The dense policy assumed the dense set fits in RAM. Qwen3.8-Flash-Next breaks
that with its 51B n-gram table (per_layer_token_embd, ~28.8 GB at IQ4_NL),
which every mode failed on in its own way. A dense tensor larger than the
kernel's MemAvailable is now held back under every mode: it stays mmap'd
with MADV_RANDOM, leaves the warm sweep, the residency sensor and the auto
cache budget, and one stderr line names it. The bound is size, not access
shape: a row-gathered table that fits keeps its mode. Inert on every other
supported model (Qwen3.6-35B matches its baselines to the decimal under
anon, warm, mmap and auto); gates pass; the guard fires on device and pinned
dense weights survive load.
Qwen3.8-Flash-Next (qwen4exp): 125B total, ~6B active, 512 routed experts at
top-10 plus one shared, 48 hybrid gated-delta SSM / sparse attention layers,
and a 51B n-gram embedding table. One registry row streams the experts; a
dense-policy guard keeps the n-gram table (larger than any phone's RAM)
mmap'd under every mode so pinned and anonymous dense weights survive load.
Runs on the 12 GB test phone at ~2 tok/s with pinned dense weights, compute-
bound, and sits in the app catalog as a three-shard download. Submodule
pinned to upstream master b10666, the first with the architecture merged,
with the expert-ready hook on top. README hero clip, changelog and docs
updated. App 0.22.0 (versionCode 37).
A benchmark contributor should be able to download one file and run
scripts/bench-report.sh without a toolchain. release-host builds a static,
portable CLI (GGML_NATIVE=OFF; x86_64 assumes AVX2, aarch64 armv8.2-a+dotprod)
from a clean checkout of the tag for linux-x86_64, linux-aarch64, macos-arm64
and windows-x86_64, and attaches the archives to the release. workflow_dispatch
with upload=false keeps them as artifacts instead, for trying the workflow on a
branch without touching a release.
Every published number comes from one phone and one laptop. This adds what a
contributor needs to add a row from hardware we do not own, without reading code:
- scripts/bench-report.sh runs the fixed README protocol on any Linux/macOS host
(256 greedy tokens, the reference prompt, auto cache, 4 lanes, overlap, dense
weights out of the page cache), records CPU / RAM / drive and the drive's
measured O_DIRECT rate at 512 KiB requests from the model file itself, and
prints the two markdown tables a report needs. Every figure is read from the
CSV `# summary` trailer by key name.
- .github/ISSUE_TEMPLATE/benchmark-report.yml collects hardware, model, engine
version and the pasted tables; tok/s alone is not accepted as a row.
- docs/community-benchmarks.md holds the protocol, the hardware wanted and why,
the reference models in the catalog quants, the meaning of each column, and
the results table seeded with the README rows.
- README, CONTRIBUTING and the docs index point at it.
Overlap stall is the union of stalled intervals, not the per-thread mean; the app panel draws compute / flash wait / cache mgmt / unattributed from measured terms. Fixes#98.
The guard added in #167 writes cache_cycle_mb into the metrics preamble, but the
app never learned the key. MetricsScreen falls back to showing unknown keys under
their raw name, sorted, at the end of the list -- so the one number this release is
about arrived as "cache_cycle_mb" detached from the cache group it belongs to,
while every sibling (cache_mb, cache_floor_mb, cache_ceil_mb) reads as prose with
an explanation behind it.
Two rows, in the two places every other config field is declared: the label in
MetricsScreen's CONFIG_ORDER, right after cache_ceil_mb so it lands with the rest
of the cache block, and the description in MetricFields.
Also makes the CHANGELOG line true: it claimed the number was recorded, and now it
is also readable where people actually look at it.
Below one token's worst-case routed bytes, global LRU evicts precisely
what it is about to read: the hit rate is 0% while the run still pays
the cache's management time and its RAM. The only protection so far was
cache_min_mb, a fixed floor that happens to sit above the cycle for the
shipped models at their default top-k and stops holding the moment
--n-expert-used widens the routing without touching the budget.
The cycle is priced at init from the model's shape alone — every bound
layer's entry_bytes times min(top_k, n_expert), at the top-k the run
actually applies — and recorded as cache_cycle_mb in the metrics
preamble next to the budget it should be judged against, so a committed
CSV answers on its own whether its cache could ever have hit. The engine
prints one stderr line when the resolved budget falls under it. It warns
rather than refuses: the budget is legal and the output byte-identical,
and the engine states the fact and leaves the choice — the argument that
--cache-mb 0 is strictly better below the cliff stays in
docs/cache-sizing.md, not in the engine's output.
Docs updated in the same commit: cache-sizing.md (the guard section,
past tense), roadmap.md (the item moves from still-worth-doing to
shipped) and telemetry.md (the new preamble key).
* Mark the two doc-invoked host scripts executable
README, AGENTS, CONTRIBUTING and docs/seam.md run scripts/build-host.sh
directly, and docs/benchmarks.md runs scripts/bench-run.sh the same way,
but both are committed 100644, so a fresh POSIX clone answers permission
denied at the first documented step. Windows trees never see the mode
bit, which is likely how it went unnoticed.
* Mark the remaining host scripts executable too
Per the discussion in #163: leaving only the two doc-invoked scripts
executable hides the next instance of the same failure behind an
inconsistent set, so all five scripts/*.sh get the bit.
project(VERSION) in the top-level CMakeLists.txt still said 0.19.0 after 0.20.0
was tagged. It is the single source of BMOE_VERSION -- the comment above it says
so, and that is the point of declaring it in one place -- so every 0.20.0 build
self-reports as 0.19.0, both in `bmoe-cli --version` and in the engine= line of
every metrics CSV it writes. Any CSV committed since 2026-08-17 names the wrong
engine, which is the kind of error that quietly poisons a benchmark read months
later. Nothing else was affected. Reported by gjjkbssg (#163).
Bumped to 0.21.0, the version being developed, with the app's versionCode and
versionName moved in step.
Batched with it, since both are small and both ship in the same release:
- Finer expert-cache rungs below 2000 MiB in the app (#146). The ladder went
500 -> 1000 -> 2000, and that x2 is where the choice is sharp: on an 8 GB phone
1000 MiB runs and 2000 MiB gets the app killed by the OS, so the step handed
the user a cliff instead of a setting. 1250, 1500 and 1750 fill it; above 2000
the existing 1000 MiB step is already a small fraction of the budget and is
unchanged. Reported by eiffel31 (#146).
Ling-3.0-flash (127B total, ~5B active, 512 routed experts) becomes one
registry row: standard split expert suffixes, biased top-k (the lfm2moe
pattern), a resident shared expert, leading dense blocks that never bind,
and a NextN/MTP block llama.cpp does not load by default. The hybrid
KDA/MLA attention stack is dense-side llama.cpp machinery, invisible to
the streaming seam.
The submodule moves to fork branch bmoe/expert-ready-hook-ling3: the same
single expert-ready-hook commit, cherry-picked clean onto upstream 3733366
("model : BailingMoE3 Support"). The old fork branch stays, so the commit
the previous pin names remains reachable. The jump crosses 496 upstream
commits and forces three adaptations, all on our side of the seam:
- llama_model_params lost use_mmap for the load_mode enum; the streamer's
required layout is now pinned with LLAMA_LOAD_MODE_MMAP.
- The nextn/MTP tensors are now skipped at load unless load_mtp is set,
and it defaults to off, so --mtp would have built its second context
over a block whose tensors were never loaded. The block is now requested
exactly when speculation asks for it.
- gpt-oss/harmony now declares its thinking tags, so the think-control
probe decides by mechanism: a prefill that moves past the closed span
into further structure of the format itself is binding; one that just
ends at the closing tag is only a suggestion (LFM2.5 stays reported as
uncontrollable).
Byte-identity gates pass on the new base (qwen3moe, gemma4, 4-shard
split). PC smoke on Qwen3.6-35B-A3B matches the recorded baseline on
every applicable cell, --mtp included (17/19 drafts accepted, 3.43 tokens
per verify decode). On device, Ling-3.0-flash loads and streams over its
512 experts; --mtp on it stops at draft rollback because upstream has no
recurrent-state rollback for bailingmoe3 yet (llm_arch_supports_rs_rollback),
a llama.cpp limitation, not a seam one.
App version 0.20.0 (versionCode 35).
nttld/setup-ndk and softprops/action-gh-release ran from mutable major
tags inside the APK job, which holds a contents:write token. Whoever
controls those tags upstream could have pointed them at unreviewed code
with permission to rewrite this repo's release assets.
Both now resolve to a fixed commit, with the human-readable version in a
trailing comment. GitHub-owned actions stay on major tags: pinning them
would cost an SHA bump on every upstream release for a materially
smaller risk.
The signing keystore was never reachable this way. It lives only in
release-apk.yml, which runs no third-party action and triggers only on
events that already require write access.
The DeepSeek figure no longer drags a paragraph of caveats and a second model's numbers behind it; what follows the hero is one line introducing the video underneath, which is what that space is for.
gpt-oss-120b no longer 'still needs a manual copy to the device'. That stopped being true when sharded downloads landed: it is a catalog entry with two shards and a single progress bar. A README that tells people to do work the app already does for them is worse than one that says nothing.
The benchmark tables name the column Active experts rather than k, matching what the app and the docs call it.
Trading quality for speed is gone as a section. In its place the Speed and quality feature block gets an introduction that says what those settings have in common, how they differ from each other, and why they exist at all on a device far past its memory, without quoting a single figure. The figures were never the point there, and each one needs its method beside it to be worth reading, which is what the linked docs are for.
