* feat(install): opt-in Vulkan llama.cpp backend and HIP gfx fallback (#7357)
Add UNSLOTH_LLAMA_BACKEND=vulkan and --llama-backend vulkan to force the
upstream Vulkan prebuilt on any host, persist llama_backend in the install
marker, and re-assert it during Studio updates.
On Windows AMD, auto-fallback to Vulkan when no detected gfx arch is in the
upstream win-hip-radeon GPU_TARGETS set (e.g. gfx803 / RX 480). Mixed setups
where at least one card is HIP-supported still default to HIP unless opted in.
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* fix(install): address Codex P2s on Vulkan gfx routing (#7357)
Honor ROCm family tokens (gfx110X), include fork-supported gfx1103, require
a known active gfx before auto-Vulkan, and base the HIP floor check on the
visible-device target instead of every physical GPU in hipinfo.
* Address Codex review: env namespace, physical-NVIDIA guard, test kwarg
- llama_backend_from_env: stop reading UNSLOTH_LLAMA_CPP_BACKEND. That is a
separate pre-existing setup variable meaning auto/cpu; setup.sh/setup.ps1
warn and ignore other values, so reading it here forced Vulkan behind that
warning. Vulkan opt-in stays on UNSLOTH_LLAMA_BACKEND / UNSLOTH_FORCE_VULKAN.
- _should_auto_vulkan_for_amd_windows: gate on not has_physical_nvidia (not
merely has_usable_nvidia). A CUDA-masked NVIDIA card keeps has_physical_nvidia
while has_usable_nvidia goes False; Vulkan ignores CUDA_VISIBLE_DEVICES and
could enumerate the reserved card. Mirrors the Intel auto path. Explicit
opt-in still overrides.
- test fakes: validate_prebuilt_attempts/validate_prebuilt_choice gained a
llama_backend kwarg; the four fake signatures in the fallback tests now
accept it, clearing the TypeError that reddened Backend CI / Repo tests (CPU).
Tests: UNSLOTH_LLAMA_CPP_BACKEND=vulkan no longer triggers Vulkan; hidden
physical NVIDIA suppresses AMD auto-Vulkan while explicit opt-in overrides.
* Keep gfx1034 on the ROCm path (fork gfx103X bundle covers it)
The WINDOWS_HIP_PREBUILT_GFX_TARGETS allow-list omitted gfx1034, so
_route_to_vulkan_prebuilt downgraded RX 6500/6400-class hosts to the upstream
Vulkan prebuilt before published_rocm_choice_for_host could match the fork
windows-rocm gfx103X bundle (whose members include gfx1034). Add gfx1034 to the
allow-list and a regression test asserting it stays on the fork ROCm asset.
* Fix auto-Vulkan stealing fork windows-rocm gfx908/gfx90a hosts for PR #7373
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* Fix Vulkan marker claiming a backend that was never installed for PR #7373
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* Tighten the Vulkan backend routing comments for PR #7373
* Keep the visible-device-aware gfx when setup forwards --rocm-gfx
setup.ps1 resolves the gfx arch from its own probe, and that pick is not
fully visible-device aware: neither the hipinfo nor the amd-smi branch
reads CUDA_VISIBLE_DEVICES, and the amd-smi branch matches a bare integer
only, so a comma-separated HIP/ROCR mask such as 1,0 also falls back to
GPU 0. The resulting arch was then forwarded through --rocm-gfx and
replaced the arch detect_host() had already resolved for the
runtime-visible GPU.
On a mixed-AMD Windows host that flipped the auto-Vulkan decision: with
GPU 0 gfx1100 and a masked-in gfx1010, the forward reinstated gfx1100,
_should_auto_vulkan_for_amd_windows() saw a HIP-supported arch and the
HIP bundle was installed for a GPU that cannot run it.
Fold the forward in as a fill rather than a replacement: it still supplies
the arch on amd-smi-only, driver-only and name-inferred hosts where the
probe reports none, which is what --rocm-gfx exists for, but no longer
overwrites a successfully detected active arch. An explicit
UNSLOTH_ROCM_GFX_ARCH stays authoritative, since it is the documented
manual override for hosts whose arch the probes get wrong.
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* Scope the Windows AMD Vulkan fallback per device and per repo
Three follow-ups on the auto-Vulkan routing for #7357.
Keep an explicit --rocm-gfx authoritative. The previous round stopped a
forwarded gfx from replacing an arch detect_host() had already resolved,
but --rocm-gfx is also the documented operator override for hosts whose
probe is wrong or stale, and both arrive as the same argv. Narrow the
advisory case to the two shapes setup can actually be describing: an arch
the probe saw on this host (setup picked a different physical GPU of the
same box), or a family label such as gfx110X, which is a bundle name the
update path derives from the marker asset rather than a real GPU arch.
Any other value is an override for an arch no probe reported and stays
authoritative. Keeping family labels advisory also preserves the rule that
an in-generation-but-unbuilt arch (gfx1033) is never upgraded into the
gfx103X bundle.
Do not auto-route to Vulkan from a HIP-only device mask. HIP_VISIBLE_DEVICES,
ROCR_VISIBLE_DEVICES and CUDA_VISIBLE_DEVICES select the active arch, but the
Vulkan runtime honours none of them: it enumerates through
GGML_VK_VISIBLE_DEVICES and Vulkan ordinals in
LlamaCppBackend._get_gpu_free_memory_vulkan. Masking down to a below-floor
card therefore used to install a backend that could still enumerate the
HIP-capable card the user deliberately hid, possibly one reserved for another
workload. Require every physical AMD gfx to be below the floor, matching the
has_physical_nvidia gate right above it. So the per-GPU list survives to that
check, a forward that agrees with the probe no longer collapses
rocm_gfx_targets to a single entry.
Make the HIP support predicate repository-specific. The floor constant is a
union of ggml-org's windows-hip gpu_targets and the fork's windows-rocm
bundles, so it only answers "is this arch served" for the fork. With
--published-repo ggml-org/llama.cpp, direct_upstream_release_plan() offers
win-hip-radeon then CPU and never Vulkan, so the four fork-only archs
(gfx908, gfx90a, gfx1034, gfx1103) were declared supported and fell through
to CPU instead of the Vulkan bundle that would actually run. Add
UPSTREAM_WINDOWS_HIP_GFX_TARGETS and select the set from the planned repo.
* Keep probe-confirmed AMD GPUs in the physical list when a gfx is forwarded
rocm_gfx_targets is the physical inventory _should_auto_vulkan_for_amd_windows()
reads, so a forwarded --rocm-gfx that the probe never reported was deleting cards
the probe had confirmed. On a mixed Windows AMD box whose active device is masked
down to a below-floor card, a stale UNSLOTH_ROCM_GFX_ARCH or a name-inferred arch
for the other GPU collapsed the list to that one arch, the floor check concluded no
AMD GPU on the host reaches the Windows HIP prebuilt, and the install auto-fell back
to Vulkan, which honours no HIP mask and would enumerate the reserved HIP-capable
card. Add the forwarded arch to the list instead of replacing it: it selects the HIP
target, it does not redefine what hardware is present.
An empty probe still yields a single-entry list, so the driver-only Windows AMD host
the forward exists for keeps its automatic Vulkan fallback, and an explicit
--llama-backend vulkan is unaffected.
* Do not auto-fall back to Vulkan when a HIP device mask filtered the probe
hipinfo is itself a HIP application, and AMD documents HIP_VISIBLE_DEVICES as
"only devices whose index is present in the sequence are visible to HIP", with
that spelling recommended on Windows. Under a mask the Windows probe therefore
enumerates the visible devices, so rocm_gfx_targets is what survived the mask
rather than the physical inventory the auto-Vulkan floor check assumes. A
masked-out gfx1100 next to a visible gfx803 made the check conclude that no AMD
GPU on the box reaches the Windows HIP prebuilt and route the install to Vulkan,
which honours none of these masks and would enumerate the reserved card.
Decline to guess when a mask is set: the physical inventory is unknowable from a
masked probe, so keep the HIP / fork / source path. This only ever turns the
automatic fallback off, never on. The driver-only single-GPU host the fallback
exists for sets no mask, an all-hiding "" / -1 mask is still handled as no active
target rather than a partial view, and an explicit --llama-backend vulkan or
UNSLOTH_LLAMA_BACKEND=vulkan is unaffected.
Reading the physical inventory through an unmasked re-probe would also correct
_pick_rocm_gfx_target, which indexes the token list by the mask value and so
already assumes an unmasked probe. That is pre-existing behaviour on main and is
left alone here.
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* Treat an all-hiding HIP device mask as suppressing the Vulkan fallback too
The mask guard exempted an empty or -1 value on the grounds that the probe reports
no active target under it, but that only holds for the probe: a forwarded
--rocm-gfx still reconstructs an active arch, and setup infers that arch from the
display-adapter name, which no HIP mask touches. A user who hid every AMD GPU from
HIP could therefore still be auto-routed to Vulkan, which honours none of these
masks and would then use all of them. That is the strongest form of the hazard the
guard exists for, not an exemption from it.
Presence of any of the three variables is now the whole test, which also removes
the value parsing. An explicit --llama-backend vulkan or UNSLOTH_LLAMA_BACKEND is
still unaffected.
* Grant the fork-only Windows HIP coverage to the fork, not to every mirror
The floor set is a union of the fork's windows-rocm bundles and only the fork is
planned from its manifest: resolve_simple_install_release_plans() compares
== DEFAULT_PUBLISHED_REPO and sends every other --published-repo through
direct_upstream_release_plan(), whose AMD branch offers win-hip-radeon then CPU
and never Vulkan. Exempting only the exact ggml-org spelling therefore told a
mirror carrying upstream-standard assets that fork-only archs such as gfx1034,
gfx1103 and gfx908 were HIP-served, landing them on HIP or CPU instead of the
Vulkan bundle that would actually run. Gate on the fork instead.
Matching the dispatch exactly, spelling included, also fixes a differently cased
repo: that really does take the upstream path, so it must be answered with
upstream coverage rather than the fork superset. An empty repo still defaults to
the fork, as the resolver does.
* Derive the Windows HIP gfx floor guard from the published manifest
The guard compared WINDOWS_HIP_PREBUILT_GFX_TARGETS against a second hardcoded
tuple in the same test file, so a windows-rocm arch newly published by the fork
passed both. Affected hosts would then be routed off the hash-approved fork ROCm
bundle onto an unhashed upstream Vulkan build with nothing failing.
Read the fork's llama-prebuilt-manifest.json through the installer's own
resolver instead, and assert the floor, the family labels, and the routing
tuple all still cover what it publishes. The manifest ships only as a release
asset, so an unreachable release skips with an explicit reason rather than
flaking. Both literals match the manifest as published today.
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* Compress the Vulkan backend routing comments and docstrings for PR #7373
* Correct the family-label rationale in the Windows HIP coverage check
The comment justified serving gfx103X / gfx110X against any repository by
claiming upstream's windows-hip targets build every member of those families.
The fork manifest maps gfx103X to gfx1030..1032 plus gfx1034 and gfx110X to
gfx1100..1102 plus gfx1103, and UPSTREAM_WINDOWS_HIP_GFX_TARGETS carries
neither gfx1034 nor gfx1103, so the stated reason is wrong even though the
answer is right.
State the real reason instead. A family label is a bundle name, not an arch,
so the concrete GPU is unknown at this point; answering unsupported to cover
the two uncovered members would move gfx1030..1032 and gfx1100..1102 off a
working HIP build onto Vulkan for a card the label cannot identify. Those two
archs still reach Vulkan through the concrete-arch branch below, which does
answer per repository.
Comment only. No behaviour change: the 5850-combination override sweep still
reports 0 rocm_gfx_target changes, 0 auto_vulkan False to True flips and 680
True to False flips all backed by a probe-confirmed HIP GPU, and both the
feature and override profile matrices are byte-identical.
* Pin that a deliberate CPU install outranks Vulkan for PR #7373
UNSLOTH_LLAMA_CPP_BACKEND (setup.sh / setup.ps1, "auto" or "cpu") and
UNSLOTH_LLAMA_BACKEND (this module, a backend name) are separate variables at
separate layers, and both accept "cpu". setup translates its own =cpu into
--force-cpu, which is what pins the CPU-only bundle on a GPU host and keeps
Intel iGPU Vulkan crashes away (#7213), so no trigger this PR adds may
outrank it.
_route_to_vulkan_prebuilt already gets this right, since force_cpu
short-circuits ahead of the forced, auto-Intel and auto-no-HIP triggers.
Cover it so it stays that way: the matrix runs [Linux, Windows, macOS] x
[NVIDIA, AMD, Intel, CPU only] x [unset, vulkan, hip, rocm, cpu] with the
legacy UNSLOTH_FORCE_VULKAN set as well, and asserts the published bundle
survives every one. WSL presents as Linux to this resolver, so it rides the
Linux row.
Also assert the guard is not vacuous: the same host still takes Vulkan once
the CPU pin is gone, so the matrix cannot pass on a resolver that had simply
stopped routing to Vulkan.
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: LeoBorcherding <borchborchmail@gmail.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Comment-only follow-up to #7454. That change carried 523 comment lines, many of
them three and four line preambles where one line says the same thing. This
collapses them and drops the ones restating what the code already says, for a
net 77 lines.
Scope is limited to comments #7454 itself introduced. The files it touched hold
about 3,761 comments in total; the rest predate it and are untouched, verified
by checking that every removed line is one that commit added.
Nothing that records why a non-obvious decision was made was dropped, only
compressed. Still stated: the normcase-before-versus-after Windows separator
trap, the innermost-indexed-model rule for nested directories, an HTTPException
being a decision rather than a failure to decide, that only an explicit False is
anonymous to huggingface_hub while None borrows the server owner's login, the
fail-closed tri-state custom-code gate, and the regressions each test was
written for.
Code is provably unchanged: comment_tools.py check reports 17/17 files
comments-only. Backend CI command 10337 passed, 0 failed. tsc -b clean.
* Vulkan GPUs: real device names and selectable ordinals
Rebases the durable half of #7356 onto the inference_gpu transport #7476
landed on main. Those two PRs solve an overlapping problem and disagree on
the data model, so merging #7356 as-is would ship two parallel Vulkan
device concepts with different index semantics. This keeps main's transport
and adds what #7356 had that #7476 does not.
- _vulkan_probe.py emits a 5th column, ggml's device description, sanitized
for the tab protocol and UTF-8 safe. Reader tolerates 4- or 5-column
output so an older probe still parses.
- llama_cpp gains _run_vulkan_probe (shared parse) and
vulkan_device_inventory (names + is_igpu + real totals).
- get_vulkan_inference_gpu_info reports the real name and an explicit
is_igpu instead of "Vulkan<i>" and a total == 0 guess.
- index_kind becomes "vulkan", not "relative", and gpu_ids picks are
supported on Vulkan builds once the probe enumerated ordinals. The XPU ban
no longer applies to them: a Vulkan pick is a ggml ordinal, not a torch-xpu
index, so it works on an Intel host too.
- Frontend picker reads the Vulkan inventory as the pickable set.
Memory deliberately still comes from _get_gpu_memory, not the inventory.
That path applies _apply_igpu_host_reserve_mib and zeroes a shared total;
budgeting an APU off its raw shared total would hand out the whole machine's
RAM with no OS headroom. Identity is joined onto it by ordinal, so a probe
failure degrades to Vulkan<i> names with the memory readings intact.
Dropped from #7356 as superseded: validate_vulkan_gpu_ids (main's
resolve_requested_gpu_ids already rejects duplicates and
_resolve_gguf_gpu_ids_for_request already probes for existence), the
gguf_devices transport, and the iGPU budget fallback in 71619891e, which
main's aggregateGpuMemoryTotalGb handles better by counting a shared pool
once.
Also keeps #7356's removal of the late diffusion raise, so the graceful
gpu_ids drop stays reachable for a GGUF only classified as diffusion after
download. #7415's real guard, _reject_vulkan_diffusion_gpu_ids_before_
teardown, is untouched.
Verified on Windows + Strix Halo: backend Vulkan/GPU-selection suites at the
same 4 pre-existing failures as main, tests/studio 1671 passed with no new
failures, frontend typecheck clean. Hardware confirmation of the underlying
behavior is on #7356 from @Bebiv24 (RX 9070 XT + RX 480).
Co-authored-by: LeoBorcherding <borchborchmail@gmail.com>
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* Studio: say which model is missing instead of "No model loaded"
A /v1 request naming a model that is not downloaded returned the generic
"No model loaded. Call POST /inference/load first.", which cannot fix it.
Return 404 model_not_found naming the model and listing what can serve,
and make the API usage examples name a model the server actually has.
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* Studio: page the API monitor, show model load/unload, pin the example quant
The monitor rendered all 50 retained entries in one scroller: page it 5 at a
time, freezing history while paged back so live traffic cannot reorder it.
Add model load/unload rows so the feed shows what the server is doing, and
stop the header rendering the loaded model as a raw host path. Advertise each
model's GGUF quant on /v1/models so the example pins repo:QUANT, and move the
auto-switch section above the monitor with shorter copy.
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* Studio: optionally download a model named in an OpenAI API request
Auto-switch only ever loaded models already on disk, so naming one this
server does not have either 404s or, when something else is loaded, gets
quietly answered by the resident model.
Add openai_api_auto_download_model (off by default, gated on auto-switch).
When on, a /v1 request naming a GGUF repo that is not downloaded starts a
background fetch and returns 503 with Retry-After and a typed
model_downloading code. The resident model keeps serving in the meantime,
and the retry after the download completes is served by the new model
through the existing auto-switch path.
The download reuses the Hub manager's service layer, which already does
repo-id validation, casing, claim bookkeeping, disk preflight, resume and
cancel. The in-loader download is deliberately not used: it silently falls
back to a smaller quant under low disk, which is wrong when the caller
named an exact one.
Admission is narrow, since a request only needs an API key:
- namespace/name only, so gpt-4 and other foreign ids fall through to the
resident model exactly as before
- GGUF only, decided from the remote file list rather than the repo name
- anything declaring auto_map is refused, so trust_remote_code stays a
deliberate opt-in in the UI and can never be granted over the API
- a single download at a time, plus a free-disk reserve
- one model_info call answers existence, gating and the quant list, so a
missing repo, a gated repo and a wrong quant each get their own error
With the setting off every one of these paths is byte-identical to before.
Also:
- monitor rows for downloads, with a live percentage
- public_model_id resolves an HF cache snapshot to its repo id, so a
cache-loaded model is no longer labelled with a commit sha; this drops
the duplicate helper added for the monitor and fixes the same leak in
the inference status response
- the unedited sk-unsloth-YOUR_KEY from the copyable examples now says so
instead of "Invalid or expired API key"; every other bad key keeps the
generic message
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* Studio: add an Unload button to the API monitor
The monitor names the loaded model but offered no way to free it. Idle
auto-unload is the only existing release path, and it needs a TTL and a
wait.
The button sits next to Refresh, appears only while a model is loaded and
is disabled mid-unload. /unload matches on the internal identifier, which
this response deliberately omits because it would be a host path, so the
click reads it from /api/inference/status the same way the chat runtime
does rather than widening the monitor payload.
Also stamp the manual unload row with the quant, read before the teardown
clears it, so it reads repo:QUANT like the load row it pairs with.
* Studio: keep the API monitor Unload button visible when idle
It only rendered while a model was loaded, which hid the one manual
release path at exactly the moment someone goes looking for it. Render it
always, disabled with a "No model is loaded" tooltip when there is nothing
to free.
* Studio: never answer a named model with a different one
Asking for a model this server is not serving returned 200 from whatever
was resident. Requesting gemma-4-E2B-it-GGUF:UD-Q6_K_XL while UD-Q4_K_XL
was loaded got a confident answer from the wrong quant, with nothing in
the response saying so.
A name carrying a namespace (org/model, optionally :QUANT) is a concrete
reference, so 404 instead, with the reason:
- wrong quant -> names the quants that are actually downloaded
- not on disk -> lists what is available
- on disk but auto-switch off -> says to turn it on
Ids without a namespace (gpt-4, claude-3, default) are foreign labels
rather than references, so they still fall through to the resident model
and drop-in clients are unaffected. A bare org/model is still satisfied by
any loaded quant of that repo; only an explicit :QUANT must match.
The check runs whatever the auto-switch and auto-download toggles are,
since serving the wrong weights is wrong in every configuration. It is
skipped when nothing is loaded, where the existing no-model-loaded error
already says the right thing, and when the model is on disk with
auto-switch on, where a failed swap should still fall back.
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* Studio: use a simpler prompt in the API usage examples
"What is Unsloth Studio?" rather than "Can Unsloth Studio do API calling?".
One constant feeds all nine snippet tabs.
* Studio: only refuse a model reference meant for this server
A namespace alone was treated as a concrete model reference, so a /v1
request naming anthropic/claude-3.5-sonnet, openai/gpt-4o or any other
LiteLLM or OpenRouter style vendor/model id started returning 404 instead
of being answered by the resident model. Refuse only on evidence the
caller meant this server: an explicit GGUF quant label, or a repo that is
actually on disk here. gpt-4 and vendor/model alike fall through again,
while the wrong-quant and wrong-repo cases this PR exists for still
refuse.
Also from review:
- Release the single download slot by object identity, not repo id. A
stale watcher could clear a newer download of the same repo and let a
second multi-GB fetch start alongside it.
- Catch BaseException around admission: CancelledError is not an
Exception, so a cancelled request stranded the slot for the process
lifetime.
- Honour the download service's accepted=False, which it returns without
raising for a cross-variant conflict, instead of promising a download
that was never dispatched.
- Treat a failed status probe as unknown rather than idle, so a transient
read cannot fail the monitor row and free the slot under a live worker.
- Check gated repos with auth_check. The Hub serves metadata for a gated
repo without granting its files, so the licence gate was being reported
as the unrelated custom-code refusal.
- Size the disk reserve from the download plan, which includes the mmproj
and MTP companions the worker fetches with every quant.
- Never fetch under the server's own HF token. The repo is named by
whoever holds an API key, so the ambient token let that key pull the
owner's private repos.
- Refuse an explicit quant on a backend with no quant identity, gated on
the suffix really being a quant so Ollama style :latest tags still match.
- Raise instead of falling through when the diagnosis fails: the mismatch
is already established by then, only the wording is uncertain.
- Report a failed switch as 503 model_switch_failed rather than answering
as the resident model.
- Fail an open monitor row under the same lock as the check, so a finish
landing in between cannot stamp an error onto a completed row.
- Usage examples never emit a hardcoded model id: the catalog is tri-state
and the panel asks for a model to be loaded instead of printing one the
server cannot serve. It also refreshes when the loaded model changes.
- Keep the monitor pager reachable while frozen entries expire.
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* Studio: scope the auto-download 404 cache to the caller's credentials
The Hub answers 404 for a private repo the caller cannot see, so caching
that verdict per repo alone let one anonymous request mark a private repo
unservable for everyone for the whole TTL. A later caller sending a valid
X-Unsloth-HF-Token skipped the probe and fell through to the resident
model instead of downloading what it asked for. Keyed on the repo id plus
a digest of the token now, so the token itself is never held.
Two more from the same review:
- Clear the chat runtime checkpoint after unloading from the API monitor,
as the chat eject flow already does. The store went on treating the
freed checkpoint as loaded and the usage examples kept naming it.
- Point gated and not-found callers at the X-Unsloth-HF-Token header.
Automatic download deliberately ignores the server's own Hugging Face
identity, so telling the user to add a token in Studio sent them round
the same 403 forever.
* Studio: tighten the comments added by this branch
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* Studio: keep API auto-download off the server's Hugging Face identity
Passing None for the caller's token was not anonymous. spawn_worker
substitutes the backend's HF_TOKEN for a falsy one, and HfApi(token=None)
falls back to a cached login, so a repo named by an API-key holder could
still be fetched under the owner's Hub identity and land in the shared
catalog. The metadata probe and auth_check now pass an explicit False,
and dispatch threads allow_ambient_token=False so the worker stays
anonymous too. The flag defaults to True, so the UI download path keeps
the ambient fallback that private repos rely on.
Three more from the same review:
- Require an exact hf_variant match only when the suffix is really a
quant. The llama.cpp branch still compared Ollama style :latest and :8b
against the loaded quant and refused the resident model, which is the
opposite of what looks_like_quant classifies them as.
- Decode an HF cache repo id only when the models-- component is followed
by snapshots. An ordinary directory whose name merely starts with
models-- was being read as an encoded repo id.
- Return the probing response before consulting the job registry when an
adopted claim has no variant yet. A stale error on the whole-repo key
could otherwise release the slot the first request's probe still holds,
letting a second large download start beside it.
* Studio: stop treating a namespace as what decides model intent
The rule refused a reference only when it carried a namespace, which was
wrong in both directions. vendor/model is how LiteLLM and OpenRouter name
every provider, and a standalone or custom-folder GGUF is advertised
without one, so asking for a path-free local id such as model-Q4_K_M was
answered by whatever else happened to be resident. The slashless early
return is gone and the same evidence test now applies to every id: an
explicit quant, or a model that actually resolves here. gpt-4 and default
still fall through because they are not local, not because of their shape.
Also:
- Recognise bits-per-weight quant labels. _extract_quant_label emits
IQ4_XS-3.53bpw and the resolver and downloader both accept it, but
_GGUF_KNOWN_QUANT_RE has no bpw group, so looks_like_quant rejected a
reference the rest of the machinery understands.
- Upper-case the synthetic names handed to _pick_best_gguf. Its preference
tokens are upper case and matched case-sensitively, so a repo with
lower-case filenames skipped the preference and took the first entry,
which can be F16.
- Only offer a downloaded but unloaded model as a runnable example when
auto-switch is on. It is off by default, so the copied snippet hit the
no-model-loaded error, which is the failure this branch exists to fix.
The tool-passthrough cancel test stubbed asyncio.to_thread module-wide, so
it cancelled at the first thread hop rather than the generation hop it
means to test. Model resolution runs off the loop before the monitor row
opens, so that stub now passes the resolver through.
* Studio: tighten the comments added since the last pass
* Studio: match a resident model through its resolver alias
A manual load stores the model by its on-disk path while the resolver and
/v1/models advertise it as publisher/model, so _loaded_satisfies could not
recognise the alias. Reducing the resolution to a boolean then threw away
the load path that would have proved the match, and the request was
refused with 404 for a model the server was serving at that moment. Common
for LM Studio models and custom-folder aliases. The resolved path is
compared against the resident backend before anything is refused.
Also:
- Size disk admission on what is left to fetch. expected_bytes is the whole
plan, so a resumed quant or a companion already pulled in by another
quant was charged for twice and could 507 a download that fits. Cached
blobs are subtracted through existing_blob_bytes, the same accounting the
worker's own preflight does, and it falls open to the full size when no
blob hashes are available.
- Report a cancelled download as cancelled. The catch-all sent every state
other than complete or idle through fail_open, so a deliberate cancel
rendered as a download failure rather than the monitor's cancelled state.
- Keep polling the servable ids while nothing is loaded. The poll settled
as soon as auto-switch was on, so turning it back off left the examples
naming an unloaded model until something else remounted the panel.
* Studio: shorten the comments added in the last pass
* Studio: keep the FLA fast-path tests hermetic across transformers versions
_discover_fla_model_types scans the *installed* transformers for modeling
files importing `from fla.`, so `models/qwen3_5/` only exists from
transformers 5.x. The backend supports transformers>=4.51, and on a 4.x
install the Qwen3.5 gate returns False, so 14 tests in
test_training_worker_flash_attn.py silently exercised a no-op instead of the
install path and failed their call-count assertions.
Pin the discovered model_type set in those 14 tests, the same way
test_hook_does_not_install_tilelang_for_model_outside_allowlist already pins
it against newly added FLA model_types. Test-only change: the production
gate and the _discover_fla_model_types unit tests are untouched.
* Studio: keep the /v1 admission check off the model-scanning path
The admission check added here runs on every /v1 request, including with
auto-switch off, where the route used to return straight away. It called
resolve_local_gguf, whose index is cached for 5s and otherwise rebuilt by
walking ./models and every HF cache root, under a lock the next caller waits
on. On an install with a large cache that scan measured 6.1s, longer than the
TTL that is meant to amortise it, so steady traffic would keep rebuilding it.
Answer from the last built index instead and never rebuild from the request
path: a stale answer is fine here, since what is on disk barely moves and a
finished download already invalidates the index. The first request, before any
scan has completed, warms the index on a background thread and skips the check
rather than blocking on it. That also makes the lookup a dict read, so it no
longer needs handing to a thread.
Cold resolve on this box goes from 6152495us to 0.4us, and the whole hook now
costs the same for a foreign label as for the resident model.
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* Studio: fix the admission hook's cold, stale and contended index paths
Five review items, four of them on the admission hook added here.
Skipping the check until the first scan lands also skipped explicit quant
mismatches, so the first request after startup could ask for :Q8_0 while
Q4_K_M was resident and be answered by it. The early return was redundant as
well: with an empty index resolved is None and here is False, so the gate below
already lets a bare name through and refuses an explicit quant, which is what
the except branch has always concluded. Dropped it and index_is_built with it.
index_is_built took _lock, which _index holds for the whole scan, so once a
warm was running every later request blocked on the event loop for exactly as
long as the scan it was there to avoid. The warm now has its own lock and reads
the timestamp unlocked, which is safe because _scan is only ever rebound.
Warming only when the index had never been built left a model fetched in the
Hub UI, or dropped into a scan folder, invisible for the life of the process,
since only the auto-download watcher calls invalidate_index. Warm on staleness
too, and unconditionally, so it refreshes within a TTL without a scan on the
request path. Rescanning is capped at a tenth of the scan's own duration: a big
install takes longer to scan than the TTL, and warming on the TTL alone would
keep a thread scanning continuously.
An Ollama-style tag names no quant, so the resolver misses it and auto-download
saw a model the resident one already answers to, then 404'd it for having no
such quant. Return early when the loaded model satisfies the reference.
Frontend: a cancelled download said "Model download failed", because the label
collapsed everything non-completed into failure.
The backend tests get an autouse fixture that stops the warm from walking the
developer's real HF caches; that scan starved the loop under the timing
sensitive streaming tests.
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* Studio: make /v1/models and the admission hook agree on what is local
Three review items, all on the seam between the catalog scan and the resolver
index, which run on separate schedules.
/v1/models can advertise a local GGUF the resolver has not indexed yet. A bare
id carries no quant to refuse on, so a client asking for one it had just been
handed was answered by the resident model instead. The hook now reads the
catalog cache as evidence too, never scanning it. It takes the path rather than
a yes/no because the converse also happens: the catalog can list the resident
weights under an alias the loaded entry does not answer to, and those must stay
served.
That alias was also emitted twice by /v1/models, once as the loaded basename a
manual load records and once as publisher/model marked unloaded, because the
dedup only compared ids. Compare the path as well.
A directly loaded standalone .gguf takes its quant from the filename, but the
resolver stores such files with no quants, so the advertised <stem>:<quant>
stopped resolving as soon as anything else loaded. Advertise a quant only when
that reference resolves, and downgrade only on a definite answer so a cold
index leaves the metadata alone.
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* Studio: tighten the comments this branch adds
Collapse the multi-line notes in the auto-download path, the /v1 admission
hook and their tests to one line each, keeping the reason and dropping the
restatement. No behaviour change.
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* Studio: four admission and catalog fixes from review
Lowercasing paths in _resolves_to_resident made /srv/models/Foo and
/srv/models/foo the same weights on any case-sensitive filesystem, so a request
for one could be answered by the other and /v1/models could mark the wrong
entry loaded. That helper now backs residency as well as admission, so use
os.path.normcase, which folds case only where the filesystem does.
Advertising a quant whenever the resolver could not disprove it kept the bug it
was meant to fix: a standalone .gguf loaded before the first scan still got
<stem>:<quant> published, and the usage examples persist that. No proof is not
proof, so omit it and warm the index instead.
A 401 from an expired or invalid X-Unsloth-HF-Token skipped the 403 and 404
branches and surfaced as "could not reach Hugging Face, retry shortly". It now
says to replace the token, kept apart from the gated refusal since a rejected
credential is not an unaccepted licence.
An image request naming an undownloaded text-only GGUF started the whole
download and only then hit the capability guard, which never sees a remote
target, so every retry 400d and the bytes were wasted. Thread require_vision
into admission and check it against the mmproj companions the disk preflight
already asks build_gguf_variant_plans for.
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* Studio: make the Hub error fixture carry a status on both hub majors
The 401 test built HfHubHTTPError directly, which works on 0.x and fails on 1.x
where response is required and keyword-only, so all four Python jobs failed
while the same test passed locally.
_hub_error already handled both constructors, but the 0.x branch left the
exception with no response at all, and hf_error_status reads the status off it
for the types that do not encode it in their name. So it could only produce a
usable error on 1.x, which is why the test bypassed it. Attach the status when
the constructed exception lacks it, and use the helper.
Cover the helper itself against stand-ins for both constructor shapes, since
whichever hub is installed only ever exercises one of them.
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* Studio: invalidate on every download, resolve bare tags, keep polling
Three review items.
Only the API auto-download watcher dropped the resolver cache, so a GGUF
fetched in the Hub UI stayed absent to the cache-only request path and the
request was answered by whatever was resident. finalize_worker_exit is the one
point every download worker exits through, so invalidate there. That closes the
window without leaning on the TTL, which the scan-duration throttle can stretch
past 5s on an install where the scan itself takes longer than that.
A downloaded but unloaded GGUF asked for as org/model:latest missed the
resolver, since the suffix was always treated as an exact quant. With
auto-download on that probed the Hub and returned a 404 for a quant that was
never a quant; with it off it refused without switching. Fall back to the base
entry when the suffix is not quant-shaped, and keep exact matching for real
quants so a swap can never serve the wrong weights under the right name.
The usage examples stopped polling once a model was resident, but idle unload
frees one without touching the store, so nothing re-ran the effect and the
examples kept naming a model that could no longer be reloaded. Slow the poll to
60s instead of stopping it.
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* Studio: hold the download slot while it is in use, and keep quants to llama.cpp
_loaded_satisfies refuses a quant reference against the Transformers backend by
name, but the path match did not carry that rule. A Transformers model active
from a directory that also holds GGUF exports therefore matched a request for
one of those quants and answered it with the safetensors weights. Only
llama.cpp has a quant identity, so admission now passes llama_only whenever the
reference is quant-qualified. A bare name still matches either backend, and
/v1/models residency keeps the default so a loaded Transformers model is still
reported loaded.
The 24 hour watch window was bounding ownership of the single-flight slot when
it should only have been bounding progress reporting, so a legitimately slow
download had its slot handed back while the worker was still writing, admitting
a second multi-gigabyte download beside it. Resolve the row on the clock, but
keep the slot on a slower poll until the job is actually terminal. Past the
deadline an unknown state does release it, since it means the worker cannot be
probed and holding it on that forever would wedge auto-download.
* Studio: keep what the resolver already knew when a download lands
Invalidating cleared the index to empty. The request path reads that cache
without scanning, so from a completed download until the rebuild landed it had
no evidence about any local model, not just the new one, and a bare request for
any of them was answered by whatever was resident. Wiring the hook into the
shared completion path in the last commit widened that from auto-download to
every download.
Mark the scan stale and keep the entries instead. Both _index and
warm_index_soon rebuild on a zero stamp, while the request path still sees
everything it knew a moment ago. Only a completed download invalidates, and
that only ever adds models, so nothing retained goes false.
Warm from the completion hook too, so the rebuild starts when the download
lands rather than when the next request happens to need it.
* Studio: match the quant, not just the directory, and default-select bare tags
Two quants of one repo share a directory, so the path match could not tell them
apart and an explicit :Q8_0 was answered by a resident Q4_K_M that
_loaded_satisfies had already refused by name. The llama_only fix in the last
commit only ruled out the wrong backend, not the wrong quant on the right one.
