What if you could slash your LLM’s size and speed it up dramatically—while keeping accuracy intact? Google’s new algo does exactly that.
Imagine deploying a massive LLM on edge devices or in production without the usual latency nightmares or sky-high compute bills. That’s the bold promise of Sequential Attention, a breakthrough from Google Research dropped yesterday that redefines model efficiency.
Google’s team unveiled Sequential Attention, a novel algorithm for feature selection in neural networks. It processes features one-by-one, dynamically calculating marginal gains to pick the most impactful ones first—without expensive recomputations. Tested on benchmarks, it hit state-of-the-art results, matching proven methods like Orthogonal Matching Pursuit (OMP) mathematically while enabling one-pass greedy selection.[2]
For developers, this is gold: apply it to prune LLMs with structured sparsity, axe redundant attention heads, or shrink embedding dimensions. In recommender systems’ large embedding models (LEMs), it optimized feature engineering under real inference constraints. The result? Drastically smaller models, faster inference, same predictive power—perfect for mobile apps, real-time services, or cost-sensitive deployments.[2]
Compare to standard pruning techniques: most require multiple passes or approximations that sacrifice accuracy. Sequential Attention++ extends to block sparsity and transformer blocks, outpacing tools like those in Hugging Face’s optimal brain surgeon. It’s not just theory—with provable guarantees and equivalence to OMP, it’s production-ready reliable.[2]
Grab the code from Google Research’s repo today, fork it into your next model pruning pipeline, and benchmark against baselines. Will this spark a wave of ultra-efficient open-weight LLMs? Watch for integrations in TensorFlow or PyTorch next.
Source: Google Research