Desktop says plainly that it is not the primary target for now.
Both features shipped in this release with no runtime coverage at all: validation tests only. That is the same shape of hole that let a zero-copy corruption reach the edge of a merge with five green checks behind it.
G13 gates the speculative loop through the n-gram source. Abstention is forced by requiring a match longer than the whole generation, so the plain path is taken by construction and the identity is structural rather than lucky; the first draft of this gate left the matcher at its default, expected a synthetic model to repeat nothing, and it drafted anyway. A third cell then turns drafting on and asserts the machinery survives, since a batched verify may legitimately order a near-tie differently and there is no reference output for that.
G14 gates route-ahead in two halves. A horizon past the last layer can override nothing, so the run must be byte-identical: that covers the plumbing without touching the lossy part. A horizon of one must commit real routings and still generate, with the override count asserted so an inert policy cannot pass vacuously.
G11 and G12 are reserved for the zero-copy branch and left unused here, which is the collision this file's index now exists to prevent.
README: the features section is one row per setting, giving the name the app uses, the flag and its accepted values. The benchmark tables all take the same shape, so their widths stop depending on how long a configuration description happened to be.
* feat(app): settings grouped by purpose, and four defects an audit found
Settings now show the recommended configuration first and fold everything else
into a collapsed Experimental group per category. That is a statement about
evidence, not about how finished the code is: inside are the levers measured on
one device, measured once, or still owed a measurement. They stay in the
release build, because testing them on other hardware is what this app is for
and a lever nobody can reach is a lever nobody can refute. The caveat is stated
once in the group header instead of leaking into some descriptions and not
others.
Every description was rewritten to say what the setting does for the person
reading it. Out went the measured figures, which need the device, the model and
the day beside them to mean anything and have none of that room under a switch,
and out went the implementation names: O_DIRECT, top-k, dma-buf, mmap and KV
cache are not what someone deciding whether to turn something on needs to know.
The metrics screen keeps the flag names, deliberately: there the reader is
matching the UI against a CSV column and the technical name IS the vocabulary.
Four defects, all found by auditing rather than by anything failing:
The session signature is now derived from the argv instead of being a
hand-written list beside it. Those two had to be kept in step with nothing
enforcing it, and forgetting a field is a silent bug: the setting appears to
change while the engine keeps running the old configuration. Three of four
rebases this week collided on exactly that list.
A malformed end-of-turn summary no longer strands the UI. The whole handler sat
inside a catch with no failure branch, so a parse error left the state in
GENERATING with no turn committed and nothing said. The streamed answer is now
kept, the reason is shown, and the state returns to READY.
MainActivity drops from about 1050 lines to under 700: the model download and
import UI moves to ModelPickerUi.kt, which shares nothing with the chat screen.
No logic moved, only its address.
Dead code removed: a field whose own comment described a use it did not have,
two functions nobody called, and five string resources describing a UI two
rewrites ago.
* docs: record the settings regrouping and the signature fix
Rule 6: the changelog and the docs a change invalidates ship with it. The app README described the settings screen as it was before the regrouping, and explained one experimental lever in terms of predictor accuracy percentages that the UI no longer shows.
* docs: the improved DeepSeek hero recording
* feat(app): keep the flag vocabulary in Settings, and make Experimental read as a boundary
The first pass at rewriting the descriptions went too far: it renamed the controls into consumer phrasing and lost the vocabulary that lets a setting here be matched against the CLI, the CSV preamble and the docs. Labels are back to the flag's own names; the descriptions are shorter than the originals rather than longer, and still carry no measured figures. The Experimental group gets a divider and a tonal bar: collapsed, it is the only thing between the recommended configuration and the levers that can change the reply, so it has to look like a boundary rather than one more row.
The flagship demo is now the 284B model generating on a 12 GB phone at about
1 tok/s, with its own recording, instead of gpt-oss carrying that slot. The
quoted 0.94 tok/s is the app's own reading and the sentence next to it says
what produced it: cold-expert dropping at full strength, which trades quality.
gpt-oss keeps its lossless and knob-on figures one paragraph down, and the
three-model clip stays where it was.
The model is named by its exact release, 0731, everywhere it appears rather
than only in the opening line. Anyone reproducing this needs to know which
DeepSeek V4 Flash it was.
The list of what ctest gates claimed the LRU cache, evictions, overlap,
temporal prefetch, the dense rebind and multi-turn sessions. It omitted
predictive prefetch and the split multi-shard model, both of which are gated,
and it did not mention that a lossy knob only gets machinery gates. Fixed to
match what the suite actually runs today.
Also: docs/ngram.md was missing from the documentation index despite being a
250-line document for a shipped flag, and the methodology caveat carried an
editorialising aside about future storage in the one paragraph that has to
stay dry.
Four things an audit found, none of which changes engine behaviour.
The demo app declares android:appCategory="game". Vendor performance layers
read that attribute to pick a governor profile, and on the OxygenOS test device
it moved the app onto the boosted path: the foreground CPU ceiling went from
1.9/1.65 GHz to the hardware maximum of 3.32/3.80 GHz, measured before and
after. Decode is the most CPU-hungry thing a phone does outside a game. The
effect belongs to the vendor rather than to Android, and Samsung's game service
has historically throttled what it classifies this way, so the manifest, the
changelog and the app README all say to treat a per-device figure as a
measurement. In-app numbers from before this are not comparable with numbers
from after it.
CI now enforces the versions it claims. The format job installs clang-format-18
by name instead of whatever the runner image ships, which happened to be 18 and
would have started failing every PR against an unannounced version on the next
image bump. The APK job builds with NDK r27c, the release that produces
published APKs, so CI stops validating a build nobody installs. It also passes
-DGGML_OPENCL=OFF, the flag whose absence once shipped a stray backend into two
releases. checkout moves to v5, since v4 pins a deprecated Node runtime.
--help lists every one of the fifty flags the CLI accepts. Six were missing.
--io-trace is the one that mattered: a fully documented, guarded diagnostic
that the usage text never mentioned, so the only way to find it was to read
docs/telemetry.md.
.gitignore covers .claude/, which until now was excluded only by a
machine-local ignore file. A clone elsewhere would have shown a second checkout
with build output and .so binaries as untracked, which is precisely the
situation the never-"git add -A" rule exists to survive.
* feat(engine): --route-ahead N — commit decode routing to the N-layers-early prediction
Every prefetch lives under the same ceiling: layer L's routing needs layer L-1's
output, so any predictor working earlier is approximate and every speculated read
can miss. This inverts the bet. The expert selection of decode layer L is REPLACED
by the ranking layer L's own gate matrix produced on the hidden state N layers back
in the same forward pass, so the selection is known N layers early and cannot miss.
With the cache on, those reads are issued the moment the selection is fixed.
Lossy by construction: it changes the output, and roughly a fifth of slots route to
a different expert than the router chose at N=1. Off by default, mutually exclusive
with both prefetchers and with the prediction probe, since each would speculate on a
future this policy has already decided.
Quality is measured rather than assumed: the committed generations match the
baseline on a four-prompt objective battery, on a long essay, and on a second model
of a different generation and quantization; output stays deterministic across
repeated runs, which expert dropping does not. See docs/route-ahead.md.
Squashed from the seventeen commits of exp/route-ahead: the branch predated the
multi-shard and speculative-decoding work, and replaying it commit by commit meant
resolving the same two collisions seventeen times over. The history is preserved on
the pull request; what lands here is what a squash-merge would have produced anyway.
* fix(engine): refuse route-ahead alongside self-speculation, and say why
Running the two together on a real model committed NOTHING: 0 routings taken,
249 passed through. A verify decode is several positions wide and the policy
correctly declines each one. But it still charged for itself — the prediction
GEMVs ran (2.6 ms/token of worker CPU) and its early reads degraded into
ordinary speculation, falling from 100% useful to 81%.
Cost with no commitment is worse than either feature alone, and nothing told
the user. validate() now rejects the pair the way it already rejects
route-ahead beside the two prefetchers, the app stops emitting the flag and
greys the row out, and the config test covers both draft sources.
Making the combination work is a different change: commit the whole verify
batch to one selection. That is written and measured, and it costs draft
acceptance (70% to 53%) while route-ahead alone still won, so the exclusion
is the honest state today rather than a limitation to be worked around.
Found by the desktop smoke run, not by the gates.
* feat(engine): MTP self-speculative decoding for Qwen3.5/3.6 (proposal)
Qwen3.5/3.6 ship a trained multi-token-prediction block inside the gguf. With
--mtp that head drafts --mtp-draft continuation tokens and the target verifies
all of them in one wider decode, confirming the longest prefix whose argmax
equals what the target itself would have produced. Nothing is approximated and
no weight is skipped, so the quality is the full model's — but it is NOT
byte-identical the way --overlap and --prefetch are, and must not be used in a
byte-identity gate: a verify pass evaluates 1+N positions in one batch, and a
batched matmul is not bit-identical to N single-token ones, so a near-tie can
flip. Off by default.
The prize is that a decode's dominant cost, moving the dense weights and the
routed expert slices, is paid once per group instead of once per token. The
counterweight is that the verify positions route independently, so a layer's
read set widens toward N*k wherever adjacent tokens disagree, and the draft
pass routes through the MTP block's own expert layer on top. Measured on the
desktop host (DRAM-bandwidth-bound, model streamed at ~1.4x RAM): +15.1% at
draft 3 with the host's best recipe (7.12 -> 8.19 tok/s), +29% without the
lossy drop knob, acceptance falling from 71% at draft 2 to 52% at draft 4, and
flash bytes per token rising 19.7 -> 33.7 MiB as the widening predicts. Draft 3
is the optimum here; 4 is worse than 2. On a flash-I/O-bound phone that balance
can invert, so the flag ships off pending the device A/B.
