We present IronLLM-0.6B, a 654M-parameter language model designed for efficient on-device inference. IronLLM-0.6B combines a hybrid attention architecture with X-MTP, a lightweight shared-KV multi-token prediction design that eliminates per-depth KV-cache replay and employs a lightweight verification head for rollback-...
Chang-Di Yang, Feng-Quan Jiao, Hao-Chih Lin et al.· 0 citations
It is demonstrated that strengthening the understanding capability of the Boogu-Image system, through a stronger multimodal encoder, agentic prompt rewriting, and related techniques, together with improvements in data quality, training pipelines, and agentic inference-time scaling, can substantially enhance generation...
Guoxuan Chen, Chufeng Xiao, Haoran Yang et al.· arXiv.org· 3 citations
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