Near-equal file bytes: EXL3 measures 0.001634 and GGUF Q6_K 0.002035 on the same suite, with engine confounding explicit

The corrected outcome. On shard 0 of the v5 held-out suite — 512 contexts, 1,048,064 scored positions, 330 source clusters, one shared BF16 head and one shared BF16 reference — this build measures 0.001634 mean KL divergence, 95 % CI [0.001541, 0.001742]. GGUF Q6_K measures 0.002035 on the identical contexts. The pre-registered prediction was 0.001488, with a registered interval of [0.001175, 0.001601] spanned by three estimators derived from overlapping evidence. The pre-registered BEATS/MATCHES/MISSES rule subtracted a cross-engine BF16 control from candidate KL. That operation is invalid: KL is neither additive nor a metric. The receipt remains immutable, but its parity verdict is withdrawn. The observable result is a complete-pipeline comparison: this EXL3 build measures lower KL than the llama.cpp Q6_K pipeline on these contexts, while the engine mismatch prevents a format-only or byte-gap attribution.

The byte axis, in the claim rather than in a footnote. The files are near-equal in size, but the comparison is not parity on the weights that do the multiplying. Counting transformer body only — payload minus embedding, output head, vision tower and MTP draft, which is the set of weights a text-only GGUF's body covers — Q6_K carries 19.3599 GiB against this build's 17.054 GiB, a deficit of 2.306 GiB, 13.5 % of ours. Our bytes go elsewhere: a BF16 embedding at 2.3682 GiB where theirs is Q6_K at 0.9713, a 0.8582 GiB BF16 vision tower that their text file does not contain at all (it ships separately as mmproj-BF16.gguf), and a 0.2847 GiB MTP draft they have no equivalent of. In the other direction, on everything you must download to serve the advertised capability, Q6_K plus its mmproj is 22.18 GiB against hydrated's 20.13 — our artifact is 2.05 GiB smaller and multimodal-complete. All of it is measured from the artifacts themselves in receipts/cross-candidate-byte-accounting.json, and it was recorded before this conversion ran. It cuts both ways, which is why it is up here.

The question this build was made to answer

The experiment was designed after GGUF Q6_K measured 0.002035 mean KL on the same 512 contexts where hydrated K5/K6 measured 0.002700. The unquantized-BF16 cross-engine control measured 0.000507, proving that the llama.cpp and vLLM pipelines differ even before quantization. It does not provide an additive correction or a quantization-only bound (receipts/cross-engine-comparator.json).

Q6_K also serializes to 21.31 GiB against hydrated's 20.10 GiB payload: +1.183 GiB over about 25.6 B quantized weights, or +0.397 bits per weight. The pre-registration hypothesized that this byte surplus explained the observed pipeline gap by applying the measured 3.73x-per-bit law. That was a testable build recipe, but the resulting cross-engine comparison cannot establish the causal byte-gap thesis.

This checkpoint therefore changes exactly one thing — MLP gate_proj and up_proj promoted K5 → K6 — adding 1.349 GiB and landing at 21.453 GiB of payload against Q6_K's 21.31 GiB. The suite and reference tokens are shared; the candidate engines are not.

What was built

The draft's gate_proj and up_proj move with the body because EXL3_BITS_OVERRIDE is matched against every module key the allocator holds, and the draft's projections are among them — the same behaviour the published hydrated build has, and it is priced into the byte prediction rather than discovered afterwards (receipts/byte-law-recipe-audit.json).

Bytes

The prediction is the published affine byte law — bytes(role, K) = fixed(role) + params(role)·K/8 — applied to the one width change, and it was committed before the conversion ran. Promoting one bit across gate_proj and up_proj costs exactly params/8 per role, 713,031,680 B each, plus 22,282,240 B for the draft's two.

Fidelity, measured

Shard 0 of the v5 held-out suite: 512 contexts, 1,048,064 scored positions, 330 source clusters, one shared BF16 lm_head, one shared BF16 reference capture, cluster-bootstrap intervals over 10,000 resamples. Every comparison below is a paired per-context difference, not a difference of two aggregates.

Against hydrated, same engine and same reference capture, no cross-engine term: this build is 0.001066 better than hydrated, paired per context: -0.001066 [-0.001173, -0.000974] over 512 contexts and 330 source clusters, winning 511 of 512.