Both path matches now require the resident hf_variant to equal the requested
quant whenever the reference is quantified; a bare name still matches on the
path alone, since it claims nothing about the weights.
The local resolver already treated a tag that names no quant as meaning the
repo, but remote admission still looked for a quant literally called "latest",
so the same reference resolved locally and 404d remotely. Branch on
looks_like_quant there too. A real quant the repo does not have is still a 404
and never a substitution, which is what separates this from the loader's
low-disk fallback.
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* Studio: one quant preference, and stop trusting a stale checkpoint
list_local_gguf_variants sorts by descending size, so the head of variants was
the biggest quant, often F16, while remote admission and a plain load both rank
through _pick_best_gguf. A bare id therefore meant a different quant depending
on which side answered it, and the local answer was the one that could evict a
working model and then fail or OOM starting an F16 next to a usable Q4.
/v1/models advertised that same head for pinning. Pull the ranking into one
preferred_quant helper and have both sides use it.
The usage examples returned a stored checkpoint without ever consulting
/v1/models, and the polling added last round was gated on not having one, so
for a stored checkpoint it never ran. An idle unload then left the panel
showing a snippet that could not run. Poll whenever mounted, and prefer the
checkpoint only while the catalog still backs it or switching can reload it. A
catalog that has not answered yet is not evidence against it.
The static contract pinned the old dependency array, so it now asserts the
intent it documents: a finished load re-runs the fetch, and the effect is not
gated on having no checkpoint.
* Studio: fix the Windows path compare, and advertise a label the worker knows
The case fix normalized the separator to "/" and then called os.path.normcase,
which on Windows folds case and rewrites the separator back to a backslash, so
the descendant checks compared against a "/" the path no longer had. A manually
loaded GGUF reached through an alias then read as a different model, giving a
false 404 and an alias marked unloaded. Run normcase first and normalize the
separator after it.
There are two quant-label extractors and they only agree while a recognized
quant token is present. With none, _extract_quant_label takes the last
hyphenated segment, "7b" of llama-7b, while build_gguf_variant_plans and the
worker key the whole stem: the plan lookup missed and the job exited on a
variant it had no shards for. Use the canonical extractor for the unrecognized
case only. Checked across real filenames first, the two match on every
recognized quant and part on bpw-qualified labels, which _extract_quant_label
keeps apart on purpose so byteshape's IQ4_XS at 3.53, 3.97 and 4.19 bpw stay
separate variants.
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* Studio: a stored checkpoint needs catalog evidence, not just the switch setting
Preferring it whenever switching was on short-circuited the catalog check, so a
checkpoint the store still held after the model was deleted or moved kept being
named even though /v1/models had already proved it absent, and the snippets 404d
instead of falling back to a model that is actually there.
A lookup rather than a disjunction, which settles the whole matrix in one place:
no answer yet keeps the checkpoint, since that is not evidence against it; listed
and resident keeps it; listed but unloaded keeps it only when switching can
reload it; absent falls back whatever the setting says.
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* Studio: normalize the quote style pre-commit would have rewritten
* Studio: cover the model that just landed, and pin the quant the catalog has
Retaining the index on invalidation protects what was already scanned and by
construction cannot contain the model that just finished downloading, so a bare
request for it in the window before the rebuild was still answered by the
resident model. Record the repo at the completion hook and treat that as
admission evidence alongside the resolver and the catalog; the next completed
scan clears the notes, since the index then covers them. Publishing a rebuilt
index before completion becomes observable would have closed it too, but that
blocks the download worker for the length of the scan.
Catalog membership proves the repo, not the saved quant, and the examples then
pinned the stored one. A quant deleted while another quant of the same repo
remained produced repo:deleted-quant, a missing-quant 404 with a runnable
alternative listed right beside it. Pin what the catalog advertises: for a
resident entry that is the resident quant, for an unloaded one it is a quant
actually on disk. The store is only consulted before /v1/models has answered.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: apply three rules everywhere they belong, not only where reported
The trust probe was the last credential handoff still passing a raw token.
huggingface_hub reads None as "use the cached login", so a caller-named repo
was read with this server's Hugging Face identity whenever the caller sent
none, which is exactly the isolation the metadata probe and the worker already
keep. It takes _hub_token now. Enumerated the rest of that path while there:
auth_check, model_info and spawn_worker were already correct.
finalize_worker_exit is shared with dataset downloads, so the resolver hook
fired for every completed dataset, scanning the model directories for nothing
and recording the dataset id as local-model evidence, which turns a bare /v1
request naming that id into a refusal instead of a foreign-id fallthrough.
Gated on repo_type.
_already_serving decided "bare" on the presence of a colon while
_loaded_satisfies and the resolver decide it on whether the suffix names a
quant, so org/model:latest against a serving Q8_0 read as a mismatch and
swapped in the preferred Q4_K_M for a request either one answers. That rule now
lives in four places, each fixed in its own round, so this time I looked for
the rest and found a fifth: describe_local_miss splits on the bare colon and
its docstring claims it splits like the resolver. It no longer did, and would
report a missing quant named "latest". Fixed here too, unreported.
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* Studio: probe before refusing busy, and scan once when the index is cold
The busy refusal fired before anything established the requested label was a
model at all, so any namespaced id a drop-in client sends was told to wait out
an unrelated download for as long as it ran. Probe first and refuse only a
label the Hub actually serves as GGUF; anything else falls through to the
resident model as before. A probe failure answers "not downloadable", since
stranding ordinary traffic costs more than missing a busy refusal.
Treating an unbuilt index as "nothing here" let the first request after startup
be answered by the resident model under another model's name. That was a
deliberate trade to keep the scan off the request path, and it was the wrong
one. Cold, the scan now runs once on a thread, bounded so a pathological
install falls through rather than hanging the request. Built, the request path
still never scans, so the latency fix stands.
The watcher freed the slot the moment it saw an error, while Retry-After is
thirty times the poll interval, so the client came back to an empty slot and
restarted the same failing download instead of being told. Hold the failure on
the slot until a retry surfaces it, and let another repo take it after three
retry intervals so a client that never returns cannot keep it.
The watcher also invalidated on completion, which now lands after
finalize_worker_exit's warm and marks that fresh scan stale, pushing a
synchronous rescan onto the retry. Removed.
_loaded_satisfies lowercased paths as well as aliases, so it returned satisfied
before the case-preserving compare below could run. Both now go through one
helper: paths compare with normcase, aliases stay case-insensitive.
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* Studio: an unfinished scan is not absence, and a decided refusal is not a failure
Bounding the cold scan then reading the bound as "not here" left the same hole
one branch over. A timeout now answers 503 model_indexing with a Retry-After
and leaves the warm running. A foreign label sent inside that window is asked
to retry rather than falling through, which is a real cost, but the window is
one request on an install whose scan exceeds ten seconds and it clears itself,
where answering with the wrong weights does not.
That uncovered a worse one. Every check here runs inside a broad except whose
job is "could not verify, so fall through", so an HTTPException raised in the
block was logged as a verification failure and the request was answered by the
resident model. Any refusal decided in there was being swallowed. Re-raise it
ahead of that handler.
Canonicalizing generic labels made them real variant keys, but the matcher
still decided on shape, so repo:llama-13b fell past an exact match and fetched
llama-7b. Match exactly first, whatever the shape; a quant-shaped suffix that
matches nothing is still a miss and never a swap.
Marking a catalog alias loaded while publishing the preferred on-disk quant
claimed alias:Q4 was loaded while Q8 was serving, and requiring the resident
quant to match then made pinning it a 404. Advertise the resident variant when
the entry resolves to the resident model.
* Studio: keep the asyncio.timeout fallback tests runnable on Python 3.10
Both tests deleted asyncio.timeout to force _wall_clock_timeout down its
pre-3.11 branch, but monkeypatch.delattr raises when the attribute is already
absent. On Python 3.10, the one version the fallback exists for, there is
nothing to delete, so the two tests errored with AttributeError before reaching
the code they cover. Passing raising=False makes the deletion a no-op there and
leaves the assertions running against the same branch on every version.
Every other delattr in the repo already passes raising=False for exactly this
reason. Verified with asyncio.timeout removed from the interpreter: the two
tests fail with the CI AttributeError before this change and pass after, and
the file still runs 89 passed on 3.13 where the deletion is real.
* Studio: decide GGUF residency, servability and variant keys by one rule each
Four admission and catalog fixes, each closing a gap between two places that
were answering the same question differently.
The /v1/models catalog asked _resolves_to_resident without llama_only, so a
Transformers model live from a directory that also holds GGUF exports marked a
GGUF alias loaded and gave it a GGUF quant. The usage examples then pinned
alias:quant that nothing could serve with switching off. Every entry in that
loop is advertised as GGUF, so residency there is llama.cpp residency.
The busy probe accepted any .gguf sibling while admission excludes mmproj, MTP
drafters and big-endian builds. A repo holding only companions is not
downloadable, so it was held at model_download_busy for the length of an
unrelated download instead of falling through to the resident model as it does
when no download is running. It now reuses _gguf_variants, the same filter.
split_model_ref refused any slash-bearing suffix, but an unrecognized GGUF below
a subdirectory keys on its path (build/llama-13b), which is_valid_gguf_variant
allows and the catalog advertises. Pinning such a variant could not parse, so
only the default-ranked one was reachable. A slash-bearing suffix is now a
variant exactly when a real Hub repo precedes it, which still leaves
C:/models/x.gguf a path rather than a quant.
The usage examples treated a downloaded-but-unloaded model as runnable only
under auto-switch, but a standalone UNSLOTH_MODEL_IDLE_TTL reloads exactly what
it freed on the next request. The panel hid runnable examples after an idle
unload. Tracked apart from auto-switch, because the stash restores the stored
checkpoint only and never an arbitrary catalog entry.
Also stub the index walk in the three cold-index tests that missed it: a real
multi-root scan inside the cold-wait budget made them time out into a 503 under
load rather than assert what they are there for. One of them flaked locally.
Verified each fix is load-bearing by reverting it and watching its test fail.
Backend CI command: 10195 passed, 0 failed. tsc -b and the frontend build clean.
* Studio: bound the Hub admission probes and stop guessing at nested model paths
Three review fixes plus a test-isolation one.
_resolves_to_resident matched on a path prefix, so two separately indexed models
that nest (/models/A alongside /models/A/sub/B) both satisfied it: loading B
made a request for A resident and answered it with B's weights, and the catalog
marked A loaded. A prefix match now counts only when no catalog entry sits
deeper, which is the innermost indexed model that actually owns the file. With
nothing indexed there is no nesting to tell apart, so the directory-to-weights
match this exists for is unchanged.
auth_check and hf_hub_download take no timeout of their own, and both ran while
the provisional single-flight slot was held, so an unresponsive Hub stalled the
request far past the metadata budget and reported every other model busy for the
duration. Both are bounded now. Each default errs the safe way: an unchecked
repo is not a cleared one, so the custom-code probe refuses on timeout, while a
slow gated-repo check stays inconclusive because the download's own auth is the
real gate.
The usage examples caught a failed refresh into an empty catalog and a disabled
auto-switch, which made a transient error authoritative and blanked every
example while the model was still servable. The catalog is deliberately
tri-state; a failure now keeps the last answer and retries.
Also start the backend tests from a built, empty model index. Stubbing only the
background warm still left the cold path walking real caches synchronously
inside the admission wait, so on a large install a test asserted against a 503
"still indexing" instead of its subject. _build_index is untouched, so the tests
that call it directly still exercise the real walk.
Verified each fix is load-bearing by reverting it and watching its test fail.
tsc -b clean. Backend CI command green apart from two failures reproduced only
on this box (a real model-dir scan and an orphan-process cleanup), neither
touched by this PR; staging CI is the gate for those.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Let a decode failure degrade instead of escaping a fail-closed helper
Pinning utf-8 makes a read that used to return mojibake on Windows raise
instead. 33 of those reads sit under a handler catching OSError or
json.JSONDecodeError but not UnicodeDecodeError, which subclasses
ValueError, so a corrupt file would now escape a helper written to return
a default. Adds UnicodeDecodeError to those tuples only.
* Treat an undecodable install lock as stale instead of retrying forever
* Pin utf-8 on shipping-code text I/O instead of the operator locale
113 read_text/write_text/open call sites across unsloth, studio and
unsloth_cli let locale.getencoding() decide the encoding. That is utf-8 on
the Linux and macOS runners and cp1252 on a stock Windows install, so the
same file decodes differently for a Windows user and silently produces
mojibake or raises UnicodeDecodeError.
Adds tests/test_runtime_text_encoding.py to keep it that way. It resolves
openers through each file's own imports rather than a fixed list of module
names, so an aliased tarfile.open or a local from PIL.Image import open is
not asked for an encoding it does not take.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Scan tracked files only and resolve the unbound Path calling forms
* Honour PEP 263 when scanning sources and migrate a legacy JSONL before appending
* Scope guard imports lexically and only migrate a legacy file when it round-trips
* Leave a legacy JSONL untouched and resolve path aliases in the foreign-opener check
* Tighten comments
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix(studio): report Vulkan GPUs in system UI
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* fix(studio): separate Vulkan inference GPU reporting
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix(studio): keep retrying Vulkan probe refreshes
* fix(studio): preserve known zero GPU budgets
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* guard llama.cpp prebuilt against out-of-disk instead of doomed source build
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* address review comments on out-of-disk guard
* keep reusable installs and Windows parity in the out-of-disk guard
* preserve the ENOSPC cause when re-raising fallback errors
* catch out-of-disk before the attempt loop and accept all llama-server layouts
* Fix out-of-disk detection gaps and false positives for PR #7420
Follow-ups found while testing the guard against a real ENOSPC (LD_PRELOAD
shim returning errno 28 under a path prefix, real network, real release):
- hydrate_source_tree retried the next mirror after an ENOSPC and only raised
on the last URL. Both source fallbacks 404 for the published mix commit, so
the reported cause was HTTP 404 and the run fell through to the source build
exactly like before the guard. Stop at the first environment-fatal error.
- The 5 GB preflight rejected hosts that install fine. A full CUDA install
peaks at 0.87 GB, the largest published bundle is 0.77 GB and macOS is
0.01 GB, so at 3 GB free the install succeeded before and exited 4 after,
with the source-build fallback suppressed too. It is now advisory, and a
real ENOSPC still exits 4. This also drops the case where an install
matching an older release plan was rejected before its reuse check.
- ENOSPC raised inside shutil.copytree arrives as shutil.Error with errno
None and no __cause__ or __context__, so it was never classified. That path
covers the hydrated source tree, the runtime overlay and the activation
fallback copy.
- _causal_chain followed __context__ even when __suppress_context__ was set,
so `raise ... from None` over an unrelated ENOSPC reported disk full and
wrongly suppressed the source build.
- TemporaryDirectory now ignores cleanup errors: an rmtree failure on the way
out replaced the in-flight SystemExit and lost EXIT_NO_SPACE.
- setup.sh skips the arm64 CPU last resort after exit 4; it re-ran the same
disk-rejected installer and buried the hint under a second error dump.
- The in-app updater turns exit 4 into a readable message instead of
"installer exited 4" plus a log tail.
Adds tests/studio/install/test_llama_prebuilt_no_space.py covering the
classifier, the advisory warning and the exit codes.
* Fix Python 3.9 breakage and Windows disk-full detection in the out-of-disk guard
Found by running the guard across the whole supported interpreter range
(requires-python is >=3.9,<3.15) and a spoofed [Linux, WSL, macOS, Windows] x
[NVIDIA, AMD, CPU] host matrix.
- TemporaryDirectory(ignore_cleanup_errors = True) is 3.10+, so the previous
commit raised TypeError at install time on 3.9 and turned a working install
into a hard failure. Replaced with a scratch_dir() contextmanager built on
mkdtemp plus rmtree(ignore_errors = True), which behaves the same on every
supported version.
- getattr(exc, "winerror", None) crashed on 3.9. urllib's HTTPError is an
OSError that proxies unknown attributes to a wrapped file object and raises
KeyError, which getattr does not swallow, so any mirror 404 during an install
would have blown up inside the classifier. Read it defensively instead.
- Classify Windows disk-full by winerror as well as errno. CPython's
PC/errmap.h maps ERROR_DISK_FULL (112) to ENOSPC but has no case for
ERROR_HANDLE_DISK_FULL (39), which arrives as EINVAL, so a Windows
os.replace() onto a full disk read as an ordinary failure and fell through to
the source build.
Tests cover both winerror codes, a non-disk winerror, and HTTPError alone and
wrapped in a PrebuiltFallback. 116 simulation cases pass on 3.9 through 3.14.
* Classify quota, flattened Windows and validate-install out-of-disk for PR #7420
- EDQUOT counts as out of space: a quota'd home has free blocks this user
cannot have, so the source build is just as doomed. Reported separately so
df does not mislead. Confirmed end to end with a real kernel EDQUOT: the
installer went from 6 retries then a source build (exit 2) to exit 4.
- Match the flattened Windows disk-full text. copytree stringifies each
per-file OSError, and OSError.__str__ returns early on winerror, so the
text reads [WinError 112] and never [Errno 28]. Captured on a real NTFS
volume. Markers are bracketed so WinError 112 does not match WinError 1120.
- --validate-install now exits 4 on a full disk. It caught PrebuiltFallback
and exited 2 before the classifier ran, and setup.sh answered 2 by deleting
the GPU build that had just succeeded and starting a CPU rebuild that needs
more of the space that ran out. Both halves are needed: the call site only
tested nonzero.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Tighten comments in the llama.cpp out-of-disk guard
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Studio: scan HF cache snapshot loads by their repo id
Inactive Hugging Face caches (legacy, default, and previously selected
download locations) are loaded by their resolved snapshot path so they
keep using the selected cache instead of re-downloading. That path is a
local filesystem path, so evaluate_file_security exempted it with
"local path; no Hub scan" and skipped Hugging Face's pickle/malware
scan. Active caches load by repo id and are still scanned, so the same
model could dodge the gate simply by being in an inactive cache.
An HF cache snapshot keeps the canonical models--org--repo/snapshots/<rev>
layout, so recover the repo id from that path and scan it instead of
exempting it. Non-cache local paths (models directory, custom folders)
still skip the scan, and a remote ref is still scanned by repo id.
Adds a regression test that a flagged pickle in an inactive-cache
snapshot path blocks the load.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: scan the exact cached commit for inactive HF caches
An HF cache snapshot path encodes the commit, not just the repo id
(models--org--repo/snapshots/<rev>). Recover the revision alongside the
repo id and pass it to model_info and the shard-index lookup so the scan
covers the exact files that will be deserialized, rather than the repo's
default branch. Without this, a pickle in an older cached commit that was
later removed from the branch would scan clean and still load.
Extends the regression test to assert the recovered revision is forwarded
to the Hub scan.
---------
Co-authored-by: danielhanchen <unslothai@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
Seed each request with the model's recommended sampling (matching the Chat UI), add per-field override flags, ignore oversized overrides, warn when sampling pins cannot apply to a reused server, and apply pins to the completions endpoint.
* Studio: fail closed on index-referenced nested pickle shards in the offline embedding gate
The offline embedding security gate (HF_HUB_OFFLINE / TRANSFORMERS_OFFLINE)
only scanned the direct files of each SentenceTransformer load root and never
parsed local weight indexes, so a cached snapshot whose pytorch_model.bin.index.json
maps a weight to a nested shard (e.g. shards/pytorch_model-00001-of-00001.bin) was
treated as inert and allowed. The loader then follows the index into the subdir and
unpickles the shard. The online gate already blocks index-referenced subdir pickles,
so the offline path was strictly weaker.
Parse each local weight index in a load root and follow weight_map into nested dirs,
flagging any referenced pickle-extension shard. Paths resolve lexically (normpath),
never Path.resolve(), since HF cache snapshot files symlink into blobs/ and resolving
would leave the snapshot dir and false-block every sharded model offline. An absolute
path, a .. traversal that escapes the snapshot, or an unreadable/invalid index fails
closed. The existing safetensors-sibling suppression is kept.
* Studio: classify offline indexed shards by torch.load path, not pickle extension
load_state_dict picks safetensors vs torch.load per shard by the shard's own
suffix, so two offline-gate gaps remained:
- A model.safetensors.index.json whose weight_map points at a .bin shard was
suppressed by has_base_safetensors (the index file itself matches the base
safetensors regex), yet Transformers still torch.loads that shard. Only the
pytorch index is superseded by a base safetensors now; a safetensors index is
the chosen archive, so its non-safetensors targets are always flagged.
- A pytorch index can map weights to arbitrary names (shards/payload,
weights.data); the loader torch.loads any target not ending in .safetensors.
Flag indexed shards by that rule instead of a pickle-extension allowlist.
Restrict the scan to the two torch-family indexes (tf/flax load via non-pickle
loaders). Add regression tests for both cases.
* Studio: match offline weight-index filenames case-insensitively
The index-name check compared the on-disk filename exactly, while the
surrounding weight and safetensors matches use case-insensitive rules. On a
case-insensitive volume (Windows or macOS) from_pretrained opens an oddly-cased
cache file such as PYTORCH_MODEL.BIN.INDEX.JSON when it requests the canonical
lowercase name, so the exact-case check skipped it and a nested pickle shard it
referenced was allowed through. Lower-case the index name before matching, as
the rest of the gate does, and add a regression test.
* Studio: match load_state_dict format/selection exactly in the offline index scan
Two edge cases in the offline weight-index scan:
- load_state_dict decides safetensors vs torch.load with a case-sensitive
endswith(".safetensors"), so a shard named payload.SAFETENSORS still
deserializes via torch.load. Classify indexed shard suffixes case-sensitively
to match, instead of lower-casing (which treated such a shard as inert).
- A complete direct model.safetensors is selected before either sharded index,
so a stale model.safetensors.index.json referencing a .bin shard never loads.
Skip both indexes when a direct model.safetensors is present, so an otherwise
loadable model is not over-blocked.
Add regression tests for both.
* Studio: read the offline weight index as UTF-8
Path.read_text() uses the locale default, which is cp1252 on Windows, so a
UTF-8 weight index with non-ASCII bytes raised UnicodeDecodeError and the gate
blocked an otherwise loadable model. JSON is UTF-8 by spec (and how the loader
reads it), so pin the encoding.
* Studio: resolve safetensors alternatives via the loader's own filename lookup
The offline gate decided a safetensors alternative existed by case-folding the
directory listing. On a case-sensitive filesystem that let an uppercase decoy
such as MODEL.SAFETENSORS suppress the pickle scan, yet from_pretrained asks for
the canonical lowercase model.safetensors, does not find the decoy, and selects
the pickle (a direct pytorch_model.bin or the pytorch index) and deserializes it.
Probe each alternative with (root / name).is_file() instead, mirroring the
loader: is_file() honors the platform's case rules, so a decoy suppresses only
where the loader would truly open it. Suppression must never fail open; detection
stays case-insensitive (fail closed). Add regression tests for the direct and
indexed pickle decoys (skipped on case-insensitive volumes, where no bypass
exists).
* Studio: resolve indexes and shards exactly as from_pretrained does
Two more loader-fidelity gaps in the offline index scan:
- Shard lookup normalized backslashes to forward slashes. On POSIX a backslash
is a literal filename character, so an index naming dir\payload.bin matches a
real pickle of that exact name that Transformers joins and deserializes, while
the normalized dir/payload.bin missed it. Join the raw weight_map value with
os.path.join so the probe mirrors the loader on each platform.
- Index detection case-folded the directory listing, so on a case-sensitive
filesystem an uppercase PYTORCH_MODEL.BIN.INDEX.JSON artifact the loader never
opens was treated as live and its shard blocked. Probe the canonical name with
the loader's own is_file lookup instead, so an index counts only where
from_pretrained would actually load it.
Update the uppercase-index tests to assert the correct per-filesystem behavior
and add a POSIX backslash-shard regression test.
---------
Co-authored-by: danielhanchen <unslothai@gmail.com>
* Studio: add Voice settings tab (dictation, dictionary, read aloud)
New Voice tab in Settings, placed just before About:
- Dictation: microphone picker, browser STT engine, recognition language,
and an inline mic test with a live transcript
- Dictation dictionary: entries rewrite matching speech to their exact
spelling and casing, applied in both dictation paths
- Recent dictations: last 20 final transcripts with copy and clear, so
text can be recovered if it lands in the wrong place
- Read aloud: optional button on assistant responses with two engines,
curated system voices (novelty and legacy voices filtered, quality
ranked, capped at 20) or the TTS audio model loaded in Unsloth via
/audio/generate (e.g. Orpheus), plus speed, pitch, volume and preview
Settings persist in localStorage (unsloth_voice_settings) and are read
at call time so changes apply without reloading the runtime. Adds en
keys plus the tab label for ja, zh-CN and pt-BR.
* Studio: drop the single option STT engine select, rename TTS option
The STT engine dropdown only had one entry, so it added noise without
giving a real choice. The engine row can come back once local STT
models land. Also renames the TTS engine option Unsloth TTS model to
Load TTS model to make the action clearer.
* Studio: harden Voice settings against edge cases found in simulation
Simulated the feature across Chromium, Firefox and WebKit plus node
level unit runs and backend contract checks. Fixes from the findings:
- Dictionary rewrite used a replacement string, so entries containing
dollar patterns corrupted transcripts (A$$AP became A$AP, $& injected
the match). Switched to the callback form of String.replace
- Persisted voice settings now validate types on hydration: non string
micDeviceId, dictationLanguage and ttsVoiceURI, and non boolean
ttsEnabled fall back to defaults instead of flowing into the UI
- Dictionary entries are trimmed, capped at 120 chars and re-sanitized
on hydration
- The Test dictation panel now falls back to the default microphone
when the saved device is unplugged, matching the composer adapter
Test coverage: 46 unit assertions (dictionary regex edge cases across
unicode, word boundaries and injection, voice curation for simulated
macOS, Windows and Linux voice inventories, corrupt storage merge),
13 backend contract checks against /audio/generate on an isolated
instance, and 60 browser assertions across the three engines covering
rendering, degradation without SpeechRecognition, curation in a real
DOM, dictionary persistence with unicode and dollar entries, the
no-model preview error path and corrupt localStorage recovery.
* Studio: address Voice settings review feedback
Verified each review comment before acting. Confirmed and fixed:
- Editing a dictionary entry was broken in two ways: the store trimmed
on every keystroke so spaces could not be typed, and clearing the
field deleted the entry and unmounted the input mid edit. Updates now
keep the raw value and a blur commit trims or removes the entry
- The unplugged mic fallback checked instanceof DOMException, but a
cross browser probe showed Firefox and WebKit throw
OverconstrainedError objects that are not DOMExceptions, so the
fallback never fired there. Matching on the error name now
- When the browser ended a dictation test on its own (silence timeout),
the mic stream stayed open. All recognition end paths now stop the
tracks and save the transcript through a single finalize path
- The studio TTS audio element now releases its WAV data URL as soon as
playback ends, fails or is cancelled
- Allow microphone now reports insecure contexts (no mediaDevices)
accurately instead of claiming access was blocked
- Voice tab copy moved into i18n keys per src/i18n/AGENTS.md, so locale
overlays can translate it; en is the baseline and parity passes
- unsloth_voice_settings added to the Reset all local preferences key
list so voice preferences obey the reset
- Non default microphones note that the system default is used when the
browser speech engine cannot bind a specific device, since browsers
without the start(track) overload ignore the argument silently
Re-ran the full simulation set after the changes: 46 unit assertions,
13 backend contract checks and 60 browser assertions across Chromium,
Firefox and WebKit all pass, plus a dedicated browser probe for the
dictionary editing behavior.
* Studio: use the chat mic icon in Voice settings for consistency
The Voice tab and its buttons used the hugeicons Mic02 glyph while the
chat composer uses a custom filled mic. Extract that composer icon into
a shared lib/mic-icon component, drop the duplicate inline copies in
thread.tsx and shared-composer.tsx, and use it for the Voice tab icon
and the tab's mic buttons so the microphone looks the same everywhere.
* Studio: address second round of Voice settings review feedback
Verified each new comment against the current code first. One item was
already fixed in the previous round (recording transcripts when the
browser ends a dictation test on its own). Confirmed and fixed:
- The microphone row showed a picker with generic names when browsers
enumerate unlabeled devices before permission, leaving no way to
grant access from the row. It now branches on whether labels are
visible and shows Allow microphone otherwise
- Compare chat dictation ignored the selected microphone. It now opens
the chosen device with the same fallback rules as the main adapter,
passes the track to recognition where supported and releases the
stream when recognition ends
- Closing the Voice tab cancelled the shared speechSynthesis even when
read aloud was playing a chat message. Cleanup now only cancels when
the tab owns an active preview
- Double clicking Start test could race two recognizers and leak the
first stream. A starting flag set before the getUserMedia await makes
start reentrancy safe
- Turning off the read aloud setting mid playback removed the only stop
control. The stop button now renders whenever a message is speaking
- When an engine lacks the start(track) overload, both dictation paths
now release the selected device stream before retrying with the
default microphone instead of holding it open
- Read aloud support no longer requires Web Speech synthesis: the
Unsloth TTS engine only needs audio playback, so it stays available
in WebViews without speechSynthesis, with a clear error if the system
engine is chosen there
Not addressed here: cancelling in flight backend TTS generation on
stop. The route runs generation in a worker thread without a
cancellation path, which is shared pre existing behavior with audio
chat generation and belongs in a backend change.
All suites re-run green: 46 unit, 13 backend contract and 60 browser
matrix assertions across Chromium, Firefox and WebKit, plus probes for
the unlabeled device branch and the double click race.
* Studio: drop empty and duplicate voiceURIs so the Voice tab never renders a crashing Select item
* Studio: guard dictation mic lifecycle in Voice test and Compare composer
Release a microphone opened after the component unmounts, and stop Compare
dictation on a permission or security failure instead of silently recording
from the default device, matching the main chat adapter.
* Studio: fix dictation and read-aloud lifecycle edge cases in Voice settings
- Join final dictation chunks with a space so recorded transcripts do not merge words
- Ignore a stale recognizer onend so a quick stop then restart is not torn down
- Use previewingRef so a double click on TTS preview does not orphan the first request
- Keep the read-aloud stop control visible when a new run starts while a message is spoken
- Stop the dictionary remove button from deleting an adjacent entry on a blur then click race
* Studio: trim redundant Voice settings comments
* Studio: fix Voice preview and Compare dictation edge cases
- Only cancel the shared speechSynthesis for a system-voice preview, so stopping
a Studio preview no longer stops an unrelated chat read-aloud
- Release the Studio preview audio and its WAV data URL on normal completion
- Iterate every finalized result in Compare dictation so batched phrases are kept
- Cap persisted recent dictations to the last 20 on hydration
* Studio: use clipboard fallback for recents and release failed preview audio
- Copy recent dictations via the copyToClipboard helper so the execCommand
fallback works in Safari and insecure http LAN contexts
- Release the Studio preview audio when play() rejects, not just on ended/error
* Studio: add local speech-to-text dictation engine
Add an offline dictation engine that transcribes with a local faster-whisper
model, alongside the existing browser (Web Speech) engine. The browser engine
streams audio to Apple or Google speech services and needs internet; the new
engine runs on the server, works offline, and drives any chat model without
evicting it (it loads in the backend process, separate from the model
subprocess). It also gives Firefox dictation, which has no Web Speech support.
Backend: a lazily-loaded, kept-warm faster-whisper sidecar and three routes
under /api/inference/audio (stt/status, stt/load, transcribe). faster-whisper
is torch-free, so this does not disturb the existing model stack.
Frontend: a Dictation engine setting (browser or local model), a curated model
picker with sizes, and MediaRecorder capture posted to the transcribe route.
The model warms automatically when the engine is selected, with live status.
* Studio: stream local STT transcription as you speak
Local dictation showed nothing until you stopped, because the whole clip was
transcribed once on stop. Now the growing recording is re-transcribed on a
fast pass every second and emitted as live interim text, with an accurate
final pass on stop. Partial recordings decode fine, and the model refines
earlier words as more audio arrives.
Adds an interim flag to the transcribe route (beam 1, no VAD) for the fast
preview pass; the final stop uses the accurate path.
* Studio: make local dictation stop instant and reliable
Stopping local dictation waited for a final network transcription before the
session ended, so the stop button did not flip and a second click ended the
session early and dropped the text. Now stop commits the live transcript
immediately, releases the mic at once, and ignores a second stop while
finalizing. Previews run more often so the committed text is current.
* Studio: record local dictation in short clips for reliable streaming
Re-transcribing a growing buffer every second got slower as it grew, flooded
the backend, showed stale words, and could leave the stop button stuck waiting
on a backlog. Record short independent clips instead and transcribe each once,
appending the text as you speak. Work per clip is bounded, so stopping is
prompt (with a hard timeout as a safety net) and long dictations stay smooth.
* Studio: dictate then transcribe once on stop, ChatGPT style
Local STT dictation streamed by re-transcribing the growing clip, which
was quadratic and saturated the backend (multi-second lag), and stop only
halted the recorder without releasing the mic, so it kept recording. Record
the microphone continuously, release it the instant the user stops, and
transcribe the whole clip once. Stopping is immediate and the transcript
lands in about a second. Also add the tiny model for the fastest option.
* Studio: surface dictation and read-aloud failures instead of failing silently
- Compare dictation reports microphone and speech-recognition errors via toast,
reusing the main chat adapter's describeMediaError and describeSpeechError
- Read-aloud toasts genuine model or synthesis failures while ignoring cancellations
* Studio: ChatGPT-style recording bar for dictation
Clicking the mic now drops the composer into a dedicated recording bar
with a live waveform, a discard (X) and a confirm (tick), instead of a
plain stop button. The tick stops recording and transcribes the clip;
the X throws the recording away and keeps whatever text was already in
the composer. The model adapter taps the mic with an analyser to drive
the waveform, and the router tracks the live session so the X can cancel
it without transcribing.
* Studio: transcribe dictation while speaking, ChatGPT layout
Match ChatGPT's recording layout: the bar now renders in place of the
input with the left plus button kept, the waveform in the middle, and
the discard and confirm buttons together on the right.
Cut the post-confirm delay by transcribing in the background as the user
talks. The audio is split at natural pauses (voice-activity detection off
the same analyser that drives the waveform) and each clip is transcribed
as it is cut, so confirming only has to finish the short final tail. The
model is also warmed when recording starts so the first run never pays a
cold load.
* Studio: ChatGPT waveform, hide tools while dictating, faster STT
Make the recording UI read like ChatGPT: the waveform is now a dense row
of round dots that rise into thin centered bars, and while dictating only
the plus button shows, with the mode badge and tool toggles hidden so the
bar is just the waveform and controls.
Speed up transcription: decode greedily (beam_size=1), which is several
times faster on CPU with negligible accuracy loss on short dictation
clips, and cap background segments at 6s so the final tail after confirm
stays short.
* Studio: finish ChatGPT voice bar and low-latency STT
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: full-width waveform with a timer that freezes on stop
Use the full-width waveform for the recording bar: brighter, bigger bars
that advance on a fixed cadence (keeping peaks between advances) so they
glide instead of racing by, inset from the composer edges. Keep a visible
timer and the green confirm button, matching the ChatGPT reference, and
freeze the timer and waveform the moment the user confirms.
* Studio: fix multilingual local dictation
* Studio: speed up dictation and release local STT
* Studio: harden dictation finalization and STT decoding
* Studio: restore Firefox dictation fallback
* Studio: add dictation history manager
* Studio: manage speech model downloads
* Studio: remove em dash from voice model label
* Studio: move dictation history into Voice
* Studio: source local STT from Unsloth Whisper models
Point the dictation STT sidecar and its Model Hub download entries at
Unsloth's Hugging Face Whisper repos (small, large-v3-turbo, large-v3)
and run them through Transformers, so Studio only ever downloads
Unsloth-uploaded weights. Drop faster-whisper and the Systran/mobiuslabs
repos; keep the Model Hub as the only download path via local_files_only,
and keep PyAV for audio decoding.
