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Reliable FP8 attention remains a barrier to fully native 8-bit large language model training. We derive how forward-backward inconsistencies produce stale delta and empirically show how it distorts training dynamics. Our stale-delta hybrid runs show a modest loss gap at 569M parameters but substantial loss increases an...
LeapQuant is proposed, a training-free method that achieves near-lossless performance under 8-bit recurrent-state quantization and substantially reduces memory and compute costs during inference.
Jet-Long is proposed, a tuning-free zero-shot method that pairs a local RoPE-faithful window with a long-range window whose rescaling factor adapts dynamically to the current sequence length via a parameter-free analytic schedule, recovering the base model exactly at short inputs while extrapolating cleanly at long one...
Lightning Weave is introduced, a post-training framework that extracts and composes independently learned capabilities in a single student through on-policy distillation and achieves a state-of-the-art accuracy-efficiency frontier.
Frontier open-weight models are increasingly available, but serving them still largely assumes datacenter infrastructure. We present FreeToken, an edge-native MoE serving system that treats a personal machine not as a small GPU, but as a unified, elastic inference platform. FreeToken co-designs the full serving stack,...
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