Against Q6_K: this build is 0.000401 better than GGUF Q6_K as measured, paired per context: -0.000401 [-0.000425, -0.000378] over 512 contexts and 330 source clusters, winning 493 of 512. This is a valid comparison of the two complete measured pipelines on identical contexts, but it is not a format-isolating comparison: the GGUF candidate was captured in llama.cpp while the reference and this build were captured in vLLM. The unquantized-BF16 cross-engine control is 0.000507 mean (receipts/gguf-report-engine-floor.json). KL is neither additive nor a metric, so subtracting that control or treating it as an upper/lower bound is invalid.

It was pre-registered

The prediction, the derivation, the exact width map, the exact command and the numeric decision rule were committed and pushed before the conversion ran, in receipts/preregistration-kld9-window.json:

• registered primary 0.001488, from the 3.73x-per-bit law charged at this build's own byte spend (+0.4526 bpw → 1.81x off hydrated's 0.002700);
• registered interval [0.001175, 0.001601], spanned by three estimators derived from the same byte law and EDA calibration evidence — not independent replications;
• the historical acceptance question classified the result relative to Q6_K measured and a floor-subtracted value. That rule is preserved in the immutable pre-registration and receipt, but its floor-subtracted branch is invalid and is not used for the corrected publication verdict.

Measured 0.001634: the registered primary was 0.001488, so the measurement is 1.10x the prediction and outside the registered interval [0.001175, 0.001601]; it sits below Q6_K's measured complete-pipeline value 0.002035; the comparison is cross-engine and does not isolate format. The body deficit against Q6_K at this file size is 2.306 GiB (13.5 % of ours), recorded before the conversion ran.

A uniform role-group promotion is the one prediction class the EDA surrogate calibration found sign-correct; the between-role reallocation at a fixed budget is the class it failed, and that failure is published too (malaiwah/Qwen3.8-27B-EXL3-EDA-research). Believing the hits requires publishing the misses.

What this does not settle

• Text-only, teacher-forced fidelity. No generation quality, no long-context retrieval, no multimodal request is measured by this number.
• One shard. 512 of the suite's 5,120 contexts. The intervals are honest about that.
• Serving cost is not fidelity. 21.453 GiB of payload is larger than hydrated's 20.10, and on a 32 GB card those bytes come out of the KV budget. If you are choosing a build for context length rather than for closeness to BF16, the context edition remains the right one.
• The byte comparison is file-to-file, and the composition differs on four axes at once: whole file against tensor payload, text-only against multimodal, non-uniform embedding and head widths per GGUF tier, and body against body. On the body axis Q6_K carries 2.306 GiB more than this build, whose body lands within 0.02 GiB of five-bit UD-Q5_K_XL's. With the int8 embedding overlay on, the embedding half of that asymmetry would narrow to about 0.21 GiB resident — but the fidelity protocol runs no overlay, so the scored artifact is the BF16-embedding one.
• The GGUF comparison stays cross-engine. Capturing Q6_K and the BF16 reference under the same engine would remove this engine mismatch; nobody has done that.

Reproducing it

# conversion (exllamav3 v1.4.2 at 5f3c537, worktree diff 578066cd...)
export EXL3_BITS_FIXED='{"^.*self_attn\\..*$": 6, "^.*linear_attn\\..*$": 6}'
export EXL3_BITS_OVERRIDE='{"^.*mlp\\.(gate|up|down)_proj$": 6}'
python convert.py -i Qwen3.8-27B -o qwen38-k6parity -w wd \
  -b 4 -hb 6 -mb 4 -vb 16 -cb mcg
python util/add_safetensors_index.py -m qwen38-k6parity --force
python util/add_quant_config.py -m qwen38-k6parity

# scoring, identical to every other candidate on this suite
python tools/fidelity.py capture --model qwen38-k6parity --suite shard-0000/suite \
  --out hidden-k6parity --quantization exl3 --quantization-config "$(cat qcfg.json)"
python tools/fidelity.py replay --reference hidden-bf16 --candidate hidden-k6parity \
  --head lm_head.safetensors --suite shard-0000/suite --out report-k6parity.json
python tools/fidelity.py paired --a report-hyd.json --b report-k6parity.json \
  --a-label hyd --b-label k6parity --bootstrap-samples 10000 --out paired.json

The calibration corpus is exllamav3's shipped default, unchanged — 250 rows of 2,048 tokens, 211 from text and 39 seeded random, whose exact token rows digest to 2b30349958715e3d3ba069a21a57a83160fde96225fd6cae26a23b240921d201 in feed order.

Provenance

Every number on this card is reproducible from the receipt, and the receipt's own content_sha256 covers it.