The orchestration is llama.cpp's own (common/speculative.h, public headers
only): no fork, no patch, no submodule bump. Self-speculation is one model with
two contexts over it — the target, created with n_rs_seq so a rejected tail is
rewound from a bounded snapshot rather than replayed, and a draft context
created with ctx_type = LLAMA_CONTEXT_TYPE_MTP. The engine builds the draft
context itself rather than through common_speculative_init_from_params because
the eval callback is per-context: the streamer only sees the MTP block's expert
layer if the draft context carries the same cb_eval.
The MTP block is streamed like any other layer. It sits at layer index n_layer,
contiguous with the trunk and using the same tensor naming, so the hook and the
expert source are sized n_layer + n_layer_nextn; left at n_layer its experts
stay silently mmap-resident. Two consequences that are easy to get wrong: the
capture warm-up has to run on the draft context too (the MTP graph is built
nowhere else), and prefill is fed through the driver so the draft context's KV
reaches the last prompt position.
The loop accepts BEFORE catching the draft context up, so the catch-up runs on
the accepted prefix instead of the whole verify batch. Acceptance depends only
on the target's logits, which are already in hand once the decode returns, and
the rejected tail was being computed only to be deleted a few statements later.
The resulting state is identical — the driver seeds from row
min(n_accepted, n_rows-1), the same row under either batch, and the surviving
KV is exactly the range the rollback used to carve out — while skipping
n_draft - n_accepted positions through the MTP block per group. Since that
block carries its own MoE FFN, on a streamed device those are expert reads that
no longer happen. It also removes the draft context's rollback entirely: it is
never given a tail to drop.
Requires an MTP-converted gguf (most quantisations strip the nextn tensors) and
greedy decoding; both are rejected at load with a message rather than silently
ignored, as is a n_ubatch narrower than the verify batch, which would split the
graph back into single-token passes and spend the draft for nothing.
Telemetry: an "mtp:" summary line, an mtp_batch per-token CSV column (a verify
decode's whole cost is charged to its group's first row, the rest carry zeros),
mtp_drafted / mtp_accepted / mtp_decodes in the CSV trailer and in BMOE_DONE,
and mtp / mtp_draft_max in the CSV preamble. The Android app exposes the flag
and the draft width, off by default.
Host gates pass. Validated on Qwen3.6-35B-A3B-MXFP4 with the streamed recipe:
draft 1 and draft 3 produce identical text, which is the invariant a broken
accept/rollback path would violate. Device A/B still owed.
* perf(mtp): shrink the draft context, make its cost measurable, record the device verdict
The first on-device A/B says MTP loses at every draft width, and the counters
say why. Same gguf with the flag on and off, shipping recipe (overlap, 3000 MiB
cache, pinned dense, drop 0.75), Qwen3.6-35B-A3B-Q4_K_M streamed:
off 5.82 / 6.14 tok/s 69.3 MiB/tok 69-109 majflt/tok
--mtp-draft 2 5.59 93.8 230
--mtp-draft 3 4.38 106.6 633
Speculation is working - 2.35-2.52 tokens per verify decode, 52-69% acceptance
- and still losing, because the prize does not exist in this regime.
stall_s/tok is 0.025-0.027 in every one of those runs, MTP on or off: 11-16% of
the token. This configuration is compute-bound, and what MTP amortises is weight
movement. The costs meanwhile are real and monotonic in the draft width: the read
set widens (+35%, +54% flash bytes per token), CPU per token rises (+28%, +67%),
and the draft context's memory tips the device into a fault storm.
Two things follow, and both are engine bugs rather than facts of nature.
The draft context's graph width drops from 256 to 32. Compute buffers are
reserved for the widest ubatch and the dominant term scales with
ubatch x vocabulary; on device that reservation measured 493 MiB - for a context
that evaluates ONE token per draft step and is handed at most 1 + draft_max
positions by the catch-up, with no logits asked for. Only prefill ever feeds it a
wide batch, and that is one layer, so splitting it costs very little. On this
engine memory is never free: it is the expert cache's, and the cache is what
decides whether the widened verify read set is a hit or a flash read.
And the cost of speculation is now measured instead of inferred. Drafting happens
between decodes, so it never entered wall_ms and tok/s never included it - a
speculated run could report a rate the user was not experiencing. New
mtp_draft_ms per-token column (a slice of loop_overhead_ms, not an addition),
mtp_draft_s/tok in the CSV trailer, mtp_draft_s_tok and loop_overhead_s_tok in
BMOE_DONE, and a second "mtp:" summary line printing the effective rate next to
the reported one.
Adds --mtp-p-min F, which stops drafting once the head's confidence in what it is
proposing falls below F. The draft loop already had this floor and the engine was
passing 0, so it always drafted the full width however unsure the head was - with
roughly half the drafts rejected at draft 3, that is the cheapest waste available
to cut. On a streamed device it pays twice: a draft not made is a pass through the
MTP block (which carries its own MoE FFN, so its own expert reads) that never
happens, AND one fewer independently routed position in the verify batch. Default
0, the setting the host numbers were measured at; the useful value is a property
of a device's balance between drafting cost and acceptance, so it is a knob to
measure rather than a constant to guess.
The Android app now reads the mtp_* keys it was already being sent: acceptance,
tokens per pass, and the effective rate. Before this the UI could not tell whether
speculation had run at all - only the session CSV could - which made the A/B this
commit reports impossible to run from the phone.
Neither mitigation changes the regime. The honest expectation is nearer
break-even, not a win, and the flag stays off by default.
Host gates pass. Note the noise floor: the two off runs did byte-identical work
and still differ by 5.6% in tok/s, and the runs were back-to-back without thermal
gating - the mechanism counters are the trustworthy part, not the exact deltas.
* perf(mtp): split the drafting flash cost from the widened verify batch
A speculated run streams more bytes per token for two unrelated reasons: the
MTP block carries its own MoE FFN, so every draft pass routes experts of its
own, and the verify batch widens the trunk's read set wherever adjacent
positions disagree. They need opposite fixes -- a narrower draft attacks the
first, only better agreement attacks the second -- and the route trace can
separate neither, since its framing brackets the target decode while the head
only ever runs in the draft context.
Measure the head's share directly by bracketing both drafting passes with the
expert source's byte counter, and report it as a third mtp: summary line.
Also record the branch-deletion rule in AGENTS.md: a branch list should only
show work in flight, and a rejected PR loses nothing.
* feat(engine): n-gram prompt-lookup draft source, and the measurement that closes it
The flash split added last commit said where MTP's cost actually is: at draft 3 on
the host, the head's own routing was 2.9% of the extra bytes a speculated run
streams and the widened verify batch was the other 97.1%. So a cheaper draft
producer is worth almost nothing, and the only property that could matter is one
the head does not have -- the ability to decline to draft at zero cost.
--ngram is that source. It takes the last few tokens, finds where that run occurred
before in the prompt or in what has been generated, and proposes whatever followed.
No head, no draft context, no decode, no expert read, and it works on any gguf
including the ones --mtp refuses for want of a nextn block. Below --ngram-min-match
it proposes nothing and the step falls through to a plain single-token decode.
Measured on the host, Qwen3.6-35B-A3B-MXFP4 streamed, 256 greedy tokens, cells
back-to-back with off run twice:
prose off 5.80 / 6.59 mtp3 7.32 eff ngram3 6.51 (cov 7.4%)
copy-heavy off 5.45 / 5.65 mtp3 6.43 eff ngram3 5.24 (cov 15%)
The zero-cost claim holds exactly -- mtp_draft_s/tok reads 0.0000 in every n-gram
cell, against 0.020-0.023 for the head plus the ~500 MiB of expert cache its draft
context takes. But the floor turns out to be per STEP, not per run: the 15% of steps
that did draft widened the read set to 67.2 MiB/token against 48-58 at baseline and,
at 44% acceptance, bought 1.20 tokens per decode. That is not enough to earn the
widening back, and a modest fraction of such steps sinks the run.
A --ngram-min-match sweep settles it rather than leaving it open. Raising the gate
3 -> 5 -> 8 lifts acceptance 44% -> 75% while coverage collapses 15% -> 3.4%, and
narrowing to --draft 1 reaches 82.6% -- the head's own figure on this prompt. Every
cell climbs toward baseline from BELOW and none crosses it; the best configuration
found lands on the floor. A knob whose optimum is its own disablement is not a
tuning problem. Acceptance, not drafting cost, is what pays for a widened batch, and
what a trained head buys is being right often enough to justify a batch that has
already been widened.
--ngram ships off. It is kept because it is the only speculation available on a
model with no head, because the per-step floor is real, and because the counters it
adds make the next speculation claim falsifiable.
Wiring. MtpConfig became SpecConfig with DraftSource {none, mtp, ngram}, and
--mtp-draft became --draft: the width belongs to the verify batch, not to whoever
filled it. --mtp and --ngram are rejected together rather than resolved by flag
order. In the session the gate split in two -- spec_on (wide batch, acceptance,
rollback: both sources) against mtp_on (draft context, common/speculative.h, the
catch-up: the head only) -- which is what lets the n-gram source reuse the whole
verify half while allocating nothing.