Device selection uses float16 on CUDA and float32 on MPS and CPU, since
Whisper's decoder is unstable in float16 on MPS and repeats tokens.
Shorten the model picker labels to name plus download size and update the
STT tests for the new backend.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: smooth dictation waveform and keep pill height
* Studio: align STT model dropdown width and tidy voice copy
* Studio: guide to local engine when browser dictation is offline
* Studio: clarify voice section and STT model copy
* Studio: keep STT warm with training-aware eviction
* Harden STT lifecycle and browser compatibility
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix model discovery test lint
* Harden cross-browser microphone errors
* Harden cross-browser microphone errors
* Surface voice test recognition errors and fall back to Studio TTS
- Voice test now toasts non-abort speech-recognition failures instead of
ending silently, matching the main and Compare dictation paths.
- Read-aloud routes to the backend model when the runtime lacks Web Speech
synthesis (audio-only WebView), so it no longer errors immediately.
* Fix reviewed STT lifecycle races
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix read-aloud fallback controls
* Guard read-aloud stop when deleting a non-speaking message
aui.message().stopSpeaking() throws unless this message is the one being
read aloud, so calling it unconditionally rejected the delete handler before
the message was removed. Only stop speech when this message is speaking.
* Cap recent dictation transcript length before persisting
Recent dictations only limited entry count, so a long transcript stored the
full text in the persisted voice settings and a few could exceed the
localStorage quota, throwing synchronously from the uncaught dictation cleanup
path. Truncate each entry on save and on hydration, matching the dictionary cap.
* Studio: keep dictation mic clickable and guide to local model
Register the dictation adapter unconditionally so the mic stays enabled
for any engine and starts working right after switching to the local
model on an already-open thread.
When the browser engine cannot run (Firefox, Brave, non-secure origins),
clicking the mic shows a toast that points to the local speech-to-text
model instead of leaving a disabled button. The toast stacks its action
below the text with a fully rounded button.
* Studio: add bottom padding below the dictation guidance toast button
* Studio: increase bottom padding under the dictation toast button
* Studio: add bottom padding inside the dictation toast button
* Studio: add five Whisper defaults and custom model search
Add private UnslothAI Tiny and Base mirrors to the curated local STT choices while keeping Small as the default. Let users search or paste a Transformers-compatible Whisper repository and validate it end to end.
Keep short dictations in one clip to avoid repeated padded encoder work, then split longer recordings near Whisper's 30-second boundary.
Update hidden model filters and tests, including the CPU-only CI runtime stub for PyAV.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: use public Unsloth Whisper repositories
Point the Tiny and Base dictation defaults to the public unsloth repositories and remove the private mirror references from model filtering and tests.
* Studio: update Whisper download sizes
Reflect the cleaned public Tiny and Base repositories in the curated model labels.
* Studio: right-align STT model size, fix dropdown wheel scroll, refresh sizes
- Show the download size on the right of each model row so long names
like Whisper Large v3 Turbo no longer hide it
- Update curated Whisper sizes to the safetensors weights actually
downloaded: Tiny 151 MB, Base 290 MB, Small 967 MB
- Drive the model list scroll from a wheel handler so the mouse wheel
scrolls it inside the Settings dialog, not just the scrollbar
- Add a search icon and shorten the placeholder to Search model
* Studio: do not search when a dictation model is picked, shrink repo label
- Treat the filled-in model text as a selection, not a query, so choosing
a model no longer kicks off a Hugging Face search
- Make the repository line under each model name smaller
* Studio: tighten dictation model and local engine descriptions
* Studio: keep model display on pick instead of the query, shrink row text
- Guard the combobox input so selecting a model shows its name and does
not echo the typed query back or start a search
- Map the item label to the friendly display so picks fill the field
- Reduce the model name and size text in each row
* Studio: show only the model name in the dictation field, shrink size label
- Drop the download size from the search field; the name alone is shown
once a model is selected, with sizes kept in the dropdown list
- Reduce the size label text in each row
* Studio: clarify the dictation model description
* Studio: drop Hugging Face from the dictation model description
* Studio: move the dictation dictionary to its own Manage subpage
- Replace the inline entry list with a Manage row, matching Dictation
history, so a long dictionary no longer crowds Voice settings
- Add a DictationDictionaryView subpage that holds the entry editor
* Studio: match STT field font, use best voice for System default
- Bump the dictation model field text to text-sm so it matches the
engine dropdown next to it
- Resolve the System default read-aloud voice to the top curated voice
instead of the browser default, which is a robotic legacy voice on macOS
* Studio: rerank read-aloud voices and drop duplicate voice entries
- Rank by vendor quality, then the user's locale, then a preferred list of
natural voices, so the best voice leads instead of the first alphabetically
- Collapse voices that macOS reports twice under one name and language
* Studio: fold dictionary and recents into the dictation section
- Drop the separate Dictation dictionary and Recent dictations headings;
their Manage rows now sit under Dictation, split by the row divider
- Shorten the custom spellings description
* Studio: add search and sort to dictation history
- Filter saved dictations by text with a search field
- Sort by newest, oldest, or A to Z; show a no-matches message
- Keep Clear all available regardless of the current filter
* Studio: settle cancelled STT loads before training and fix dictation review items
Wait for a cancelled STT load to exit and release its memory before
reporting it freed for training, so the loader cannot still be inside
from_pretrained()/.to(device) holding VRAM when the training subprocess
starts. A load that finishes before observing the cancel now gets
unloaded so the memory is actually reclaimed.
Clear the accelerator cache before the CPU fallback in load() so a failed
CUDA/MPS load does not strand reserved VRAM once the sidecar is marked
CPU-resident.
Send the saved Hugging Face token when polling STT download progress so a
gated or private repo resolves and shows the correct Load/Downloaded
state instead of reporting missing.
Mark the composer Dictate button as type="button" so clicking it does not
also submit the draft when the composer already has text or attachments.
* Studio: pin dictation settings per session and close STT startup races
Capture the STT model and language when a dictation session starts and
pass them to every queued segment and the warm-up load, so changing the
model or language mid-recording no longer transcribes the same clip with
the wrong model or a model that is not downloaded.
Check the local runtime at the top of transcribe(), before the model
cache lookup and the bounded audio decode, so a server missing PyTorch or
Transformers returns 501 up front instead of decoding a long clip first.
Treat the training startup window as active for STT device selection.
start_training frees VRAM in before_spawn but only assigns _proc later, so
a concurrent STT load could take the GPU that was just cleared. A startup
flag now reports training active from the free until the process is live,
forcing those loads to CPU; a finally clears it on every exit.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: stub the STT runtime check in transcribe orchestration tests
transcribe() now verifies the local runtime up front, so the unit tests
that exercise transcription orchestration must treat the runtime as
present to keep passing where PyTorch, Transformers, and PyAV are not
installed. Stub ensure_stt_available in the shared fixture and restore
the real check in the availability and load-rejection tests.
* Harden custom Whisper dictation models
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add whisper.cpp dictation engine with per-engine downloads and history rework
Engines
- New GGML STT sidecar that runs a managed whisper-server subprocess with
idle unload, plus a pinned static build script (scripts/build_whisper_cpp.sh)
- Dictation engine picker now offers Browser, Local transcription
(whisper.cpp), and Local transcription (Transformers)
- Both local engines serve the same five curated Whisper models and download
them directly with byte-level progress reported by /audio/stt/status
- Models auto load on selection and when their download finishes
- Unload and training admission account for both engines
Benchmarks (Apple Silicon, greedy, warm, same checkpoints)
- whisper.cpp transcribes 2.4x to 5x faster than Transformers and loads in
about 0.45s vs 0.86s for Whisper Small
- whisper.cpp GGUF path is unchanged by the Transformers addition
(load 0.445s -> 0.444s, short clip 0.391s -> 0.347s, long 1.197s -> 1.129s)
Voice settings UI
- Plain curated model select replaces the searchable combobox
- Single download progress bar with transfer rate for both engines
- Dictation history now stores every dictation with Show more pagination,
a top Clear history action, and links back to the chat it was spoken into
- Archived chats dialog gets the same pagination
- Delete dialog offers deleting a dictation together with its chat
Tests: 88 backend STT tests pass, including new snapshot download coverage.
Frontend typecheck, lint, i18n parity, and production build pass.
* Merge local engines into one option and source GGML models from unslothai
Engine selection
- The dictation engine dropdown is back to two choices: Browser and Local
transcription. The selected model decides the backend: curated ids run
GGML checkpoints through whisper.cpp, searched Hugging Face repositories
run safetensors through Transformers
- Model picker lists the curated models and searches Hugging Face for other
Whisper repositories, validating them before selection. The trigger is a
plain button so the selection never renders inside a text input
- /audio/stt/status accepts a model query param so downloaded state works
for custom repositories; the engine param on load, transcribe, and
download routes is derived from the model everywhere
Model source
- Curated GGML checkpoints now download from the Unsloth-hosted
unslothai/whisper-*-GGUF repositories (one repo per model) instead of
ggerganov/whisper.cpp; cache lookups, progress totals, and in-flight blob
tracking are per-model
Fixes
- Voice settings and dictation history were not persisting: the quota-safe
localStorage wrapper was declared after the store that uses it, so the
persist storage factory failed silently. Every settings write also threw
mid-click, which kept the model picker popover from closing on selection
- is_model_downloaded now verifies config, preprocessor config, and real
weight files instead of trusting an offline snapshot lookup, so a partial
download left by an aborted fetch shows the Download button instead of
failing to load
- Removed whisper.cpp mentions from user-facing text: the ready status
shows Loaded instead of the runtime name, picker rows show the source
repository, and runtime error messages say local transcription runtime
Verified with automated browser sessions and live API checks: selection
closes the picker with no page errors, persisted settings hydrate on
reload, a stale partial snapshot triggers download then loads on MPS and
transcribes, and curated models download from the unslothai repos. 88
backend STT tests, typecheck, lint, i18n parity, and build pass.
* Skip the duplicate source line for custom models in the STT picker
A custom repository's display name is its id, so search results and the
appended current selection rendered the same string twice. The source
line now only renders when it differs from the name; curated rows keep
their name, unslothai source repository, and download size.
* Verify every shard of a sharded checkpoint in the downloaded check
A snapshot holding one of N shards (or a corrupt shard index) passed the
downloaded check and then failed at load. When model.safetensors.index.json
exists, every shard in its weight map must now be present. Found by
simulation; covered by a regression test.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Rename stale _starting references in the pump resilience tests
The startup flag on TrainingBackend was renamed to _spawn_in_progress but
two tests added alongside it still asserted on the old name, failing the
Python 3.11 to 3.13 CI jobs.
* Make the selected model row clearly highlighted in the STT picker
The current selection was a faint background tint. It now uses the accent
background with a medium weight name. Two line rows use a small corner
radius; single line custom repo rows keep the pill shape.
* Address review feedback on STT snapshot checks, VRAM release, and dictation UX
Verify snapshot completeness in the load preflight so a partial download
fails before the audio is decoded, for curated and custom repos alike.
Drop the failed accelerator traceback before the CPU retry so the cache
clear can actually release that memory. Keep unloading the GGUF sidecar
after cancelling an in-flight Transformers load; both engines can hold
memory at once. Allow Auto language with English-only .en checkpoints,
matching the backend which sends no forced language. Keep the discard
button usable while a transcription is pending so a slow or hung request
cannot trap the composer in dictation mode. Stop linking Compare and
settings test dictations to the unrelated active single chat thread.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Move the CPU retry out of the exception handler
On Python 3.10 the interpreter exception state keeps its own reference
to the traceback, so dropping it from the caught exception was not
enough to release the failed accelerator load during the retry. Leaving
the handler before clearing the cache works on every supported version.
* Address review feedback on session handoff, chat pinning, and server lifetime
Starting a dictation from a second entry point now cancels the session
it replaces, so the old recording cannot keep the microphone open or
save a transcript with no discard button pointing at it. The linked
chat is pinned when recording starts, so switching threads while a
transcription finalizes cannot relink the transcript to the newly
opened chat. whisper-server is now bound to Studio's lifetime like the
other long-lived children: PDEATHSIG on Linux, the parent job object on
Windows, and pid adoption so the shutdown sweep reaps it; before this
it survived a Ctrl+C exit as an orphan still holding the model.
* Remove the dictation mic test from Voice settings
The composer dictate button covers the same check, so the test row, its
transcript panel, the unsupported fallback row, and their strings and
search entry are gone.
* Studio STT: gate GGUF whisper-server on training and fix dictation retry and dictionary edits
GGUF (whisper.cpp) sidecar:
- Launch whisper-server with --no-gpu while training is active, mirroring the Transformers sidecar's CPU device choice, so a mid-training dictation cannot reclaim the VRAM training just freed.
- Report is_loading() during whisper-server startup so training VRAM admission accounts for the accelerator memory it is about to bind.
- Require PyAV in is_available() so /audio/stt/status reports the engine unavailable when uploads cannot be decoded, instead of loading fine and then 501ing at transcription.
- Reject a missing model before decoding audio, matching the Transformers download preflight.
Voice settings:
- The download Retry button now restarts the download; the sidecar error is sticky until a new start(), so re-polling alone never cleared it.
Dictation dictionary:
- Tabbing from an emptied entry to its remove button no longer commit-splices the row first, which shifted indices and deleted the wrong entry.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio STT: fix curated GGUF whisper filenames to match hosted repos
The unslothai/whisper-*-GGUF repos host the checkpoint as whisper-<id>.bin,
not ggml-<id>.bin, so every curated dictation download and cached-path
lookup 404'd and the whisper.cpp engine could never load a model. Point
GGML_STT_MODELS at the real filenames and guard the naming with a test.
* Studio STT: validate a custom dictation repo before downloading it
The Transformers STT engine accepts an arbitrary owner/model repo, but the
download route handed it straight to snapshot_download, pulling a possibly large
non-Whisper repository into the shared HF cache. Confirm the repo is a Whisper
checkpoint first with the existing metadata-only validate_remote_model (no
weights); curated ids short-circuit and the GGUF engine (curated-only) is
unaffected. A non-Whisper repo now 422s before any download.
* Studio STT: preempt a still-loading GGUF server for training admission
A whisper-server still in its startup window binds accelerator memory but has no
loaded_model yet, so training admission could miss it and launch into an OOM.
Make the GGUF startup cancellable (cancel_pending_load signals an abort event and
terminates the starting process without the load lock; _wait_for_server observes
it and raises SttLoadCancelledError; wait_for_load_to_settle blocks on the lock
until the killed server is reaped), and always fold the GGUF sidecar into the
resident-STT summary so a resident Transformers model cannot mask a loading GGUF
server. free_stt_model_for_training now cancels an in-flight load and waits for it
to settle before training claims the memory.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio STT: fall back to Transformers when whisper-server is absent
A curated dictation model (including the default small) hard-pinned the GGUF
engine, but standard installs do not ship whisper-server, so every recording
501'd instead of using the Transformers engine that serves the same checkpoint
-- the GGUF sidecar's own documented contract. Add _resolve_serving_stt_engine:
a GGUF request for a curated id (the only ids GGUF accepts, all Transformers-
servable) downgrades to Transformers when whisper-server is unavailable, applied
consistently to download, load and transcribe (not unload, which targets a
specific engine). The Voice tab likewise falls back to the Transformers status so
the model is not shown unavailable and download is not blocked.
* Studio STT: hide custom Whisper caches from the legacy model pickers
The legacy /cached-models (and /cached-gguf) routes called is_hidden_model with
only the owner/model id, which cannot reach the config-based Whisper check, so a
downloaded custom (non-curated) Whisper checkpoint was still offered as a chat
model. Pass the cached snapshot path so _path_is_whisper_model inspects the repo
config and hides it, matching the discovery route.
* Studio STT: hide GGUF dictation repos, lock-free status, unload fallback, split training eviction
- Hide the curated GGUF dictation repos (unslothai/whisper-*-GGUF) from the chat
model inventory and pickers, backend and frontend. Only their Transformers
safetensors companions were hidden; the GGUF repos use a different org and a
-GGUF suffix and carry a raw .bin with no whisper config.json, so they leaked
into chat pickers.
- Make the GGUF sidecar loaded_model/device accessors lock-free, mirroring the
Transformers sidecar. transcribe() holds self._lock across the whole inference
call, so /audio/stt status polls and training admission previously blocked
behind an in-flight transcription.
- stt_unload resolves through the serving resolver: a "gguf" pick on a host
without whisper-server is served by the Transformers fallback, so unload must
target that engine or the resident model is never freed. Unload also attempts
every engine even if one raises, so a failure freeing one backend no longer
skips the other.
- free_stt_model_for_training frees the Transformers and GGUF sidecars under
independent exception boundaries so a failure unloading one no longer skips
the other before training claims the memory.
Adds tests/test_stt_review_fixes.py covering all four.
* Studio STT: resolve Auto dictation language for the model engine + snapshot process liveness
- The model dictation adapter sent the raw setting (the literal "auto") to the
backend, while the browser engine resolves Auto via resolveDictationLanguage.
A batch of non-English voice notes came back mostly English on Auto. Add
resolveModelDictationLanguage: only the literal "auto" is resolved to a
concrete locale, gated so it becomes a language the model AND Whisper can
honor (mirroring the backend's known-whisper-languages set); an explicit
language, or a locale Whisper cannot honor, stays unchanged/auto-detect. Wire
it into both adapter call sites.
- GgmlSttSidecar._process_alive() read self._process twice; a concurrent
unload() nulls it under the lock while loaded_model/device read lock-free, so
a null between the two reads called None.poll(). Snapshot once. Adds a
deterministic regression test.
* studio: tighten comments and docstrings in the dictation modules
* studio: harden dictation model downloads, GGML readiness, and recording paths
Address review findings on the STT dictation feature:
- build_whisper_cpp.sh refuses to delete a whisper.cpp tree under a custom
Studio home unless it carries the Studio ownership marker, matching the
setup.sh policy, and marks trees it creates
- _snapshot_is_complete validates every shard of a sharded PyTorch
(pytorch_model.bin.index.json) checkpoint like the safetensors path, and
requires tokenizer assets (tokenizer.json or vocab.json + merges.txt)
- custom-repo downloads pin the revision resolved at validation time and
restrict snapshot_download to the model/tokenizer/config/preprocessor file
classes Studio loads
- the GGML sidecar holds its port reservation until just before spawning
whisper-server and only accepts readiness from a responder that both looks
like whisper.cpp's server and belongs to the still-running managed child,
probing twice, so mic audio cannot be posted to a foreign local process
- the recording adapter transcribes every non-empty segment; the RMS meter
only shapes segment boundaries and can no longer discard quiet speech
- Compare-pane dictation can cancel a pending transcription on second click,
with the button relabeled while finalizing
- localStorage quota recovery halves the dictation history until the save
fits, so small histories shrink too
- the System default TTS voice resolves to the platform default voice
- new dictation UI imports go through the chat and hub feature barrels
Regression tests cover the build-script gate, sharded PyTorch and tokenizer
completeness, revision pinning and allow patterns, and the whisper-server
readiness probe.
* Fix STT download and voice picker follow-ups
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add dictation button regression coverage
* Studio: prebuilt whisper.cpp via the shared llama.cpp install core, slim bundles paired to the llama prebuilt (#7294)
* Studio STT: add prebuilt whisper.cpp (whisper-server) installer
New install_whisper_prebuilt.py downloads a per-platform whisper-server
bundle published by the unslothai/whisper.cpp prebuilt CI into the managed
whisper.cpp dir (build/bin/whisper-server) so local dictation needs no
compiler. Mirrors install_node_prebuilt.py / install_llama_prebuilt.py:
host + backend detection, sha256 pins (whisper_prebuilt_pins.json) as the
trust anchor, staging + install lock + atomic swap, traversal-safe extract,
co-located shared libs (RUNPATH=$ORIGIN), an UNSLOTH_WHISPER_PREBUILT_INFO.json
marker with idempotent "already matches", and exit codes 0/1/2/3. Not wired
into setup yet; the pins ship empty so every asset fails closed until the
first fork release is published and its digests are reviewed in.
* Studio STT: install prebuilt whisper.cpp during setup and update
Add a fail-open whisper.cpp block to setup.sh after the llama.cpp section so
`unsloth studio update` (and a fresh install) fetch the prebuilt whisper-server
into the managed whisper.cpp dir the sidecar discovers. It skips a user-set
WHISPER_SERVER_PATH/UNSLOTH_WHISPER_CPP_PATH, honors UNSLOTH_SKIP_WHISPER_INSTALL,
forwards the resolved ROCm gfx, and never aborts setup: a busy install keeps the
existing runtime, and an unavailable prebuilt stays quiet (source build is opt-in
via UNSLOTH_WHISPER_FORCE_COMPILE) since Transformers STT and browser dictation
remain. Register UNSLOTH_WHISPER_PREBUILT_INFO.json as Studio-owned evidence.
* Studio STT: harden whisper-server child env + WSL ROCm detection
- Sidecar spawns whisper-server with a scrubbed child env that prepends the
binary dir (co-located GPU libs) to the loader path, and on WSL2 ROCm loads
the system HIP first (HSA_ENABLE_DXG_DETECTION=1) so a bundle's bare-metal HIP
does not segfault on /dev/dxg. Secret-bearing vars are dropped from the child.
- find_whisper_server_binary now requires an executable, not just a file.
- Installer rocm probe passes HSA_ENABLE_DXG_DETECTION and falls back to
/opt/rocm/bin/rocminfo so a WSL ROCm host is not misdetected as CPU-only;
gfx parsing skips the gfx000 CPU agent and generic ISA lines.
- Tests for the child env (secret scrub, lib dir, WSL HIP precedence), the
executable check, and the WSL rocm detection.
* Studio STT: in-app whisper.cpp prebuilt update stack + ship pins in the wheel
Mirror the llama.cpp update stack for the whisper.cpp prebuilt so Studio can
detect and install a newer whisper-server release from inside the app:
- backend/utils/whisper_cpp_freshness.py: read UNSLOTH_WHISPER_PREBUILT_INFO.json
and compare the installed release against the newest unslothai/whisper.cpp
release. Whisper tags are v<upstream>-unsloth.<N>, so is_behind compares a
(major, minor, patch, serial) key with a strict downgrade guard; 24h cache;
fail-open.
- backend/utils/whisper_cpp_update.py: run install_whisper_prebuilt.py to fetch
and atomically swap the newest bundle, unloading the warm GGUF sidecar first.
- backend/routes/whisper.py mounted at /api/whisper (update-status + update).
- pyproject: add whisper_prebuilt_pins.json to studio package-data so the
installer's trust anchor ships in the wheel (it is a data file, not a .py
module, so package discovery alone does not include it; node_prebuilt_pins.json
is listed for the same reason). Without this a pip-installed wheel had no pins
and the prebuilt install aborted to Transformers STT.
Adds test_whisper_cpp_freshness.py (version parser, is_behind matrix + downgrade
guard, marker layouts, stale decision, fail-open).
* Studio STT: verify whisper prebuilts via the release checksum index, like llama.cpp
Re-align the whisper.cpp prebuilt installer to install_llama_prebuilt.py's trust
model: instead of a committed whisper_prebuilt_pins.json, verify every download
against the release's own whisper-prebuilt-sha256.json checksum index, fetched
from the same GitHub release.
- parse_release_checksums / fetch_release_checksums / expected_sha256_for replace
the pins layer. The index is validated for schema/component and that its
release_tag matches the resolved release; an asset absent from it, a release
that does not publish it, or a manifest sha256 that disagrees with it all fail
closed to a source build.
- resolve_release_tag now resolves the newest published release at runtime (or an
explicit --published-release-tag), matching llama and the freshness check;
removed the pinned-default and the UNSLOTH_WHISPER_ALLOW_UNVERIFIED opt-in.
- Delete studio/whisper_prebuilt_pins.json and drop its pyproject package-data
entry (nothing to ship now, same as llama which has no committed pins).
- Adds test_install_whisper_prebuilt_checksums.py (index parser, fail-closed on
uncovered asset, tampered-manifest guard, newest-release resolution).
This is a same-origin checksum (integrity, not authenticity), identical to the
llama.cpp installer; pair releases with GitHub artifact attestations for provenance.
* Resolve whisper prebuilt release via the download host (no GitHub API)
Mirror install_llama_prebuilt.py's fast path: resolve the release tag from
the releases/latest redirect and fetch the manifest + checksum index from
constructed releases/download URLs, so the common install path makes zero
api.github.com calls (unauthenticated api.github.com is capped at 60 req/hour
per IP; the download host is not). Fall back to the GitHub API only on a 404,
malformed asset, or tag mismatch.
* Studio STT: coverage-aware whisper prebuilt selection via a shared core
whisper's select_artifact returned the first os/arch/backend manifest match and
ignored the SM-coverage fields the release manifest already carries, so a
Blackwell B200 (sm_100) was served cuda12-legacy (sms 50-61) -- runnable only via
forward PTX JIT. install_llama_prebuilt.py on the same host correctly picks
cuda13-newer.
Extract the coverage-aware selection into a shared, component-agnostic core under
studio/backend/utils/prebuilt/ (selection + GPU host-capability detection), lifted
from llama's linux_cuda_choice_from_release / _artifact_covers_sms / _sm_range and
generalised over a normalised artifact. whisper's HostInfo now records the GPU
compute caps + driver CUDA version (honoring CUDA_VISIBLE_DEVICES), and
select_artifact routes CUDA/ROCm through the shared selector: every visible SM
must be covered, the tightest-covering profile wins (Blackwell-aware runtime-line
ordering), ROCm matches the gfx target exactly, and an uncovered GPU falls back to
the CPU bundle. CPU/Metal/Vulkan keep first-match. The resolver JSON, exit codes,
and "already matches" contract are unchanged.
On the B200 the installer now resolves cuda13-newer, matching llama.
* Studio STT: gate whisper CUDA selection on the on-disk runtime, like llama
The prebuilt CUDA bundles are dynamically linked and intentionally do NOT ship
libcudart/libcublas -- they load the same runtime the host already has. So the
driver's advertised CUDA version is only an upper bound: a cuda13 bundle still
needs cuda13 runtime libraries present on disk. Port llama's on-disk runtime
scan (detected_linux_runtime_lines / detected_windows_runtime_lines) into the
shared core and intersect it with the driver-compatible lines in
select_cuda_attempts. A host with a cuda13 driver but only cuda12 runtime (e.g.
torch-cuda12) now correctly gets a cuda12 bundle instead of an unloadable cuda13
one; a host with no CUDA runtime at all falls back to CPU.
Fixes a glob bug in the port (any(Path(d).glob(p) for d in dirs) tests generator
truthiness, not a match) that made every major report present; add a real
filesystem test that exercises the scan.
* studio: harden shared prebuilt core to full llama parity
Apply the review findings on the shared coverage-aware prebuilt-consumer
core so whisper.cpp selection is exactly equivalent to the llama.cpp path.
hosts.py: port llama's CUDA_VISIBLE_DEVICES handling. A GPU hidden by an
index/UUID selector now reports has_usable_nvidia False instead of staying
usable, via supports_explicit_visible_device_matching plus the physical /
explicit-match branches, and _select_visible_rows now matches rows the way
llama does (index or UUID, gpu- prefix optional) and skips unmatched tokens
rather than keeping all rows. Adds the Linux /proc/driver/nvidia/gpus
fallback and has_physical_nvidia. Adds parse_macos_version.
runtime_libs.py: the Linux on-disk scan now requires the exact libcudart /
libcublas SONAME (libcudart.so.13), not a libcudart.so.13* glob, so a bare
versioned file without the SONAME symlink no longer counts as loadable.
Hardens the ldconfig parse against an empty left-hand side.
selection.py: fix the Blackwell/torch reordering so it keys on the covering
runtime lines (falls through to the torch preference when the covering lines
were filtered out), matching linux_cuda_choice_from_release. Corrects the
compatible_runtime_lines_for_driver docstring: the bundles do not ship the
CUDA runtime, so the driver version is only an upper bound and the caller
must intersect with the on-disk scan.
install_whisper_prebuilt.py: enforce a macOS artifact's min_os (new
HostInfo.macos_version) so a bundle that cannot load on the host OS version
is dropped. Keep resolver stdout to only the JSON line by leaving logs on
stderr in --resolve-prebuilt mode, and map an unexpected probe failure to
prebuilt_available False instead of a traceback.
Tests: new host-probe suite for the visible-device logic, exact-SONAME
runtime-scan cases, macOS min_os filtering, resolver stdout-only-JSON,
exit-code mapping, and the repo key.
* studio: fix whisper prebuilt selection + launch parity gaps from review
A parallel review surfaced integration defects where the whisper path could
select or launch a bundle that cannot run on a concrete host. Each is fixed to
match install_llama_prebuilt.py.
macOS min_os: the manifest labels macOS requirements as macos-<version>
(e.g. macos-14.0), which the version parser could not read, so the guard was a
no-op and a macOS-13 host would install the macos-14 Metal bundle. Strip the
platform prefix before parsing.
ROCm gfx detection: _detect_rocm_gfx returned the first gfx token and ignored
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES / CUDA_VISIBLE_DEVICES. Since exact
ROCm matching treats that token as the active GPU, a mixed APU + dGPU host
(gfx1151 + gfx1100) with HIP_VISIBLE_DEVICES=1 installed the wrong archive. Route
through a shared pick_rocm_gfx_target (lifted from llama) that parses per-GPU
sections and honors the visibility vars (empty / -1 -> no AMD GPU).
--rocm-gfx override: recording the arch without setting has_rocm left the host on
its CUDA/CPU path so the ROCm bundle was never picked. --rocm-gfx now implies
has_rocm and clears NVIDIA state, like llama's _apply_host_overrides.
CUDA launch env: a CUDA bundle ships the ggml CUDA backend but not
libcudart/libcublas, and the sidecar launch env exposed only the bundle dir, so
on a host whose CUDA runtime lives only in the PyTorch wheels the selection would
gate cuda usable but the server could not load it. Add the CUDA-from-PyTorch
runtime dirs to the child loader path for CUDA bundles (bundle dir still first),
mirroring binary_env.
Also normalize a manifest artifact's supported_sms defensively (parity with
llama's parser) and document that blackwell_min_toolkit_for_caps is retained for
the Phase B llama Windows path.
Not changed (verified parity, not defects): Linux/Windows min_os is enforced
nowhere in llama (macOS only); the resolver is optimistic about the checksum
index and the install path verifies.
* studio: tighten prebuilt-core code comments
* studio: lift shared prebuilt installer core out of the whisper installer
* studio: reuse the llama.cpp prebuilt installer machinery for whisper
* studio: unify llama and whisper prebuilt installers on a shared descriptor core
* studio: consolidate prebuilt installer tests into the shared core suite
Grow tests/studio/install/test_prebuilt_core.py from 62 to 164 tests so every
component-agnostic behavior runs against both descriptors: the full seven
profile CUDA release matrix (multi-GPU, on-disk runtime gating, shuffle
stability, missing SM metadata, dotted SM normalization, no-driver fallback
policy), the ROCm gfx family matrix, macOS min_os gating and its helper,
backend resolution incl. cpu-fallback precedence and Intel-mac auto detect,
checksum-index non-object and plain-lookup cases, the tar symlink/hardlink
extraction guards moved from the llama suite, and the compute-cap, visible
device, runtime-line and Blackwell helper value tables moved verbatim from
the llama characterization suites.
Delete only tests whose exact behavior the master now asserts for the same
component: 40 pure-alias helper cases in test_selection_logic.py (replaced by
value-identical master tables plus an alias-identity pin), 6 extraction moves
and the master-absorbed zip-symlink case in the llama logic suite, 3 routing
twins in test_rocm_support.py already pinned byte-for-byte in
test_selection_logic.py, the 2 Blackwell helper tables in the backend resolve
suite, 28 whisper logic tests and 10 whisper checksum tests re-asserted by
the master whisper parameterization. Wrapper wiring pins, the llama release
plan dialect, fingerprints and every llama-only behavior stay untouched.
* studio: dedupe sidecar and update helpers into the backend prebuilt package
* studio: chain whisper.cpp prebuilt updates onto the llama.cpp update flow
* studio: consume paired slim whisper prebuilts via the llama ggml runtime
* studio: serve every whisper backend from slim prebuilts
* studio: drop the whisper fat per-accelerator selection chain
unslothai/whisper.cpp releases are slim-only from v1.9.1-unsloth.2: one
ggml-less bundle per os/arch, paired to the llama.cpp prebuilt that provides
every ggml backend. Delete the whisper-side fat CUDA/ROCm/metal/vulkan
selection glue; keep slim selection + pairing, link_ggml_runtime, and one
legacy shape, the published fat CPU bundle of an explicitly pinned pre-slim
release. Exit 2 now reads as prebuilt unavailable (whisper never source
builds); setup already treats it that way.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Wire libomp runtime DLL alongside ggml in slim whisper installs
llama's clang-built windows-arm64 ggml-base.dll imports
libomp140.aarch64.dll, shipped in the llama bundle but not a system DLL.
Without it next to whisper-server.exe the loader fails with
STATUS_DLL_NOT_FOUND before main. MSVC x64 links vcomp140.dll from
System32 and Linux ggml uses system libgomp.so.1, so only windows-arm64
was affected. The empty-runtime guard still requires a real ggml
library; libomp alone is not a pairing.
* studio: drop whisper-side fat-selection support structure
Slim whisper bundles are selected per os/arch only; all accelerator
capability comes from the installed llama.cpp prebuilt, whose installer
already did the coverage-aware selection. Remove the machinery that only
existed to pick among fat per-accelerator whisper bundles:
- prebuilt_core: delete the generic CUDA/ROCm coverage selection
(select_cuda_artifact, select_rocm_artifact, ArtifactView adapters,
detected_cuda_runtime_lines, the exact-SONAME linux probe) that no
shipped component routes through; llama keeps its own selection chain
and whisper shadows select_artifact with the slim-only version.
select_artifact is now a plain os/arch/backend first-match.
- install_whisper_prebuilt: drop the HostInfo CUDA fields
(compute_caps, driver_cuda_version, torch_runtime_line) and the torch
runtime probe that populated them; nothing reachable reads them, and
the resolver payload sources runtime_line from the artifact.
- whisper_cpp_update: delete the standalone start_update job worker;
whisper applies only run as the chained phase of the combined
llama+whisper update. The status payload keeps its job field (idle).
- routes/whisper: drop the progress logger that could never fire.
- tests: remove tests of the deleted paths and tests duplicating the
descriptor-parameterized core suite or the llama freshness suite.
Contracts unchanged: resolver JSON keys, exit codes, marker fields,
pairing logs, and the pinned pre-slim fat CPU escape hatch.
* Address review feedback on the whisper prebuilt update and install paths
- Pin the chained whisper phase to the release the freshness check
offered, so the download-host latest pointer cannot reinstall an
older build in a loop
- Wire the whisper prebuilt install into setup.ps1 (Windows setup
previously skipped it entirely)
- Treat a non-executable server or missing wired ggml libraries as a
broken install instead of reporting already matches
- Keep whisper sidecar reloads out of the job-level reload flag and
resync chat state after a partial chained update that unloaded llama
- Repoint home and profile vars for the whisper-server subprocess at a
managed scratch dir and drop credential-store pointers
- Clear the prebuilt marker before the opt-in source build overwrite
- Write the prebuilt marker with explicit utf-8 encoding
* Tighten comments in the whisper prebuilt consumer
* Harden the Windows whisper setup phase and the chained update edges
- setup.ps1: honor WHISPER_SERVER_PATH / UNSLOTH_WHISPER_CPP_PATH /
UNSLOTH_SKIP_WHISPER_INSTALL, run the custom-home ownership guard
before the atomic install, and forward the release-tag pin and ROCm
hints like setup.sh
- sidecar: a cpu-selected install launches whisper-server with --no-gpu
(slim wiring links every llama backend, so the flag is what keeps a
deliberate CPU choice off the GPU)
- chained update: leave whisper unpinned on macOS (the llama phase can
walk back there, and a newest-tag pin could be an impossible pairing
on every retry) and treat installer exit 2 as kept-existing-runtime
instead of failing the combined job
- job.to_tag now comes only from the llama phase, so a whisper-only
round cannot report a llama update that never ran
* Fix slim whisper runtime follow-ups
* Address remaining whisper update reviews
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address remaining prebuilt update reviews
* Fix remaining chained update reviews
* Fix remaining whisper runtime review edges
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: danielhanchen <unslothai@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
---------
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
Adds a configurable Hugging Face model download cache location to Unsloth Studio, selectable from Settings, with per-cache download manifests, scoped deletion, and read-only inventory of previously selected caches.
