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  accabb4cb83cf180ce18e4c5c5bbc3901ef45e219578237b94692f69ef4541a6
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.