A step that drafts nothing now takes the plain path: llama_batch_get_one with a
logits row of -1, byte for byte the unspeculated decode. It used to build the wide
batch anyway. Required for --ngram, and it tightens --mtp-p-min's zero-draft steps
for free.
The matcher is pure policy over token ids with no llama.cpp at all -- not even
llama.h, since llama_token is int32_t -- so it sits on the clean side of the seam,
adds no dependency on the common layer, and is unit-tested with no model
(tests/ngram_test.cpp covers tie-breaks, clipping, self-match exclusion and the gate
boundary). Telemetry: spec= / spec_draft_max= / ngram_min_match= in the CSV
preamble, a new drafted_steps key in the trailer and BMOE_DONE, and an ngram: line
reporting coverage -- without which a delta cannot be divided by the fraction of the
run it applies to. The per-token and trailer counters keep their mtp_ names: they
always described the loop rather than a source, spec_* already means the temporal
prefetch in that trailer, and renaming would break every CSV already holding a
measurement. The Android setting became a three-way picker, migrating the old
boolean preference.
The device A/B agrees and adds a cost the host could not show. Thermally gated cells
(a 120 s settle, then a battery-temperature gate, so all six start between 35.3 and
36.4 C): prose 4.90 inside a 4.59-5.17 band, copy-heavy 3.14 against 4.43 -- a 29%
loss, worse than MTP's 18%. Major faults per token go 126 -> 1427 for a source that
allocates no draft context at all, and that is the rollback snapshots: n_rs_seq =
draft_max is asked for by ANY speculation, since rejecting a draft means rewinding the
KV, and on a hybrid attention/SSM model that snapshot is a real allocation scaling with
the context. The n-gram source escapes MTP's draft context but not the loop's own
memory, and on device that memory is the expert cache's.
The same run re-measured MTP with the thermal confound removed -- 3.64 effective
against 4.43, so the earlier device verdict was not an artefact of benching without a
cooldown gate -- and reproduced the flash split at 3.7% head against 96.3% widened
verify batch, matching the host's 2.9-3.0%.
Byte-identity gates pass; speculation stays out of them for the reason docs/mtp.md
gives.
The app's CSV configuration surface follows: the three new preamble keys get their own
glossary entries rather than falling through to the unexplained-key renderer, and the
draft source joins the short run label. A speculated run is not the same KIND of run --
under speculation a decode confirms a whole group, so its per-token rows are not even
accounted the same way -- and two compare legends differing by it must not read alike.
* feat(app): sharded model downloads — DeepSeek V4 Flash in the catalog, gpt-oss one-tap
A catalog entry can now list shard files. They download sequentially through one
WorkManager chain (per-file HTTP Range resume, one aggregate progress bar, free
space checked once against the whole remaining set), the model picker offers only
the first shard — the file the engine opens — and deleting a sharded entry deletes
the whole set, so no 40 GB tail is ever orphaned.
DeepSeek V4 Flash UD-IQ2_M (~91 GB, three shards) joins the catalog, and
gpt-oss-120b turns from a "merge it on a PC" manual recipe into a one-tap
download; a merged single file from an earlier release still counts as on-device.
App version 0.19.0 (versionCode 34).
* docs(readme): DeepSeek V4 Flash becomes the flagship claim, video slot staged
* docs(readme): standardize benchmark tables (slowest to fastest, one label scheme), drop dashes
* fix(app): a split model's first shard is a MoE model too
The picker's MoE filter looked for an expert tensor inside the file it was
handed. A split gguf's first shard carries the metadata and, in the layout
large quants ship in, almost no tensors: DeepSeek V4 Flash was therefore
classified dense and never appeared in the model dropdown, with all 91 GB
sitting on the device. The header walk now also accepts the metadata key
<arch>.expert_count, which is the definitive MoE signal and always lives in
the first shard; the tensor-name check stays as the fallback.
* fix(app): bound the session's ubatch so compute buffers stop eating the model's RAM
A session opened at ctx 4096 with no --ubatch reserves compute buffers for the
whole width, though decode only ever computes one token. That reservation is
memory the expert cache and the dense weights do not get, and the CLI has
measured it as an 18% decode lever for a while; the app never passed the flag,
so every in-app run since gave it away.
On DeepSeek V4 it is not 18% but the whole result: the same configuration read
14.58 s/token in-app against 2.22 s over adb, with identical flash I/O (2.74 vs
2.89 s) and 3.6x the major faults. The 13.9 s of 'compute' were page faults, the
process swapping while it worked. Prefill pays instead, and barely: chunking it
costs ~7.7x the flash reads for ~6% of prefill wall time.
* feat(app): context is a setting, not a constant
The session opened at a fixed 4096 tokens. That is also memory — the KV cache is
sized for it once at open — so on a model that already fills RAM it competes with
the weights, and there was no way to trade conversation length for room without a
rebuild. It joins the other tunables (default unchanged), and the ubatch is
clamped to it so a graph is never reserved wider than the context. The service
reads the running session's context from its own argv for the 'ctx used/total'
readout, so the number describes the process rather than the current setting.
Measured on DeepSeek V4: the KV is 44 MiB at 512 and ~270 MiB at 4096, small
thanks to the compressed attention, so on that model the setting is not the lever
its size suggests. It is on models with ordinary attention.
* fix(app): review pass on sharded downloads
Four defects, all from the same blind spot: code that asked whether a filename
belongs to a catalog entry compared it against the entry's own name, which for a
sharded model is one file out of several.
- A sharded entry never reached ON_DEVICE. The present-files set was recomputed
only when the SELECTABLE model list changed, but shards 2..N are hidden from it
by design, so finishing a 41 GB shard left it byte-identical: the row offered
Download for a model already fully downloaded, and pressing it did nothing
until the app was restarted. Keyed on the in-flight names as well, which change
exactly when a shard starts or finishes.
- Shards also rendered as pasted-URL downloads, whose Cancel deleted the .part
the worker was still writing while cancelling nothing (the chain is registered
under the entry name). The transfer then ran on an unlinked file for tens of GB
before failing to finalize.
- A sharded gpt-oss also appeared under Imported models, with a Delete that
removed shard 1 and orphaned the rest — the exact failure the delete dialog
exists to prevent.
- That dialog replaced the entry name with the shard names instead of adding
them, so a gpt-oss merged by an earlier release became undeletable.
ModelCatalog.fileNamesOf/isCatalogFile is now the single answer to 'does this
file belong to an entry', and all four sites go through it. Also: a queued shard
reports zero bytes, so aggregate progress now falls back to its .part length
rather than appearing to lose ground on a resumed 50 GB transfer.
* feat(moe): stream split multi-shard ggufs natively + DeepSeek V4 Flash recipe
Hugging Face rejects single files above 50 GB, so every large model ships as
-00001-of-0000N.gguf shards; until now the streamer assumed one file, forcing
a merge with double the disk. gguf_offsets now fans the first shard out to the
whole set and resolves every tensor to (shard, offset); the expert streamer
and the dense loader open one positioned reader per shard and route each read
by the tensor's shard index. Pass the first shard, exactly as llama.cpp takes
it; a missing sibling fails the load with the shard named.
Add the deepseek4 recipe row: V3.2-style routing (256 routed experts, a
per-expert bias like lfm2moe, an always-on shared expert that stays resident)
over the standard split expert suffixes. The V4 compressed-attention machinery
is dense-side llama.cpp code, invisible to the streaming seam.
The byte-identity gates gain a 4-shard qwen3moe fixture (metadata-only first
shard, the layout large quants actually use); make-tiny-moe.py learns
--split-max-tensors. All gates pass, split included.
* fix(moe): cache auto must budget for the anon dense conversion
The auto budget read MemAvailable while the dense weights were still reclaimable
page cache, then dense-weights=anon converted them into buffers the kernel cannot
take back: the same bytes planned twice. Latent since the anon policy shipped
(dense sets were 2-3 GiB and explicit budgets were the benched path); DeepSeek V4
Flash's 6.5 GiB dense set turned it into a device-taking overcommit on first load.
The budget now deducts the pending conversion and says so in the log.
* fix(moe): review pass on the multi-shard path
Three defects the split rewrite introduced, none of which the gates could see:
- The shard index rode in an int8_t, so a model past 127 shards wrapped to a
negative index into the reader vector. The bounds check could never catch it:
it validated the untruncated value. Widened to int16_t, which covers the whole
-%05d-of-%05d filename space.
- DenseWeights::warm() reused one flag as both the inner loop condition and the
partial-warm report, so the first shard that failed to open silently skipped
the warm-up of every later shard. Per-shard condition, sticky report.
- The dense readers stayed allocated for the session after read_anonymous had
copied and rebound every tensor: fds and a per-lane bounce buffer per shard,
sitting next to a cache counting every MiB. Released at the end of init.
Also: the streaming banner read O_DIRECT off shard 0, which under the
small-first-shard layout is metadata only and too short to verify, so it could
claim a mode the shards carrying experts had not got. It now reports the weakest
of the readers.
* build: the engine version says 0.19.0, like the changelog does
The version is declared in CMakeLists.txt and reported by `--version` and by the
run-parameter preamble of every metrics CSV, so a committed benchmark file names
the engine that produced it. This release section landed while the number stayed
at 0.18.0, which would have stamped the wrong engine on every CSV this branch
produces, defeating the one purpose the string has.