* studio: classify embedding models from the HF cache and honor offline mode
is_embedding_model() went straight to huggingface_hub.model_info() for any repo
id, so in offline mode (no DNS, or HF_HUB_OFFLINE set) selecting an
already-downloaded model hung on network retries that could never succeed and
training/export never started (#6817).
Check the local HF cache first: a sentence-transformers repo carries
modules.json in its snapshot (the same marker used for local paths), so a cached
model is classified with no network call. When HF_HUB_OFFLINE / TRANSFORMERS_OFFLINE
is set, anything not positively an embedding model returns False without a
network call instead of retrying a doomed request. Online, uncached lookups still
fall through to model_info(), so tag-only embedding models (feature-extraction)
are unaffected.
Adds _embedding_marker_in_hf_cache() over the existing _iter_hf_cache_snapshots.
* studio: judge the active cached revision, harden the cache probe, stop stub leaks
Three review fixes on the cache-first embedding detection:
1. Prefer the revision refs/main resolves to. The HF cache keeps snapshots of
older revisions, so an any-snapshot scan could classify a repo by a stale
revision -- e.g. a repo that used to be a sentence-transformers model would
short-circuit even the online lookup. When refs/main is recorded, only its
snapshot is consulted; the newest-first scan remains the fallback for caches
with no ref.
2. Keep the cache probe inside the detection error boundary. The snapshot
iterator stat()s entries and could raise if a cached model is deleted
concurrently, propagating a 500 out of the config/check-embedding routes.
_embedding_marker_in_hf_cache now catches everything and reads as
not-cached, so callers keep their normal Hub/offline fallback.
3. Stub loggers/structlog in the test only when the real modules are absent
(try-import, mirroring test_windows_gpu_detection_mock), so collecting this
file first can no longer shadow the real packages for later tests in the
same pytest process.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: treat a missing active-ref snapshot as a cache miss, don't cache offline misses
Two review fixes on the cache-first embedding detection:
1. When refs/main is recorded but points at a commit whose snapshot dir is
absent (partial download / cache pruning), the recorded ref is still
authoritative: return None (cache miss) instead of falling through to scan
older snapshots, which could report a stale historical revision's
modules.json as the active one -- the same stale-cache class this helper
avoids.
2. Do not cache the offline negative. When HF_HUB_OFFLINE/TRANSFORMERS_OFFLINE
is set and the repo is not positively an ST model from modules.json,
is_embedding_model stored False under the (model_name, hf_token) key shared
with online lookups; after the env var cleared in the same process, a
tag-only (feature-extraction) embedder returned the cached False and never
reached model_info(). The offline negative is now returned without caching.
* studio: defer online embedding detection to the Hub, re-probe offline
The local modules.json marker short-circuited is_embedding_model() even
online, so a repo that dropped (or added) the marker since it was cached
was judged by its stale local revision instead of the current remote one.
Online now treats model_info() as authoritative and uses the cache marker
only as an uncached fallback when the Hub is unreachable, so a transient
failure never poisons the memo. Offline re-probes the marker on every call
without consulting or populating the memo, so a model downloaded later in
the session (or a cached online negative that predates the download) is
detected. _embedding_marker_in_hf_cache() now treats an unreadable refs/main
(a non-FileNotFoundError OSError) as a cache miss rather than scanning stale
history -- only a genuinely missing ref enables the fallback scan.
* studio: harden offline embedding detection against empty refs, offline flips, and cache casing
- _embedding_marker_in_hf_cache: an existing-but-empty/whitespace refs/main
(a partial write or in-progress truncate-and-rewrite) now reads as a cache
miss (None) instead of falling through to scan stale snapshots; only a
genuinely missing ref enables the historical scan.
- is_embedding_model: while offline, retain a positive already confirmed online
this session (model_info only ever memoizes Hub-derived results), so
_hf_offline_if_dns_dead() flipping the process to offline mid-load can't
downgrade a verified tag-only embedder to False. Cached negatives are still
bypassed and re-probed.
- resolve_cached_repo_casing + settings route: persist the embedding model in
the casing its local HF cache dir uses. Validation accepts a case-insensitive
cache hit, but an offline SentenceTransformer load resolves the cache by exact
case, so storing the requested spelling (baai/bge-m3 vs models--BAAI--bge-m3)
made the model fail to load on a case-sensitive filesystem.
* studio: reuse the exact-match-first case resolver and preserve the default
Replace the ad-hoc resolve_cached_repo_casing with the existing
resolve_cached_repo_id_case, which already prefers the exact-case cache dir
before any case variant and tie-breaks variants deterministically -- so an
exact requested id is never rewritten to a differently cased directory just
because iterdir() happened to yield it first.
Skip the normalization entirely when the submitted model equals the default:
rewriting its casing would make set_rag_embedding_model()'s exact-string
default comparison treat it as a custom override, pinning it so later changes
to the configured default stop taking effect.
* studio: don't let a stale cache marker mask a permanent Hub error
is_embedding_model's Hub-failure fallback consulted the local modules.json
marker for ANY model_info() exception, so a permanent error -- a deleted repo,
a gated repo without credentials, or a typo that matches stale cache casing --
could pass online validation on a stale marker instead of returning the
documented 409, and the persisted model could then fail when the loader
refreshes from the Hub. Classify permanent Hub errors (RepositoryNotFound,
GatedRepo, RevisionNotFound, EntryNotFound) as False, matching the nearby
GGUF/vision detectors, and reserve the cache fallback for transient/5xx failures.
* studio: honor TRANSFORMERS_OFFLINE in the embedding preflight, skip casing for local paths
- The embedding-model save reached the offline-aware is_embedding_model() only
after two preflight helpers made direct huggingface_hub calls that honor just
HF_HUB_OFFLINE: _st_module_subdirs() downloads modules.json and the security
scan fetches Hub metadata twice. In a TRANSFORMERS_OFFLINE-only session those
blocked on network timeouts before the offline return, so saving an already
cached model stalled. Both now consult a canonical hf_env_offline() helper --
the download passes local_files_only, and the metadata-only security scan
short-circuits to its documented fail-open instead of burning both timeouts.
- Skip cache-casing normalization for local paths: a relative directory such as
"org/model" is loaded from disk, so rewriting it to a case-insensitive HF
cache collision ("Org/model") would stop resolving to that directory and be
read as a Hub repo id instead.
* studio: never skip the security scan on TRANSFORMERS_OFFLINE alone
The previous commit skipped the Hub security scan whenever either offline flag
was set, but huggingface_hub honors only HF_HUB_OFFLINE: under a
TRANSFORMERS_OFFLINE-only session the later SentenceTransformer load still
reaches the network, so the scan was being skipped while the repo's pickle could
still be downloaded and deserialized -- waving through exactly what
_guard_model_security exists to block.
Split the flags: hf_hub_offline() (HF_HUB_OFFLINE, the only one that actually
prevents a fetch) gates the security short-circuit, while hf_env_offline()
(either flag, the user's intent) is used only where local-only behavior is
forced explicitly. The SentenceTransformer load now passes local_files_only from
that intent, so TRANSFORMERS_OFFLINE genuinely stops the loader fetching instead
of merely being assumed to.
* studio: short-circuit the security preflight under either offline flag
With the loader now pinned to the local cache by local_files_only =
hf_env_offline(), a TRANSFORMERS_OFFLINE-only session can no longer fetch
anything -- yet the preflight still fell through to two model_info() attempts on
10s and 20s timeouts, stalling every save and load of an already-cached embedder
for half a minute before failing open anyway.
Skip the metadata-only scan whenever either flag is set. The scan's job is to
stop a poisoned pickle being downloaded and deserialized, and nothing can be
downloaded under that predicate; the residual case -- a model cached BEFORE it
was flagged -- is the same fail-open this function has always documented for an
unavailable scan, and is exactly what HF_HUB_OFFLINE already did.
That safety argument depends on every loader behind the gate honoring the same
predicate, so it is pinned as a test invariant instead of a comment: removing
local_files_only from the SentenceTransformer construction now fails the suite.
Drops the short-lived hf_hub_offline() helper, which no longer has a caller.
* studio: scope the offline scan bypass to callers that load local-only
The previous commit put the offline short-circuit inside _fetch_security_status,
which is the malware gate shared by every loader -- so TRANSFORMERS_OFFLINE=1
disabled it for all of them, while only the RAG embedder had been changed to
pass local_files_only. MLX inference (core/inference/worker.py -> FastMLXModel
.from_pretrained), training and export call from_pretrained with no local-only
argument, and huggingface_hub ignores that flag, so those paths could still
fetch and deserialize an unscanned model with the gate switched off.
The bypass is now an explicit local_only_load argument, defaulting to False, and
only the two RAG embedding callers -- whose loader is pinned to the local cache
by the same predicate -- opt in. Tests pin both halves: the shared gate must
still scan under either offline flag by default, and no other caller may pass
local_only_load without constraining its loader.
* studio: capture offline state once, and probe the ST cache root
Two holes in the offline embedding path:
- _get() read hf_env_offline() twice: once inside _guard_model_security and
again for local_files_only. _hf_offline_if_dns_dead() mutates the process-wide
offline vars and restores them on exit, so a concurrent load could see True in
the guard -- skipping the Hub malware scan -- and False by the time the
constructor ran, fetching and deserializing the unscanned repo and breaking
the very invariant that licenses the bypass. The value is now read once in
_get() and passed to both; _guard_model_security takes it as an argument
instead of re-deriving it.
- The cache probe searched only HF_HUB_CACHE. SentenceTransformer downloads into
SENTENCE_TRANSFORMERS_HOME when that is set, using the same
models--org--name/snapshots layout under a different root, so a model fully
present there looked uncached and was rejected with a 409 offline even though
the local-only loader could load it. Snapshot lookup now covers both roots.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: probe the cache the ST loader actually uses, and require it be loadable
Adding SENTENCE_TRANSFORMERS_HOME to the shared snapshot iterator was too broad
in one direction and too narrow in another:
- _get() builds SentenceTransformer with no cache_folder, so with ST_HOME set it
searches THAT root only, never the Hub cache. Probing the union let offline
validation pass on a repo cached only in the Hub cache, after which the loader
looked in ST_HOME and failed. The Sentence-Transformers probe now resolves to
exactly one root: ST_HOME when set, the Hub cache otherwise.
- The shared iterator is also used by the GGUF detectors, whose downloads go
through hf_hub_download with no cache_dir and therefore really do use the Hub
cache. It is back to Hub-cache-only so detection cannot pick a snapshot the
GGUF load will not find.
- Casing normalization ran through resolve_cached_repo_id_case, which scans the
Hub cache, so with ST_HOME set the requested spelling was persisted unchanged
and the exact-case offline load missed the differently cased directory that
detection had just accepted. It now resolves against the same roots detection
uses, exact match first.
- A snapshot carrying only modules.json no longer counts as cached: the online
security preflight downloads that single file itself, and a partial download
leaves it behind, so validation passed for a snapshot with no weights and the
first RAG load then failed. A hit now requires the marker plus a config and at
least one weight file.
* studio: thread the captured offline state into the module probe, fix the gate shard
- _st_module_subdirs() re-read the process env for its local_files_only. With
_hf_offline_if_dns_dead() flipping those vars from another thread, a load that
captured local_only=False could still force this probe local-only, get () back
because modules.json is not cached, and leave the scan with NO module load
roots -- a Hub-flagged pickle under 0_Transformer/ would then pass as an
unreferenced nested artifact while the loader fetched and deserialized it. It
now takes the captured predicate as an argument, and the settings route reads
the state once and uses that single value for both the probe and the scan.
- Skip ST-cache casing on the llama-server backend. Nothing there loads through
SentenceTransformer: the embedder derives a GGUF companion from the saved
spelling and fetches it from the HUB cache, so normalizing to an ST_HOME
spelling would point it at a repo _hf_gguf_backend_error() never validated
(BAAI/bge-m3-GGUF instead of the checked baai/bge-m3-GGUF).
- Fix the security-gate shard, which the signature change had broken: the direct
_guard_model_security / _st_module_subdirs callers now pass the new argument
(they were raising TypeError before reaching any assertion), and the casing
tests patch utils.models.resolve_st_cached_repo_id_case, which the route
actually calls, instead of the Hub-only resolver it no longer uses -- those
patches were being silently ignored.
* studio: accept only torch-loadable weights in the offline ST probe; fix re-export lint
_snapshot_is_loadable_st_model accepted a cached snapshot whose only weights
were .onnx (or .pt), but the RAG loader builds SentenceTransformer with the
default torch backend, so such a snapshot passed offline validation and then
failed on the first load, the exact validate-then-fail this helper exists to
prevent. Restrict _ST_WEIGHT_SUFFIXES to .safetensors and .bin and add a
regression test for an ONNX-only snapshot.
Also teach scripts/verify_import_hoist.py that names listed in a module-level
__all__ are uses, so the legitimately added resolve_st_cached_repo_id_case
re-export in utils/models/__init__.py no longer trips HOISTED-IMPORT-UNUSED.
Covered by two new self-test cases.
* studio: probe the exact repo dir and revision an offline load resolves
The cache probe modelled the cache loosely rather than modelling what
SentenceTransformer actually does with local_files_only=True:
- It merged snapshots across every case-variant repo dir and then read refs/main
from whichever held the newest one. With both models--baai--bge-m3 and
models--BAAI--bge-m3 present, a complete embedding snapshot in the directory
the loader opens could be judged by a newer partial snapshot in the other,
failing validation for a usable model. It now selects the ONE directory the
loader opens, by the same exact-case-first rule resolve_st_cached_repo_id_case
uses to choose the spelling that gets persisted.
- It fell back to scanning historical snapshots when refs/main was absent. With
local_files_only the default revision is resolved THROUGH that ref, so a
snapshot directory alone is not discoverable: the settings request succeeded
and the loader then failed at first indexing. A missing, empty or unreadable
ref is now a cache miss, and the historical scan is gone.
The tests exercise the real lookup against a built cache tree instead of
patching the snapshot iterator, so they now cover the directory selection and
ref resolution the loader depends on.
* studio: record refs/main in the ONNX-only probe test
The ONNX-only regression test predates the refs/main requirement, so after that
change it returned None (a cache miss for want of a ref) before ever reaching
the weight-format check it exists to make. Recording the ref restores its
intent: the snapshot resolves, and the answer is False because an ONNX export is
not loadable by the RAG loader's default Torch backend.
* studio: recognize base-model weight files and gate the offline positive on a materialized snapshot
_snapshot_is_loadable_st_model matched any .safetensors/.bin by suffix, so a
partial cache carrying only a commonly published non-weight bin such as
training_args.bin (or an adapter-only artifact) passed offline validation and
then failed the local_files_only load at first indexing. Match recognized Torch
base-model weight filenames (model / pytorch_model, including sharded) by name.
is_embedding_model retained an online-confirmed positive offline even when no
files were cached, so a metadata-only /check-embedding result let an uncached
repo be saved and then fail at first indexing. Retain the positive only when the
active revision is materialized locally, which still covers a downloaded tag-only
embedder whose snapshot carries no modules.json.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: require a complete weight set offline and persist embedder verdicts across restarts
Two follow-ups to the offline embedding-model classifier:
- _snapshot_is_loadable_st_model now requires a COMPLETE Torch base-model
weight set in one snapshot directory, not just any single recognized weight
file. A partially downloaded sharded model (model-00001-of-00002 without its
sibling) no longer passes offline validation and then fails at first indexing
under local_files_only. Weight files are grouped by directory and a directory
counts only when it holds a single model.safetensors / pytorch_model.bin or a
full shard set whose indices cover 1..total.
- Online-confirmed embedder verdicts are now recorded under the resolved Studio
home (embedding_verdicts.json). The session memo is lost on exit, so a
downloaded tag-only feature-extraction embedder (snapshot present but no
modules.json) was misclassified as non-embedding the first offline call after
a restart. The offline branch consults this durable allowlist in addition to
the memo, still gated on the active revision being materialized on disk, so an
uncached repo is never trusted. Writes are best-effort and only positive
verdicts are stored.
* studio: require complete weights (with shard index) and resolve default casing offline
Follow-ups to the offline embedding-model classifier from the latest review:
- Trust a recorded embedder verdict (session memo or persisted allowlist) offline
only when the active snapshot carries a COMPLETE, loadable weight set, not merely
that it is materialized. A partial download (config present, weights missing or an
incomplete shard set) makes _embedding_marker_in_hf_cache read False rather than
None, so the previous marker-is-not-None gate wrongly returned True and the
local_files_only load then failed. Split out _snapshot_has_complete_weights (config
plus complete weights, modules.json aside) and _active_snapshot_dir, and gate the
known-embedder positive on the weight set.
- Require a sharded checkpoint's index map (model.safetensors.index.json /
pytorch_model.bin.index.json) in addition to every shard before accepting it:
transformers discovers and wires shards through that index, so a complete shard set
without it fails the local-only load.
- Resolve the embedding model name to its exact cache casing in the RAG loader before
constructing SentenceTransformer. The settings route persists that spelling for a
custom override but deliberately leaves the configured default verbatim, so a
default whose casing differs from the cache dir would miss it and fail offline.
Resolving at load time covers the default too; a no-op for a local path or when
nothing case-matching is cached, and idempotent for an already-normalized override.
Adds regression tests for the partial-snapshot verdict, the missing shard index, and
the loader casing resolution; updates the offline-invariant source assertion to the
resolved-name variable.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: require a tokenizer, case-fold verdict ids, and serialize verdict writes
Three follow-ups to the offline embedding-model classifier from the latest review:
- _snapshot_has_complete_weights now also requires a tokenizer asset. A
SentenceTransformer Transformer module builds an AutoTokenizer, so a snapshot with
a complete weight set but no tokenizer.json / tokenizer_config.json / vocab still
fails the local_files_only load. The check is a permissive union over the common
fast-tokenizer, config, and WordPiece/BPE/SentencePiece assets, so an unusual but
valid layout is not rejected -- only a genuinely tokenizer-less partial download.
- The persisted embedder allowlist is now keyed case-insensitively. model_info() is
queried under the requested casing while the settings route saves the cache-resolved
casing, so an exact-string lookup missed the persisted positive after a restart
(baai/model recorded, BAAI/model looked up) and a loadable tag-only embedder was
rejected. Both persist and lookup case-fold the id.
- _persist_embedder serializes its read-modify-write under a lock and writes through a
per-thread temp file, so concurrent confirmations of different embedders no longer
drop each other's entry or collide on the temp path. Cross-process writers stay
best-effort (os.replace is atomic; a dropped verdict is only an optimization miss a
later online re-confirmation heals).
Adds regression tests for the missing-tokenizer reject, alternate tokenizer assets,
cross-casing verdict match, and concurrent verdict writes; updates the snapshot test
helpers to materialize a tokenizer alongside config and weights.
* studio: tighten comments in the offline embedding-model classifier
Comment-only pass over the PR's changed files. Collapse the long block
comments and docstrings around is_embedding_model, the cache-snapshot and
weight-completeness helpers, the embedder-verdict persistence, the offline
security gate, and the offline/casing tests to short one- or two-line forms.
Preserve the rationale (issue #6817, the local_files_only invariant, the
casing and weight-gate reasons) in far fewer words. No code changes.
* studio: drop redundant comments in the offline embedding-model classifier
Second comment-reduction pass over the offline embedding-model cache work:
delete comments and trailing notes that restate the adjacent code or an
assertion, and trim the remaining docstrings and rationale comments to their
load-bearing invariants. Comments and docstrings only; no code changes.
* studio: pin embedder verdicts to a revision, canonicalize default aliases
- A persisted verdict recorded that the Hub tagged ONE revision an embedder, but
was stored per repo. Once refs/main advanced to a complete but non-embedding
Transformer snapshot, the offline path still returned True: the settings route
accepted the updated model without force and RAG could silently load it as an
embedder. Verdicts now carry the commit they were confirmed at and are trusted
only while the active revision matches. One confirmed before the repo was
cached has no revision to compare, so the first revision observed afterwards is
pinned then -- which is what lets a later advance be caught. The persisted file
gains a {id: commit} form and still reads the previous list format.
- tokenizer_config.json no longer counts as a tokenizer asset. It only DESCRIBES
a tokenizer, so a snapshot with config, weights and just that file passed
validation and then failed AutoTokenizer.from_pretrained(local_files_only=True)
at first indexing for common BERT/GPT-style models.
- A casing-only alias of the default is canonicalized to the default up front.
Repo ids are case-insensitive but every gate here compares exact strings, so
saving "Unsloth/bge-m3" against a default of "unsloth/bge-m3" ran the
verification and scan for a custom model and then persisted an override --
after which later changes to the configured default stopped applying.
- verify_import_hoist.py replays __all__ assignments in order instead of unioning
them. Only the final value exports anything, so a later plain "=" that drops a
name must leave its import counted as unused; "+=" still extends, and an
unreadable rebind keeps the earlier names rather than flagging real re-exports.
* studio: validate the real ST load root, and pin verdicts to the Hub revision
Four ways the offline probe still disagreed with what the loader does:
- Verdicts were pinned to the LOCAL refs/main, but model_info() describes the
current HUB revision. With a stale cache the two differ, so an older snapshot
nobody verified was allowlisted. The pin is now info.sha, taken from the
ModelInfo that produced the positive. A verdict carrying no revision (a legacy
entry) is no longer trusted at all -- trusting it meant pinning whatever
happened to be cached, which is the same bug; the next online check re-records
it properly.
- config, tokenizer and weights had to exist somewhere in the snapshot, not
together. modules.json can send SentenceTransformer at 0_Transformer/, which is
loaded FROM that directory, so a cache with the config at the root and only
0_Transformer/model.safetensors passed and then failed the local-only load.
Each directory is now checked as a complete load root, which covers both the
plain HF layout and the ST module layout.
- vocab.json and merges.txt counted independently, but BPE needs the pair unless
a serialized tokenizer.json is present, so half a pair validated and then
failed AutoTokenizer.from_pretrained(local_files_only=True).
- A slashless short name like all-MiniLM-L6-v2 is a supported ST alias that the
loader resolves through the sentence-transformers/ organization, so its
snapshot is cached under that full id. Probing only the bare name reported a
miss and 409'd a model that was cached and loadable; the bare id is still tried
first, matching the loader's own order.
* studio: fail closed for an offline security scan instead of failing open
A local_only (offline) load cannot fetch Hugging Face's malware scan, and the previous
behaviour skipped the scan and failed OPEN, so a cached repo with a poisoned pickle weight
could deserialize under SentenceTransformer(local_files_only=True). Evaluate it fail-CLOSED
against the cached files instead: block a base-model pickle weight the load would deserialize
(pytorch_model.bin and its shards, in a directory with no safetensors alternative) and allow a
pickle-free (safetensors / gguf are inert) cache. A cached pickle model must be reloaded online
once to be scanned, or shipped as safetensors. Nothing cached is not a security event.
_fetch_security_status no longer needs the local_only_load skip (the offline branch is handled
in evaluate_file_security). Adds a regression test covering the safetensors-allow and
pickle-block paths with no Hub call.
* studio: only suppress an offline pickle when a loadable safetensors weight exists
The offline security gate treated any .safetensors in a directory as covering a
pickle weight, so a cache with pytorch_model.bin beside a bare adapter_model.safetensors
(or an orphan shard with no index) passed the fail-closed check even though
from_pretrained still selects and deserializes the pickle. Require a genuinely loadable
safetensors weight -- an unsharded base file or a complete indexed shard set -- before
treating the pickle as covered.
Also make the import-hoist analyzer preserve uncertainty when __all__ is extended by a
value it cannot read statically (__all__ += dynamic()), matching how it already handles
an unreadable rebind, so a dynamically-supplied re-export is not flagged HOISTED-IMPORT-UNUSED.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: scope the offline pickle scan to load paths; reset __all__ opacity on rebind
Address three review follow-ups on the offline security gate and the import-hoist analyzer:
- The offline pickle scan walked the whole snapshot, so a stray pickle in a non-load
subdirectory (archive/, nemo/) that SentenceTransformer never deserializes was blocked.
Scope it to real from_pretrained load roots -- the snapshot root, or a subdir that holds
its own config.json -- matching the online scan's load-path scoping.
- _collect_dunder_all kept a sticky opaque flag: a readable replacing assignment after an
unreadable extend (__all__ += dynamic(); __all__ = []) still credited every import, so a
genuinely unused hoist went unreported. A replacing assignment now resets opacity.
- A bare __all__: list[str] annotation has no runtime value; it was treated as an unreadable
assignment and marked the export set opaque. Skip annotation-only declarations.
* studio: recase slashless ST aliases and accept a pinned embedder after a transient failure
Two offline-detection gaps on well-formed input:
- resolve_st_cached_repo_id_case bailed on every slashless name, so a differently-cased
short alias (all-minilm-l6-v2) validated case-insensitively but was loaded verbatim; the
SentenceTransformer loader rewrites it to sentence-transformers/all-minilm-l6-v2 and looks
it up case-sensitively, missing the canonical sentence-transformers/all-MiniLM-L6-v2 cache
dir. Resolve through _st_cache_repo_dir, which follows the same org alias, and hand back the
on-disk casing.
- On a transient (non-permanent) Hub failure, is_embedding_model only accepted a cached
modules.json marker, so a downloaded tag-only embedder (no modules.json) with a verdict
pinned to the active revision was rejected even though the offline branch accepts the
identical cache. Mirror the offline branch's pinned-verdict acceptance.
* studio: scan modules.json-declared module roots in the offline pickle gate
The offline pickle scan treated only the snapshot root and config.json-bearing subdirs as
load roots, so a pickle in a non-Transformer SentenceTransformer module directory that has no
config.json (e.g. a 0_WordEmbeddings/ module: wordembedding_config.json + pytorch_model.bin)
was skipped even though the loader deserializes it. Parse modules.json (and thread through
load_subdirs) to treat every declared module directory as a load root, so such a pickle is
scanned and fail-closed offline.
* studio: classify cached non-Transformer SentenceTransformer models offline
_snapshot_has_complete_weights recognized only a Transformer-shaped load root (config +
tokenizer + weights co-located), so a fully-cached model built from a non-Transformer module
(0_WordEmbeddings uses wordembedding_config.json + embedding weights and its own tokenizer, no
HF config.json; BoW keeps its vocab in config.json) was classified non-embedding offline and
the settings endpoint returned 409.
Add _snapshot_modules_all_loadable, which parses modules.json and accepts a snapshot when every
declared module's path directory carries the files that module class's own load() reads (a
Transformer/root module still needs the full HF load root; a WordEmbeddings module needs its
config plus a complete weight set; other modules need their *_config.json), and at least one
embedding-producing module is present. It is OR-ed after the Transformer check, so it only ever
accepts more and cannot regress the existing path or reject a pruned cache.
* studio: scan PEFT adapter pickle weights in the offline security gate
from_pretrained auto-detects an adapter_config.json in the load root and deserializes the
adapter weights on top of the base model, so adapter_model.bin is a separate pickle RCE vector
that a safetensors base weight does not cover. The offline scan matched only base-model pickle
names, so an offline local-only load with safetensors base weights plus a cached
adapter_model.bin was allowed despite the live adapter pickle. Scan adapter pickles too, scoped
to a load root where adapter_config.json is present and no adapter_model.safetensors exists.
* studio: require weights for Dense/CNN/LSTM SentenceTransformer modules offline
_module_dir_is_loadable accepted a Dense, CNN, or LSTM module dir with only its config, but
those modules' load() hard-load model.safetensors else pytorch_model.bin (verified against
sentence-transformers source: no fallback, raises if neither exists) -- exactly like
WordEmbeddings. A cache with such a module's config but no weights would validate and then
fail the local_files_only load. Require a complete weight set for every weighted module, not
just WordEmbeddings.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: scan root-index subdir pickle shards offline; handle __all__.append/.extend
- The offline pickle scan followed only load-root directories, so a shard mapped by a root
pytorch_model.bin.index.json into a non-root subdirectory was skipped even though
from_pretrained follows the index weight_map and deserializes it (a layout an attacker can
craft to evade the scanner). Read the local index and scan its referenced pickle shards,
covered by a loadable base safetensors at the index root -- mirroring the online scan.
- The import-hoist analyzer ignored __all__.append("X") / __all__.extend([...]) runtime
re-export mutators, so an import added solely for one tripped HOISTED-IMPORT-UNUSED. Read
their string args like +=, and treat any other __all__ method call as opaque.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: classify StaticEmbedding offline, require WordEmbeddings tokenizer, bound model_info
- A StaticEmbedding module (e.g. sentence-transformers/static-retrieval-mrl-en-v1's
0_StaticEmbedding/) holds tokenizer.json + weights and NO config, so the config-gated
non-Transformer path 409'd it offline. Recognize it by what StaticEmbedding.load() reads: a
tokenizer.json plus a complete Torch weight set.
- WordEmbeddings.load() rebuilds its tokenizer via the configured tokenizer_class.load() from the
module dir, so a WordEmbeddings module now also requires a tokenizer artifact
(whitespacetokenizer_config.json / phrasetokenizer_config.json, or a shared HF tokenizer asset),
not just its config + weights.
- With neither offline env var set, an unbounded model_info() could hang on connect/DNS retries
for networkless users (the #6817 symptom). Bound it with a 15s timeout so a dead network fails
fast and the existing transient-failure cache fallback resolves a cached model, while a
reachable Hub still wins. (Documented caveat: a stalled DNS getaddrinfo may exceed this.)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Resolve indexed safetensors shards relative to their index
_safetensors_index_complete compared shard basenames against the flat
set of files in the index directory, so an index whose weight_map names
shards in a subdirectory was treated as incomplete whenever a legacy
pytorch_model.bin sat beside it. That falsely blocked a snapshot whose
pickle weights are fully covered by a complete, loadable safetensors
shard set. Resolve each shard path relative to the index directory
instead, and add a regression test for the subdir-mapped shard case.
* Restrict offline weight-completeness check to declared load roots
_snapshot_has_complete_weights scanned every directory in a snapshot and
accepted it when ANY directory was a complete Transformer load root. When
modules.json is present a SentenceTransformer load only opens the declared
module paths, so a snapshot whose declared modules are incomplete but which
happens to contain an unrelated complete directory was accepted offline and
then failed at the first local_files_only load. Restrict the candidate
directories to the roots a load actually opens: the snapshot root plus each
modules.json module path. For a well-formed snapshot the verdict is
unchanged; only a complete directory at an undeclared path no longer vouches
for an otherwise-incomplete snapshot.
* Scan SentenceTransformer Router child module weights offline
A Router (legacy Asym) snapshot declares its child sub-modules only in
router_config.json, not the top-level modules.json, and Router.load()
deserializes each child's weights from its own subdir. A config.json-less
child such as query_0_WordEmbeddings (wordembedding_config.json plus a
pickle pytorch_model.bin loaded via torch.load) was therefore neither a
modules.json-declared load root nor a config.json-bearing dir, so the
offline gate skipped its pickle even though the loader deserializes it.
Parse router_config.json at each load root and treat every declared child
subdir as a load root (bounded BFS, so nested routers are covered), so
those child pickles are scanned. Add Router regression tests: a pickle
child blocks, a safetensors child is allowed, and a Router in a declared
subfolder is followed.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Do not treat an unreferenced config subdir as an offline load root
The offline pickle gate skipped a directory only when it was neither a
declared load root nor held a config.json. Because _st_load_roots already
resolves every real load root (snapshot root, modules.json / load_subdirs
dirs, Router children), the config.json fallback only ever promoted an
UNREFERENCED subdir -- a nested checkpoint-500/ or archive/ that ships its
own config.json + pytorch_model.bin -- to a load root. from_pretrained
never descends into such a subdir and the online scan ignores the same
unindexed pickle, so offline mode wrongly blocked a model the loader reads
from a clean safetensors root. Scope the pickle to directory in roots
only, and add a regression test (a stray checkpoint-500/ no longer blocks;
a modules.json-declared module dir still does).
* Classify a root Router (Asym) model as loadable offline
_module_dir_is_loadable applied Transformer root requirements (config +
tokenizer + weights) to every root module, so a Router saved at the
snapshot root -- which carries only modules.json + router_config.json and
loads its weights from child subdirs -- was classified not loadable
offline, and is_embedding_model missed a cached Router embedder. Dispatch
on the module class before the root Transformer fallback: a Router/Asym
dir is loadable when router_config.json parses and every declared child
subdir is loadable (validated recursively through _module_dir_is_loadable,
so nested routers and every child type are covered) with at least one
embedding-producing child. This also tightens a non-root Router, which
previously validated on the mere presence of router_config.json without
checking its children. Add Router regression tests (root and declared
subfolder, complete and incomplete-child).
* Require every declared module before accepting an offline cache
_snapshot_is_loadable_st_model returned has_complete_weights OR
modules_all_loadable, so a complete 0_Transformer short-circuited the or
and vouched for the whole snapshot even when a declared sibling module was
missing its serialized weights; SentenceTransformer builds every module in
modules.json, so that snapshot passed offline validation and then failed
the local-only load. When modules.json declares a non-empty list it is now
authoritative (modules_all_loadable validates every declared module);
has_complete_weights stays the fallback only for an empty/non-list
modules.json (the plain from_pretrained root). Also add the weight-bearing
modules whose load() hard-loads via load_torch_weights and previously fell
to the config-only path -- LayerNorm, WeightedLayerPooling, SparseAutoEncoder
-- to _ST_WEIGHTED_MODULE_NAMES, with source citations and the deliberate
exclusions (Pooling/Normalize/BoW/WordWeights read no weights on load).
Add parametrized regression tests over LayerNorm/WeightedLayerPooling/Dense
(a weightless sibling rejects, a complete sibling accepts).
* Reject self-referential Router children instead of recursing forever
_router_dir_is_loadable validates each router_config.json child through
_module_dir_is_loadable, which re-enters _router_dir_is_loadable for a
Router child. A malformed types entry naming the router's own directory
(a key of ".", which normalizes to the same dir) made that recursion
never descend, so it looped until RecursionError -- breaking the
documented never-raises contract and turning a crafted/corrupted cached
model into a 500 from is_embedding_model instead of a graceful
unverifiable result. A real child reference is a subdir and always
resolves deeper, so reject any child whose resolved path is the router
dir itself. Add a regression test (a router_config naming "." as a
Router child returns False without raising).
* Treat a destructuring __all__ assignment as opaque
_collect_dunder_all detected __all__ only as a direct ast.Name assignment
target, so a binding through a destructuring target (__all__, meta = [...],
v -> an ast.Tuple) was skipped entirely, leaving an empty, non-opaque
export set. A newly hoisted import re-exported only through that assignment
was then falsely flagged HOISTED-IMPORT-UNUSED. Its value cannot be mapped
statically, so mark the export set opaque when __all__ is reached only
through a destructuring / item / attr target, matching how the collector
already handles other unreadable __all__ forms. Add a self-test case.
* Canonicalize declared module paths before scoping the offline pickle gate
A repo could declare a traversing module path such as 0/../evil in
modules.json (or a router_config child), which SentenceTransformer resolves
to evil/ and deserializes evil/pytorch_model.bin. _st_load_roots recorded
the raw snap/"0/../evil", which never equals the snap/evil that rglob
yields, so the offline pickle gate skipped that directory and a malicious
repo slipped a pickle past the newly added gate. Add _canonical_load_dir
to collapse ./ and ../ components lexically and reject an upward escape,
and route the modules.json paths, load_subdirs and router children through
it so the gate scopes the same normalized directory the loader opens. Add
regression tests for a traversing modules.json path and router child.
