Geometric-AI/DeepSeek-V4-Flash-0731-ROCmFP3-MIX

DeepSeek-V4-Flash-0731 — Option 1: adaptive down-experts (ROCmFP3-MIX)

The same bytes as the uniform ROCmFPX baseline, holding a measurably better model.

The 43 down-projection expert tensors move from qtype 104 (Q3_0_ROCMFPX, uniform) to qtype 105 (Q3_1_ROCMFP3_MIX, adaptive). Both are 14 bytes per 32 weights, so the file is byte-for-byte the same size, reads the same bytes per token, and costs the kernels the same work. Nothing else in the model changes: attention, dense, gate/up, norms and embeddings are identical to the baseline.

What "adaptive" means here

A uniform format compiles one interpretation into the format itself — the same codebook for every block in the model. An adaptive format moves that interpretation into the artifact: each expert carries its own small learned table, chosen by a per-expert search against Hessian-weighted sensitivity rather than by a rule applied to the whole tensor.

Those tables are ~375 KB against ~102 GB. That asymmetry is the whole trick — a per-expert choice costs almost nothing to record and buys a much better fit, because expert weight distributions genuinely differ.

Measured quality

From the quality-transfer ledger that selected this assignment (fake-quant, against the unquantized model, 2 seeds x 2 corpora x 5 policies):

	uniform (P0)	adaptive
wikitext2 PPL	3.8573	3.6760
wikitext2 KL	0.2157	0.0953
c4 PPL	12.5342	11.5245
c4 KL	0.2064	0.0677

Aggregate: KL -57.0%, perplexity -5.6%, at identical footprint.

KL is the number that matters most: it measures divergence from the unquantized model, so it says this artifact reproduces the original's output distribution roughly twice as faithfully as the uniform recipe does — using the same bytes.

Two honest caveats:

• 96.4% weighted expert coverage. The calibration corpus never routed to some experts; those fall back to the conservative uniform rung rather than being guessed at. The gains above are earned on the covered majority.
• These are fake-quant research measurements against the native model, not end-to-end serving benchmarks. Throughput and speculative-decode figures are reported separately, under one protocol with the prompt mix stated.

Single file — no sidecar

Earlier adaptive artifacts shipped as a model plus a required .p4mix.bin companion: separate the two and the tensors are undecodable. The learned tables are now carried inside the GGUF, as a verbatim byte blob in the KV block:

deepseek4.p4mix.sidecar   375,320 bytes

Loaders prefer the embedded copy and fall back to a loose file, so existing two-file artifacts keep working unchanged.

Requirements

Needs a server build with the qtype-105 decode kernels — upstream lucebox plus the adaptive series, on branch feat/ds4-adaptive-on-upstream (upstream main + 20 reviewable commits, full suite green). A stock build without those kernels will not decode qtype 105.

Pair with DeepSeek-V4-Flash-0731-DSpark-Drafter-GGUF for speculative decode, and compare against DeepSeek-V4-Flash-0731-ROCMFPX, the uniform baseline this is measured against.

Integrity

sha256  a431f955c639107d3f34ab4cc65edc806038588a0c258abd0bc3e9074b908096
file    ds4-0731-opt1.gguf  (102,321,006,592 bytes)

That is +375,392 bytes over the uniform baseline — the embedded codebooks and their KV overhead, and the entire size cost of the adaptive encoding.

License

MIT, inherited from the base model.

Checksum correction (2026-08-05). An earlier revision of this card listed accabb4c…, which was the hash of a pre-release build, not of the published file. A correct download failed that check. The value above (a431f955…) is the real one and matches the git-LFS oid and x-linked-etag. Thanks to @hycris for catching it.

Serving: keep the model's six routed experts

Do not pass --ds4-expert-top-k 4 with this artifact. That flag keeps only four of the six routed experts and renormalizes. On the uniform ROCMFPX artifact it is harmless; on this adaptive one it produces degenerate output.

Measured 2026-08-05, exact-copy fidelity over 20 identifiers x 3 repeats at temperature 0 (the model is asked to echo a line of Python already present in its prompt, so anything but a verbatim copy is a defect):

	top-k 6 (model default)	top-k 4
this artifact (adaptive)	95.0%	60.0%
uniform ROCMFPX	100%	100%

The failures are not degraded paraphrases -- they are " 0 0 0 0 0 ...", repeated markdown fragments and empty strings, on prompts as trivial as "repeat this line". Deterministic, and reproducible at two context sizes and with fused decode on or off.

Some published example commands (including in this project's own server/docs/DS4.md until today) included --ds4-expert-top-k 4. Omit it, or pass 0, to use the model default.