In r/MachineLearning, a builder reports a hybrid linear quadratic linear attention modification that speeds inference substantially with small perplexity loss, but finds dataset size improvements outweigh architectural tweaks.
Hybrid attention for small code models: 50x faster inference, but data scaling still dominates
Changed attention so its linear first layer , middle quadratic layer, last linear layer
The main result is that increasing dataset size mattered more than any architectural c
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.
Forked pytorch and triton internals . Changed attention so its linear first layer , middle quadratic layer, last linear layer
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