* Close offline embedding-classification completeness gaps
Five real offline misclassifications, each a false negative (the #6817 hang
recurs) or false positive (accepted then 409s at the local_files_only load).
Dispatch _module_dir_is_loadable on the module class before the root
Transformer fallback. A module with save_in_root=True (every InputModule:
WordEmbeddings, StaticEmbedding, SparseStaticEmbedding, Transformer, Router)
is saved at the snapshot root, so a root WordEmbeddings was wrongly held to
Transformer requirements (an HF tokenizer it never writes) and classified not
loadable.
CLIPModel is Transformer-shaped: CLIPModel.load() reads AutoModel weights plus
AutoProcessor, so a config-only CLIP dir must not validate.
SparseStaticEmbedding needs a tokenizer plus either idf.json or a complete
torch weight set (conditionally weight-bearing); a config alone is not enough.
A present but empty or malformed modules.json is not loadable and does not fall
back to a root Transformer: with modules.json present the loader never takes
the plain-Transformer path (base/model.py _load_config_modules). The tag-only
no-modules.json embedder is classified separately via
_snapshot_has_complete_weights.
Validate a sharded weight index against its weight_map (every mapped shard
present, resolved relative to the index dir) instead of trusting the index
file's mere existence, mirroring the security-side check.
Add regression tests for all five.
* Close case-folding and online-traversal holes in the offline pickle gate
Two gate bypasses where the security scan credited or scoped a path
differently from what the loader actually resolves:
The safetensors credit was case-folded. _cached_pickle_weight_files lowercases
every filename, and the loadable-safetensors and adapter checks tested those
folded keys against the exact-lowercase names. On a case-sensitive filesystem
(Linux, the Studio default) a crafted repo shipping Model.SafeTensors plus a
malicious pytorch_model.bin makes transformers and sentence-transformers miss
the exact-name model.safetensors and deserialize the pickle, while the gate
credited an inert safetensors and did not block. Credit safetensors
case-sensitively against real filenames, and drop pytorch_model.safetensors
from the credit set (transformers loads only model.safetensors, never that
name). Pickle matching stays case-insensitive (over-blocking a mis-cased
pickle the loader would not load is the safe direction).
The online scan did not canonicalize traversing paths while the offline gate
did. A repo-controlled modules.json path (threaded into the online scan via
the RAG guard) or a weight_map shard entry like 0/../evil / ../evil was
compared verbatim, so a flagged evil/pytorch_model.bin never matched and
evaded the online scan though the loader resolves and deserializes it.
Canonicalize the repo-controlled load-subdir prefixes and weight_map shards
the same way the offline gate does, so offline and online agree.
Add regression tests for both bypasses.
* Treat a conditional __all__ mutation as opaque in the import-hoist linter
_collect_dunder_all replayed only top-level module statements, so an __all__
assignment or mutation inside a module-level if / try / for / while / with /
match (or a deeper scope) was ignored, leaving the export set understated. A
newly hoisted import re-exported only through such a conditional __all__ was
then falsely flagged HOISTED-IMPORT-UNUSED, blocking a valid change. A
conditional value cannot be replayed statically, so mark the export set opaque
when __all__ is bound or mutated anywhere other than a top-level statement.
Add a self-test case.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Scope Router child sub-modules as load roots in the online embedding scan
The RAG embedding security guard unions the SentenceTransformer module dirs
from modules.json into the load roots it scopes for the Hub scan, so a flagged
pickle directly under a Transformer module blocks. A Router (legacy Asym)
module declares its child sub-modules only in router_config.json, not in
modules.json, and Router.load() deserializes each child from its own subdir.
The online scan therefore dropped a flagged child pickle (for example
query_0_WordEmbeddings/pytorch_model.bin) as an unreferenced nested shard while
the loader still deserialized it, the counterpart to the offline gate which
already expands router children via _router_child_dirs.
_st_module_subdirs now reads router_config.json for any Router-typed module and
adds each declared child (joined onto the module path, canonicalized so a
traversing entry is dropped) to the load roots. The config is read only for a
Router-typed module, so a plain embedder pays no extra fetch, and every failure
path still returns () so the guard never bricks the embedder.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Allow a recorded-clean pickle embedder to load offline
The offline embedding security gate is fail-closed: with no network to reach
Hugging Face's scan, a cached pickle weight cannot be verified, so it is blocked
and a model the user already downloaded and used online will not load offline.
This adds a persistent cache of clean Hub verdicts so that exact content can load
offline, without weakening the gate for an unknown or never-scanned pickle.
When an embedding repo is loaded online and HF's scan returns a completed clean
verdict, the load roots are hashed and recorded under the scanned commit as an
exact map of snapshot-relative pickle name to sha256, in a per-user JSON store at
studio_root()/security/embedding_scan_verdicts.json (atomic write, 0600, thread
and cross-process locked, 30-day TTL). Offline, a cached pickle model loads only
when the active cached commit and every load-root pickle's sha256 match the
recorded verdict; a missing record, moved commit, changed or added pickle,
expired record, or any error keeps blocking. Online loads always re-query the
Hub and an authoritative unsafe verdict deletes any stale record, so a
now-flagged commit cannot keep loading on an old clean record.
The store binds repo id, full commit, and a per-file sha256 map so a locally
swapped pickle at the same commit, a branch advance, or an added load-relevant
pickle is detected. A same-user attacker who can rewrite the model cache or the
store is outside the enforceable boundary and this is documented; the sha256 is
computed just before load, so a narrow verify-to-load window remains, and a Hub
scanner false negative is recorded faithfully (safetensors stays the stronger
defense).
Recording is triggered post-load in the RAG embedder because the settings route
only validates and the pre-load guard runs before the constructor downloads;
recording is skipped when the loaded commit differs from the scanned commit. The
blocked-pickle enumerator now returns snapshot-relative Paths so two module dirs
that ship the same pickle basename are hashed and reported distinctly.
* Harden the embedding verdict cache against review findings
Tighten the offline verdict cache and its enumeration so every uncertain or
malformed input fails closed and the recorded hashes always match the files the
loader reads:
- Hash every case-colliding pickle in a load root, not one representative. On a
case-sensitive filesystem pytorch_model.bin and PYTORCH_MODEL.BIN are distinct
files; keying by lowered name dropped one and could hash a decoy instead of the
loader's target. The enumerator now returns every variant Path.
- Only persist a clean verdict for a COMPLETED, entirely-benign scan. Require
scansDone to be the boolean True (not a truthy string), filesWithIssues to be a
well-formed list, and every flagged file to be a definitively-safe level; a
pending, error, unknown, or malformed entry no longer records as clean. The
online block decision is unchanged.
- Fail closed when the offline cache cannot be inspected: an rglob error now
propagates and blocks instead of reading as pickle-free, and a snapshot that
errors on resolution (vs a clean not-cached) blocks. The offline guard also
raises instead of returning when its own inspection throws, so the constructor
never deserializes an unverified cached pickle.
- Expand online Router children recursively (bounded BFS with a seen set),
mirroring the offline load-root expansion, so a flagged grandchild pickle is
scoped online and cannot be recorded clean.
- Reject absolute and drive/UNC declared paths in the load-root canonicalizers;
the loader would resolve them outside the snapshot, so collapsing them to an
in-snapshot relative dir scoped the wrong place.
- Pin verdict recording to the scanned commit's snapshot and take the offline
verify commit from the snapshot directory name, removing a second refs/main read
and the skew it allowed.
- Drop the now-unused pickle-name wrapper.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Tighten offline embedding classification and the pickle gate
Close a set of offline edge cases where validation accepted a cache the
local_files_only load then rejects, and one gate bypass:
- Credit a sharded model.safetensors.index.json for a pickle sibling only at a
from_pretrained root. A non-Transformer SentenceTransformer module (Dense,
WordEmbeddings, StaticEmbedding) loads via Module.load_torch_weights, which
reads model.safetensors then pytorch_model.bin and never the index, so a sharded
safetensors index in such a module dir must not vouch for its pytorch_model.bin.
- Stop counting pytorch_model.safetensors as loadable in the offline classifier:
the loader probes model.safetensors (then its index) or pytorch_model.bin, never
pytorch_model.safetensors, matching the gate that already treats it as a decoy.
- Treat a present but unreadable weight index as incomplete: transformers opens
and parses any present index, so a malformed one or one without a weight_map
fails the load rather than falling back to filename-numbered shards.
- Require the CLIP image-processor config (preprocessor_config.json) for a CLIP
module: CLIPModel.load builds a CLIPProcessor that needs it, so a tokenizer
alone is not enough.
- Require a SparseStaticEmbedding config to actually select idf.json (a path
ending .json) or ship loadable weights; a bare idf.json the config does not name
falls through to load_torch_weights and raises.
- Do not use the tag-only recorded-verdict fallback when modules.json is present:
with the file present the loader takes the modules.json path, so a present but
empty or malformed manifest must not be validated as a plain root Transformer.
- Import-hoist linter: only a module-level conditional mutation or a function that
declares global __all__ makes the export set opaque; a __all__ bound as a local
in a nested function or class no longer masks a genuinely unused hoisted import.
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* Scope Router-child pickles to their deepest load root and gate the ST offline kwarg
The online scan stripped the first matching load-subdir prefix from a flagged file, so a
nested Router child pickle (0_Router/query_0_WordEmbeddings/pytorch_model.bin) matched the
parent 0_Router root, looked like an unreferenced nested shard, and slipped the gate even
though Router.load() deserializes that child directly. Match the deepest (longest) load
subdir instead, so the child becomes root-level under its own load root and blocks.
pyproject sets no lower bound on sentence-transformers and the local_files_only constructor
arg is absent on older releases, so always forwarding it broke every embedder warm on those
installs. Pass it only for an offline load; an online warm never forwards it and works as
before, while the offline capability still requires a version that supports it.
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* Reject snapshot-escaping shard paths and credit Transformer submodule safetensors
The offline pickle enumerator joined a weight-index weight_map value straight to the load
root and followed it, so a repo-controlled index mapping "../.." into a sibling snapshot
made an offline from_pretrained deserialize an out-of-snapshot pickle, and an online load
would then hash and record that external file as the scanned commit's clean content. Reject
any shard path that escapes the snapshot root and fail closed, mirroring the canonical-root
check the online shard scan already applies.
A complete model.safetensors.index.json was credited over a sibling pickle only at the
snapshot root, but a Transformer module subdirectory (0_Transformer/) is loaded via
AutoModel.from_pretrained, which honors that shard set and never reads the pickle. Credit the
sharded index for Transformer-typed modules declared in modules.json so a cached model that
ships both a sharded safetensors checkpoint and an unused PyTorch checkpoint is no longer
falsely blocked offline. Non-Transformer modules (Dense, WordEmbeddings, StaticEmbedding) read
a flat weight with no index and keep their pickle blocked.
Limit the import-hoist verifier's global __all__ scan to the declaring function's own scope so
a nested inner-scope local __all__ no longer marks the module export set opaque and mask an
unused hoisted import.
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* Scope Router children against the snapshot and mirror the ST alias rewrite
Router.load resolves each child at Path(subfolder, model_id) relative to the Router dir, so a
nested 1_Router with a "../evil" child points at evil/ inside the snapshot and the loader
deserializes evil/pytorch_model.bin. The offline enumerator canonicalized the child against
the Router dir alone and dropped anything with "..", so that pickle was never scanned and the
gate reported the cache pickle-free. Canonicalize router children against the snapshot,
retaining in-snapshot siblings as load roots and failing closed on a child that escapes the
snapshot itself, matching the online scan which already joins the prefix before normalizing.
The security gate resolved a slashless model id by probing the bare cache dir first, but the
SentenceTransformer constructor rewrites a non-basic slashless name to sentence-transformers/
<name> and loads THAT snapshot (only the basic ORIGINAL_TRANSFORMER_MODELS load bare). With
both models--<name> and models--sentence-transformers--<name> cached, the gate inspected the
bare dir while the loader read the namespaced one, so a pickle there bypassed the local-only
gate. Mirror the constructor: try the namespaced candidate first for non-basic slashless names.
Add the same not (snapshot / modules.json).is_file() guard to the transient-Hub-failure
tag-only fallback that the offline branch already carries, so a cache whose present manifest is
empty or malformed is no longer reported as a loadable embedder.
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* Tighten root shard credit, module-path escapes, and weight-set probe order
Credit the sharded safetensors index at the snapshot ROOT only when the root is actually loaded
through an AutoModel/from_pretrained path. A modules.json root module of a non-Transformer type
(StaticEmbedding / WordEmbeddings / Dense) loads via load_torch_weights, which reads
pytorch_model.bin and ignores the index, so crediting a root shard index there suppressed a live
root pickle and let the offline gate report the cache pickle-free.
Recognize the Transformer subclasses CLIPModel and MLMTransformer as index-honoring load roots
(they load via from_pretrained), so a sharded-safetensors CLIP/MLM submodule with a legacy
pytorch_model.bin sibling is no longer falsely blocked offline. Mirrors the classifier dispatch.
Fail closed on an absolute or snapshot-escaping modules.json module path (or load_subdirs entry)
instead of silently dropping it: SentenceTransformer resolves such a path outside the snapshot and
would deserialize an external pytorch_model.bin the gate cannot scan.
On the classifier side, walk the weight set in the exact from_pretrained probe order
(model.safetensors, its index, pytorch_model.bin, its index) so a pickle behind a malformed
safetensors index is no longer accepted as complete, and restrict shard names to the loader-probed
stem/ext pairs so a decoy model-*.bin / pytorch_model-*.safetensors set is not treated as loadable.
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* Restore scripts/verify_import_hoist.py to main
The offline embedding cache fix does not depend on the __all__ scope
handling that had accumulated in this linter, so revert the file to its
main version and keep the PR focused on the feature. The feature modules
still pass the existing import hoist check unchanged.
* Reuse a shared HF cache skeleton in the offline classification tests
Extract _mk_repo and _activate helpers for the repeated snapshot cache
setup that every per-type builder duplicated, and fold the two
StaticEmbedding missing-asset cases into one parametrized test. Same 125
collected items, all still passing.
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* Reclassify embedding models from the cache on every offline call
is_embedding_model consulted its process memo before the offline branch, so an
online lookup that memoized True from tags (without caching any weights) was returned
unchanged once the session went offline -- the studio flips HF_HUB_OFFLINE in-process
on a dead DNS, and the ungated check-embedding route can populate the memo. Settings
would then accept a repo the offline loader cannot open. Run the offline
cache-marker reclassification ahead of the memo and never record it, so an offline
verdict always reflects the local cache and a later cache materialization is not
masked by a stale negative. Add regression tests.
* Tighten comments on the offline embedding path
Condense the offline-embedding helper docstrings and inline comments added in
this PR to fewer, clearer lines, keeping the non-obvious security and offline
rationale. Comments and docstrings only; no code change.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <unslothai@gmail.com>
* studio: show system-wide VRAM in the multi-GPU System tab view on ROCm
The System tab's per-GPU list comes from get_visible_gpu_utilization. When
amd-smi is unavailable (always on Windows, minimal Linux installs) it fell back
to torch, whose readings are process-local: on Windows WDDM hands each process
its own budget, so a model held by the separate llama-server process read as
~0 VRAM used even with the GPU full (#7072). The primary-GPU endpoint already
compensates with system-wide sources -- Windows Performance Counters (Task
Manager's source) and Linux DRM sysfs -- but the multi-device endpoint never
got those fallbacks.
Add per-GPU variants of both sources and overlay them onto the torch fallback:
_rocm_windows_perf_counter_vram_per_adapter_gb() attributes Dedicated Usage per
physical adapter (phys_<N> in the counter instance name), and
_rocm_linux_sysfs_vram_per_card_gb() reads mem_info_vram_{used,total} per DRM
card. _overlay_system_wide_vram() applies them to the device list, ROCm-only,
best-effort: unmatched adapters and ambiguous card counts keep the torch
figures, and NVIDIA paths are untouched.
Fixes#7072
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* studio: match VRAM overlay sources by device, honor unified memory, unblock the loop
Five review fixes on the multi-GPU system-wide VRAM overlay:
1. Linux: match DRM cards to devices by PHYSICAL index instead of a positional
zip, so a reordering visibility mask (HIP_VISIBLE_DEVICES=1,0) no longer
swaps each card's figures onto the other GPU (which would mislead
auto_select_gpu_ids and the coexistence checks). An index with no matching
card keeps its torch figures.
2. Linux: skip the overlay for a device whose sysfs total is below torch's --
on unified-memory APUs (Strix Halo) mem_info_vram_total is only the small
dedicated slice while torch sees the GTT-backed pool, and
_apply_unified_memory_correction already defines larger-total-wins.
3. Windows: group counter instances by adapter LUID, not the phys_<N> suffix --
separate adapters each read phys_0, which collapsed every GPU into key 0.
LUIDs are mapped to 0-based positions by ascending value as the closest
stand-in for device order.
4. Windows: pair the system-wide usage with the physical capacity from
get_device_properties (as the primary-GPU fallback does) -- under WDDM
mem_get_info's "total" is the process budget, which misreported capacity
and pushed utilization to 100%.
5. Run get_visible_gpu_utilization off the event loop in the /hardware/visible
route (asyncio.to_thread, the repo's convention): the ROCm fallbacks can
shell out to PowerShell with a 5s timeout, which would stall every other
request while the System view polls.
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* studio: skip the system-wide VRAM overlay for relative GPU indices
The overlay matches its per-GPU sources (Windows perf counters, Linux sysfs) by
physical device index, but under a UUID/MIG visibility mask the torch fallback
enumerates ordinals and reports index_kind == "relative", where `index` is a
visible ordinal, not a physical id. Applying the overlay there let card/adapter
0's system-wide VRAM overwrite the torch reading of a process that actually
exposes physical GPU 1, misleading auto_select_gpu_ids and the coexistence
checks. Gate the overlay on index_kind == "physical"; relative-index paths keep
the torch fallback.
* studio: drop the unreliable Windows VRAM overlay, keep the Linux one
The multi-GPU system-wide VRAM overlay is now Linux-only. The Windows
per-adapter Performance Counter path could not be made correct: the wildcard
Get-Counter query also returns non-ROCm/iGPU adapters and LUID order is not the
ROCm device order, so an adapter's usage could be overlaid onto the wrong GPU;
and it read only Dedicated Usage, missing WDDM shared memory on unified-memory
GPUs (Strix Halo), overstating free VRAM. Rather than misattribute VRAM and
skew placement decisions, Windows keeps the process-local torch fallback (no
regression vs before this PR); Linux DRM sysfs -- matched by physical index --
still fixes#7072 for the reporter's native-Linux ROCm case.
Removes _rocm_windows_perf_counter_vram_per_adapter_gb and _torch_props_total_gb.
* studio: key sysfs VRAM by DRM card number so filtering can't renumber cards
_rocm_linux_sysfs_vram_per_card_gb dropped cards with a zero total or unreadable
files and then the overlay enumerated the compacted list, so if card0 was
dropped, card1's usage was assigned to physical GPU index 0 (equal-capacity GPUs
slip past the unified-memory total guard). Return {card_number: (used, total)}
and match a device to its card number directly: a hole stays a hole -- device 0
keeps its torch figures when card0 is absent, and card1 maps to device 1.
* studio: key system-wide VRAM by ROCm ordinal, not raw DRM card number
When a non-amdgpu adapter (Intel iGPU, a display-only card) owns an earlier
DRM slot, DRM card numbers stop equalling ROCm device ordinals -- Intel card0
plus AMD card1/card2 gives ROCm devices 0/1, so keying the sysfs overlay by
card number handed ROCm device 1 card1's data (AMD device 0) and left device 0
on stale torch figures, corrupting free-VRAM placement on equal-capacity GPUs.
Only amdgpu cards expose mem_info_vram_*, so the glob already excludes foreign
adapters; order the surviving cards by their PCI address (ROCm/HIP's default
device order, read from each card's device symlink) and key by that position --
the ROCm physical ordinal, which is what the overlay matches against dev index.
An unreadable / zero-total amdgpu card still consumes its ordinal so a later
card is never renumbered onto its slot.
* studio: skip the VRAM overlay under layered HIP-over-ROCR masks
ROCR_VISIBLE_DEVICES filters physical GPUs at the HSA/ROCr layer, and a
HIP_VISIBLE_DEVICES set on top selects WITHIN that already-filtered set
(apply_gpu_ids sets HIP while leaving an inherited ROCR mask in place). When
both are active _get_parent_visible_gpu_spec() prefers the HIP value, so the
reported device index is a ROCR-relative ordinal, not a physical GPU id --
overlaying DRM-sysfs figures by that index would pull another GPU's usage
(e.g. ROCR=2,3 + HIP=1 is physical GPU 3, but the overlay would read card 1),
and equal-capacity cards bypass the total-size safeguard. Detect layered masks
and keep torch's process-local figures there rather than risk misattribution;
a single mask still leaves the index physical and is overlaid as before.
* studio: only overlay whole-card VRAM onto 1:1 ROCm devices
The overlay guard only skipped the case where sysfs total < torch total
(unified-memory APUs), so a partitioned ROCm device (MI300 in CPX mode) --
where HIP exposes several logical devices per physical card but sysfs reports
the whole card's aggregate -- passed the guard: the card total exceeds a
partition's torch total, and the overlay overwrote the partition with
whole-card usage and capacity, letting downstream selection think a partition
had the entire card free. Require the sysfs card total to match the torch
device total (within ~10%) so a mismatch in either direction -- unified memory
(sysfs smaller) or partitioning (sysfs larger) -- keeps torch's figures.
* studio: treat CUDA-over-ROCR as layered, enumerate AMD cards by driver
Two remaining mismatches between the reported device index and the DRM card the
overlay reads:
- On ROCm the HIP layer honors CUDA_VISIBLE_DEVICES as well as
HIP_VISIBLE_DEVICES, so a CUDA mask composed over ROCR layers identically:
ROCR=2,3 with CUDA=1 is physical GPU 3, yet the spec reports the ROCR value
[2,3] and the device was labeled index 2, overlaying card 2's usage onto GPU 3.
The layered check now treats ROCR combined with either HIP or CUDA as layered.
- The ROCm device set is now enumerated by bound driver (device/driver resolves
to amdgpu) instead of by the presence of mem_info_vram_*. An AMD device with
incomplete sysfs support (some APUs expose no VRAM files at all) was omitted
by the glob entirely and shifted every later card down one ordinal, letting a
similar-capacity GPU pass the total guard with another device's usage. Such a
card now consumes its ordinal and simply yields no entry.
* studio: honor GPU_DEVICE_ORDINAL and require an unambiguous card mapping
Two remaining ways the reported device index could be matched to the wrong DRM
card:
- GPU_DEVICE_ORDINAL is a supported ROCm visibility variable that
_get_parent_visible_gpu_spec() never consults, so GPU_DEVICE_ORDINAL=1
surfaces physical GPU 1 as torch ordinal 0 and it was mislabeled index 0,
overlaying card 0's usage onto GPU 1. The mask check now covers it, and is
renamed _rocm_device_index_unreliable() to say what it actually decides.
- driver == amdgpu is only a SUPERSET of the ROCm-visible set: an amdgpu-bound
adapter HIP cannot enumerate (an unsupported older AMD GPU beside a supported
one) still took an ordinal and shifted every real compute device. There is no
torch-side PCI identity to match against, so the overlay now requires the
amdgpu card count to equal the device count -- exactly the condition under
which position-in-PCI-order is a sound 1:1 mapping. Any disagreement keeps
torch's process-local figures: less informative, never misattributed.
* studio: keep the VRAM overlay working for masked GPU subsets
The card-count guard compared the amdgpu card list against the VISIBLE device
list, so any visibility mask disabled the overlay outright: HIP_VISIBLE_DEVICES=1,3
on a four-GPU host gives two devices against four cards. Those masked GPUs then
kept reporting process-local torch usage, hiding VRAM held by llama-server and
letting the training/chat placement checks overestimate free memory -- the exact
problem the overlay exists to fix.
The count check now applies only when no visibility mask is active, which is the
case where the reported devices really are the whole host and a mismatch means an
amdgpu adapter ROCm cannot enumerate is shifting the ordinals. Under a mask the
subset is expected, so each device's physical index is validated individually
instead: the per-card lookup bounds-checks it and the total-size guard rejects a
card whose capacity does not match the device's.
* studio: match GPUs to DRM cards by PCI identity, not by position
Every mapping bug on this PR came from the same root cause: there was no
authoritative link between a reported device index and a DRM card, so the
overlay kept inferring one positionally and each heuristic broke on a new host
shape -- foreign adapters on earlier DRM slots, cards with no VRAM sysfs, and
most recently amdgpu-bound adapters HIP cannot enumerate, which the count guard
could only catch on an unmasked host and therefore missed under any mask.
Use the link ROCm itself enumerates from. KFD topology
(/sys/class/kfd/kfd/topology/nodes/<N>/properties) lists exactly the GPUs HIP
exposes -- GPU nodes in node-id order are HIP's device order -- and each carries
its PCI location, so index N there IS physical device N with a stable identity.
DRM sysfs now supplies system-wide VRAM keyed by that same PCI address, and the
overlay is a join on it.
Every previous skew becomes a failed join rather than a misattribution: an
unenumerable adapter has no KFD node so it never takes an ordinal, a foreign
adapter contributes no entry, and a masked subset resolves each physical index
directly. That removes the count heuristic and its mask exception entirely. With
no KFD topology there is no identity to join on, so the overlay is skipped rather
than guessing positionally.
* studio: require verified host visibility and AMD-only KFD nodes
Three ways the identity map could still be built on a false premise:
- The NVIDIA open kernel module registers KFD topology nodes with a positive
SIMD count, so an earlier NVIDIA node shifted every AMD ordinal and ROCm
device 1 resolved to AMD GPU 0. GPU nodes now require vendor_id 4098 (0x1002),
the same filter install.sh already applies for this exact reason.
- A GPU node with an unreadable properties file or no location_id was skipped,
which silently shifted every later ordinal. Both now fail the whole map
closed, so the overlay is disabled rather than misattributing.
- A container exposing only some render devices through device cgroups sets no
visibility variable, yet torch compacts what it can see to ordinals from zero
while the host-mounted KFD and DRM trees still list every GPU. Nothing in the
reported payload distinguishes that from a full host, and torch exposes no PCI
id to check against, so the overlay now runs only when host visibility is
positively verified: no visibility mask AND device count equal to the host GPU
count. That also subsumes the previous layered-mask and GPU_DEVICE_ORDINAL
checks, so _rocm_device_index_unreliable() is gone.
This trades coverage for correctness: masked subsets and filtered containers now
keep torch's process-local figures instead of a mapping that cannot be verified.
* Fix the multi-GPU VRAM overlay docstring for PR #7216
The docstring claimed a reordering mask keeps each card on the right GPU,
but the overlay skips any active visibility mask and keeps torch's figures.
State the actual gating instead.
* Tighten comments in the multi-GPU VRAM overlay and its tests
Collapse the verbose docstrings and inline explanations added for the Linux
ROCm system-wide VRAM overlay to succinct one-liners, keeping the non-obvious
rationale (fail-closed KFD mapping, PCI-identity join, mask gating, the 10%
whole-card guard). Comments only, no behavior change.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* refactor(studio): move chat model picker into features/model-picker
Relocate model-selector + its support files from components/assistant-ui
into a self-contained features/model-picker feature (own barrel), mirroring
the modular Hub layout. Pure move + import repoint; no behaviour change.
* feat(model-picker): add per-model config persistence layer
Superset PerModelConfig (customContextLength, kvCacheDtype, speculativeType,
specDraftNMax, tensorParallel, chatTemplateOverride, trustRemoteCode) persisted
to localStorage (unsloth_model_configs) with schema versioning + LRU budget.
KV-dtype and speculative value sets match main's sidebar (no q4_0/ngram-simple).
Reuses features/hub/lib/model-identity for normalization; adds storage-key layer
and applyPerModelConfigToRuntime (sets tensorParallel, which the old PR omitted).
* feat(picker): modular backend for chat-template validate + default fetch
New studio/backend/picker package (schemas/service/routes) mounted at /api/picker:
- POST /api/picker/validate-chat-template (Jinja syntax validation, no false positives)
- GET /api/picker/chat-template/{model_name} (default template from tokenizer_config.json,
reusing get_cache_path/resolve_cached_repo_id_case; graceful null, no model-code exec)
Frontend api/templates.ts client + hooks/use-model-defaults lazy cache. No backend
changes to the existing inference load route (per-model load fields already supported).
* feat(model-picker): bind picker on-device list to shared hub inventory
Picker now sources cached + local models from useHubInventory (the Hub's shared
store) via a thin adapter, replacing its own /api/models/* fetchers + module
caches. Hub, download manager, and picker now share one source of truth, so
completed downloads reflect in the picker automatically. Partial/live-download
rows are filtered from the cached lists (unchanged rendering). Local naming/search
preserved via additive LocalInventoryRow modelId/displayName. Variant expander,
scan-folder management, recommended-fit, search, external providers untouched.
Known minor: cached 'Downloaded date' sort tiebreak degrades to alphabetical
(hub cached rows carry no mtime); default 'recent' (load-time) sort preserved.
* feat(model-picker): per-model config step inside the picker
Picking a (non-external) model now opens an in-picker config view built from
main's current load controls (context length, KV cache dtype, speculative
decoding, draft tokens, tensor parallel) plus a chat-template editor backed by
the picker validate/default endpoints. 'Remember for this model' persists the
config per model+variant; Run forwards the config to the existing load flow via
meta.config. External models bypass the step. Two-view orchestration lives in
model-selector (single interception point); pickers.tsx call sites untouched.
trustRemoteCode dropped from PerModelConfig to preserve main's per-load consent.
* feat(chat): apply/persist per-model config through the load flow
handleCheckpointChange threads meta.config into the selection; stageOrLoad and
the autoload/Hub-run paths now apply the picker config (explicit pick or saved
remembered config) via applyPerModelConfigToRuntime before staging/loading, with
keepSpeculative set so a remembered speculative mode survives the model switch.
Replaces the old remembered-load-settings seeding (resolveInitialConfig now the
single source). SelectedModelInput carries config.
* refactor(chat): remove per-model load config from the right sidebar
The load knobs (context, KV cache, speculative, draft tokens, tensor parallel)
and the chat-template editor now live only in the picker config step. The sheet's
Model section keeps the staged Load/Cancel flow (config is applied at pick time);
sampling params, system prompt, and RAG are unchanged. Deletes the superseded
remembered-load-settings module + the store's applyRememberedLoadSettings action,
removes the now-dead sheet state/imports, and points the settings reset at
unsloth_model_configs. Delete-cleanup deferred (stale config is LRU-capped).
* fix(model-picker): remove leftover sidebar-staging cogwheel + empty Model section
The downloaded-variant gear (ModelLoadSettingsAction) staged a model straight
into the right-sidebar Run-settings flow -- the old 'configure before load' path
now fully replaced by the in-picker config step. Removed the gear + its component.
Also gate the sheet's 'Model' section to staged picks only (pendingSelection):
after the load-knob strip its content is staged-only, so it was rendering an
empty section header whenever a model was merely loaded.
* chore(chat): remove dead per-model-config setters + modelControlsDisabled
After the load-config UI moved into the picker, the store's per-model setters
(setKvCacheDtype/setSpeculativeType/setSpecDraftNMax/setTensorParallel/
setCustomContextLength/setChatTemplateOverride) had zero callers
(applyPerModelConfigToRuntime writes via setState), and the sheet's
modelControlsDisabled was unreferenced. Verified dead across the whole tree.
* fix(chat): config-step Load actually loads (ignore Load-on-selection)
Root cause: with Settings > Chat > 'Load on selection' turned OFF, the config
step's load went down the deferred-staging path -- opening the right sidebar with
'<model> is staged, not loaded yet / Choose Load model'. The in-picker config step
IS the deliberate load action, so its Load now loads immediately (or downloads +
auto-loads when not cached) regardless of the toggle. Renamed the button
'Run model' -> 'Load model' to match. Native/dropped picks still honor the toggle.
* refactor(chat,hub): retire 'Load on selection' — config step is the only load flow
The in-picker config step (and the Hub Run button) now fully supersede the old
stage-to-sidebar flow, so the Load-on-selection toggle is removed everywhere:
- chat stageOrLoad: every pick loads immediately, or downloads + auto-loads when
not cached (the previous default behaviour, now universal).
- hub Run: drops the stage branch; downloaded GGUFs load directly with their saved
per-model config (no collision with the chat config step — both end at selectModel).
- store: removed loadOnSelection field/setter/key/default; Settings>Chat toggle and
its settings-reset entry removed.
- staged sidebar section is now a download-progress view (auto-loads on completion).
No manual staging remains; stageModel is used only for background auto-load downloads.
* feat(model-picker): default chat template from GGUF + thread variant through config flow
Read the embedded tokenizer.chat_template from GGUF files (read_gguf_chat_template
in gguf_metadata) and use it as the per-model default. Plumb gguf_variant through
the picker service, /api/picker/chat-template route, frontend templates API, and
use-model-defaults so the right variant's template is fetched.
Also refine the picker config-page/model-selector wiring, drop the dead
ggufNativeContextLength runtime path, and add the per-model-config storage keys to
the settings prefs export.
* feat(model-picker): read safetensors chat template + hide editor where it has no effect
Resolve the default chat template for safetensors models: prefer the modern
chat_template.jinja, fall back to the tokenizer_config.json chat_template field,
then chat_template.json (multimodal processor), then the GGUF embedded template.
Applied to local dirs, the HF cache snapshot scan, and the HF remote fetch.
Hide the chat-template editor in the picker for safetensors models — the override
is only applied at load by the GGUF/llama.cpp backend, so editing it on safetensors
currently has no effect. GGUF keeps the editor. Nothing removed; the dialog stays
for when the safetensors apply path is wired up in a later branch.
* fix(model-picker): set legacy-migration flag only after the write succeeds
Set unsloth_model_configs_migrated only once writeMap confirms the migrated
map persisted, so a quota/storage failure no longer marks migration done and
silently drops the user's pre-existing remembered settings — the next load retries.
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* MVP model picker fixes
* MVP picker config fix
* MVP safetensors config
* MVP max seq config
* MVP max seq fix
* Fix static max tokens cap ignoring model context
* Fix picker GGUF scan parity
* fix(studio): harden model picker config loading
Apply remembered per-model configs consistently from picker and Hub loads, keep default configs from overriding standing speculative settings, add config access for direct local GGUF files, and support saving or forgetting active model settings without a reload.
* Fix model picker config flow
* Fix model picker config loads
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* Avoid recursive per-model config migration reads
* Apply the displayed context length when loading a GGUF
* Fix template validation, cached template lookup, and failed load rollback
- Validate chat templates with the loopcontrols extension so templates
that use break or continue tags pass the picker validator, matching the
inference renderer that already accepts them.
- Read the default chat template from the newest cache snapshot rather than
an arbitrary iterdir order, so an older cached revision no longer prefills
a stale template.
- Capture the runtime per-model config before a load and reapply it when the
load fails, so a failed switch leaves the active model context, KV cache,
template, and speculative settings as they were.
* Make chat template view only for safetensors models
Custom chat template overrides are applied at inference only for GGUF
models, which pass the template to llama-server. The safetensors backend
renders with the model built-in template and ignores the override, so
editing it would save a value that never loads. For safetensors the
config page now opens the template as a read-only preview with a note
that editing is not available yet. This can become editable once
inference support for custom safetensors templates lands in main.
