Experimental & Emerging Work / Independent exploration
GPT-2 Architecture Exploration
An educational transformer and local-assistant implementation centered on inspectable causal attention.
Problem
Implementing the attention mechanism directly makes its masking and tensor transformations easier to study.
Implementation
CausalSelfAttention projects queries, keys, and values, reshapes them into heads, applies a lower-triangular mask, uses scaled dot-product attention and dropout, then projects the merged heads.
Results
VERIFIED: the attention mechanism exists in source. The repository also contains assistant/provider abstractions, but no pretrained-GPT-2 equivalence or independently trained language-model quality is claimed.
Limitations
Architectural similarity does not establish weight compatibility, benchmark parity, or a new model contribution.
Reproducibility
Reviewed at commit c07656c4cb67 on 2026-11-09. Source inspection establishes implementation structure; repository-authored results are not independent measurements. The upstream application and benchmark suite were not rerun during this review.