Aryan Kumar
212f558aab
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.
FastVideo documentation
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python -m unittest discover -s docs/tests -p 'test_*.py'
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