* Fix model picker config edge cases
- Restore prior runtime config when a load no-ops for the active model
- Cap the picker validator request body via the protected prefixes
- Keep the GGUF context slider max above the loaded context
- Fetch subfolder chat templates for uncached Hub repos
- Show the compare side config when reopening the picker
* Keep saved GGUF context above the fallback ceiling
* Show the model config in the run settings sidebar
* Fix model config sidebar reset and context slider
- Stack the remember toggle and action buttons in the sidebar
- Reset the config to defaults instead of the loaded values
- Fetch the native context so the slider max is not the loaded value
* Fix model picker config and download regressions
- Run picker chat template routes off the event loop
- Depth and root guard local template directory scans
- Restore download manager flow for uncached hub picks
- Apply per model context length on reload
- Import model picker symbols from the feature barrel
* Fix model picker config and cached download sorting
- Restore load settings when a Hub run is rejected mid load
- Reuse one NumericValueInput instead of a duplicate copy
- Fix double decode of the model name in the template route
- Remove the unused reset-to-loaded settings action
- Fix cached model download sorting
* Fix model picker per-model config edge cases
Honor a saved or typed max seq length above the model's native context so
RoPE extended values are no longer clamped and silently overwritten. Allow
typing past native while the slider keeps native as a soft ceiling.
Guard the fetch success paths in use-model-defaults against an aborted
signal, and refetch when the HF token changes.
Hash the chat template content in the sidebar remount key instead of its
length. Enable reset for a GGUF whose native context is unknown, and floor
the context slider max so it can never fall below the min.
* Fix GGUF context auto-fit and gated model config token
Stop forcing a 32768 context when a GGUF native context is unknown so the backend auto-fits to VRAM again, while still honoring an explicit context edit.
Send the HF token as a query param so gated safetensors models resolve their max position embeddings.
Derive model default state during render to drop the set-state-in-effect calls.
* Fix native GGUF context ceiling and guard picker template reads
Restore the native context store field so the sidebar slider keeps the
full ceiling for drag and drop GGUFs. Limit local chat template reads to
the browse allowlist, skip malformed repo ids, and drop unused model
picker exports.
* Fix model picker lint boundaries
* Fix model picker review findings
Chat template editor never seeded its draft. Radix only calls onOpenChange
from internal events, so the seed in the nextOpen branch was dead and a model
with a saved override opened empty. Saving then cleared the override. Drop the
dead branch, treat draft as an untouched sentinel, and reset it on every close.
Uncached Hub picks could auto load a model after the user left the chat. Main
detached the staged pick on route exit and on chat context change. Carry the
context key on the pending pick and skip the load when it no longer matches.
Also clear configTarget when the picker closes, restore the onUpdated ref so
variant rows stop resubscribing on every parent render, skip the LRU write when
the entry is already most recent, import NumericValueInput relatively, and drop
the unused ModelUpdateAction barrel export.
* Preserve GGUF context on active reload
* Fix model picker per-model config regressions
- Stop reloading the already loaded model on re-pick
- Hide infra models from the chat picker
- Detect vision support on cached GGUF repos
- Honor saved maxSeqLength on auto load
- Restore default chat template for local GGUFs
- Warn on save failure and revert config on cancel
- Refetch picker inventory on open
- Persist read only per model config safely
* Fix stale model auto load
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* Fix model picker numeric input sizing and constraints
Size value inputs to their content so long context lengths are not clipped,
restrict them to numeric characters, and stop the speculative decoding label
from truncating in the sidebar.
* Fix picker CI tests and harden chat template resolution for PR #6647
- tests: point the descender guard at the moved model-selector.tsx path
- tests: exclude the disabled Reload model button from the regenerate locator so .first targets the real Regenerate
- picker/service.py: reject symlinked template/gguf leaves that resolve outside the browse allowlist (HF cache reads unchanged)
- compare mode: resolve each pane's own remembered chat template instead of inheriting the other pane's from the store
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* Protect future-schema per-model configs from deletion for PR #6647
savePerModelConfig already refuses to overwrite a stored config whose schema version is newer than this client understands, but deletePerModelConfig did not. Unchecking Remember on an older client therefore silently destroyed a newer client's saved config. Apply the same guard on delete and surface the blocked case through the existing saveFailed toast.
* Protect future-schema per-model configs from quota eviction for PR #6647
The save and delete guards already refuse to touch a stored config whose schema version is newer than this client understands, but the quota-eviction path did not, so a full store on an older client could still evict a newer client's config. Skip future-schema entries when evicting and fail the save if the budget cannot be met without them.
* Fix GGUF context persistence, compare context, and rollback settings for PR #6647
Persist a GGUF context override from the user's intent instead of collapsing it against the loaded context, which reintroduced the context-reset (f4838782cb reverted the native-baseline fix). model-config-page now collapses the saved value against native, and use-chat-model-runtime and chat-adapter retain the requested context on load so re-saving another setting keeps the override; a null request stays null so a VRAM auto-fit never becomes a stored override.
shared-composer: a compare pane with no explicit GGUF context now loads at native (0) like single-view, not the session maxSeqLength that silently shrank the shown context.
use-chat-model-runtime: restore the previous model's KV cache dtype and chat template on a failed-load rollback so it runs as it was, not with backend defaults.
* Preserve native path token when reloading the active model for PR #6647
handleReloadActiveModel rebuilt the selection without the store's activeNativePathToken, so reloading a file-picked GGUF after a settings change validated the display label as a repo/path and failed. Thread the active native token through the reload selection so native-loaded models reopen correctly.
* Make picker template validation resilient and accept HF generation tags for PR #6647
Import Jinja lazily inside validate_chat_template so a backend without the optional jinja2 package (GGUF-only installs) still starts instead of raising ModuleNotFoundError at import time. Register a no-op extension for the Transformers {% generation %} assistant-mask tag so pasting a valid HF chat template validates, matching the renderer, rather than being rejected as an unknown tag.
* Honor remembered compare config and parse processor chat_template.json for PR #6647
* Fix failed-load rollback context and processor template map fallback for PR #6647
* Restore speculative decoding config on failed-switch rollback
When a model switch fails after the previous model was unloaded, the
rollback reload restored tensor_parallel, KV cache dtype and the chat
template override, but omitted speculative_type and spec_draft_n_max and
cleared their loaded shadows to null. The previous model therefore came
back running at backend defaults (speculation off) while the UI still
showed it enabled, and the status resync confirmed the off state. Resend
the previous model's speculative settings in the rollback load and keep
the store's active and loaded speculative fields in sync with them.
* Reset max sequence length when a model has no saved config
applyPerModelConfigToRuntime reset every per-model field except
maxSeqLength, which it only wrote when the incoming config had one.
maxSeqLength is the sole field carried on store.params, so selecting a
model with no remembered config left the previous model's value in place
and later loaded the new model at that leaked length. Fall back to the
standing default so an unremembered model loads at its own default.
* Surface a message when a variant update cannot start
startManagedUpdate handled the conflict and error start outcomes but let
busy fall through as if the update began, so the confirm dialog closed
with no job created and the cached variant stayed stale. Show an info
message when the repo is busy with a sibling transfer so the click is
not silently dropped.
* Keep per-model speculative choices out of the global default
A staged load with a per-model or one-off config sets keepSpeculative,
which already skips reading the global speculative preference. The
matching save still ran unconditionally, so the model-specific choice was
written to the global unsloth_chat_speculative_type and a later model with
no saved config started from it instead of Auto. Skip saveSpeculativeType
when keepSpeculative so the per-model choice stays isolated.
* Seed non-active model settings from the app default max length
The Run settings page captured initialMaxSeqLength from the loaded
model's runtime params and fell back to it for a model with no saved
config. Opening settings for a different, unloaded model and clicking
Load then sent the active model's context (for example 64k) instead of
the 4096 default, risking validation failures or OOMs. Seed the default
for non-active models and keep the runtime value only for the active one.
* Prefer sidecar tokenizer chat template over the GGUF copy for variants
_chat_template_from_dir returned the embedded GGUF template first when a
variant was selected, reversing the tokenizer-first precedence of the
no-variant path. A model whose chat_template.jinja or tokenizer_config.json
supersedes a stale embedded template then got the wrong template on
variant selection. Keep tokenizer files first regardless of variant; the
variant only picks which GGUF is the fallback. Adds regression tests for
both the tokenizer-wins and gguf-fallback cases.
* Keep per-model speculative choices load-local in autoload and compare
The interactive load path treats a per-model speculative choice as
load-local and skips writing it to the global default. Autoload and
generalized compare still called saveSpeculativeType unconditionally, so a
remembered off or ngram setting leaked into unsloth_chat_speculative_type
and later models with no saved config inherited it. Persist the global
preference only when the value came from the global settings.
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* Studio: record the compare pane's loaded context in runtime state so the active model's settings and any reload or save use it, not the previous context
* Studio: notify the user when a Hub autoload can't start because another download for the model is already running, instead of silently dropping it
* Studio: drop the merge's orphaned staged-model store helpers and unused alert imports
The main merge left isPendingGguf and pendingSelectionMatches referencing the
removed PendingModelSelection type, and the alert-dialog/alert imports unused
after the permission-mode dropdown replaced the bypass dialog, so tsc -b failed.
* Studio: cache a null default chat template so the viewer stops re-fetching it
A model with no sidecar or embedded template resolves to a terminal null, but
that result was never cached, so reopening the template viewer re-ran the
backend and Hugging Face lookup every time.
* Studio: detect direct-file GGUFs in run settings so Max Tokens uses their context
A GGUF loaded from a local file or custom folder has no variant label, so the
run-settings panel treated it as non-GGUF and clamped Max Tokens to the session
max_seq_length instead of the loaded GGUF context. Detect it via the reported
GGUF context and the .gguf checkpoint suffix, matching the chat page.
* Studio: prompt to re-select a local model file when its lease expired before reload
A file-picked GGUF is reachable only through a native path token that the
desktop host prunes after a TTL. Reloading reused that token blindly, so a
reload long after the initial load failed with an opaque error. Track the
token's expiry and, when it has passed, ask the user to re-select the file
instead of attempting a doomed reload.
* Fix descender-clipping test to tolerate sidebar layout utilities
The sidebar account-block div carries layout utilities (min-w-0, flex-1)
between 'flex' and 'flex-col', so the descender-clipping guard's regex,
which required 'flex' immediately followed by 'flex-col', no longer matched
and the test failed to locate the account-block div. Generalize the prefix
to allow intervening flex utilities while still capturing the leading-*
class before the collapsible visibility utility and asserting leading-tight,
so the guard against clipped glyph descenders is fully preserved.
* Harden picker chat-template resolution
Enforce the 64 KiB chat-template contract at the validate endpoint's request
model so a direct caller cannot submit a template far larger than the frontend
allows (MaxBodyMiddleware only bounds the whole request body, not this field);
oversized templates now return a clean 422.
Apply sidecar-over-GGUF template precedence globally across cached snapshots
instead of per snapshot. A repo with multiple cached revisions previously
returned the first snapshot's template, so a newer GGUF-only revision could
win over an older revision's maintained chat_template.jinja sidecar, which
contradicted the documented intent that sidecars supersede the embedded copy.
* Guard per-model config against future-schema and lossy migration
Two forward-compatibility gaps in the versioned per-model config store:
- The load/apply path returned and normalized a stored record without checking
its schema version, so a record written by a newer client was reinterpreted
under the current schema and applied to a live model load, even though save,
delete and eviction all refuse to touch future-schema records. Reject
future-schema records on load too.
- The one-time legacy migration enforced the storage budget without protecting
the entries it had just migrated and set the completion flag unconditionally.
When storage was already full of future-schema records (which are unevictable
by an older client), the migrated entries were the only evictable ones and
could be dropped while migration was still marked complete. Protect the
migrated keys during eviction and only mark migration complete when they
survive, so it retries once space frees up.
* Discard chat-template validation results after the dialog closes
Server-side template validation is async, but closing or cancelling the editor
did not abort it, so a late-arriving valid response still called onSave and
applied a template the user had already dismissed. Track a validation token
that is bumped on close and ignore any validation result whose token is stale.
* Record native lease expiry when loading a picked GGUF from the chip
The pending-native-model chip loaded via stageOrLoad directly, bypassing
loadNativeModelIntent, so activeNativePathExpiresAtMs was never recorded for a
chip-loaded file. A later reload then either skipped the lease-expiry guard
entirely (expiry left null) or compared against a previously loaded file's
stale expiry, so reload could reuse an already-pruned token or wrongly block a
still-valid one. Route the chip through loadNativeModelIntent, which builds the
same selection and records the expiry.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Prefer sidecar template for a directly selected local GGUF file
A direct .gguf file path read its embedded chat template without checking the
parent directory for a maintained sidecar (chat_template.jinja /
tokenizer_config.json), while directory and variant selections already prefer
the sidecar. That let the config editor preview or save a stale embedded
template for the same model depending on how it was selected. Check the parent
directory sidecars first, then fall back to the embedded copy, and cover both
paths with tests.
* Resolve cached chat template per revision, newest first
The earlier change searched every cached snapshot for a sidecar before
considering any snapshot's embedded GGUF template, which let an obsolete sidecar
from an older revision override the newest revision's template. Restore
per-snapshot resolution (newest first): a revision's sidecar still supersedes
its own embedded GGUF copy, but a newer revision is no longer overridden by an
older revision's sidecar.
* Preserve autoload transport conflicts and surface background busy downloads
- When a Hub autoload hits a transport conflict, keep pendingHubAutoLoad bound
instead of clearing it. Clearing it re-keyed the download surface and its
cleanup cancelled the conflict the toast tells the user to resolve, so the
Hub resume affordance was gone the moment it appeared. Return early on
conflict, mirroring the started branch, so resolving it from the Hub still
auto-loads on completion.
- The background-download branch handled started and conflict but silently
dropped a busy outcome, leaving the user with no feedback when a peer variant
of the same repo was already downloading. Surface the same busy toast the
autoload path uses.
* Fix context length, GGUF template, fetch state and lease expiry bugs
Keep explicit context length values instead of collapsing to null at
native. The collapse made the slider jump back at the native maximum
and made Reload load the previous context instead of the chosen one.
Prefer the first split when resolving a GGUF without a variant. Later
splits carry no chat template metadata, so picking the largest file
could return no template for a sharded model.
Clear stale fetch state when template and metadata lookups retry, so
a previous terminal error is not shown while a new fetch is running.
Record native path lease expiry together with the token when a load
commits. The expiry was written by only one load path and even when
the load did not start, so a reload could be blocked with an expired
file message for a still valid token.
* fix(model-picker): resolve review findings across config, inventory, and templates
- Apply remembered per-model config in the training-compare chat handoff so a
prior model's customContextLength no longer leaks into the next load
- Match GGUF variant labels with the inventory extractor too, so cached
no-quant-token files resolve their default chat template
- Show "Auto" instead of a fabricated 32768 when native context is unknown
- Reuse the identical staged auto-load object on same-pick so a re-pick during
download pre-flight no longer disarms auto-load via "busy"
- Union supports_vision when deduping cross-cache inventory rows
- Serve hidden-model needles from a new GET /api/hub/hidden-models endpoint and
merge them client-side, covering runtime-configured RAG embedders
- Clamp GET chat templates to MAX_CHAT_TEMPLATE_BYTES (route + jinja sidecar),
matching the validate endpoint's contract
- Lower-clamp stored customContextLength to shared CONTEXT_LENGTH_MIN
- Wipe unsloth_chat_load_on_selection in Settings "Reset all"
- Drop stale pendingHasContext comment describing deleted staging machinery
* Fix stale defaults cache, token in query string and rounded up context ceiling
Refresh cached chat template and max position data when a model update
completes. Send the HF token for model config requests in the dedicated
header instead of the URL. Snap the native sequence length ceiling down
to the nearest step so the slider cannot exceed the declared maximum.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix compare pane reverting active checkpoint on non-GGUF load
Re-read runtime params after setCheckpoint so the fresh checkpoint is
kept instead of being overwritten by the pre-setCheckpoint snapshot.
* Send the HF token via header for the vision and embedding checks
checkVisionModel and checkEmbeddingModel still passed the HuggingFace
token as a ?hf_token= query parameter, so it landed in server access
logs, proxy logs, and browser history. Move them to the
X-Unsloth-HF-Token header like getModelConfig already does, and accept
the header on the check-vision and check-embedding routes with the
existing query parameter kept as a fallback for older clients.
* Cap the chat template on the model load path
The load endpoint accepted an unbounded chat_template_override, so a
direct caller could hand llama.cpp an arbitrarily large Jinja template
even though the frontend, the validate endpoint, and the read paths all
enforce the 64 KiB limit. Reuse MAX_CHAT_TEMPLATE_BYTES in the
LoadRequest validator, rejecting oversized templates with a fast
character-count check before the exact UTF-8 byte check.
* Protect existing per-model configs during legacy migration
When the one-time legacy import pushes the store over budget, eviction
now protects the entries the user already has and drops only the
just-migrated legacy entries, so importing old load settings can never
discard a newer per-model config.
* Reset clears the context override instead of pinning the native value
Reset wrote the discovered native context into customContextLength for
GGUF models, but isDefaultConfig treats any non-null customContextLength
as an explicit pin, so Reset with Remember enabled persisted a fixed
context and future loads stopped using the native auto context. Reset
now restores the full default (customContextLength null); the native
value is still shown through the existing display fallback.
* Bound chat-template sidecar reads to a size limit
The chat_template.json, tokenizer_config.json, and Hub-downloaded sidecar
readers decoded and json-parsed the whole file before the extracted
template hit the 64 KiB response cap, so an oversized metadata file could
exhaust memory. Read them through a bounded reader (4 MiB envelope) that
returns None when the file is larger, matching the existing chat_template.jinja
size guard. Adds tests for oversized tokenizer_config.json and chat_template.json.
* Keep the native-path token and lease expiry in sync
Rollback after a failed reload restored the previous token but left the
failed load's expiry in the store, so a later reload could be falsely
blocked as expired (token A paired with load B's lease). Restore the
previous lease alongside the token, and clear the expiry wherever the
token is cleared on a non-GGUF transition, so the two never diverge.
* Clear the native file lease on compare-pane loads
* Studio: add regression tests for the model-picker per-model-config
Guard the specific regressions that reverted the predecessor change:
- backend pytest (studio/backend/tests/test_model_picker_regression.py):
infra-model hiding, HF token via header with query fallback, and the
chat-template byte caps.
- source contracts (tests/studio/test_model_picker_contracts.py): the token
stays out of the URL, the context ceiling is floored, the native lease is
cleared on compare-load and restored on rollback, the default caches key on
the inventory version, and the hidden needles stay present.
- Playwright E2E (tests/studio/playwright_model_config.py) wired into
studio-ui-smoke.yml on port 18898: Context Length persists across a reload,
Reset clears the stored override, and infra models are absent from the picker.
- optional GPU-gated inference smoke (tests/studio/test_gpu_inference_smoke.py)
that auto-skips on GPU-less CI and stays short on a GPU.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: model pinning, row menus, hub inference settings, and inventory filters
Pinning
- Add a pinned models store (localStorage) with repo and per-quant pins
- Pinned section in the model selector's On Device list and the hub inventory,
with newest pins first so Pin to top lands on top
- Deleting a repo drops its pins
Row menus
- Replace loose row icons with a shared 3-dots menu (pin, reveal in file
manager, copy identifier, copy path, delete) on picker rows, hub quant rows,
the hub run bar, and on-device inventory rows
- Menus only render for models actually on disk; platform-aware reveal labels
- Backend: cached-model-path and reveal-cached-model endpoints resolving
managed HF-cache repos only
Hub inference settings
- Gear in the GGUF run bar opens an Inference settings dialog reusing the chat
page's controls: model config (context length, KV cache, speculative
decoding, chat template), system prompt, reasoning, sampling, tools and
retrieval
Inventory
- Model-type filter (text, vision, embedding, STT, TTS, diffusion) beside the
sort pill, both with a sort icon, capped widths and truncation so the
On device heading never wraps
- Unsloth-owned repos without an upstream provider logo fall back to the
Unsloth mascot avatar
- Discover / On Device tabs widened; hub search bar narrowed to match
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: revert the Unsloth mascot avatar fallback
Unsloth-owned repos without an upstream provider match go back to the
colored-initial tile, and unslothai is no longer a relabeled owner.
* Studio: run-bar options on single models, and aligned type/capability filters
- Give single-model (non-GGUF) run bars the same 3-dots options menu and
settings gear as GGUF, at repo level
- Drop Pin to top from the run-bar menus; pinning stays in the On Device list
- Add an Image to text (diffusion) capability with detection, and surface it
in both the hub Discover capability filter and the On Device type filter
- Align the On Device type filter with the Discover capability options and
share the same detection so both dropdowns match
* Studio: apply hub inference config on reload, eject action, and run-bar polish
- Fix inference settings not applying: the hub dialog now writes the config to
the runtime before reload, matching the chat page (selectModel reads runtime
state, not the selection)
- Order the settings gear before the 3-dots menu in the run bars
- Replace the loaded-model run-bar action (New Chat) with Eject, wired through
the inspector to the hub's ejectModel
- Truncate the results heading so a long search query clips instead of
overlapping the header pills in split view
- Use a plain magnifying-glass icon for the no-results empty state
* Studio: fix GPU settings loss, load guards, pins, filters, and cached paths
Reloading a model from the chat sidebar or the hub gear dialog rebuilt the
per-model config without the GPU memory fields, so manual GPU layers, MoE
placement, and the GPU pick were reset on every reload and could be saved
over a remembered config. The active config now comes from a shared
useActiveModelConfig hook that carries the GPU fields for GGUF models, and
the sidebar remount signature tracks them through a shared gpuFieldsSignature
helper.
The in-flight load guard lived in a ref inside each useChatModelRuntime
instance, so the chat page, hub page, and gear dialog could not see each
other's loads. A load started from the gear dialog left the hub page free to
eject the model mid-reload or start a second concurrent load. The runtime
store now records the loading pick, selectModel checks it across instances,
and ejectModel refuses with a toast while any load is in flight.
The cached-model-path endpoint matched GGUF files by basename and excluded
only mmproj, so Copy path and Reveal could return an MTP drafter for a quant
and returned 404 for directory layouts like BF16/model-00001.gguf. Variant
files are now resolved from snapshot-relative paths with the same drafter,
mmproj, and big-endian exclusions as the load path, shared through a new
_main_variant_gguf_label helper.
Hub and picker fixes:
- rename the diffusion capability label from "Image to text" to
"Image generation", since it detects image generators
- validate pinned quants through the cached variant listing, keep the last
verified set while revalidating, and drop deleted quants immediately
- pass a measured scroll margin to the on-device virtual list so rows past
the overscan stay visible below the pinned block
- keep the delete menu for stopped partial safetensors downloads
- give the inventory type filter a reset in Clear filters, a truthful empty
state with a Show all types action, and hide it on the datasets view
- order picker pinned rows by pin recency, include pinned matches in the
empty-state check, and sync pins across browser tabs
- count only the visible rows in the On device list header
Tests: contract checks for each fix in test_model_picker_contracts.py and a
backend test for the variant label selection.
* Studio: reveal cached models in Windows Explorer under WSL
The reveal endpoint only branched on macOS, Windows, and generic Linux.
Under WSL the Linux branch spawned xdg-open, which is missing on a stock
distro without a Linux desktop, so the request failed with a 500 and the
UI showed a failed to open file manager error.
WSL is now detected with the existing helper and the path is converted
with wslpath before opening explorer.exe, selecting the file the same
way native Windows does. Directories open directly. When interop is
unavailable the old xdg-open fallback still runs. The macOS, native
Windows, and native Linux branches are unchanged, and the Tauri app is
covered since its hub reveal calls this same local endpoint.
Tests: platform guards for the WSL reveal, the interop fallback, and
the unchanged native Linux behavior in tests/studio/test_reveal_file_manager.py.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Adjust model picker row spacing and cogwheel hover consistency
* Studio: exact hidden model ids and newest revision cached paths
A custom RAG embedder repo was published to the frontend as a basename
substring needle, so a generic name like org/model could hide unrelated
models in the pickers. The hidden-models endpoint now sends full repo ids
that are matched exactly.
Copy path and Reveal picked a GGUF variant from an arbitrary cache
revision when the same file existed in more than one. The newest revision
now wins, matching the whole repo lookup.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix model picker GPU config, metadata, and cache selection
Load each compare model with its saved GPU memory mode, GPU layers, CPU MoE layers, and selected GPU IDs. Reconcile saved GPU IDs with the current hardware. Include the active native GGUF path token in metadata checks. Search all Hugging Face cache roots when resolving cached models and select the largest visible cache entry. Remove obsolete barrel exports and the staging-only GPU memory helper.
* Studio: hide hub inference settings gear for now
The cogwheel in the hub download cards is out of scope for this PR. The
dialog component stays in place and a TODO marks where the button
returns in a future PR.
* Refresh hidden model matchers
* Fix GGUF detection, compare context pin, and picker delete staleness
Treat any pick with a GGUF variant as GGUF in selectModel so the first
load after downloading an uncached quant validates and sizes with the
right GPU settings instead of unloading the current model on a wrong
preflight. Variant picks now also set isGguf on their selection meta.
Stop compare panes from inheriting the active model's context pin when
their own saved config says Auto. Null context in a remembered config
now means no pin, matching how the pane settings are shown.
Route picker deletes through the hub inventory client, which
invalidates the HF cache scan and the variants cache. The legacy
delete route left the scan cache warm, so deleted models reappeared
in the picker until the TTL expired. Removed the now unused legacy
delete client and updated the contract test to match.
* Studio: fix stale GGUF load-marker ordering test
The load-in-flight marker still precedes the hub-download guard and the
unload, but the llama_extra_args inheritance that used to sit between the
marker and the guard now runs ahead of the GGUF branch, so it is no
longer a landmark inside the sliced source. Drop it from the ordering
assertion and keep the marker -> guard -> unload invariant.
* Studio: fix per-model config edge cases in compare loads and saved defaults
- chat-settings-sheet: gate the MTP fallback note and context/VRAM warning on
the broader isGguf (variant, loaded gguf context, or .gguf suffix) instead of
isLoadedGguf, so direct-file and custom-folder GGUF loads still surface
those diagnostics.
- shared-composer: a compare pane's context now comes from its own config only
(a saved pin, else null for Auto/native). It no longer inherits the active
model's shared snapshot, which resolveFitMaxSeqLength treated as an explicit
pin and could load a pane at another model's context (VRAM/OOM), matching the
single-model load path.
- model-config-page: when an auto-fit GGUF is saved with fixed GPU layers
(Manual) and Remember, pin the displayed fitted context so a later fresh load
keeps the placement instead of sending native/0 and recreating the OOM.
- per-model-config: treat Auto GPU memory mode and Auto/default speculative type
as follow-global defaults; do not persist them as per-model overrides so later
global preference changes keep applying.
* Studio: gate vision capability on GGUF projectors and bound remote template downloads
- cache_inventory: only mark a cached repo vision-capable when it holds an actual
GGUF mmproj projector, not any file whose name merely contains "mmproj" (e.g.
mmproj_config.json), matching the runtime's GGUF-only projector detection.
- picker/service: pre-check the remote file size before downloading an uncached
repo's chat template / tokenizer config, so a maliciously large sidecar is
skipped instead of fetched and retained in full, mirroring the size gate the
local-file path already applies.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: add source-contract guards for the per-model-config edge-case fixes
Guard the four per-model-config fixes against silent regression in CI:
- local GGUF diagnostics gate on the broad isGguf, not the variant-only isLoadedGguf
- fixed-layer GGUF saves pin the displayed context
- Auto GPU mode and Auto/default speculative are not persisted as per-model overrides
- a compare pane's context comes from its own config, not the active model's snapshot
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: clear manual GPU knobs on Default and resolve local embedders before repo-id
- model-config-page: switching GPU Memory back to Default now clears the Manual-only
knobs (gpuLayers/nCpuMoe/selectedGpuIds); otherwise a remembered config kept stale
pins that a later load re-applied when the global GPU preference was Manual, despite
the page showing Default.
- routes/models hidden_model_matchers: resolve an existing local path before the repo-id
regex, mirroring is_hidden_model, so a local embedder shaped like "models/embedder" is
hidden by exact path instead of leaking as a chat model.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: add _is_mtp_drafter to the model_config stub in the export-paths test
routes/models.py imports _is_mtp_drafter from utils.models.model_config at module
load, but the lightweight stub in test_export_absolute_paths.py did not provide it,
so loading the module under the stub raised ImportError on Backend CI. Add the stub.
* Studio: read a picked GGUF's chat template through the native path lease
The picker chat-template GET has no native-path-lease plumbing, so a
desktop-picked (drag-drop) GGUF could not show its default chat template
in Run Settings until the model was loaded: the endpoint only receives
the display label, not the leased file path.
Read the embedded template through the existing lease-aware
/api/inference/validate probe instead. A new include_chat_template flag
resolves the granted canonical path and returns the GGUF's own embedded
template, never a sibling sidecar (the grant authorizes just that one
file); it skips the training guard like include_context_length and is
bounded by MAX_CHAT_TEMPLATE_BYTES. The frontend fetch mints a one-shot
validate-model lease when a native token is present and keeps the plain
GET path for HF and allowlisted local models.
Adds backend and source-contract regression tests.
* Studio: call worker.direct_wheel_url in the ROCm wheel-url test
The ROCm Mamba/SSM test referenced worker.py's private _direct_wheel_url,
but the worker imports the wheel helper under its public name
direct_wheel_url (utils.wheel_utils). When the worker module loads (its
imports resolve in CI), worker_mod._direct_wheel_url raised AttributeError;
the test only masked it by skipping when the worker could not be imported.
Call the name that actually exists so the assertion runs; it still returns
None for an empty cuda_major (ROCm).
* Studio: reset max sequence length to the app default, not the loaded value
For a non-GGUF active model, the per-model config seeds maxSeqLength from
the loaded runtime value so the panel opens showing the running context.
Reset set config.maxSeqLength to null, but the null fallback resolved back
to that captured runtime value, so the field kept showing the old custom
length and the config saved/reloaded it again. A remembered or active
max-length override therefore could not be cleared from Run settings.
Fall the null/default case back to the app default (clamped to the model's
native ceiling) instead of the active runtime snapshot, so Reset actually
clears the override. The initial view is unaffected: an active model's
config.maxSeqLength is already non-null, so it still shows the loaded value.
Adds a source-contract regression guard.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: persist default max length, refresh deleted quants, hide non-chat locals
Three follow-up fixes from review of the per-model-config picker:
- Max sequence length: the persisted per-model record now keeps config's
maxSeqLength (null after Reset) so isDefaultConfig can clear a remembered
override; the resolved app-default is substituted only into the load
request, never the saved record. Previously Reset saved the concrete
default and left the model pinned/remembered.
- GGUF variant expander: deleting a downloaded quant from a repo that still
has other cached quants now bumps the expander refresh key, so the removed
quant stops showing as downloaded and clickable (which would try to reload
the deleted file) until the repo is collapsed and reopened.
- Local picker rows: require capabilities.canChat before listing a local
models-folder / LM Studio row. A weightless folder (only config.json) is
classified non-chat, and toLocalModelInfo drops capabilities, so selecting
such a row would try to load a path the inventory already marked non-chat.
Adds source-contract regression guards for all three.
* Fix compare-pane and Reset context defaults in model picker
Two related per-model-config default regressions:
- A non-GGUF compare pane with no saved maxSeqLength fell back to the
active model's shared runtime snapshot, so comparing a saved 128K model
against an unconfigured pane loaded the latter at 128K and could OOM. It
now falls back to the shared app default (DEFAULT_MAX_SEQ_LENGTH), the
same fallback the single-model config path uses.
- contextAtDefault treated an explicit customContextLength equal to the
native ceiling as a default, which wedged the Reset button disabled for
a deliberate pin-to-native. It now counts as default only when there is
no override at all.
DEFAULT_MAX_SEQ_LENGTH becomes a single exported constant in
per-model-config.ts so the single-model config and the compare path share
one source of truth. Adds source-contract guards for both fixes.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Skip over-cap remote Jinja templates so the tokenizer template wins
The remote chat-template resolver bounded raw chat_template.jinja downloads
only by MAX_TEMPLATE_METADATA_BYTES (4 MiB), then returned the first
non-empty Jinja unconditionally. The picker route drops any template larger
than MAX_CHAT_TEMPLATE_BYTES (64 KiB), so an uncached repo whose
chat_template.jinja sits between 64 KiB and 4 MiB returned no template at
all, even when a valid smaller tokenizer_config.json template existed. The
local path already skips oversized .jinja files and falls through.
Gate the extracted Jinja on MAX_CHAT_TEMPLATE_BYTES and continue searching
when it exceeds the cap, matching _chat_template_from_jinja_file. The 4 MiB
download bound stays for JSON files that merely embed a small template. Adds
a regression test that a big Jinja plus a valid tokenizer config resolves to
the tokenizer template.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Guard legacy per-model-config migration idempotency
The v1->v2 localStorage migration (unsloth_load_settings ->
unsloth_model_configs) runs on every store read, so it must migrate exactly
once and never re-run, duplicate, or clobber a newer per-model config on a
reload or restart. That was covered only by a manual proof, so add durable
guards:
- Source-contract test pinning the three idempotency layers (the in-memory
legacyMigrationChecked guard, the persistent unsloth_model_configs_migrated
flag set in every terminal branch, and the non-overwriting Object.hasOwn
merge-skip) plus the readMap invocation. Reddens if any layer is dropped.
- Playwright model-config E2E: promote the legacy-migration step to a gating
check (soft_fail, which gates under the CI STUDIO_UI_STRICT=1) that the
migrated value is preserved and the flag is set, then reload again with a
fresh legacy seed present and assert the stored key set is unchanged, so a
second reload cannot re-migrate, duplicate, or clobber.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Note the migration E2E now gates idempotency under STUDIO_UI_STRICT
* Tighten model-picker per-model-config code comments
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Co-authored-by: shimmyshimmer <shimmyshimmer@users.noreply.github.com>
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
* Studio: validate Hugging Face tokens before use
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: keep token validation failures non-blocking
* Studio: harden Hugging Face token preflight
* Studio: make token validation effect lint-safe
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* Studio: fix per-GPU VRAM reporting on Windows ROCm
On Windows ROCm without a HIP SDK, amd-smi is disabled and the System tab fell
back to torch mem_get_info, which reports free==total there (ROCm/ROCm#1909), so
used VRAM showed as 0. The perf-counter fallback also summed every adapter into a
single device with only GPU 0's total, hiding the second GPU.
Read per-adapter Dedicated Usage (LUID-instanced) for used and take each GPU's
total from torch properties, and treat the free==total case as unknown rather
than 0, so every GPU shows real usage. NVIDIA, Linux ROCm, Apple and CPU paths
are unchanged. Final validation needs a real Windows AMD box.
Fixes#7072
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: report unknown VRAM instead of fabricating or zeroing it
Two gaps in the Windows ROCm VRAM path. When more adapters are actively using
VRAM than are visible to the process (a GPU outside the visibility mask), the
per-adapter attribution paired usage by size and fabricated a per-GPU value;
report unknown for every device in that case rather than mis-assign. And the
System API turned an unknown (None) used value into 0 with ``or 0``, then
reported the full card as free, re-hiding the exact case this change surfaces;
keep None so the UI shows unknown.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Render unknown VRAM as Unknown instead of zero in the System tab
The backend reports null usage when it is unknown (e.g. the Windows ROCm
perf counter is unavailable or localized), but the System tab coerced
null to 0 and derived free from it, fabricating a 0-used/full-free total.
Preserve null and render the translated Unknown for per-device used, free
and utilization, and mark the aggregate VRAM tile unknown when any device
is unknown.
* Render unknown VRAM as Unknown in the floating monitor and the util tile
The floating VRAM monitor and the aggregate utilization ring both still
coerced a null usage to 0, showing a fabricated 0.00 GiB / full free / 0%
on the same Windows ROCm no-counter case the resources tab already
handles. Guard both on whether every device reports a finite usage and
render Unknown (value and percent) instead of a concrete 0.
