Model files come from untrusted mirrors. One choke point in load_cfg bounds
every dimension (hostile config.json now dies with a clear message instead
of reaching allocation sites); falloc guards the n*sizeof(float) wrap.
No hot-path calloc churn — the report's patch was declined in review, the
valid kernel of the idea is taken here.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
serve mode persists the compressed MLA KV-cache incrementally after every
turn (~182 KB/token appended, header count written last = crash-safe) and
resumes it at startup: the model remembers the whole conversation and zero
re-prefill happens. :reset and context-full restarts truncate the file.
The MTP layer's KV row is not saved; kv_start=-1 re-arms its decode window.
Validated: split-session answer byte-identical to an uninterrupted session
(tiny oracle, TEMP=0), and on the real 744B model a restarted chat resumed
58 tokens in 0.0s and recalled a fact from the previous session while
prefilling only the new question.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Measured on GLM-5.2 (48 tok, greedy): next-layer routing is 71.6% predictable
one full layer ahead (79.4% skipping attention only; 41.3% previous-token).
PILOT=1 issues next-layer expert WILLNEED from a dedicated I/O thread while
the current layer computes — inline fadvise BLOCKS ~0.5ms/call on a saturated
disk queue (+92s/48 tok, measured), hence the lock-free ring + worker.
Neutral-in-noise on this dev box (disk already ~80% duty); expected to pay on
balanced machines (#12: 43% disk / 46% matmul) — opt-in, default off.
cap_for_ram now RAISES the LRU cap up to the RAM budget (ceiling n_experts,
CAP_RAISE=0 opt-out): big-RAM machines were silently running with cap=8
(#12: 128GB box using 22GB of a 110GB budget; #13: 92GB box, same).
DRAFT=3 on cold cache measured locally: 1399s vs 880s baseline for the same
48 tokens (acceptance 16%, experts/token 1809 vs 800) — confirms #8; DRAFT
re-evaluation belongs to warm-cache serve sessions.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- dot_i8i8/dot_i4i8: guarded __AVX512VNNI__ branches using vpdpbusd
(64 bytes/iter, s32 accumulation, no 16-bit intermediate). AVX-512 has
no vpsignb, so the sign trick becomes abs(w) + mask-negate on x.
- dot_i4i8: nibble unpack stays 256-bit; activation blocks are permuted
to match the per-lane value order instead of re-sorting weights.
- The int4 IDOT gate (was hardcoded S>=2, from AVX2 measurements) becomes
g_i4s with a VNNI-aware default of 1: single-row decode goes through the
integer path where it now pays. I4S=n overrides for A/B; I4S=2 restores
the old behavior exactly. AVX2/NEON/scalar builds are unchanged.
- Startup banner reports the active idot kernel (avx512-vnni/avx2/neon/scalar).
Measured on Xeon 6 (24C, AVX512-VNNI), DDR5-5600, ~19.8 GB/s NVMe RAID0,
gcc 13.3 -march=native: 256-tok greedy decode 1.72 -> 2.08 tok/s (+21%),
expert-matmul -24%; second prompt +20%. Integer-path outputs bit-identical
(associative s32 adds, same no-saturation bounds); S=1 int4 adopts the same
int8-activation tradeoff IDOT already makes for S>=2.
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
First real datapoint for the 'Got a better machine?' section: disk iobench
plus stock and --topp 0.7 inference numbers, with the RAM-bound takeaway.
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Fenced code blocks become bordered boxes with the language label, **bold**
renders as real bold, `inline code` colored, # headers, - bullets. Works
char-by-char on the live stream (markers split across chunks are held back),
inline state resets per line, and orphan ``` fences right after a close
(a known int4 glitch) are swallowed. COLI_RAW=1 restores raw output.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Each architecture maps to its own engine binary (glm today; gptoss, qwenmoe
reserved). Registry in c/models.json (local, gitignored); chat shows a picker
when more than one model is installed. Dense models stay llama.cpp territory
- documented honestly in the README.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Faithful to the official modeling (transformers glm_moe_dsa): q from the
q_a latent via wq_b (32 heads x 128), k = LayerNorm(wk(h)) shared across
heads, interleaved RoPE on the first 64 dims, ReLU(q.k/sqrt(128)) weighted
by weights_proj(h)/sqrt(32), causal top-2048 per query.
- 'full' layers compute the selection (+ maintain the indexer k-cache from
token 0); 'shared' layers reuse it (IndexShare, index_topk_freq=4).
- Selection restricts both attention paths (absorbed decode + prefill
reconstruction). MTP row stays dense.
- Auto-detected like MTP: if out-idx-* weights are present for all full
layers, DSA arms itself; DSA=0 disables; DSA_FORCE/DSA_TOPK for testing.
- Validated on the tiny oracle (which ships indexer weights): selection
machinery forced on with keep=all keys reproduces dense attention exactly
(TF 32/32, gen 20/20); sparse smoke runs clean; kill switch verified.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Learning cache: expert usage persists in <SNAP>/.coli_usage across sessions
(atomic save every turn); at startup the hottest experts are auto-pinned in
RAM with half the expert budget (AUTOPIN=0 disables). The engine gets faster
the more you use it.
- Sampling: temperature + nucleus (official 1.0/0.95 defaults in chat; TEMP=0
= greedy). MTP/n-gram speculation stays lossless via rejection sampling
(accept draft w.p. p(draft); on reject resample with draft banned).
- coli: --temp flag.
- Converter: --indexer mode extracts DSA lightning-indexer weights
(resumable; needed for future sparse attention beyond 2048 ctx).
- pin_load/stats include the MTP row; usage histogram covers layer 78.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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