memory: record prefill result (roughly doubled) on the gen-seat mixed requant
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@@ -89,6 +89,9 @@ names — 0 overlap, MLP union = layers 0-63 — before any GPU time was spent.
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| metric | W4A16 | mixed | delta |
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|---|---|---|---|
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| decode tok/s | 80.12 | **94.53** | **+18.0%** |
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| prefill tok/s (~6.7k prompt) | 3,206 | **6,334** | **+98%** |
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| prefill tok/s (~27k prompt) | 2,862 | **5,085** | **+78%** |
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| TTFT on a ~27k doc | 9.43 s | **5.31 s** | −44% |
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| MTP acceptance | 47.8% | 47.7% | unchanged |
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| perplexity (6 passages) | 6.941 | 7.059 | +1.7% worse |
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| abliteration compliance | 4/4 | 4/4 | preserved |
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@@ -107,6 +110,10 @@ routing. Commit `74f596b`.
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uninitialised → 0% acceptance). `post_quant.py` now **re-injects it after the
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graft and re-verifies**. That check *fired on this run* — it was not
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hypothetical.
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- **Prefix caching silently fakes prefill numbers too.** The prefill harness originally used a
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*seeded* nonce, so run 2 regenerated run 1's prompts verbatim and read **~41k tok/s of
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cache-hit** instead of ~5k of real prefill. Same class of error as the decode bench. Use
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`SystemRandom`; never seed a cache-busting nonce.
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- **vLLM's `prompt_logprobs` are garbage while speculative decoding is on** —
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~uniform over the vocab (median rank ~10⁵, logprob ≈ log(1/vocab); " Paris"
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after "The capital of France is" ranked 69698). Perplexity must be measured on
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