During 2026-03, delivered RMSNorm-based normalization before the final projection for Llama models by adding a configurable norm_before_fc option in the Eagle3 speculator. This enables RMSNorm usage in gpt-oss draft models and supports a broader range of training configurations, contributing to improved stability and performance. A bug-fix was included to propagate norm_before_fc from the Eagle3 speculator to downstream components, ensuring consistent behavior across the speculative decoding pipeline. Delivered via two commits, establishing a stronger foundation for robust training workflows and faster experimentation. Technologies demonstrated: RMSNorm, Llama model normalization, speculative decoding workflows, Eagle3 speculator integration, cross-repo code propagation.

April 2025 monthly summary for EvolvingLMMs-Lab (lmms-eval). Focused on enhancing multilingual evaluation for Whisper-vLLM, delivering a robust FLEURS evaluation workflow with language-aware prompts and CI-ready formatting. The work strengthens multilingual ASR evaluation reliability and positions the LMMS-eval module for broader language support.