TensorLLM
Free while signed in. Answers cite the passages they came from.
First page
The curator’s take
Proposes a framework that performs MHA compression through a multi-head tensorisation process and the Tucker decomposition. Achieves a compression rate of up to ∼ 250x in the MHA weights, without requiring any additional data, training, or fine-tuning.
Every Monday
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack