Paper: frozen 16 MB embedding table can match learned embeddings in small models
A paper posted to r/LocalLLaMA, titled "Embeddings Are Overpriced?", proposes replacing a small language model's trainable embedding table with a frozen 16 MB table of 512-bit codes that has no trainable parameters, derived once from a pretrained embedding via principal-component projection and sign binarisation. The authors trained the same Gemma-shaped body from scratch on 2.05B tokens with and without the table; they report the learned table buys 0.20 nats of validation loss for 2.6 times the trainable parameters, while the frozen-table model is better or tied on LAMBADA, HellaSwag and PIQA, with only ARC-Easy favouring the learned table. They also report that with a ternary BitNet b1.58 body the whole model is a 46 MB file that ties the float model on LAMBADA and PIQA and generates 615 to 740 tokens per second on a laptop CPU in 62 MB of memory, and that weights, data and evaluation answers are released.
If the authors' self-reported results hold, a frozen, parameter-free vocabulary could sharply cut the memory and file size of small and ternary models while preserving benchmark performance.
| In a small language model the vocabulary is the single largest part. In Gemma 3 270M the embedding table holds 168M of 268M parameters, and because quantisation methods leave the embedding in 16-bit, it is also most of the file once the rest of the model is compressed. We ask what that table actually buys. We replace it with one frozen table of 512-bit codes, 16 MB, with no trainable parameters, derived once from a pretrained embedding by principal-component projection and sign binarisation, and we train the same Gemma-shaped body from scratch on the same 2.05B tokens with and without it. The learned table buys 0.20 nats of validation loss for 2.6 times the trainable parameters, but on LAMBADA, HellaSwag and PIQA the frozen-table model is better or tied; only ARC-Easy favours the learned table. With a ternary BitNet b1.58 body the whole model is a 46 MB file, still ties the float model on LAMBADA and PIQA, and on a laptop CPU generates 615 to 740 tokens per second in 62 MB of memory, five to six times the speed of the float model's 8-bit GGUF in this http URL at a seventh of the memory. The frozen table wins by 1.7 to 2.3 nats on rare words, accepts 10,000 new words after training with a 3% cost where the learned table collapses by 43%, and all of it is released with the weights, the data and every evaluation answer. submitted by /u/pmttyji [link] [comments] |