* fix(app): show the whole run configuration a metrics CSV carries (#136)
The engine has written every resolved knob into the `# bmoe_metrics v2` preamble for
several releases; the app displayed hand-picked subsets of it. The header card of an
opened file listed eight fields, and the compare view rendered a 17-key whitelist that
had drifted behind the metrics sink. Expert dropping, predictive prefetch and its
speculation width, the sampling parameters and the engine build were all in the file and
none of them reachable — a saved run could not say whether it dropped experts, let alone
at what fraction, and an A/B differing only by one of those levers rendered two
configuration cards that looked identical.
Both views now go through one renderer over the whole preamble: the curated keys in
order, then every remaining key under its own name. The order list stops being a filter,
so a knob added to the sink becomes visible without an app change — which is the actual
fix, the missing keys were only the symptom. The single-file card keeps its glance line
and gains the full table behind a tap; `drop` and `predict` also join the short run label
the compare legends show.
Version bumped to 0.18.1 (versionCode 33).
* fix(app): state the whole run configuration on the main screen too (#136)
The reminder line under the prompt was the same hand-picked subset as the metrics
views: cache, lanes, overlap, threads, top-k. It left out the dense-weight policy,
both prefetches and cache-aware expert dropping - which defaults to 75%, so the
out-of-the-box configuration changed the answers and the screen said nothing.
The short line now carries what makes a run a different KIND of run, gated exactly
as sessionArgv gates the flags themselves so it cannot name a lever the CLI is
never told about. The whole configuration sits one tap below it, read back from
that same argv rather than from a second list kept by hand: a knob added to
sessionArgv shows up on its own, which is the property this display kept losing.
* fix(app): explain the configuration the metrics views now show (#136)
Surfacing the whole preamble is half an answer while the keys it names go
unexplained: several are unguessable from the key alone. The glossary behind ?
gains a second section describing every configuration key, worded from where each
knob is defined, and is now reachable from Compare too. It also finally describes
loop_overhead_ms, a column the engine has written since 0.17.0.
predict_spec_max renders as inert when predictive prefetch was off. The engine
records its own default (2) there - the one field of that block session.cpp does
not neutralise - so a file claimed two speculated misses per layer for a run that
speculated nothing. Only the record was wrong: with the feature off the value is
never read, so no run behaved differently than reported.
* docs(readme): reposition around large MoE models in general, add logo and TOC
The front page led with gpt-oss-120b and read as a single-stunt repo. It now
leads with what the engine is for: running MoE models past RAM on phones and
PCs, on llama.cpp's public API, with every llama.cpp quantization coming for
free. The 120B stays as the most extreme proof, not the pitch.
Structure follows the usual professional layout: logo (chip-buddy, light and
dark variants under docs/assets/logo/), badges, table of contents, a "Why this
exists" section that covers all three regimes (far past RAM, just past RAM,
and models that barely fit, which streaming keeps inside a chosen budget),
and Features grouped the way the app's Settings groups them. Benchmark tables
and the evidence sections are unchanged in substance; the test device is
phrased generically.
* docs(agents): make AGENTS.md the canonical agent guide, CLAUDE.md points to it
The agent guide lived in CLAUDE.md with AGENTS.md as a stub pointing at it,
which is backwards: AGENTS.md is the cross-tool convention (Codex, Copilot,
Cursor and others read it), CLAUDE.md is one tool's name for the same file.
The full guide now lives in AGENTS.md and CLAUDE.md is a one-line import.
While moving it, the guide gains the release rules that were only tribal
knowledge: release APKs come from the release-apk workflow, never a local
build; every released feature bumps versionCode/versionName in the same PR;
release titles are the bare version; on-device validation before a release.
The style section now names the CI clang-format version (18), since a
mismatched local formatter passes locally and fails the check.
* docs(readme): state per-platform host status honestly
Linux is CI-verified, Windows is where the desktop bench ran (but needs CMake
directly, not the bash script, and MSVC's Release output path), macOS builds
from the same sources but is unvalidated and has no O_DIRECT.
A cold layer's batch became visible to the I/O lanes only after every
miss took its page commits - up to three vm_commit syscalls per cold
expert of bookkeeping sitting in front of the first byte of I/O, which
is the latency-to-first-slice the sidecar refutation (PR #90) identified
as the binding constraint. (#118)
With the flag on, only the first present projection - the one
mul_mat_id blocks on first - is committed up front; its jobs publish
and wake the lanes immediately, and the remaining projections are
committed and appended while the lanes already read.
The drain protocol grew the one thing this needs:
- io_drain copies each job out under the lock, so jobs_ growing (and
possibly reallocating) mid-batch cannot leave a worker holding a
dangling reference;
- the worker wait predicate admits next_idx_ < batch_njobs_, so a
worker that drained wave one and left comes back for a batch that
grew in the SAME generation - the gen comparison alone never would;
- a wave-two commit failure goes fatal and wakes the ready waiters,
because wave one already published flags this batch will never flip.
Batch completion cannot fire between the waves: the only thread that
waits on done_cnt_ == batch_njobs_ is the eval thread, and it is the
one appending wave two.
Overlap + LRU cache only (validate() enforces both); recorded in the
CSV preamble as io_two_wave. Default off: the win is bounded by the
commit cost per cold layer, and the failure mode of a drain-protocol
bug is a hang the host cannot reproduce - so a new gate (G4d) holds
two-wave output byte-identical to serial streaming, and the flag stays
off until the on-device A/B (#120) says the win is real.
Every per-token line repeated the whole answer and reasoning so far, so
a generation of n tokens wrote, JSON-escaped and made the app parse
O(n^2) bytes - megabytes of pipe traffic to deliver a few kilobytes of
text on a reasoning model (#119).
The line now carries delta_reasoning/delta_text - the tail since the
previous line - and the reader appends. A pure append-only protocol
cannot express the one thing common_chat_parse does retroactively:
when a closing think tag arrives, text already reported as answer
becomes reasoning. That case falls back to a full snapshot with
"reset":1, and the reader replaces instead of appending.
Both emitters (one-shot --progress and --session) share the single
format string, so they changed together; the app's TelemetryParser
accumulates in StringBuilders (appending to a String re-copied the
whole answer per token) and resets them with the generation. The full
final text still travels in BMOE_DONE, untouched.
The engine-side re-parse per token remains (common_chat_parse cannot
resume); this removes the pipe, escape and app-parse cost, which is
what loop_overhead_ms can now see. On-device numbers are part of the
#120 A/B.
Closes#119.
Three findings from the 2026-07-28 audit's leftover list (#123), all on
paths that run for every routed expert of every layer of every token:
- on_expert_ready looked the expert tensor up in an unordered_map; the
map is static after init, so it is now a flat sorted array probed with
a binary search, and the pre-block spin of 2048 sched_yield syscalls
(up to a millisecond of scheduler churn per genuinely slow slice) is
now 256 single-instruction pauses (isb on arm64, pause on x86) before
the thread registers as a waiter.
- a cache hit in load_layer_async paid an LRU unlink+push in
touch_entry that the token-major promote loop overwrote
unconditionally two steps later; touch_entry now takes promote=false
from the overlap path. The serial path, which has no trailing loop
for first touches, keeps promoting. Final LRU order is unchanged.
- the gguf header was parsed once for the tensor offsets and once for
the model info — two full KV walks of a multi-GB file's header.
read_gguf_meta parses once and serves both; the session's lazy
accessor now hands the same parse to the top-k override, the route
trace, the run info and the streamer.
Items 4-6 of #123 stay parked: the no-consumer metrics guard only
matters to a library embedder, the O_DIRECT misreport only to macOS,
and the llama.cpp context defaults want a bench, not a code change.
Byte-identity gates pass; LRU semantics and readiness protocol are
untouched.
Release assets were built on a developer machine and uploaded by hand,
which is how a stale cmake cache shipped an OpenCL backend into two
releases. A release-apk workflow now runs when a release is published:
clean checkout of the tag, NDK build of the CLI with the same flags and
explicit staging list as scripts/build-android.ps1, APK build signed
with the stable release key from repository secrets, a content check
that fails on any stray library, and the assets attached to the
release. workflow_dispatch allows rebuilding assets for an old tag.
Both build types now sign with the stable key when available, so the
debug APK also updates in place instead of demanding an uninstall that
wipes downloaded models.
* build(android): stage an explicit library list, and force GGML_OPENCL off
The staging step swept every libggml*.so it found anywhere in the build
tree into the app's jniLibs, and never cleaned jniLibs between builds.
The android build directory's cmake cache still carried GGML_OPENCL=ON
from a GPU experiment, so a libggml-opencl.so that no shipped
configuration ever loads was rebuilt on every run and rode along into
the v0.16.0 and v0.17.0 APKs.
The script now pins GGML_OPENCL=OFF at configure time (a cached ON from
an old experiment no longer survives), wipes jniLibs/*.so before
staging, and copies an explicit list of libraries. If a submodule bump
adds a library the CLI needs, the copy fails the build loudly instead of
a stray binary shipping silently.
* docs(agent): commits are grouped - one coherent change, no single-tweak commits
build_moe_ffn emits a chain of router-weight nodes per layer — ffn_moe_weights,
then optionally _softmax or _norm, then optionally _scaled — each a refinement
of the last. The engine asked the eval callback to isolate all of them, because
last-wins is how it stays architecture-independent: it learns which node ends
the chain instead of tabulating the gating per model.
But that learning finishes on the first graph. From then on only the terminal
node is read: the drop policy decides there, and the route trace's last-wins
gather lands there. The other two or three asks per layer were still made, and
each one is a graph split plus a full compute-thread synchronization, on a
tensor of a few floats — per layer, per token, for the whole run.
