Continual training recipe upcycles dense Qwen2.5-8B LLM to 4x channel-sparse model via predictor-gated bank-wise sparsity in SwiGLU FFN with a single-layer repair for long-context failure on RULER-CWE.
Mixture-of-channels: Exploiting sparse ffns for efficient llms pre-training and inference.ArXiv preprint, abs/2511.09323, 2025
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Continual LLM Upcycling: A Predictor-Gated Bank-Wise Sparsity Training Recipe for Dense-to-Sparse LLMs
Continual training recipe upcycles dense Qwen2.5-8B LLM to 4x channel-sparse model via predictor-gated bank-wise sparsity in SwiGLU FFN with a single-layer repair for long-context failure on RULER-CWE.