docs: Gemma-4-E4B-It perplexity benchmark — turbo4 vs F16 cross-corpus

[WillowOneVision]

Summary

Adds docs/benchmarks/gemma_e4b_turbo4_ppl.md reporting empirical perplexity measurements on Raspberry Pi 16 GB (Cortex-A76 aarch64) comparing F16 dense KV vs turbo4 K + turbo4 V on gemma-4-E4B-it-Q4_K_M.gguf.

Headline finding

Corpus	F16 PPL	turbo4 PPL	Δ relative	Paired pattern
WikiText-2 raw test	55.0055 ± 8.83	50.6767 ± 7.96	−7.87%	turbo4 lower 4/4 chunks
HumanEval (prompt + canonical_solution)	4.2657 ± 0.469	4.1310 ± 0.450	−3.16%	turbo4 lower 4/4 chunks

turbo4 produces lower perplexity than F16 dense on both corpora, with the paired direction consistent across all 4 chunks of each bench. Most likely explanation : Walsh-Hadamard Transform (WHT) rotation applied pre-quant by TurboQuant acts as light implicit regularization (analog to QuaRot / SpinQuant smoothing). For an instruction-tuned model evaluated outside its native chat-format distribution, this smoothing slightly improves attention quality even after 4-bit quantization.

Why submit this

For users deciding whether to adopt turbo4 on ARM edge hardware : the usual mental model "lossy quant = quality trade-off" does not apply on this model class. turbo4 is quality-neutral or slightly positive on plain text and code corpora.

Cited in the doc :

• Build : cecil/phase-c2-dispatch HEAD ab632e4
• Direct llama-perplexity invocation (MTP + mmproj disabled to isolate KV quant impact)
• Standard -c 512 --chunks 4 -t 4 protocol, paired-difference test across chunks
• Reproducibility instructions inline

Caveats listed in the doc

• Chunks=4 wide CI (paired direction robust, absolute magnitude noisy)
• Two corpora tested ; chat-format prompts (model's native domain) not measured
• Single hardware target (Cortex-A76 aarch64) ; x86_64 SIMD path not validated

Test plan

• Maintainer review of measurement methodology
• Optional : tighter chunks=32 re-bench if absolute magnitude relevant
• Optional : chat-format corpus addition to close native-domain gap

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