Ask for the rest of the chain only while term_variant_[il] is unlearned. That
covers the first graph of a run and any graph after close_drop_layer forgets a
terminal that stopped appearing, so re-widening is automatic and the recovery
path for a graph that moved is unchanged: the deferral still fails to be
honoured, the layer still loads undropped, the terminal is still forgotten.
The drop policy is on by default in the app, so this cost sat inside every
--drop-cold-experts measurement taken so far.
Gates 7/7, run three times, including the two that exercise the deferral (G8a,
G8c). Route trace verified by hand on Qwen3-30B: 1920/1920 rows still carry a
non-zero router weight, range 0.0011-0.9862 — the terminal-only gather sees
exactly what the full-chain one did.
No throughput claim: still no device. Folded into the owed A/B (#120).
Carve the accumulated Unreleased section into a dated release, bump the app's
versionCode and versionName, and say in the README what the release changed
about how a run reports itself.
0.17.0 is the engine/core audit: three latent correctness bugs, four pieces of
per-token work that ran whether or not anything consumed them, and the two
measurement gaps that made the rest hard to judge — a metrics file that did not
record half the configuration it was produced under, and a tok/s that excluded
everything between the decodes.
The audit flagged the bounce buffer as the largest avoidable CPU cost on the
read path: every streamed byte is pulled into a per-lane staging buffer and
then memcpy'd to its cache slot, so the CPU touches 100-200 MB per token twice,
on the I/O lanes, alongside a decode that is already compute-bound.
Skipping the copy needs the read to be block-aligned at offset, length AND
destination. Measured against the three shipped models: bytes-per-expert IS
4096-aligned, but the tensor's file offset is not — 512, 1152 and 2272 bytes
past a block on Qwen3-30B, Qwen3.6-35B and gpt-oss-120b respectively, because
gguf aligns tensor data to general.alignment (32), not to a device block. So an
O_DIRECT window always rounds outward and always spills one block into the
neighbouring experts' bytes.
That spill writes the file's own bytes into buffer positions that hold exactly
those bytes, so it is content-identical. It is still not safe: the pages belong
to other cache entries, which may have been evicted, and touching them faults a
page back with nothing accounting for it — the residency budget stops matching
what is resident, and that budget is what keeps a >RAM model running. Offsetting
each buffer's base by file_off % align, the trick that makes the destination
inherit the file's misalignment, shrinks the spill but does not remove it.
An implementation of the safe subset — take the direct path only when all three
are already aligned — was written and thrown away: it is dead code on every
model in the catalog, and a branch on the hot read path that never fires, with a
startup line that would always report the fallback, is worse than the honest
note.
No code change; the finding is the deliverable.
Two gaps, both about a benchmark file being unable to explain itself.
The CSV preamble had eighteen keys and had fallen behind several releases.
Missing: n_ubatch, which sets the compute-buffer reservation and therefore
moves the very memory columns printed underneath it; predict_log,
predict_spec_max and prefetch_sync, so a probed run was indistinguishable from
a benchmark run the docs explicitly say it is not; drop_renorm and
drop_prefill, which change how much mass dropping discards; cache_floor_mb, the
input behind an auto-sized cache_mb; load_all, whose read_bytes mean something
else entirely; every sampling parameter, so a stochastic run read as a greedy
one; and compute_trace_layers. All are recorded now, under a bumped
"# bmoe_metrics v2" banner.
`think` stays out on purpose: it is a property of a request, not of a session,
so one value in a session-wide preamble would be wrong for every turn that
asked for the other.
The file also now names the build that wrote it. There was no version string
anywhere in the engine — the CMake project had none — so a committed CSV could
only be dated by the commit that copied it in. Added as a project VERSION, a
BMOE_VERSION define, bmoe::version(), `bmoe-cli --version`, and engine= in the
preamble. It sits on its own line so the model= line still BEGINS with model=,
which is how the app's CSV reader finds a run's name; the app's lookup is made
order-independent too, so the next key to be appended cannot break it again.
Second gap: wall_ms brackets llama_decode and nothing else. That is what makes
compute_ms a clean residual, and it also means sampling, detokenization,
rendering and the sink writes are outside wall_ms, outside gen_seconds and
outside the reported tok/s. Work moved into or out of that region was
unmeasurable by the number the project optimizes. loop_overhead_ms now reports
it per token, and loop_overhead_s/tok in the summary closes the accounting with
the tail after the last token that no row can carry.
docs/telemetry.md documents the preamble — it specified every other # block but
not this one — the new column, and the stall_ms divisor: stall is a per-thread
mean, so a stall that is not simultaneous across threads is under-stated and
compute_ms absorbs the difference.
Gates 7/7; preamble, column and --version verified against the tiny gate model.
Both readers checked: scripts/bench-analyze.py reads columns by name and skips
unknown # lines; the app's Csv.read keeps unknown keys and picks the new column
up automatically.
Producing TokenMetrics::text means parsing the entire generation so far —
common_chat_parse takes the whole string and cannot resume — so the cost is
O(n) per token and O(n-squared) across a turn, growing as the answer does. Off
the chat path it was not free either: shown_view returns the raw string, so
every token copied everything generated so far into the metrics struct.
It ran for every token of every run. The plain CLI path writes m.piece and
never looks at m.text; a benchmark run reads neither. Only the BMOE_PROGRESS
line protocol actually renders it.
GenerateRequest::render_text now carries that decision. The session loop sets
it (the protocol puts the parsed answer on every line); run() ties it to
--progress; it defaults to true so an embedder that has never seen the flag
behaves exactly as before. Note this cost sat OUTSIDE the wall bracket that
feeds gen_seconds, so it never showed up in the reported tok/s while being paid
on every benchmark run.
The end-of-turn parse was also done twice — once for RunResult::generated_text,
once to commit the assistant message to history — over the same string with the
same parameters. Parse once, use twice; the prefilled-turn and parse-failure
fallbacks are unchanged and now stated in one place.
Also: three back-to-back stats() snapshots to read three fields become one, and
--compute-trace / --io-trace are actually passed to the session loop. The CLI
parsed those flags, opened the files, wrote their headers and then dropped both
sinks when entering --session, so the user got an empty trace and no
explanation.
Gates 7/7. Both CLI output paths verified by hand on the tiny gate model:
--progress still accumulates the parsed text per line, the plain path still
streams pieces.
Two defects in the overlap/speculation paths, found auditing core/ with a
performance lens.
prefetch() pushed a speculative read job per projection as it built them, and
set the entry's pending count only after the last one succeeded. A commit that
failed part-way through an expert therefore returned with jobs already queued
against a count that was never set. A worker draining one of those decrements
spec_remaining_ from zero to negative: the entry can never reach zero, so it
never completes; quiesce_spec never sees it in spec_touched_, so its pages are
never released; and the "already queued" test at the top of prefetch skips that
expert forever after. One transient failure, permanent damage — in precisely
the low-memory situation this path exists to degrade gracefully in.
Stage an expert's jobs locally and publish them as a unit once all of its
projections are committed; on a part-way failure, hand back the pages it did
commit and stop. The staging vectors are members, so the path stays
allocation-free after the first call like the rest of the per-load scratch.
The commits also move OUT of io_mtx_: prefetch runs on the eval thread right
after a real batch was published, so a syscall per projection inside the lock
stalled the lanes trying to pull real read indices out of it — undercutting the
one invariant speculation has to keep.
Separately, every completed slice read took ready_mtx_ and called notify_all,
waking every compute thread blocked on any expert so each could re-check a
predicate that was almost never its own, and paying the mutex even when nobody
was blocked — the usual case, since a slice normally lands inside the spin.
Waiters now register in an atomic count that the publisher consults first.
Both the registration and the flag publication are seq_cst, so the two cannot
miss each other: either the waiter observes the flag and never sleeps, or the
publisher observes the registration and notifies.
Considered and left alone: folding the done_cnt_ increment into the same
critical section as the next-index fetch. It saves one uncontended mutex
acquisition per job against reads that cost ~100 us, which is not worth
perturbing the drain protocol for.
Gates 7/7, and the overlap set (G4a/b/c) run five times over to give a missed
wakeup a chance to hang. Format clean.
The eval callback sees every node of every graph. For each one the ask pass ran
sscanf("ffn_moe_topk-%d") — and, with the drop policy armed, match_weights on
top of it, and with the probe on, a third — to decide a question the first eight
characters already answer. sscanf drags a format-string parser and locale
machinery through a hot path that a fixed-length memcmp settles.
Gate all three matchers behind one memcmp against "ffn_moe_", the prefix every
routing node in this engine shares. The overwhelming majority of a graph's nodes
fail it and leave without touching anything else. Parse the trailing layer index
with a digit loop instead of "%d"; that is also stricter, rejecting a tail like
"12abc" that sscanf would have read as layer 12 — the same strictness node_layer
already applies next door.
Remember a layer's terminal weight-chain node as the INDEX of its name rather
than the name. The four chain names are mutually exclusive (the '-' check makes
them so), so the index identifies it exactly, and the old std::string assignment
was heap-allocating on every weight node of every layer of every token whenever
dropping was armed — the names run past the small-string buffer — with a string
compare next to it. term_node_ becomes term_variant_, a vector<int8_t>.
No routing decision changes. Byte-identity gates pass 7/7, including the two
that exercise the deferral this touches (G8a, G8c).
weight_at is the single source of truth for where token j's slot k sits inside
a weight node — the route trace reads through it and the drop policy writes
through it, so the two agreeing is what keeps the policy from zeroing another
token's slot.
