library_name: vllm
tags:
- arvq
- nvfp4
- experimental
GLM-5.3-Vision-NVFP4-ARVQ-hybrid
Per-expert ARVQ v3 cold experts, NVFP4 hot experts, ARVQ-scored REAP allocation. 75/75 MoE layers replaced by the full-corpus sequential PV campaign. Other layers retain their previously published weights; see pv_progress.json. Each layer's two tensor files and reports are replaced together in one commit.
Training draws sequentially from 18,001,846 text tokens at context 1024. Fixed validation and development-audit sets each contain 16,384 tokens. Adam trains FP4-constrained per-expert books and FP8-constrained per-block scales. The effective batch is 262,144 tokens, accumulated in four 65,536-token passes. Layers 4–26 use 69 updates: book/scale LR .048/.032 through update 45, then .012/.008. From layer27, 69 updates is the maximum: three validation checks more than 0.1% worse than best trigger an earlier LR reduction; 15 updates without improvement after the reduction permit stopping. Layer30 retains audit-qualified update15 after its validation-best update20 failed the development audit. Layer 3 retains its separately qualified lower-LR refinement. Output-gradient index reassignment occurs every 20 updates; validation every five updates retains the best checkpoint. Audit non-regression and export replay gate publication.
Student inputs reflect retained tuned upstream layers. Fixed reference targets use original FP8-source routed experts and the donor backbone. A rolling cache carries both trajectories. Cold arithmetic emulates FP4 activation planes and FP16 boundaries; native SM120 parity and full-model quality remain unverified. The audit set is historical development data, not an untouched final test. Hot experts, backbone, BF16 MTP, vision components and allocation are unchanged.