test: add a MoE fixture, and fix a flaky assertion in the fixture suite

[solderzzc]

Follow-up to #143. Two things, both found by running the suite rather than reasoning about it.

1. A MoE shape

No MoE model appears anywhere in the CI matrix, so the sanitize path that stacks per-expert mlp.experts.N.* tensors into switch_mlp was untested at any level. This fixture is a Qwen3-style MoE with per-expert tensors, an expert count nested under text_config, and a decoy count under vision_config that detection must ignore. 230 KB.

It does not reproduce #112 — and I checked rather than assumed

I built this intending to close #112's shape, then red-checked it the way the b674 fixture was checked, and it failed to reproduce the bug.

#112 was num_local_experts being the only spelling checked, so a config declaring num_experts was called dense, --stream-experts was silently dropped, and a 397B model was materialised whole until the OS killed it. But modelTypeImpliesMoE treats any model_type containing "moe" as MoE regardless of config keys — so qwen3_moe is caught by that fallback no matter what the spelling is.

Verified by reverting detection to the single-key top-level form: this fixture still enables streaming.

Reproducing #112 end to end needs a MoE architecture whose model_type lacks "moe" — deepseek_v3, with n_routed_experts. Its MLA attention makes that a substantially larger fixture, so it stays a follow-up; ModelProfilerMoEDetectionTests covers the profiler function directly meanwhile. The docstring records all of this so the next person doesn't re-derive it.

The suite does assert --stream-experts is honoured for this fixture, which is the closest end-to-end check available — a model classified dense has the flag dropped silently, and that silence is what turned #112 into an OOM kill rather than an error message.

2. A flaky assertion — mine, from #143

The check required completion_tokens >= 1. With random weights and the default temperature, sampling varies run to run. On one run kv-shared-absent drew EOS immediately, returned zero tokens, and failed — having passed minutes earlier on identical code:

❌ FAIL: kv-shared-absent: no completion — {"choices":[{"message":{"content":""...
         "finish_reason":"length"

That would have been an intermittent red check for everyone. The assertion now pins temperature: 0 and checks prompt_tokens plus a well-formed response — which is what "the checkpoint loaded and a forward pass ran" actually means. Token count never was the thing being proved.

Three consecutive runs after the fix: 6 passed, 0 failed each time.

Net

shapes	6 assertions across 5 fixtures
size	1.9 MB total committed
runtime	~15 s, no network, no cache

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