It told the 3-D [1, nu, nt] node apart from the norm variant's pre-reshape 2-D
[nu, nt] by asking whether ne[0] == 1. That works only while the routing is
wider than one expert. At n_expert_used == 1 the 2-D node IS [1, nt]: it passes
the test, gets read with nb[2] as the token stride instead of nb[1], and every
token after the first is taken from the wrong row. With --drop-cold-experts
armed in prefill the same misreading sends the policy's zeroing and renorm
writes to the wrong slot.
Match the full extents against the routing's own nu and nt instead, which the
two callers already have. That separates the shapes exactly; where both fit —
a single token at top-1 — they name the same element, so either answer is
right. An unrecognised shape keeps the old test rather than inventing an offset.
Latent, not active: no model in the catalog routes top-1, which is also why the
top-1 gate (G8c) passes either way — it decodes one token at a time, and at
nt == 1 the wrong stride is multiplied by zero.
Three defects found auditing the I/O layer, all in the same seam between what
O_DIRECT requires and what the buffered fallback actually needs.
FileReader::read freed the lane's bounce before allocating its replacement but
left bounce_sz_ at the old capacity. After a failed realloc the lane advertised
a buffer it no longer had: the next smaller read passed the capacity test, took
the null pointer and preadd into it, and kept failing for the rest of the run.
Clear the size with the pointer.
The buffered path — taken when the platform refuses O_DIRECT, or when the
open-time verify catches storage that mis-serves it — shared the direct path's
mechanics for no reason. Buffered pread has no alignment constraint, so the
outward-aligned window, the staging buffer and the interior memcpy were pure
cost in the mode that is already the slow one. Read straight into the caller's
memory and report the bytes actually moved; the aligned window is a direct-mode
concept and only direct mode returns it now.
The per-lane buffered tail fd was opened without checking. When it failed, a
read reaching the file's sub-alignment EOF tail fell back to the O_DIRECT fd,
which must reject a length that short — so fd exhaustion surfaced as an
unexplained read error far from its cause. Report it at open, and name the
missing fd if a tail read is ever attempted without it.
Also: vm_reserve gains MAP_NORESERVE, so the address-only reservation is one by
contract and not merely by the default overcommit heuristic, and file_size uses
fstat rather than mutating the shared fd position to answer a question about the
file.
Host gates pass (7/7).
The pull_request trigger ignores markdown, docs/, LICENSE and .gitignore; the push
trigger does not — so merging a docs PR re-ran format and the full submodule build
plus ctest on a tree the PR had already gated.
paths-ignore cannot simply be copied onto the push trigger: that trigger also carries
the release tags, and a tag push has no commit range for a path filter to read, which
would silence the tag build that attaches the APK. So the question is answered once by
a ten-second guard job that diffs the pushed range, and the build jobs are gated on it.
Everything that is not a branch push — pull requests, manual runs, tags — answers yes
unconditionally, as does a push whose previous head is unknown (branch creation, force
push): the guard only ever saves work it can prove is redundant.
--ubatch is a memory-reservation knob, not a capability, and the list is what a
reader scans to learn what the engine does. The remaining entries now say what
each setting is for; the numbers stay where they can carry their caveats — the
benchmark tables and the bench-data write-ups they link to.
* chore(release): 0.16.0 (versionCode 30)
Carves the accumulated [Unreleased] entries into a dated section and advances the
example app to match: --ubatch N, and the expert-prediction work — --predict-log
plus --predict-prefetch, the latter shipping off by default because its own
matched-pair measurement refuted it.
* docs(changelog): restore the blank line before 0.15.1's Fixed heading
* feat(moe): --predict-log, measure how predictable expert routing is
Temporal prefetch was built on a predictor nobody had priced. The
previous-token bet turned out to be right ~38% of the time on Qwen and
~18% on gpt-oss, which cannot pay for the reads it speculates. The
lesson was not that prefetch is impossible but that a predictor should
be measured before it is wired into anything. This adds the instrument,
not a policy.
--predict-log ranks each layer's experts a layer early, by running the
NEXT layer's router matrix on the CURRENT layer's gate input. The
residual stream barely moves between layers, so the stale input ranks
nearly as the real one will -- the mechanism FATE (arXiv 2502.12224)
reports 78.8% for. It is training-free and changes no model: the router
matrix is a dense weight already resident, and the prediction is one
GEMV per layer. The matrix is learned from the graph (the gate matmul's
first source) rather than looked up by tensor name, so no architecture
is named anywhere in the path; being a weight leaf, its pointer stays
valid into layers the current token has not reached.
Three predictors are scored against the routing the router actually
produced, so they are comparable on one run: the stale gate, the
previous-token bet --prefetch already places, and a zero-staleness
control. The control is the load-bearing part. It shares every line of
code with the prediction under test and differs only in using the
layer's own matrix, so it must reproduce the selection llama.cpp
computes from those same two tensors. A transposed matrix, a mis-strided
row or the wrong token of the batch collapses it toward chance while the
stale figure would stay superficially plausible; an architecture that
selects by something other than raw-logit ranking (an additive bias,
group-limited routing) puts it below 100% and by that much the stale
figure understates the method. The CLI says so rather than letting the
gap be blamed on staleness.
Reported per layer as well as in aggregate, because an aggregate
flatters a prefetch: what a prefetch costs is set by the layers it gets
wrong, and a MoE model's first layers route far less predictably than
its last. Denominators are printed per predictor -- the stale gate
structurally cannot rank layer 0 (nothing precedes it) or the first
token of a run, and those routings are counted as unscored rather than
folded in, since a routing that was not ranked is not a wrong guess. A
predictor with no routings at a layer prints "-", never 0.0.
Diagnostics only: nothing it computes reaches load_layer, the cache or
the graph, so a probed run reads exactly the bytes an unprobed one does.
G9a gates that byte identity and G9b gates the control, which reads
100.0% on the tiny model. It is not free -- one isolated node and two
GEMVs per layer on the eval thread -- so a probed run is not a benchmark
run, and it requires --moe-stream since routing does not depend on how
the weights reached memory.
docs/expert-prediction.md also records the caveat the number will need:
a high score would say the routing is knowable earlier, not that knowing
it earlier makes decode faster. On a flash already saturated, starting a
read sooner adds no bandwidth -- which is why prefetch, layer-LFU and
the expert sidecar all lost despite improving the metric each was
designed around.
* feat(moe): --predict-prefetch, speculate on the stale-gate prediction
The probe said the routing is knowable a layer early (~89% of routed
slots on a 128-expert model, vs ~43% for the previous-token bet the
temporal prefetch acts on). This wires that prediction into the existing
speculative read path -- same cache buffers, same accounting, same
settle, same moe-prefetch summary line (tagged [stale-gate]) -- so the
only thing that changes is which guess rides the idle lanes.
Two decisions carry the design:
Speculation is issued AFTER the current layer's load, not at prediction
time. Every load path begins by quiescing speculation, and a layer's own
load sits a few graph nodes after its gate matmul -- reads queued at
prediction time would be cancelled before a lane picked them up. Each of
the three load sites (plain topk, deferred drop, drop fallback) issues
the pending next-layer prediction right after its load_layer, restoring
the same read-ahead window the temporal prefetch gets. On the tiny-model
gates this is the difference between 0% and 27% of speculated experts
proving useful -- the latter matching the probe's measured accuracy on
that model, which is the accounting agreeing with itself.
It is drop-aware. With --drop-cold-experts armed, a predicted expert
whose predicted routing weight (softmax over the predicted top-k)
falls below the drop threshold is not speculated: if it misses, the
policy discards it unread, so reading it ahead would spend the exact
I/O the policy exists to save. The top prediction is always kept,
mirroring the policy's own pin of the top-weighted expert. The known
interplay is inherited from the temporal prefetch and deliberate: a
correct guess un-drops an expert, buying quality at the same threshold
rather than speed.
The routing width the prefetch predicts at is learned from the topk
node, not read from config, so an --n-expert-used override stays honest
with no extra plumbing. Mutually exclusive with --prefetch (two
predictors would double-speculate the same future); requires the LRU
cache; decode only. The control GEMV remains probe-only, so the
production path costs one gate GEMV per MoE layer per token.
Gates: G10a proves byte-identity through the speculative path
(prefetch-sync, forced small cache, hits and evictions both occur);
G10b proves the run actually speculated and that useful-hit accounting
tracks the probe's accuracy. Off by default, pending an on-device A/B.
* feat(moe): cap predictive speculation at the top 2 predicted misses per layer
Speculating the whole predicted routing was measured on device at -38%
against its own baseline despite every intermediate metric improving
(hit 77.6->88.9%, stall 32->9 ms, 84% of speculations useful): the +33%
flash bytes and the vm commits fighting a full cache (major faults x3)
cost several times the stall removed. The stall a prefetch can remove is
head-of-line only -- overlap already hides the tail behind the expert
matmul -- so the cap keeps the part of the bet that can pay and drops
the part that provably cannot. Residency-aware: a predicted expert
already in cache does not burn a slot of the cap.
* perf(moe): rebuild the predictive prefetch around its measured costs
The observer-tax run priced the naive implementation: ~35-45 ms per
GEMV pass (ggml_fp16_to_fp32 is a function call per weight element --
21M calls/token) and ~20 ms/token for the extra isolated node, against
a speculation machinery that costs ~15-25. The GEMV and the barrier
were the feature; this commit removes both.
- gate_scores converts F16 natively on aarch64 (one instruction,
vectorizable) instead of a function call per element.
