Resolve the FP4 attention kernel and backend conflicts with main's #1980 versions
(re-adding install_fp4_vsa_attention), keep main's FP32 FSDP grouping, and keep both
save_inference and save_tagged in the checkpointer.
Three source recipes (T2VA, keyframes, Ref2VA) on one model page under
MiniMax H3, with knobs for task, resolution tier, aspect bucket,
duration, NVFP4 decoder and text encoder, offload mode, and GPU count.
The page uses the official Pika-Labs Hugging Face avatar.
Add the OmniRef (Ref2VA, VSA tile 128) and V2/V2.1 Consumer (T2VA,
VSA tile 64) profiles of one quantization-aware distillation recipe:
NVFP4 W4A4 q/k/v/out and FFN linears in all 50 blocks, the FP4
sparse-attention emulator, frozen VSA gates and norms, output plus
layer-reconstruction regression against a frozen bf16 teacher, and
best-by-score checkpoints with audio eligibility guards.
Training code: the PDD-QAD method, the two H3 model plugins, the
NVFP4 emulated linear, and the tile-64/128 Triton emulator with its
PyTorch numeric reference. The VSA attention impl gains a fine
attention override hook and lets tile 128 run grad-capable forwards
through it. The trainer gains opt-in tagged checkpoints and early
stop hooks for the QAD guards.
Also add two runnable configs, the training guide, and CPU/GPU tests.
Aryan Kumar