thunlp / hybrid-linear-attention

What is hybrid-linear-attention?

This Python repo delivers code and pretrained models for hybrid linear attention architectures, distilling standard Transformers like Qwen into efficient hybrids that crush extremely long contexts. It solves the quadratic memory bottleneck in LLMs by blending classic attention with linear alternatives via the HALO procedure—run stage-wise scripts for alignment, distillation, and finetuning to get Hugging Face-ready checkpoints. Developers get plug-and-play tools to train models rivaling Qwen3 on perf-efficiency, perfect for code models llm handling massive inputs.

Why is it gaining traction?

It nails superior speedups on long contexts with just 2.3B training tokens, beating homogeneous Transformers on benchmarks while keeping inference lean. The hook: dead-simple scripts convert any pretrained model, outputting architectures tuned for length generalization—no from-scratch training needed. Users notice snappier generation on code github repository scans or code benchmark models without exploding VRAM.

Who should use this?

ML engineers tweaking code models leaderboard contenders or ai models for code with long docs, like code github python repos or code models ollama setups. Suited for researchers evaluating hybrid linear attention on code models benchmark tasks, or teams distilling Qwen-like bases for edge deployment.

Verdict

Grab it for hybrid linear attention experiments—solid paper-backed results make it a fresh alternative to pure RNNs or Transformers. At 19 stars and 1.0% credibility, it's early (thin docs, no broad tests), so prototype locally first.

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