* Attribute per-adapter VRAM usage only when capacity forces the mapping
On Windows/ROCm there is no shared key between LUID performance-counter
instances and torch ordinals, so usage was paired to devices purely by capacity
ranking. That pairing is only trustworthy when capacity forces it (a usage
larger than every smaller device can sit on one card). When a smaller-capacity
device could equally hold a strictly larger usage (for example an 8 GiB card
near full beside a lightly used 48 GiB card), the two values are swappable
without violating any capacity, so the ranking is a guess with no key to break
the tie. A wrong guess both mislabels the System tab and feeds
routes/training_vram.py a wrong per-index free value, driving a wrong
keep-resident decision.
Report unknown for every device when the assignment is ambiguous, keeping the
attribution only for the capacity-forced case. Returning None is the
conservative direction: training_vram treats a missing index as zero free, so it
never keeps a chat model into an OOM. Add regression tests for the
not-capacity-ordered, same-capacity, single-fits-both, and capacity-forced
cases.
* Report unknown VRAM usage when a hidden adapter survives the noise filter
When HIP_VISIBLE_DEVICES exposes a subset of the physical adapters, the LUID
usage counters cover cards outside the visibility mask too. The sub-64 MiB noise
filter could drop a genuinely-idle visible card's real usage while keeping a
hidden larger card's high usage, which was then clamped onto the smaller visible
device and reported as fully used (for example a hidden 48 GiB card at 40 GiB
shown as a visible 8 GiB card fully used, with its true 10 MiB usage filtered
out). That fabricated reading also feeds routes/training_vram.py a wrong
per-index free value.
Flag extra adapters on the raw counter count (before the noise filter, since an
idle visible card can itself fall below the floor) and, when a kept usage exceeds
its ranked visible capacity, report unknown rather than clamp a hidden card's
usage onto a visible device. The genuinely-idle-noise and capacity-forced
single-model cases are unchanged. Add a regression test for the hidden
high-use-adapter case in both counter orders.
* Report unknown when only a placeholder adapter counter survives the noise filter
When more raw counters than visible devices are present but every counter sits
below the 64 MiB noise floor (an idle real GPU alongside a Windows Basic Render
Driver placeholder), the non_trivial-or-raw fallback resurrected the raw
magnitude-sorted counters and could attribute the placeholder to a real GPU while
dropping a real card's reading. With a single visible device the swap-ambiguity
check cannot catch it (it needs at least two ranks), so the fabricated value
reached the System tab and automatic GPU selection.
Return unknown for every device in that case instead of falling back to raw
counters. With the earlier guards this completes the invariant: a concrete
per-GPU usage is emitted only when the assignment is capacity-forced, and every
ambiguous, extra-adapter, placeholder-fallback, or count-mismatch path reports
unknown. Add a regression test for the placeholder fallback in both counter
orders and the two-idle-GPU case.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: attribute Windows/ROCm VRAM only when capacity forces a clean bijection
With more raw adapter counters than visible devices, a survivor that merely
fits a visible card was pinned to it by magnitude ranking, fabricating a hidden
GPU's usage onto an idle visible card whose true reading was dropped by the
sub-threshold noise filter (two visible 48/8 GiB cards using 40 GiB / 10 MiB
beside a hidden 6 GiB adapter returned [40, 6]). Emit a concrete per-device
value only when the supra-threshold counters number exactly the visible devices
(every visible card has one real reading, the extras were sub-threshold
placeholders) AND the ranked usage strictly exceeds every smaller visible card's
capacity. When a visible card is idle (fewer supra-threshold counters than
devices) a survivor could be the hidden GPU's usage, so every device reports
unknown; more active counters than visible cards, the smallest card, and any
merely-fitting usage stay unknown too. The reporter's loaded-card display is
preserved (40 GiB / 0.5 GiB across 48/8 GiB -> [40, None]). Adds a regression
test for the reported case plus an exhaustive capacity-forced/bijection matrix.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: keep the unified-memory total when Windows-ROCm used is unknown
_apply_unified_memory_correction gated both the total and the used update on
torch_used_gb being known, so on a unified-memory APU (Strix Halo) where torch
reports used=None (the Windows-ROCm free==total sentinel) but an authoritative
full-GTT total, the device kept amd-smi's small dedicated carve-out and
underreported its capacity on the System tab. Adopt torch's larger total
independently of used; overwrite used only when torch's is known (otherwise keep
amd-smi's dedicated-usage figure) and recompute utilization against the
corrected total. Adds regression tests.
* Tighten comments in the ROCm/Windows VRAM reporting path
* Tighten comments further in the ROCm/Windows VRAM reporting path
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <unslothshared@gmail.com>
* test(studio): add e2e test for cpu-fallback overriding vulkan
* feat(studio): add UNSLOTH_LLAMA_CPP_BACKEND env var
* feat(studio): add UNSLOTH_LLAMA_CPP_BACKEND env var
* Preserve UNSLOTH_LLAMA_CPP_BACKEND=cpu across llama.cpp updates for PR #7228
The in-app updater rebuilt the installer command without --cpu-fallback and
only re-asserted Vulkan, so accepting a llama.cpp update after forcing CPU on
an Intel iGPU host re-ran host detection and routed back to the crashing Vulkan
bundle (#7213). Record install_kind in the prebuilt marker and re-assert
--cpu-fallback on update when the installed bundle is CPU.
Also make setup.sh's UNSLOTH_LLAMA_CPP_BACKEND check case-insensitive to match
setup.ps1, and add tests for the updater CPU preservation and the setup.sh flag
plumbing.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Trim and validate UNSLOTH_LLAMA_CPP_BACKEND, warn on unknown values for PR #7228
Trim surrounding whitespace and lowercase the value in both setup.sh and
setup.ps1, so values like ' cpu ' or 'CPU' still force the CPU-only prebuilt.
An unrecognized value (e.g. 'gpu') now prints a warning instead of silently
falling back to auto. Extend test_setup_llama_cpp_backend.py to cover both
scripts, including trimmed, empty and unknown values.
* Preserve arm64 CPU installs on update and honor CPU override in Windows prune for PR #7228
The update-path CPU preservation only matched install_kind ending in -cpu, so
arm64 CPU bundles (linux-arm64, windows-arm64) were re-routed to a GPU or source
build on update. Match the full set of CPU-only kinds instead.
Persisting install_kind also activated the previously inert Windows
mismatch-prune in setup.ps1: on a GPU host with UNSLOTH_LLAMA_CPP_BACKEND=cpu it
saw the windows-cpu marker as mismatched and deleted it every rerun. Normalize
the override once and make CPU expected so a deliberate CPU install is kept.
Extend the tests to cover both.
* Document legacy llama.cpp markers keep heal-to-GPU on update for PR #7228
Legacy prebuilt markers written before install_kind was persisted intentionally
do not force --cpu-fallback on update: the in-app updater lets them re-resolve
(heal to a GPU bundle) per the existing behavior from #6097, and only markers
that explicitly record a CPU install_kind are pinned to CPU. Add a comment and a
regression case documenting the boundary.
* Tighten llama.cpp CPU-fallback comments for PR #7228
* Fix Windows install-prune to keep valid Intel/fallback bundles for PR #7228
Persisting install_kind activated the setup.ps1 mismatch-prune, whose
expectedKinds was incomplete: the non-NVIDIA/non-AMD branch omitted
windows-vulkan (the Intel auto-route) and the GPU branches omitted the
windows-cpu/windows-arm64 fallback the installer uses when a GPU prebuilt is
missing. That made every setup rerun delete and re-download a valid Intel Vulkan
(or CPU-fallback) install. List all kinds the installer can produce per host so
only a bundle the host cannot run is pruned. Cover the full matrix in tests.
* Persist force_cpu marker flag so only forced CPU installs re-assert on update for PR #7228
* Add --force-cpu for deliberate CPU installs and warn on macOS for PR #7228
* Record force_cpu when reusing a matching CPU bundle for PR #7228
* Accept force_cpu keyword in installer test validator fakes for PR #7228
---------
Co-authored-by: danielhanchen <unslothai@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: persist llama.cpp KV cache across idle auto-unload (slot save/restore)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: address KV persistence review feedback
* Studio: guard KV restore on launch config
* Studio: fix KV resume purge race, fingerprint requested ctx, purge on disable
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: re-check idle/keep-KV settings after slot save, ns file identity
* Studio: shard-aware KV guard, honor user --no-cache-prompt, early save cap
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: honor LLAMA_ARG_CACHE_PROMPT env in slot-save guard
* Studio: derive prompt-cache state from final argv for slot saves
* Studio: stat LoRA/control-vector sidecars in KV restore fingerprint
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: parse csv and FNAME:SCALE sidecar syntax in KV fingerprint
* Studio: address codex review on idle-unload KV resume
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: harden slot-save cleanup, cap accounting, stale-KV guard, save timeout
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: treat unavailable KV estimate as full-cap for slot-save disk check
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: GPU memory dropdown — llama.cpp --fit on and manual gpu-layers/cpu-moe
* Studio: simplify GPU memory changes (reuse ParamSlider, GPU_LAYERS_ALL, loadedGpuMemoryFields helper)
* Studio: GPU picker — choose which GPUs a GGUF model loads on (gpu_ids)
* Studio: simplify GPU picker (share /api/system fetch, validate gpu_ids)
* Studio: GPU picker review fixes (gate relative indices, no cross-model leak, validate, types)
* Studio: group GPU controls under a collapsible GPU section
* Studio: GPU feature review fixes (fix fit-ctx test, behavior-test the floor, comment accuracy)
* Studio: make GPU a top-level settings section (not nested under Model)
* Studio: flatten GPU controls into the Model section, group by GPU/context/generation
* Studio: move GPU Memory to the bottom of Model with its dependent controls beneath it
* Studio: move GPU Memory below Tensor Parallelism and GPUs below GPU Memory
* Studio: tighten GPU Memory and GPU Layers tooltip copy
* Studio: fix fit-mode context slider track-click, restore GPU Memory tooltip, shorten fit dropdown label
* Studio: GPU Memory tooltip one mode per line, briefer
* Studio: note HIP_VISIBLE_DEVICES (ROCm) in the GPUs picker tooltip
* Studio: narrow the GPU Memory dropdown to fit the shortened label
* Studio: use 'llama.cpp --fit' in the GPU Memory tooltip for consistency
* Studio: allow Tensor Parallelism in Manual GPU mode
* Studio: graduated MoE-on-CPU offload (--n-cpu-moe) replacing the all-or-nothing toggle
* Studio: size the MoE-offload slider for staged (deferred-load) models
* Studio: share one GGUF header walk for the context-length and MoE-count readers
* Studio: size the GPU Layers slider for staged models (one staged-header read)
* Studio: move Tensor Parallelism below the GPUs picker
* Studio: GPU split (--tensor-split) per-GPU model share in Manual mode
* Studio: tolerate whitespace in GPU split input, move it below GPU Layers
* Studio: rename the GPU split control to "Split ratio"
* Studio: Split ratio sends explicit even input; fix blank=free-VRAM (not even) copy
* Studio: tighten llama.cpp --fit VRAM margin with --fit-target 512
* Studio: GPU memory review fixes (rollback re-baseline, single-GPU TP gate, accurate copy)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: move Split ratio below MoE Layers on CPU
* Studio: address PR review (fix GPU-info hydration race, share fit context-length across load paths)
* Studio: address codex review (manual single-GPU TP guard, GPU-aware spec defaults in fit/manual, GGUF-only context/preference)
* Studio: address codex review round 2 (gpu_present seed, single-GPU tensor-split guard, staged manual-knob reset, strip inherited offload flags)
* Studio: address codex review round 3 (strip inherited --n-cpu-moe, CPU-fallback warning in Manual mode)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: address codex review round 4 (preserve pinned fit context across a later Apply)
* Studio: address codex review round 5 (honor GPU picker for diffusion GGUFs, clear fit pin on cross-model switch)
* Studio: preserve the pending GPU Memory mode when staging a model
* Studio: pin diffusion GPU device order and reset GPU-memory state for diffusion loads
* Studio: address codex review round 6 (fit-Auto rollback context, preserve manual non-tensor split modes, persist GPU mode on load not select)
* Studio: persist the applied GPU Memory mode, not the requested one (skip diffusion loads)
* Studio: replace Manual-mode split-ratio field with per-GPU layer sliders
* Studio: clarify per-GPU layer split hint for tensor-parallel mode
* Studio: address codex review round 7 (allow GGUF gpu_ids past the legacy guard, replay GPU-memory fields on respawn)
* Studio: address codex review round 8 (size the validate preflight like the load in fit mode, across both load paths)
* Studio: skip the training-OOM guard for llama.cpp --fit GGUF loads (they spill to RAM)
* Studio: drop the now-redundant compare-path validate sizing (the --fit guard skip makes it moot)
* Studio: address codex review round 9 (keep the training guard for fit loads, forward gpu_ids to validate, strip inherited manual tensor-split)
* Studio: address codex review round 10 (gate GPU-memory adoption on is_gguf, record manual knobs only in Manual mode)
* Studio: handle diffusion GGUFs symmetrically in the GPU Memory controls (preserve the standing mode preference, hide the inapplicable mode/TP controls)
* Studio: remember the GPU Memory settings per model
* Studio: consolidate --fit mode and Manual mode into a single Manual mode
* Studio: preserve the per-GPU layer split across GPU Layers changes
* Studio: trim overly long GPU Memory comments
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* address GPU memory config review comments
* trim redundant GPU memory tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Reconcile manual-mode TP drops with the #6659 drop-site invariants
* Preserve quantized KV in manual --fit, charge GGUF companions in full, reconcile GPU pick on load
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* Clear stale GPU baseline on non-GGUF loads so it can't read as dirty
* Fix no-context-shift test for the conditional -c flag
* Credit manual GPU-layer offload for cached HF GGUFs
* Reset per-model load knobs on GGUF quant switch
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* Strip inherited tensor-split when manual ratio is cleared
* Match auto-load validation to safetensors placement
* Reset editable manual knobs after Auto GGUF loads
* Record a single device for diffusion GPU picks
* Reset per-model GPU knobs before applying saved settings
* Address review comments
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* Guard manual tensor splits and keep remembered context on auto-load
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Snapshot compare knobs, seed splits from free VRAM, flag zero-offload loads
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* Exempt CPU-only loads from the guard floor and harden compare and reseed paths
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* Reach full offload from the layers slider and charge extras drafters in the guard
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Warm the GPU device cache before pick reconciles and disable staged GPU controls
* Align the training guard with inherited extras, spec mode, and compare targets
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* Hide GPUs from companion-less zero-offload loads
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Size diffusion picks per device, own manual offload flags, reject XPU picks
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Drop tensor flags at zero layers and exempt CPU-pinned drafters
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Allowlist the zero-layer tensor parallel drop site
* Keep validate and load guards on the same extras and refresh stale baselines
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* Drop mismatched manual tensor splits before launch
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Gate XPU picks on the real backend field and harden split and hydration paths
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Weight full GPUs as zero, clamp split shares, and refine the zero-layer mask gate
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Carry fit context across mode changes and align drafter and picker gates
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Catch variant switches, uncached diffusion repos, and text-only mmproj skips
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Check companions on the first device and size native and remote zero-layer loads
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* Replace the training guard's precise VRAM modeling with a conservative bound
* Baseline context pins on non-GGUF hydration and reprobe list-seeded staged GGUFs
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* Size manual splits by their largest share and preserve resolved context from Default
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Default-deny unsized required companions and price KV at the effective cache dtype
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* Reserve MTP draft KV and MLA target-copy in the training guard
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* Size tensor-parallel loads per device and show GPU controls for native GGUFs
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* Reserve MTP overhead for uncached remote GGUFs and the mmproj runtime factor
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* Drop the training-coexistence VRAM estimation this PR added
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* Gate remembered load settings to GGUF picks
* Lock the remaining load-time controls during a staged load
* Clear the stale native-path token on compare loads
* Drop a stale guard reference from the zero-offload masking comment
* Seed GPU baselines from the rollback response and drop never-emitted offload flags
* Match validate's training guard to load and keep the native reload token
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Trim verbose GPU-memory comments
* Thread the variants header walk off the event loop, honor device pins on zero-offload, and hold staged GPU edits
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* Honor manual placement and classify pinned zero-offload loads
* Close diffusion admission and status hydration gaps
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* Check the actual diffusion GPU during training
* Align staged baselines and manual reload dedupe
* Fix GGUF placement and rollback state
* Harden manual GGUF placement boundaries
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* Remove unused resolve_tensor_parallel import in llama_cpp.py
The name is used only in llama_server_args.py, routes/inference.py, and tests,
not in llama_cpp.py; the unused hoisted import trips the import-hoist verifier
in the source-lint CI job.
* Fix diffusion GPU dedup and training guard for non-numeric device tokens
The diffusion runner drives only its single lowest device and the backend
records that one device (self._gpu_ids = [sorted(gpu_ids)[0]]), but the reload
dedupe compared it against the full requested list, so a multi-GPU pick that
resolves to the same device forced a needless reload. Normalize the request the
same way for a loaded diffusion model in both _already_in_target_state and the
route _request_matches_loaded_settings.
The chat-during-training coexistence guard called int() on the single-device
token and hard-rejected when it could not parse. A non-numeric token (a CUDA
UUID / MIG handle) now sizes against the whole visible pool like the GGUF guard
instead of falsely blocking the load, and an empty token (a CPU-only runner such
as a CPU diffusion GGUF) is allowed outright since it uses no GPU VRAM.
* Tighten comments added by the GPU memory config changes
* Harden GGUF placement from independent review: VRAM sizing, diffusion TP reset, tensor_split validation
- Training coexistence guard: a single-device runner pinned through an
unresolvable UUID/MIG token was sized against the aggregate visible-VRAM pool,
so a load could pass on capacity it cannot use and then OOM active training.
Size against the worst-case visible device (min free) instead, keeping the
guard's documented default-deny contract. The empty-token (CPU-only runner)
allow path is unchanged.
- Diffusion startup: _start_diffusion_server now resets self._tensor_parallel to
False alongside the other placement resets. A prior tensor-parallel chat load
(process killed but not fully unload-reset) otherwise left /status misreporting
tensor parallelism and made an identical diffusion re-Apply reload against the
stale state.
- tensor_split: reject negative / non-finite / all-zero splits up front. They
were dropped at launch but still compared raw in the reload dedupe, so an
identical Apply reloaded indefinitely.
- Tests: the shared httpx stub was incomplete and, installed via setdefault
before real httpx loaded, broke a combined pytest run (collection errors on
httpx.Response). Import the real installed httpx instead.
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---------
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Co-authored-by: danielhanchen <unslothshared@gmail.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Studio: hide infra models from the hub cached inventory
The hub inventory scans behind /api/hub/cached-gguf and /api/hub/cached-models
returned the llama.cpp install validation probe (ggml-org/models) and the RAG
embedder (unsloth/bge-small-en-v1.5[-GGUF]) as on-device models. Share the
hidden-model check from routes/models.py via utils/models/hidden_models.py and
apply it in both scans. A GGUF infra repo stays visible when the user
explicitly downloaded a variant through the Hub, since variant manifests only
exist for user-initiated downloads.
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* Studio: make On Device trust the hub inventory, match repo ids exactly, lighten the hidden-model import
Follow-up on the hub cached-inventory hidden-model change, addressing the review.
On Device now trusts the Hub inventory API for cached rows. The backend already
hides the RAG embedder and the llama.cpp probe and re-includes a GGUF infra repo
once the user downloads a variant through the Hub, but the frontend was
re-hiding it by repo id, so the user-downloaded variant never appeared in the On
Device list or the count. isVisibleInventoryRow now short-circuits cached rows
(kind === "cache") to visible and keeps client-side needle hiding only for local
filesystem rows and Discover.
is_hidden_model matches Hub repo ids exactly (case-insensitive) against the probe
plus the effective embedder and its GGUF companion, instead of substring
matching the configured-embedder basename. A custom embedder with a generic
basename like org/model no longer hides unrelated cached repos such as
user/model-chat or org/model-instruct. The probe filename and local-path
embedders keep exact matching.
The helper moves to utils/hidden_models.py and is imported at module scope in the
hub cache scanner, so it no longer pulls in utils/models/__init__ (the eager
model-config/checkpoint stack) and a broken import fails at startup instead of
being swallowed per-repo and silently emptying the inventory. routes.models
keeps the _is_hidden_model and _safe_resolve aliases and drops the unused
_HF_REPO_ID_RE re-export that was failing source lint.
Tests: exact repo-id matching with a custom embedder, the cached-models scan
keeping an unrelated repo, and a clean-interpreter check that the helper imports
without the model-config stack.
* Studio: match the llama.cpp probe filename on both path separators
The hidden-model check compared the probe's on-disk filename with
Path(value).name, which on a POSIX interpreter does not split a Windows-style
path ("...\stories260K.gguf") and would let the probe through. Split on both
separators so the probe is matched regardless of which OS produced the path,
matching the tolerance of the previous substring check. Adds a Windows-path
assertion to the probe test.
* Studio: harden hidden infra model handling
* Fix hidden cache row confirmation
* Fix hidden local rows and confirmed hint merges
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* Handle snapshot-configured hidden models
* Hide basename-only default embedders
* Fix dynamic embedder inventory filtering
* Studio: hide the configured RAG embedder from Discover and feed rows
---------
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* Replace standalone Studio wording with Unsloth
Replace the single word Studio with Unsloth wherever it is used as
shorthand for Unsloth Studio in docs, CLI output, UI strings, i18n
locales, workflow display names, comments and docstrings.
Kept unchanged: the full name Unsloth Studio, third party product
names (LM Studio, Visual Studio, Mac Studio), feature names
(Recipe Studio, Fine-tuning Studio and its translations), and all
identifiers such as env vars, commands, paths and filenames.
* Address review feedback on the Studio wording rename
Use "an" before Unsloth where the rename left the article as "a".
Restore the split brand where Unsloth and Studio render as two halves
of the full product name: the onboarding sidebar subtitle and the
IPv6 localhost warning. Scope two messages to the full name Unsloth
Studio where plain Unsloth was misleading: the AMD README bullet and
the CLI studio setup error.
* Studio: enforce 60s minimum on idle auto-unload TTL (0 stays off)
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* Drop decorative section separator from idle TTL floor tests
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* refactor(studio): move chat model picker into features/model-picker
Relocate model-selector + its support files from components/assistant-ui
into a self-contained features/model-picker feature (own barrel), mirroring
the modular Hub layout. Pure move + import repoint; no behaviour change.
* feat(model-picker): add per-model config persistence layer
Superset PerModelConfig (customContextLength, kvCacheDtype, speculativeType,
specDraftNMax, tensorParallel, chatTemplateOverride, trustRemoteCode) persisted
to localStorage (unsloth_model_configs) with schema versioning + LRU budget.
KV-dtype and speculative value sets match main's sidebar (no q4_0/ngram-simple).
Reuses features/hub/lib/model-identity for normalization; adds storage-key layer
and applyPerModelConfigToRuntime (sets tensorParallel, which the old PR omitted).
* feat(picker): modular backend for chat-template validate + default fetch
New studio/backend/picker package (schemas/service/routes) mounted at /api/picker:
- POST /api/picker/validate-chat-template (Jinja syntax validation, no false positives)
- GET /api/picker/chat-template/{model_name} (default template from tokenizer_config.json,
reusing get_cache_path/resolve_cached_repo_id_case; graceful null, no model-code exec)
Frontend api/templates.ts client + hooks/use-model-defaults lazy cache. No backend
changes to the existing inference load route (per-model load fields already supported).
* feat(model-picker): bind picker on-device list to shared hub inventory
Picker now sources cached + local models from useHubInventory (the Hub's shared
store) via a thin adapter, replacing its own /api/models/* fetchers + module
caches. Hub, download manager, and picker now share one source of truth, so
completed downloads reflect in the picker automatically. Partial/live-download
rows are filtered from the cached lists (unchanged rendering). Local naming/search
preserved via additive LocalInventoryRow modelId/displayName. Variant expander,
scan-folder management, recommended-fit, search, external providers untouched.
Known minor: cached 'Downloaded date' sort tiebreak degrades to alphabetical
(hub cached rows carry no mtime); default 'recent' (load-time) sort preserved.
* feat(model-picker): per-model config step inside the picker
Picking a (non-external) model now opens an in-picker config view built from
main's current load controls (context length, KV cache dtype, speculative
decoding, draft tokens, tensor parallel) plus a chat-template editor backed by
the picker validate/default endpoints. 'Remember for this model' persists the
config per model+variant; Run forwards the config to the existing load flow via
meta.config. External models bypass the step. Two-view orchestration lives in
model-selector (single interception point); pickers.tsx call sites untouched.
trustRemoteCode dropped from PerModelConfig to preserve main's per-load consent.
* feat(chat): apply/persist per-model config through the load flow
handleCheckpointChange threads meta.config into the selection; stageOrLoad and
the autoload/Hub-run paths now apply the picker config (explicit pick or saved
remembered config) via applyPerModelConfigToRuntime before staging/loading, with
keepSpeculative set so a remembered speculative mode survives the model switch.
Replaces the old remembered-load-settings seeding (resolveInitialConfig now the
single source). SelectedModelInput carries config.
* refactor(chat): remove per-model load config from the right sidebar
The load knobs (context, KV cache, speculative, draft tokens, tensor parallel)
and the chat-template editor now live only in the picker config step. The sheet's
Model section keeps the staged Load/Cancel flow (config is applied at pick time);
sampling params, system prompt, and RAG are unchanged. Deletes the superseded
remembered-load-settings module + the store's applyRememberedLoadSettings action,
removes the now-dead sheet state/imports, and points the settings reset at
unsloth_model_configs. Delete-cleanup deferred (stale config is LRU-capped).
* fix(model-picker): remove leftover sidebar-staging cogwheel + empty Model section
The downloaded-variant gear (ModelLoadSettingsAction) staged a model straight
into the right-sidebar Run-settings flow -- the old 'configure before load' path
now fully replaced by the in-picker config step. Removed the gear + its component.
Also gate the sheet's 'Model' section to staged picks only (pendingSelection):
after the load-knob strip its content is staged-only, so it was rendering an
empty section header whenever a model was merely loaded.
* chore(chat): remove dead per-model-config setters + modelControlsDisabled
After the load-config UI moved into the picker, the store's per-model setters
(setKvCacheDtype/setSpeculativeType/setSpecDraftNMax/setTensorParallel/
setCustomContextLength/setChatTemplateOverride) had zero callers
(applyPerModelConfigToRuntime writes via setState), and the sheet's
modelControlsDisabled was unreferenced. Verified dead across the whole tree.
* fix(chat): config-step Load actually loads (ignore Load-on-selection)
Root cause: with Settings > Chat > 'Load on selection' turned OFF, the config
step's load went down the deferred-staging path -- opening the right sidebar with
'<model> is staged, not loaded yet / Choose Load model'. The in-picker config step
IS the deliberate load action, so its Load now loads immediately (or downloads +
auto-loads when not cached) regardless of the toggle. Renamed the button
'Run model' -> 'Load model' to match. Native/dropped picks still honor the toggle.
* refactor(chat,hub): retire 'Load on selection' — config step is the only load flow
The in-picker config step (and the Hub Run button) now fully supersede the old
stage-to-sidebar flow, so the Load-on-selection toggle is removed everywhere:
- chat stageOrLoad: every pick loads immediately, or downloads + auto-loads when
not cached (the previous default behaviour, now universal).
- hub Run: drops the stage branch; downloaded GGUFs load directly with their saved
per-model config (no collision with the chat config step — both end at selectModel).
- store: removed loadOnSelection field/setter/key/default; Settings>Chat toggle and
its settings-reset entry removed.
- staged sidebar section is now a download-progress view (auto-loads on completion).
No manual staging remains; stageModel is used only for background auto-load downloads.
* feat(model-picker): default chat template from GGUF + thread variant through config flow
Read the embedded tokenizer.chat_template from GGUF files (read_gguf_chat_template
in gguf_metadata) and use it as the per-model default. Plumb gguf_variant through
the picker service, /api/picker/chat-template route, frontend templates API, and
use-model-defaults so the right variant's template is fetched.
Also refine the picker config-page/model-selector wiring, drop the dead
ggufNativeContextLength runtime path, and add the per-model-config storage keys to
the settings prefs export.
* feat(model-picker): read safetensors chat template + hide editor where it has no effect
Resolve the default chat template for safetensors models: prefer the modern
chat_template.jinja, fall back to the tokenizer_config.json chat_template field,
then chat_template.json (multimodal processor), then the GGUF embedded template.
Applied to local dirs, the HF cache snapshot scan, and the HF remote fetch.
Hide the chat-template editor in the picker for safetensors models — the override
is only applied at load by the GGUF/llama.cpp backend, so editing it on safetensors
currently has no effect. GGUF keeps the editor. Nothing removed; the dialog stays
for when the safetensors apply path is wired up in a later branch.
* fix(model-picker): set legacy-migration flag only after the write succeeds
Set unsloth_model_configs_migrated only once writeMap confirms the migrated
map persisted, so a quota/storage failure no longer marks migration done and
silently drops the user's pre-existing remembered settings — the next load retries.
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* MVP model picker fixes
* MVP picker config fix
* MVP safetensors config
* MVP max seq config
* MVP max seq fix
* Fix static max tokens cap ignoring model context
* Fix picker GGUF scan parity
* fix(studio): harden model picker config loading
Apply remembered per-model configs consistently from picker and Hub loads, keep default configs from overriding standing speculative settings, add config access for direct local GGUF files, and support saving or forgetting active model settings without a reload.
* Fix model picker config flow
* Fix model picker config loads
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Avoid recursive per-model config migration reads
* Apply the displayed context length when loading a GGUF
* Fix template validation, cached template lookup, and failed load rollback
- Validate chat templates with the loopcontrols extension so templates
that use break or continue tags pass the picker validator, matching the
inference renderer that already accepts them.
- Read the default chat template from the newest cache snapshot rather than
an arbitrary iterdir order, so an older cached revision no longer prefills
a stale template.
- Capture the runtime per-model config before a load and reapply it when the
load fails, so a failed switch leaves the active model context, KV cache,
template, and speculative settings as they were.
* Make chat template view only for safetensors models
Custom chat template overrides are applied at inference only for GGUF
models, which pass the template to llama-server. The safetensors backend
renders with the model built-in template and ignores the override, so
editing it would save a value that never loads. For safetensors the
config page now opens the template as a read-only preview with a note
that editing is not available yet. This can become editable once
inference support for custom safetensors templates lands in main.
* Fix model picker config edge cases
- Restore prior runtime config when a load no-ops for the active model
- Cap the picker validator request body via the protected prefixes
- Keep the GGUF context slider max above the loaded context
- Fetch subfolder chat templates for uncached Hub repos
- Show the compare side config when reopening the picker
* Keep saved GGUF context above the fallback ceiling
* Show the model config in the run settings sidebar
* Fix model config sidebar reset and context slider
- Stack the remember toggle and action buttons in the sidebar
- Reset the config to defaults instead of the loaded values
- Fetch the native context so the slider max is not the loaded value
* Fix model picker config and download regressions
- Run picker chat template routes off the event loop
- Depth and root guard local template directory scans
- Restore download manager flow for uncached hub picks
- Apply per model context length on reload
- Import model picker symbols from the feature barrel
* Fix model picker config and cached download sorting
- Restore load settings when a Hub run is rejected mid load
- Reuse one NumericValueInput instead of a duplicate copy
- Fix double decode of the model name in the template route
- Remove the unused reset-to-loaded settings action
- Fix cached model download sorting
* Fix model picker per-model config edge cases
Honor a saved or typed max seq length above the model's native context so
RoPE extended values are no longer clamped and silently overwritten. Allow
typing past native while the slider keeps native as a soft ceiling.
Guard the fetch success paths in use-model-defaults against an aborted
signal, and refetch when the HF token changes.
Hash the chat template content in the sidebar remount key instead of its
length. Enable reset for a GGUF whose native context is unknown, and floor
the context slider max so it can never fall below the min.
* Fix GGUF context auto-fit and gated model config token
Stop forcing a 32768 context when a GGUF native context is unknown so the backend auto-fits to VRAM again, while still honoring an explicit context edit.
Send the HF token as a query param so gated safetensors models resolve their max position embeddings.
Derive model default state during render to drop the set-state-in-effect calls.
* Fix native GGUF context ceiling and guard picker template reads
Restore the native context store field so the sidebar slider keeps the
full ceiling for drag and drop GGUFs. Limit local chat template reads to
the browse allowlist, skip malformed repo ids, and drop unused model
picker exports.
* Fix model picker lint boundaries
* Fix model picker review findings
Chat template editor never seeded its draft. Radix only calls onOpenChange
from internal events, so the seed in the nextOpen branch was dead and a model
with a saved override opened empty. Saving then cleared the override. Drop the
dead branch, treat draft as an untouched sentinel, and reset it on every close.
Uncached Hub picks could auto load a model after the user left the chat. Main
detached the staged pick on route exit and on chat context change. Carry the
context key on the pending pick and skip the load when it no longer matches.
Also clear configTarget when the picker closes, restore the onUpdated ref so
variant rows stop resubscribing on every parent render, skip the LRU write when
the entry is already most recent, import NumericValueInput relatively, and drop
the unused ModelUpdateAction barrel export.
* Preserve GGUF context on active reload
* Fix model picker per-model config regressions
- Stop reloading the already loaded model on re-pick
- Hide infra models from the chat picker
- Detect vision support on cached GGUF repos
- Honor saved maxSeqLength on auto load
- Restore default chat template for local GGUFs
- Warn on save failure and revert config on cancel
- Refetch picker inventory on open
- Persist read only per model config safely
* Fix stale model auto load
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* Fix model picker numeric input sizing and constraints
Size value inputs to their content so long context lengths are not clipped,
restrict them to numeric characters, and stop the speculative decoding label
from truncating in the sidebar.
* Fix picker CI tests and harden chat template resolution for PR #6647
- tests: point the descender guard at the moved model-selector.tsx path
- tests: exclude the disabled Reload model button from the regenerate locator so .first targets the real Regenerate
- picker/service.py: reject symlinked template/gguf leaves that resolve outside the browse allowlist (HF cache reads unchanged)
- compare mode: resolve each pane's own remembered chat template instead of inheriting the other pane's from the store
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* Protect future-schema per-model configs from deletion for PR #6647
savePerModelConfig already refuses to overwrite a stored config whose schema version is newer than this client understands, but deletePerModelConfig did not. Unchecking Remember on an older client therefore silently destroyed a newer client's saved config. Apply the same guard on delete and surface the blocked case through the existing saveFailed toast.
* Protect future-schema per-model configs from quota eviction for PR #6647
The save and delete guards already refuse to touch a stored config whose schema version is newer than this client understands, but the quota-eviction path did not, so a full store on an older client could still evict a newer client's config. Skip future-schema entries when evicting and fail the save if the budget cannot be met without them.
* Fix GGUF context persistence, compare context, and rollback settings for PR #6647
Persist a GGUF context override from the user's intent instead of collapsing it against the loaded context, which reintroduced the context-reset (f4838782cb reverted the native-baseline fix). model-config-page now collapses the saved value against native, and use-chat-model-runtime and chat-adapter retain the requested context on load so re-saving another setting keeps the override; a null request stays null so a VRAM auto-fit never becomes a stored override.
shared-composer: a compare pane with no explicit GGUF context now loads at native (0) like single-view, not the session maxSeqLength that silently shrank the shown context.
use-chat-model-runtime: restore the previous model's KV cache dtype and chat template on a failed-load rollback so it runs as it was, not with backend defaults.
* Preserve native path token when reloading the active model for PR #6647
handleReloadActiveModel rebuilt the selection without the store's activeNativePathToken, so reloading a file-picked GGUF after a settings change validated the display label as a repo/path and failed. Thread the active native token through the reload selection so native-loaded models reopen correctly.
* Make picker template validation resilient and accept HF generation tags for PR #6647
Import Jinja lazily inside validate_chat_template so a backend without the optional jinja2 package (GGUF-only installs) still starts instead of raising ModuleNotFoundError at import time. Register a no-op extension for the Transformers {% generation %} assistant-mask tag so pasting a valid HF chat template validates, matching the renderer, rather than being rejected as an unknown tag.
