, or other), architecture comparison for your task, model size and compute tradeoff analysis, fine-tuning versus prompting decision, and a recommended model with implementation guidance.