[R] Hybrid attention for small code models: 50x faster inference, but data scaling still dominates
Reddit - Machine Learning
1 min read
About this article
TLDR: Forked pytorch and triton internals . Changed attention so its linear first layer , middle quadratic layer, last linear layer Inference got much faster with a low perplexity hit in tests . I trained a 25.6M parameter Rust-focused language model from scratch using a byte-level GPT-style decoder. The main result is that increasing dataset size mattered more than any architectural change. Expanding the corpus from about 31MB of core Rust sources to roughly 173MB by adding a few hundred cra...
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Originally published on April 07, 2026.
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