The base-viability pre-flight had three checks (fits / MoE expert mapping / LoRA
support) and would have passed Qwen3.5-0.8B-Base clean while it trained 2.6x
slower than a dense model 2.3x its size. Check 4 closes that: read `layer_types`
for a linear_attention majority AND probe for mamba_ssm / causal_conv1d / fla /
kernels. It is the intersection that is slow -- a hybrid shape with the kernel
present is fine, a dense shape does not care.
Carries the measured table (gx10 GB10, n=10/arm, spreads 0.6-2.6%), plus the two
things a hybrid Base checkpoint brings that a dense one does not: a vision tower
and MTP head that target_modules="all-linear" would train on text, and the module
rename that AutoModelForCausalLM introduces relative to the vLLM serving class;
and unsafe cross-document packing, since SSM state ignores the attention mask.
Section heading corrected from "three greps" to "four checks". The example was
made runnable and verified on the box rather than shipped untested.
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