Custom Transformer Architecture — Novel Model Design & Build

I will design and implement a custom transformer architecture for your specific requirements — covering architectural innovations appropriate to your data type and task (modified attention mechanisms, hybrid CNN-transformer designs, efficient attention for long sequences, or domain-specific positional encodings) — and benchmark the custom architecture against standard transformer baselines to validate the architectural choices. Custom transformer architectures are warranted when standard architectures have known limitations for your specific scenario — very long sequences that quadratic attention makes impractical, highly structured data that benefits from domain-specific inductive biases, or efficiency constraints that require departing from the standard multi-head self-attention design.

The implementation covers architectural design with justification for each component choice, PyTorch or JAX implementation, ablation study validating each novel component, benchmark comparison against standard transformer baselines, and a technical report documenting the design decisions.