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- The torchao 0.17 MX training path swaps a matched frozen Linear's weight for a wrapper tensor whose linear override computes input @ weight_t and drops the bias, so mxfp8'ing a biased frozen linear silently loses its bias and corrupts the base output the LoRA regresses against (verified on Blackwell: the bias term is fully dropped). Skip biased linears in _mx_module_filter. - _resolve_base_precision re-checked explicit dense modes against the live device but only rejected CPU, so an explicit mxfp8 request on a non-Blackwell CUDA GPU passed and then crashed at the first MX GEMM after a full dense-transformer load. /info only advertises mxfp8 on sm100+; mirror that gate here and fail fast for a stale or direct client below Blackwell. |
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| .. | ||
| assets | ||
| auth | ||
| core | ||
| hub | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| cloudflare_tunnel.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||