dapack-math

dapack — math pack (graded precision, 10.7 GB)

Qwen3.5-35B-A3B compressed for mathematics and quantitative reasoning: 10.7 GB instead of 21.2 GB, with every expert still present. The 143 experts this domain routes to are kept at q2_K; the remaining 113 are held at IQ2_XXS (2.06 bpw) under an importance matrix — graded, not deleted, so the pack degrades out of domain instead of breaking.

⚠️ This file requires the dapack runtime

Graded packs carry two expert tensors per layer at different precisions (ffn_*_exps_cold + dapack.hot_experts_per_layer). Stock llama.cpp / ollama / LM Studio cannot load them. Build the runtime from:

https://github.com/R3n3r0/dapack — ready-to-run binaries under Releases (Linux x86_64, ROCm), nothing to compile:

tar xzf dapack-v0.1.0-linux-x86_64-rocm-gfx1151.tar.gz
cd dapack-v0.1.0-* && ./dapack serve ~/catalog.dapack   # web chat + OpenAI API + routing

On other GPUs, build from the same repo (one command, scripts/rebuild_fork.sh --apply after a recursive clone).

Measured capabilities

These numbers are behavioural measurements, not estimates. They ship in the pack's manifest, and the dapack router treats them as hard constraints — a request needing a capability this pack has lost is routed to a pack that has it.

What it lost — on purpose, and declared: translation collapsed (100% → 20%) and the pack failed Italian certification outright, because a mathematics calibration held the language experts at 2 bits. Its manifest says languages: [en], and under the dapack router non-English requests never reach it. If you serve this file standalone, use it in English.

Why graded instead of pruned

On the identical expert selection, we measured:

On the second architecture deletion loses 53 points and grading loses none. (35B columns measured on this pack's language-domain twin; same base, same recipe.)

Deletion costs ~1 point of reasoning per 1% of experts chosen wrongly and is unrecoverable. A 2-bit expert is present and merely imprecise. Compute cost is unchanged: the top-k budget is partitioned across the two banks, so exactly 8 experts run per token.

Files

Full documentation, tools to build your own domain packs, and every measurement behind this card: https://github.com/R3n3r0/dapack