- The prefetch no longer isolates the gate matmul: the ask pass hands
over its source pointers for free, and the gate-input row is read at
the topk callback with no barrier of its own. A sampled watchdog
(the zero-staleness control, every 512 routings) validates the
barrier-less read and disarms the prefetch out loud if the memory
planner ever reuses that buffer -- without it, a future llama.cpp
bump could silently turn the predictor into a noise generator.
- The GEMV runs on a dedicated worker at a TWO-layer horizon: one
layer ahead has no landing spot (the callbacks between a layer's
gate and its own load are microseconds apart), while at l+2 the
worker has a whole layer for a ~0.5M-MAC job and the result inherits
the same post-load issue window as before. The probe now also scores
stale-2, so the extra layer of staleness is priced per model rather
than assumed.
- The prediction's residents are RETAINED (new IExpertSource::retain:
move-to-MRU, deliberately not a cache hit so the hit-rate metric
stays honest) -- protecting a predicted expert costs zero bytes,
unlike prefetching it. Only predicted misses are speculated, still
capped at 2.
The probe path keeps its barrier and both GEMVs: a probed run is
diagnostics, and its job is to be right, not fast.
* feat(moe): --predict-spec-max N — how much flash the prediction may spend (0 = retention only)
The cap was a constant; the retention-only point (0) is the config the
whole experiment now hinges on -- the prediction protecting predicted
residents from eviction while spending no flash at all -- and a
measured constant that cannot be varied is not a mechanism. Validated
[0, 8]; retention happens at every value because it is free.
* docs(predict): record the 2026-07-23 campaign — accuracy confirmed, throughput verdict open
Accuracy on device: stale-gate 88.6% (Qwen3-30B) / 80.7% (Qwen3.6),
control exactly 100.0% on every layer of both, prev-token 43/35% --
corroborating the offline route-trace estimates.
Throughput: the day's C-vs-B losses are recorded WITH their
invalidation. Re-running the reference on the by-then-hot device gave
3.93 tok/s against the cool morning's 6.54 with byte-identical I/O,
hit and drop counts -- the engine is deterministic, the -40% was
silent thermal capping, and every variant had been compared against
the cool number. The one thermally matched pair that was measured
(speculation on 8 io lanes, -28%, effective flash bandwidth 585->392
MiB/s) kills the more-lanes hypothesis specifically; spec-max 2 and
retention-only still owe a matched cool pair.
What did survive: the observer-tax decomposition (109 ms/token: the
per-element exported-function F16 conversion at 21M calls/token, plus
the barrier), and retention moving hit rate 0.1pp on a 3000 MiB cache
-- the offline replay bound confirmed from inside the engine.
* feat(app): expose the predictive prefetch as an experimental Streaming toggle
Off by default. Gated on streaming + a live cache like the temporal
prefetch, and the two settings disable each other in the UI -- the
engine refuses the pair, and a control the engine will reject is worse
than one that cannot be set. The spec-max rung selector (0/1/2/4)
surfaces the retention-only point, which is the configuration the open
throughput question most needs measured from the app. Session
signature includes both fields so flipping them reopens the process.
No versionCode bump: this is a PR-branch test build, not a release.
* docs(predict): record the 2026-07-24 matched pairs — read-ahead refuted, retention hit-neutral
A four-cell session (B, retention-only, spec-2, B sentinel) run at fixed 30 s
spacing re-proved the thermal-contamination mechanism (sentinel −17%, clusters
silently capped from cell 2) and yielded one genuinely matched pair: spec-2 vs
the B sentinel at the same caps and battery temperature, 3.14 vs 3.96 tok/s
(−21%) with hit rate up 4.3pp and 79% of speculations useful. Speculation
improves every metric it owns and still loses the wall clock — the flash has no
spare bandwidth to spend. Retention-only again moved the hit rate by nothing
(77.2% vs 77.6%), as the offline replay bound predicted.
* feat(app): contrast the two prefetch predictors in the UI, default spec-max to 0
The temporal and predictive toggles now say what actually differs — the bet
("repeats the previous token", ~40%) vs the question ("ask the next router a
layer early", ~85%) — instead of describing mechanisms side by side. Spec-max
defaults to 0 (retention only): the matched-pair A/B showed the read-ahead
losing −21% on a saturated flash, so 0 is the only rung the measurements did
not refute, and the helper text says so.
* chore(predict): file the changelog under Unreleased, drop a dead member, record the verdict
Three loose ends found reviewing the branch for merge:
- The changelog entries had been appended to the already-released 0.15.1 section;
they belong under [Unreleased], where the release commit carves them out.
- nu_hint_ was written on every routing and read nowhere: a leftover of the
synchronous first design, whose successor passes the routing width straight into
the prediction job.
- docs/roadmap.md still closed the routing-prediction question on the 2026-07-12
removal. It now records what reopening it with a training-free predictor found:
the accuracy is real and the throughput is not, for the same reason more lanes
and the sidecar lost.
Compute buffers are reserved for the worst-case graph, and this engine had
always set n_ubatch = n_ctx so that any fitting prompt prefills in one pass.
That quietly ties RESIDENT MEMORY to the context rather than to the work:
320 MiB reserved at n_ctx 2048, falling to 80 MiB at 512, scaling exactly with
the context and nothing else.
On an engine whose entire problem is that the expert cache and the dense weights
compete for the same RAM, that is a real budget and it was invisible. Decode is
unaffected -- a decode graph is one token wide whatever this says -- so the cost
is prefill throughput, which processes a long prompt in more, smaller passes.
Default 0 keeps the previous behaviour exactly.
validate() rejects a negative value and one above n_ctx: reserving for a batch
that cannot arrive is the inverse of what the knob is for.
Found while chasing what looked like a catastrophic slowdown and turned out to
be this reservation pushing an already-tight system into reclaim at nearly
6 000 major faults per token, none of them the workload's fault. That happened
during GPU work, where the reservation was largest, but the coupling it exposed
is the engine's own and applies to every configuration. Salvaged here because
the GPU offload it was found alongside measured negative and was closed (#104).
Also fixed, all found alongside and unrelated to each other:
* A stray % in the --dense-weights help text was read by printf as a conversion
specifier, so that line printed garbage and read past the argument list.
* scripts/build-android.ps1 judged cmake by whether it wrote to stderr rather
than by its exit code, which broke it under Windows PowerShell 5.1 -- the NDK
toolchain prints progress to stderr and 5.1 turns that into a terminating
error under ErrorActionPreference = Stop.
* .gitignore now covers third_party/opencl-sdk/, 5.3 MB of Khronos headers and
an ICD loader that fetch-opencl-android.ps1 drops into the tree and nothing
was ignoring.
Qwen3.6-35B (22.3 GB, ~1.5x RAM) on a Windows x86 laptop, engine
unmodified: baseline 4.78 tok/s, best recipe --overlap --cache-mb auto
--drop-cold-experts 0.75 = 7.33 (+55%), above the phone's 5.0-5.8 on
the same model.
Streamed decode is DRAM-bandwidth-bound there, the opposite of the
phone: compute sits at ~0.11 s/token in every cell (a ~9 tok/s ceiling
at zero I/O), doubling compute threads buys 2 ms, and io8 = io4 to the
millisecond once the cache is fixed — round 1's apparent +17% from
lanes was entirely a --cache-mb auto budget confound (5.3-7.4 GiB
across cells), recorded with its resolution. Aggregate flash read
stays ~900 MiB/s on a ~3 GB/s NVMe regardless of lanes, and with
overlap on it drops to ~330 MiB/s: async reads compete with FFN
compute for the same DRAM bandwidth.
README gets a dedicated Desktop section (table + the flip) replacing
the old one-line quick check; raw CSVs and findings.md land under
docs/bench-data/2026-07-24-desktop-qwen36/.
Ships the narrow-routing warning that landed after 0.15.0 was tagged.
0.15.0 shipped the app default at 75% with no caveat for models that
route few experts. On gpt-oss (4 of 128) that default puts the threshold
at 18.8% of the routing against the 9.4% every published number was
measured at, and nothing said so. The warning closes that gap in the
engine and in the app.
Also corrects a contradiction inside the 0.15.0 section itself: the
app-default entry still called the quality cost unquantified while the
Measured block below it reported the GSM8K result.
The threshold is a fraction of the uniform share 1/top-k, so what it
removes scales with how wide the routing is. At top-k 8 -- where every
number in docs/expert-dropping.md was collected -- 0.75 means "below 9.4%
of the routing", a tail trim. At top-k 4 it means "below 18.8%", and at
top-k 2 "below 37.5%", which on a miss discards the whole minority
expert: closer to halving the routing than trimming it, and unmeasured.
The engine now says so once at load when dropping is armed and the
effective top-k is 4 or fewer, quoting the actual share for the model in
hand. It warns rather than clamping or refusing: it cannot know whether
that trade is acceptable for a given model and task, and silently
adjusting a number the caller chose would be worse than a loud caveat.
MoeStreamConfig::drop_low_topk_warn is documented as an EVIDENCE
boundary, not a physical one -- nothing in the streaming path reads it.
The app shows the same caveat inline under the setting, in the error
colour, computed from the width the loaded model reports rather than
assumed -- and only once a model is loaded, since guessing would be worse
than staying quiet. gpt-oss is the case this exists for: it routes 4 of
128, and the app default is 75%.
To make that possible, BMOE_READY gains n_expert_used (the effective
width after any override, 0 on a non-MoE model) and Session exposes
n_expert_used() for embedders. Additive: older consumers ignore it.
Say explicitly that the in-app downloader takes any direct gguf URL, so
any model from the supported architecture families streams the same way
as the catalog entries. Also swaps an em-dash for a colon in that
paragraph.