* Honor remembered compare config and parse processor chat_template.json for PR #6647
* Fix failed-load rollback context and processor template map fallback for PR #6647
* Restore speculative decoding config on failed-switch rollback
When a model switch fails after the previous model was unloaded, the
rollback reload restored tensor_parallel, KV cache dtype and the chat
template override, but omitted speculative_type and spec_draft_n_max and
cleared their loaded shadows to null. The previous model therefore came
back running at backend defaults (speculation off) while the UI still
showed it enabled, and the status resync confirmed the off state. Resend
the previous model's speculative settings in the rollback load and keep
the store's active and loaded speculative fields in sync with them.
* Reset max sequence length when a model has no saved config
applyPerModelConfigToRuntime reset every per-model field except
maxSeqLength, which it only wrote when the incoming config had one.
maxSeqLength is the sole field carried on store.params, so selecting a
model with no remembered config left the previous model's value in place
and later loaded the new model at that leaked length. Fall back to the
standing default so an unremembered model loads at its own default.
* Surface a message when a variant update cannot start
startManagedUpdate handled the conflict and error start outcomes but let
busy fall through as if the update began, so the confirm dialog closed
with no job created and the cached variant stayed stale. Show an info
message when the repo is busy with a sibling transfer so the click is
not silently dropped.
* Keep per-model speculative choices out of the global default
A staged load with a per-model or one-off config sets keepSpeculative,
which already skips reading the global speculative preference. The
matching save still ran unconditionally, so the model-specific choice was
written to the global unsloth_chat_speculative_type and a later model with
no saved config started from it instead of Auto. Skip saveSpeculativeType
when keepSpeculative so the per-model choice stays isolated.
* Seed non-active model settings from the app default max length
The Run settings page captured initialMaxSeqLength from the loaded
model's runtime params and fell back to it for a model with no saved
config. Opening settings for a different, unloaded model and clicking
Load then sent the active model's context (for example 64k) instead of
the 4096 default, risking validation failures or OOMs. Seed the default
for non-active models and keep the runtime value only for the active one.
* Prefer sidecar tokenizer chat template over the GGUF copy for variants
_chat_template_from_dir returned the embedded GGUF template first when a
variant was selected, reversing the tokenizer-first precedence of the
no-variant path. A model whose chat_template.jinja or tokenizer_config.json
supersedes a stale embedded template then got the wrong template on
variant selection. Keep tokenizer files first regardless of variant; the
variant only picks which GGUF is the fallback. Adds regression tests for
both the tokenizer-wins and gguf-fallback cases.
* Keep per-model speculative choices load-local in autoload and compare
The interactive load path treats a per-model speculative choice as
load-local and skips writing it to the global default. Autoload and
generalized compare still called saveSpeculativeType unconditionally, so a
remembered off or ngram setting leaked into unsloth_chat_speculative_type
and later models with no saved config inherited it. Persist the global
preference only when the value came from the global settings.
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* Studio: record the compare pane's loaded context in runtime state so the active model's settings and any reload or save use it, not the previous context
* Studio: notify the user when a Hub autoload can't start because another download for the model is already running, instead of silently dropping it
* Studio: drop the merge's orphaned staged-model store helpers and unused alert imports
The main merge left isPendingGguf and pendingSelectionMatches referencing the
removed PendingModelSelection type, and the alert-dialog/alert imports unused
after the permission-mode dropdown replaced the bypass dialog, so tsc -b failed.
* Studio: cache a null default chat template so the viewer stops re-fetching it
A model with no sidecar or embedded template resolves to a terminal null, but
that result was never cached, so reopening the template viewer re-ran the
backend and Hugging Face lookup every time.
* Studio: detect direct-file GGUFs in run settings so Max Tokens uses their context
A GGUF loaded from a local file or custom folder has no variant label, so the
run-settings panel treated it as non-GGUF and clamped Max Tokens to the session
max_seq_length instead of the loaded GGUF context. Detect it via the reported
GGUF context and the .gguf checkpoint suffix, matching the chat page.
* Studio: prompt to re-select a local model file when its lease expired before reload
A file-picked GGUF is reachable only through a native path token that the
desktop host prunes after a TTL. Reloading reused that token blindly, so a
reload long after the initial load failed with an opaque error. Track the
token's expiry and, when it has passed, ask the user to re-select the file
instead of attempting a doomed reload.
* Fix descender-clipping test to tolerate sidebar layout utilities
The sidebar account-block div carries layout utilities (min-w-0, flex-1)
between 'flex' and 'flex-col', so the descender-clipping guard's regex,
which required 'flex' immediately followed by 'flex-col', no longer matched
and the test failed to locate the account-block div. Generalize the prefix
to allow intervening flex utilities while still capturing the leading-*
class before the collapsible visibility utility and asserting leading-tight,
so the guard against clipped glyph descenders is fully preserved.
* Harden picker chat-template resolution
Enforce the 64 KiB chat-template contract at the validate endpoint's request
model so a direct caller cannot submit a template far larger than the frontend
allows (MaxBodyMiddleware only bounds the whole request body, not this field);
oversized templates now return a clean 422.
Apply sidecar-over-GGUF template precedence globally across cached snapshots
instead of per snapshot. A repo with multiple cached revisions previously
returned the first snapshot's template, so a newer GGUF-only revision could
win over an older revision's maintained chat_template.jinja sidecar, which
contradicted the documented intent that sidecars supersede the embedded copy.
* Guard per-model config against future-schema and lossy migration
Two forward-compatibility gaps in the versioned per-model config store:
- The load/apply path returned and normalized a stored record without checking
its schema version, so a record written by a newer client was reinterpreted
under the current schema and applied to a live model load, even though save,
delete and eviction all refuse to touch future-schema records. Reject
future-schema records on load too.
- The one-time legacy migration enforced the storage budget without protecting
the entries it had just migrated and set the completion flag unconditionally.
When storage was already full of future-schema records (which are unevictable
by an older client), the migrated entries were the only evictable ones and
could be dropped while migration was still marked complete. Protect the
migrated keys during eviction and only mark migration complete when they
survive, so it retries once space frees up.
* Discard chat-template validation results after the dialog closes
Server-side template validation is async, but closing or cancelling the editor
did not abort it, so a late-arriving valid response still called onSave and
applied a template the user had already dismissed. Track a validation token
that is bumped on close and ignore any validation result whose token is stale.
* Record native lease expiry when loading a picked GGUF from the chip
The pending-native-model chip loaded via stageOrLoad directly, bypassing
loadNativeModelIntent, so activeNativePathExpiresAtMs was never recorded for a
chip-loaded file. A later reload then either skipped the lease-expiry guard
entirely (expiry left null) or compared against a previously loaded file's
stale expiry, so reload could reuse an already-pruned token or wrongly block a
still-valid one. Route the chip through loadNativeModelIntent, which builds the
same selection and records the expiry.
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* Prefer sidecar template for a directly selected local GGUF file
A direct .gguf file path read its embedded chat template without checking the
parent directory for a maintained sidecar (chat_template.jinja /
tokenizer_config.json), while directory and variant selections already prefer
the sidecar. That let the config editor preview or save a stale embedded
template for the same model depending on how it was selected. Check the parent
directory sidecars first, then fall back to the embedded copy, and cover both
paths with tests.
* Resolve cached chat template per revision, newest first
The earlier change searched every cached snapshot for a sidecar before
considering any snapshot's embedded GGUF template, which let an obsolete sidecar
from an older revision override the newest revision's template. Restore
per-snapshot resolution (newest first): a revision's sidecar still supersedes
its own embedded GGUF copy, but a newer revision is no longer overridden by an
older revision's sidecar.
* Preserve autoload transport conflicts and surface background busy downloads
- When a Hub autoload hits a transport conflict, keep pendingHubAutoLoad bound
instead of clearing it. Clearing it re-keyed the download surface and its
cleanup cancelled the conflict the toast tells the user to resolve, so the
Hub resume affordance was gone the moment it appeared. Return early on
conflict, mirroring the started branch, so resolving it from the Hub still
auto-loads on completion.
- The background-download branch handled started and conflict but silently
dropped a busy outcome, leaving the user with no feedback when a peer variant
of the same repo was already downloading. Surface the same busy toast the
autoload path uses.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* Studio: exclude /api/export/status from request access logs
The frontend polls /api/export/status every 5s to detect export start, so it
fires continuously even when idle. Each poll emitted an info request_completed
access line, making up most of the server access logs. Add it to _EXCLUDED_PATHS
alongside /api/train/status. The endpoint is unchanged; export state is still
logged by the export modules and streamed over SSE, so no signal is lost.
* Studio: collapse hub download-progress polls in the access log
download-status and gguf-download-progress (plus the dataset equivalents)
are polled about twice a second for the whole download, so each emitted an
info request_completed line. Add them to _QUIET_POLL_PATHS so they collapse
to one heartbeat line per 10s instead of one per poll.
* Studio: log hub download progress at 10% steps
The access log carried no real progress, only poll pings. Emit one
hub_download_progress line per 10% step from the shared snapshot progress
reader, so an active download shows actual percentage without a line per
poll. Throttled per job and resynced if the same download restarts.
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* Studio: drop successful chat thread/project CRUD from the access log
A single chat turn fans out about twenty requests under /api/chat/threads
and /api/chat/projects (list, fetch, per-message forks, and the message
writes) that only reflect the UI re-rendering. Suppress their 2xx access
line so the log keeps the signal (generation, tool calls, code execution,
engine stats) and errors. Non-2xx on these paths still log.
* Studio: silence transformers torch_dtype deprecation warning
transformers logs "`torch_dtype` is deprecated! Use `dtype` instead!" once at
model-config load via logger.warning_once (logging, not warnings), so a warnings
filter cannot catch it. Attach a small logging.Filter in setup_logging, which
runs before any model config is parsed, to drop that record on the transformers
loggers that emit it.
* Studio: quiet inference load-progress polls and log throttled load progress
The frontend polls /api/inference/load-progress about twice a second for the
whole model load, so each emitted a request_completed line. Add it to
_QUIET_POLL_PATHS (heartbeat) and emit one inference_load_progress line per 10%
step from the load-progress route, so a load shows real percentage instead of a
line per poll.
* Studio: fully suppress download/load progress poll access lines
The download-status, download-progress, gguf-download-progress, active-downloads
and transport-status polls (model and dataset), plus inference load-progress,
fire ~2x/s for the whole download or load. Their progress is now reported by the
hub_download_progress / inference_load_progress events (and the viewer's progress
line), so the per-poll access line adds nothing. Drop it on 2xx and keep it on
errors, instead of the prior 10s heartbeat. Chat CRUD suppression is folded into
the same _is_quiet_success helper.
* Studio: suppress training-tab model/dataset download-progress polls
The training tab polls /api/models/download-progress and
/api/datasets/download-progress about twice a second for the whole prep phase.
These are separate routes from the /api/hub equivalents and only scan the cache,
so their 2xx access line adds nothing (on Windows they always read 0 since the
bytes live in snapshots/, not blobs/). Suppress the 2xx line and keep errors,
alongside /api/models/gguf-download-progress.
* Studio: drop transient pre-auth 401 on chat thread/project polls
On first load the SPA fires chat thread/project GETs before the initial token
refresh, so they 401 until /api/auth/refresh runs and the retries succeed. That
pre-auth 401 is a bootstrap artifact, not an error; suppress it alongside the
already-quiet 2xx line. Genuine 4xx/5xx on these paths, the download/load poll
401s, and all /api/auth/* still log.
* Studio: quiet tab-switch list polls and per-poll scan/reconnect logs
Switching between the Train, Export, and Chat tabs refetches list endpoints on a
timer, and each hit re-logs internal detail. Heartbeat /api/train/runs,
/api/models/checkpoints, /api/models/local and /api/rag/knowledge-bases (10s
window, first hit and errors still log), and downgrade two per-poll INFO lines to
debug: the checkpoints scan summary ("Found N training runs") and the
per-reconnect SSE resume line. The meaningful "replayed N missed steps" line,
logged only when steps were actually replayed, stays at info.
* Studio: enable tokenizer parallelism for dataset prep on Windows/macOS
TOKENIZERS_PARALLELISM was forced off everywhere to stop datasets' forked map()
workers from deadlocking, but that fork only happens on Linux. On spawn platforms
(Windows/macOS) dataset.map() runs in-process (dataset_map_num_proc returns None),
so disabling tokenizer parallelism leaves the fast tokenizer single-threaded and
dataset prep runs serially on one core. Keep it off on Linux (fork safety) and on
for spawn platforms, where there is no fork to deadlock. Measured ~7x faster
tokenization (12.5s -> 1.7s for 20k rows on a 32-core Windows box).
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* Studio: log throttled training status to the server log
Training step/loss/epoch only went to the UI via SSE, so the server log showed
inference engine_stats and train/runs heartbeats but nothing about the actual
run. Emit one throttled training_progress line (step/total, percent, loss, epoch,
eta) from the CUDA event pump: the first step, then at most every 30s, plus the
final step, resyncing when a new run restarts the counter. Per-step UI streaming
is unchanged.
* Studio: quiet llama.cpp update-status polls and log throttled update progress
The prebuilt llama.cpp update polls /api/llama/update-status about twice a second
for the whole download and install. Suppress its 2xx access line (errors still
log) and emit one throttled llama_update_progress line per 10% step from the
status route, so the update shows progress without a line per poll. The existing
"llama update: installing" and "llama update: success" events still bracket it.
* Studio: quiet the export log-tail poll
The Export tab polls /api/export/logs about once a second to stream the export
subprocess output into the UI panel. Suppress its 2xx access line; the real
progress is already logged as event-driven "Export subprocess status: <phase>"
lines plus the subprocess start and checkpoint-loaded events, and errors still log.
* studio: keep errors and mutations visible in access-log suppression
Make the quiet-success access-log suppression GET-only so chat thread/project
mutations (POST/PUT/DELETE) still log; only their list-poll 2xx and the
transient pre-auth 401 are dropped.
Suppress /api/export/status 2xx only (move it out of the all-status exclude
set) so a 401/403/500 on it stays visible.
Legacy /api/models and /api/datasets download-progress polls emit no
hub_download_progress events, so heartbeat them via the 10s quiet-poll window
instead of suppressing outright, keeping download visibility (notably on
Linux). The event-emitting /api/hub download polls stay fully suppressed.
Update and extend the middleware tests to cover GET-only suppression, the
export-status error path, and the legacy download heartbeat.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: tighten access-log and training-progress comments
Comment-only pass: collapse the multi-line explanations in the logging
middleware and the throttled training-progress logger to fewer lines while
keeping the rationale. No behavior change.
* studio: log structured export_progress phases
Emit a structured export_progress event per phase (consolidated in the server
log like training and download progress) instead of a plain status string, and
add a phase milestone at the start of the heavy export step so the
merge/save/convert is visible in the server log, not only in the forwarded
stdout panel.
* Studio: reset training-progress log throttle on each new run
start_training rebuilds the per-run progress state but left _last_progress_log_ts/_last_progress_log_step at their prior values. A run started within 30s of a previous one whose last logged step matched the new run's first step would hit the step == prev short-circuit and drop the promised first training_progress line, then stay suppressed until the old 30s window expired. Reset both fields when a new job is accepted.
* Studio: keep post-bootstrap chat 401s visible in the access log
The chat thread/project 401 suppression dropped every GET 401 on those prefixes, so a genuine expired-session 401 vanished alongside the transient pre-auth race. Gate the 401 drop on a per-middleware bootstrap latch that flips once /api/auth/refresh first succeeds: before that the 401s are the pre-refresh race and are suppressed; after it any chat 401 is a real failure and logs. Add a test for the post-refresh case.
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* Studio: limit chat access-log suppression to the exact list polls
The chat thread/project suppression matched by startswith, so it also dropped the 2xx access line for detail and message reads (/threads/{id}, /threads/{id}/messages, /threads/{id}/messages/{id}, /projects/{id}) that are not the high-frequency list polls, losing their access and latency logging. Match the two list paths exactly instead, so only the intended list polls (and their pre-auth 401 race) are suppressed while detail and message reads keep their access line. Add a regression test.
* Studio: reset inference load-progress throttle for each load
The load-progress throttle (_last_load_progress_step) is a module global that persisted across loads, so a cached or small load whose first sampled /api/inference/load-progress response already reported fraction=1.0 hit step == prev (10) from a prior completed load and emitted no inference_load_progress line, while that endpoint's access log is suppressed, leaving the new load with no progress signal. Arm the throttle at load initiation in _load_model_impl so each load's first step always logs. Add a regression test.
* Studio: tighten logging comments
Collapse a few verbose comments (tokenizer-parallelism note, torch_dtype filter, legacy download-poll heartbeat, chat list-path suppression) to fewer lines without changing intent or code.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: offer the latest transformers release for brand-new architectures
When a model's config.json model_type is absent from every installed
transformers overlay (base 4.57.x and the .venv_t5_530/550/510 sidecars),
Studio now checks, unauthenticated and cached, whether the newest
transformers ships it:
- utils/transformers_latest.py fetches the latest release version from
https://pypi.org/pypi/transformers/json and the CONFIG_MAPPING_NAMES
sources for that tag and for main from raw.githubusercontent.com
(never api.github.com), parsing them with the same AST extractor the
static router uses (no code execution, no trust_remote_code). Results
are cached in memory and in a JSON snapshot under studio_root()/cache
with a one day ttl; fetches are bounded to 5s with one retry and a
failure backoff, and offline mode or the new kill switch
UNSLOTH_STUDIO_NO_LATEST_TRANSFORMERS=1 short-circuits to None.
- POST /api/inference/validate gains requires_transformers_upgrade plus
a transformers_upgrade payload (model_type, pypi_version,
supported_in_pypi, supported_in_main) so the frontend can raise the
install consent dialog before /load, mirroring the existing
remote-code consent flow. The check fires only when the model_type is
unknown to all installed overlays and the hardcoded tier tables.
- POST /api/inference/install-latest-transformers provisions a new
persistent .venv_t5_latest sidecar after user consent, pinned to the
exact PyPI version (re-verified server-side) with the same
--target/--no-deps recipe as the fixed sidecars. A JSON pin marker
inside the dir records the installed package set, so restarts
revalidate it and routing resolves the new highest-ranked tier
automatically. A dependency preflight (compat_plan) compares the
release's requires_dist against the running env: unsatisfied
tokenizers/safetensors floors are shadow-installed as exact pins into
the sidecar, anything else unsatisfied blocks the install with a
clear message.
Routing for every already-supported model_type is unchanged: the
hardcoded lists and the 530/550/510 static resolver run first, the new
tier only participates once its venv exists, and the probe order gains
the latest sidecar only when provisioned. Verified against live PyPI
and GitHub (transformers 5.13.0: 674 model_types, 26 absent from all
installed overlays, e.g. cosmos3_omni; 4 dev-only on main) and with a
real sidecar install plus restart persistence. 64 new tests; the
existing 200-test transformers_version suite passes unchanged.
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* Latest-transformers check: fetch outside the lock, serialize installs
Release the module lock during the network refresh so a slow fetch cannot
stall other threads in the ASGI pool; concurrent callers during a fetch get
None (the graceful fallthrough) via an in-flight flag instead of stacking
fetches. Serialize install_latest_transformers with an in-progress flag so
concurrent consents cannot race the sidecar delete and recreate; the loser
gets a structured already-in-progress refusal.
* Latest-transformers check: LoRA bases, pin-gated mapping, live reverify
Run the upgrade check over the [adapter, base] target set so a LoRA whose
base model is a brand-new architecture surfaces the prompt (the worker
activates transformers for the base, not the adapter).
Gate the latest overlay's mapping lookup on a valid pin marker, matching
activation and the probe order, so a partial or manual .venv_t5_latest dir
cannot be routed to and then refused at activation.
Re-verify the requested version against a live PyPI snapshot at install
time, falling back to the cached one on fetch failure, so a release
published inside the cache TTL is not silently missed.
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* Latest-transformers check: nested config types and latest-tier vision probe
Collect every model_type in the config (top level plus each nested
sub-config) and signal on the first one missing from all installed
overlays, so a supported wrapper carrying a brand-new backbone still
surfaces the upgrade prompt; wrappers instantiate sub-configs through
CONFIG_MAPPING and would fail on the nested type.
Route the vision capability subprocess through the pinned latest sidecar
when the model resolves to the latest tier, so latest-only VLMs are not
misclassified as text-only; every other tier keeps the 5.5 sidecar used
today.
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* Latest tier: nested routing, vision probe after raw miss, safe upgrades
Route by every model_type in the config: a nested sub-config type can raise
the tier (wrappers instantiate sub-configs through CONFIG_MAPPING), so a
supported wrapper with a latest-only backbone routes to latest once
installed instead of staying on default. An unknown nested type never
vetoes; the primary type keeps its previous semantics. The collector is
shared with the upgrade checker.
Vision detection: when the raw heuristics say False for a model that routes
to the latest tier, run the AutoConfig subprocess under the pinned latest
sidecar instead of trusting heuristics built from older transformers.
Provisioning: stage-and-swap. Build the new sidecar in .venv_t5_latest.staging
and swap it in only when the install and pin marker are complete, so a failed
upgrade never destroys a previously working sidecar; restore the old dir if
the final swap fails.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Latest-transformers checker, vision subprocess, and cache fixes
Require the latest release to support every missing model_type (the
primary included) before prompting; a nested-only match cannot make the
model loadable, so no install is offered for it.
The vision-check subprocess now unions the active sidecar's own
registry mappings into the inlined parent-process detection sets, so
architectures only the sidecar knows classify correctly.
A successful sidecar install clears the tier probe cache, the latest
tier's model_type mapping, and the vision-detection cache so the new
venv takes effect without a restart. Tests for all three.
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* Aggregate upgrade support flags and keep install off /v1
The upgrade signal now reports supported_in_pypi only when the latest
release covers every missing model_type; a mix with a main-only nested
type surfaces as dev-only so no PyPI install is offered that would
still fail at load. The consented install endpoint moves to
studio_router so it is not reachable through the OpenAI-compatible /v1
mount. Tests for both.
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* Honor the latest-transformers kill switch in routing
With UNSLOTH_STUDIO_NO_LATEST_TRANSFORMERS set after the sidecar was
provisioned, the latest tier still joined mapping and probe routing
because only the pin was checked. Both admission points now also check
the kill switch, so operators can roll back a problematic sidecar
without deleting files.
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* Repair the latest sidecar through stage-and-swap
The lazy repair path installed into the live .venv_t5_latest, which
_ensure_venv_dir wipes first, so a failed repair deleted the pinned
sidecar and its marker. Both the consented install and the repair now
share one stage-and-swap helper: the incomplete-but-pinned dir survives
any failure and a later attempt can still repair it.
* Tighten comments
* Remove the staging dir when a latest-sidecar install fails
A pip failure inside _ensure_venv_dir returns False without raising, so
the except cleanup never ran and the partial .venv_t5_latest.staging
leaked until a later attempt. Also note on the validate response fields
that frontend consumption ships in the follow-up PR.
* Add the transformers-upgrade consent dialog to the frontend
When /validate reports requires_transformers_upgrade, every explicit load
path (chat runtime and the compare composer) now pauses on a consent
dialog modeled on the remote-code one: it names the model_type and the
latest PyPI transformers version, and on Accept calls
/api/inference/install-latest-transformers itself, shows an installing
state, and resumes the original load automatically on success. Errors
surface in the dialog with a retry; Cancel aborts the load like the
trust dialog's deny path. Architectures shipped only on transformers
main get a dev-only notice with no install button. Background auto-load
skips upgrade-requiring candidates instead of prompting, mirroring the
trust_remote_code rule. The dialog mounts once in the root layout and
runs before the security dialogs, since no load can proceed without the
runtime.
* Route a non-installable new architecture to the custom-code consent as a last resort
When the upgrade dialog has no installable PyPI release (the architecture
is only on transformers main, which Studio never installs), the dialog now
says so explicitly, and when the model also declares custom (auto_map)
code it offers Continue with custom code: resolving the paused load into
the existing trust_remote_code consent gate instead of hard-aborting.
Models with no custom code keep the Cancel-only notice. The backend
returns no upgrade signal at all for architectures unknown to both PyPI
and main, so those still route straight to the unchanged security gate.
* Force a 16-bit load for models on the latest-transformers sidecar
Live validation with Zyphra/ZAYA1-8B (model_type zaya, shipped by
transformers 5.13.1 but unknown to every installed tier) surfaced a
generation crash when the consented sidecar load kept the default bnb
4-bit quantization: transformers' grouped-MoE kernels feed the packed
uint8 expert weights straight into torch._grouped_mm, and generation
dies (plain 16-bit works). New latest_tier_active_for() mirrors the
sidecar activation's tier resolution and never raises; the inference
worker flips load_in_4bit off when it reports true, and the load route
applies the same flip so the pre-load VRAM guard and the worker command
agree. Fixed tiers are untouched. With the guard, ZAYA1-8B loads and
generates correctly in Studio chat.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Offer the custom-code fallback when a latest-sidecar install fails
* Fail remote mapping fetches wholesale and mirror the 16-bit flip in validate
A transient fetch or parse failure of one auto-mapping file no longer caches
a partial latest-release map for the TTL (a real 404 on pre-5.10 tags is
still tolerated), and validate_model now applies the same latest-sidecar
16-bit sizing flip as /load before the training guard so the two agree.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Tighten comments in the latest-transformers changes
* Resolve remote LoRA bases, fold nested tiers, and guard the sidecar swap
latest_tier_active_for now resolves a remote adapter's base model the same
way worker pre-activation does (and returns early without a sidecar pin), a
hardcoded fast-path tier is raised when a nested sub-config's model_type
needs a higher sidecar, and the install route refuses to swap .venv_t5_latest
while training runs on it and unloads a latest-tier chat model first.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Gate the sidecar install on worker liveness and size installable upgrades 16-bit
The install route now refuses while any training or export runs (tier
re-resolution without the load token is unreliable for gated repos), holds
the inference lifecycle gate across the unload and the swap so no load can
interleave, and passes the model name to unload_model. validate_model runs
the upgrade check before the training guard and sizes an installable
upgrade as 16-bit, matching what /load and the worker will force after the
consented install.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Close the sidecar install races and honor the kill switch over cached mappings
Training starts and mutating export routes now refuse while a transformers
install is in progress (shared is_install_in_progress flag), the chat unload
and idle export-worker teardown moved into a before_swap hook that runs only
once the staged install succeeded, and _config_model_types checks the kill
switch before returning a cached latest mapping.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Reserve the sidecar swap before the gate wait and abort it on failed teardown
The install-in-progress flag moved into a shared sidecar swap reservation in
transformers_version, taken by the install route before awaiting the
inference lifecycle gate (so training and export starts see it for the whole
window) and by the lazy .venv_t5_latest repair path. The before_swap hook
now raises when the chat unload or export teardown reports failure, leaving
the previous sidecar untouched.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Back the sidecar swap reservation with a cross-process lock file
The lazy repair runs inside worker subprocesses, where a module-level flag
is invisible to the parent's route checks. The reservation now also creates
a lock file next to .venv_t5_latest (O_EXCL, owner-only removal, stale after
two hours for crashed owners), so is_install_in_progress sees a repair from
any Studio process.
* Hand the swap reservation to the installer thread and harden pre-swap teardown
A cancelled install request no longer releases the reservation while the
installer thread is still staging (the thread owns and releases it, shielded
from cancellation). The route refuses while another inference request is
generating, export teardown runs before the chat unload and is judged by
worker liveness rather than the cleanup return value, and a live inference
worker with no active model (failed load residue) is shut down before the
swap.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Keep the lifecycle gate with the installer and recheck the swap at spawn time
The gate moved into the shielded install task so a cancelled POST cannot
release the guard /load honors while the installer still runs, cached latest
probe results are ignored while the kill switch is set, and the training and
export subprocess spawns recheck the sidecar swap reservation right before
spawning (the route-level guards are one-shot and validation can outlast an
install's start).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Close the spawn-registration windows against the sidecar install
Training marks the spawn in progress before its reservation recheck and
is_training_active honors the flag, so the install route sees a start that
has passed proc.start() but not yet recorded _proc. Export load-checkpoint
rechecks the reservation after setting _export_active and before tearing
down the old worker, so losing the race keeps the loaded checkpoint instead
of surfacing a 500.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Refine the install-window interleavings around worker teardown
The inference busy count is rechecked under the lifecycle gate (streams
start by taking that gate, so nothing slips past a held gate), the training
handshake moved ahead of the VRAM-freeing before_spawn hook so a lost race
leaves chat/export intact, the export spawn-time check is op-aware (inside
an active op the install is the side that aborts), and the Xet-stall respawn
waits out a transient reservation instead of stranding the run.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Track the install's server-side unload and guard export ops against the swap
The upgrade dialog store records when its install actually ran (the server
unloads the active chat model before swapping), and the load flow then marks
the previous model as unloaded so a later cancelled gate still triggers
rollback; the custom-code fallback leaves the flag unset. _run_export gained
the same reservation handshake as load_checkpoint so an install cannot block
behind an hours-long export op instead of returning 409.
* Tighten comments in the install-guard and upgrade-consent changes
* Surface install-race refusals cleanly and roll back after a failed swap unload
/load refuses while the sidecar swap is reserved so a load cannot succeed
and immediately be unloaded by the pre-swap teardown, worker starts that
lose the install race raise a typed SidecarSwapInProgress mapped to 409
instead of a 500, the install response reports model_unloaded even on a
structured failure so the client can restore its state, and the compare
flow tracks the server-side unload like the primary load path and clears a
stale checkpoint on abort.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Type the export install races, scope the lock release, and keep the unload signal
Export load-checkpoint and export ops raise SidecarSwapInProgress (mapped to
409 in every export route) instead of a 400-shaped failure, the export spawn
check distinguishes repair reservations (always refused) from install ones
(op-aware), the swap lock release only unlinks a lock this process wrote so
a stale-superseded owner cannot drop the new owner's live lock, and the
frontend unload signal survives a superseding consent via read-and-clear
consumption instead of a reset.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Finalize a stalled run when the respawn loses the install race and latch the unload signal
The Xet-stall respawn timeout now finalizes the run as a failure instead of
raising into the pump's broad finalization catch (which stranded it in a
training state with no worker), and a successful install retry ORs the
model_unloaded signal with the latched value so a failed-after-unload first
attempt still triggers rollback.
* Recheck the swap under the load gate and latch the unload before resolver checks
/load rechecks the sidecar reservation after acquiring the lifecycle gate
(an install can reserve while the load queues on it), and the dialog store
latches model_unloaded as soon as the install response arrives, before any
resolver-identity guard, so a superseded consent's unload still reaches
whichever load consumes the signal next.
* Report cleared-state unload failures, guard queued installs, and fold name tiers
A failed chat unload that still cleared the orchestrator's model state now
reports model_unloaded so the client rolls back, the installer aborts with
a 409 when a model load completed while it waited on the lifecycle gate,
and the fixed-tier name fast path consults the config mapping when a latest
sidecar is pinned so an accepted upgrade routes to the sidecar it installed
(no I/O added to the unpinned path).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Report cleared-state unload failures and harden the spawn handshake flag
The failed-unload branch in before_swap now detects that the orchestrator
cleared its model state and reports model_unloaded before aborting (the
earlier commit claimed this fix but a scripting error dropped the edit),
the installer's queued-load check compares a load generation counter so a
same-model reload is caught, and both training spawn sites wrap everything
after the handshake in a guard that resets _spawn_in_progress on any
exception so a failed start cannot wedge is_training_active.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Bump the load generation when the load is published, not at load start
A start-time bump is already visible when the installer snapshots mid-load,
so a same-model reload completing after the snapshot looked unchanged and
could be unloaded by the swap. The counter now increments alongside the
active_model_name publish.
* Self-heal a broken pinned sidecar, guard lazy repairs, and refresh stale retries
A valid pin whose transformers source dir vanished now triggers the repair
from the routing path (with a five minute backoff after failures) instead of
silently routing latest-only models to older tiers, the lazy repair refuses
while parent-visible chat/training/export workers are active since it has no
teardown of its own, and a version-mismatch install failure carries the
superseding release so the dialog's Retry re-requests a version that can
succeed.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Flip latest-tier loads to 16-bit outside chat and protect export state
Training and export workers now apply the same latest-sidecar 16-bit flip
as the chat worker so a brand-new grouped-MoE architecture cannot reach bnb
4-bit through those paths, the latest-tier vision override returns None on
an inconclusive probe so a transient failure is not cached as not-vision,
and the install route refuses while an idle export checkpoint is loaded
rather than discard it with no rollback signal on a failed swap.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address parallel-review findings on the sidecar guards and install checks
The training route sizes latest-tier jobs 16-bit before GPU selection, the
inference subprocess spawn rechecks the swap reservation like training and
export (covering the OpenAI auto-switch path) with the typed error mapped
to a retryable 409, compat_plan blocks the install when dependency metadata
cannot be fetched instead of proceeding unverified, snapshot model-type
lists must contain only strings, and pin-marker package specs are validated
against the sidecar's own package set before ever reaching pip.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Parent-only repairs, live-owner locks, remote-base activation, pre-teardown recheck
Lazy sidecar repairs now refuse inside worker children (whose empty backend
singletons cannot see live siblings) and run only in the parent where the
active-worker guard is real, swap-lock staleness requires the owner pid to
be dead so a slow live install is never superseded, both activation entry
points resolve a remote adapter's base model like the inference worker and
latest_tier_active_for already do, and load_model rechecks the reservation
before tearing down the old worker so losing the race keeps the current
model loaded.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Check workers under the repair reservation and keep state on refused swaps
The lazy repair now reserves first and checks workers under the reservation
(worker starts set their active markers before rechecking, so every
interleaving aborts one side), with export ops and in-flight inference loads
counted as active. The inference pre-teardown and spawn guards refuse only
repair reservations since an install shares the load's lifecycle gate and
aborts via its queued-load snapshot, a SidecarSwapInProgress raised before
teardown no longer clears the live model mirrors, and an export spawn abort
after teardown clears current_checkpoint so the page cannot claim a loaded
checkpoint with no worker.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Repair a present-but-incomplete latest sidecar from routing
The routing self-heal only fired when the pinned sidecar's transformers/
dir was missing. A sidecar that kept transformers/ but lost another pinned
package still routed models to the latest tier, and workers refuse
parent-only repairs, so every load failed until a manual reinstall. Routing
now validates the full pin (via _venv_dir_is_valid) and repairs any
incomplete sidecar under the same swap reservation and 5-minute backoff.
* Treat an unrepaired latest sidecar as unavailable in routing
When the pinned sidecar is incomplete and the lazy repair fails (offline,
pip failure, workers active) or is inside the backoff window, routing
returned the source dir anyway, sending models to a tier whose worker
activation is known to fail. Return None instead so models an older tier
supports keep loading there until a repair succeeds, matching the behavior
when the sidecar dir is missing entirely.
* Harden sidecar swap and repair against crash, survivor, and 16-bit paths
Reclaim a swap lock as soon as its recorded owner PID is dead instead of
waiting out the two-hour cutoff, so a crash mid-install no longer wedges
/load, training, export, and repair for hours. A lock whose PID cannot be
read yet still uses the long cutoff so the create-before-write window is
never mistaken for dead.
Probe process liveness with OpenProcess on Windows: os.kill(pid, 0) there
is CTRL_C_EVENT (a real Ctrl+C via GenerateConsoleCtrlEvent), not a
harmless check, and psutil is not always present.
Return whether _shutdown_subprocess actually killed the worker and keep the
live handle when it survives terminate/kill (an uninterruptible CUDA
syscall can outlive SIGKILL). The pre-swap liveness guard now trusts that
result, so the destructive .venv_t5_latest rename cannot proceed while a
live worker still holds sidecar modules.
Recover a sidecar stranded at .old when a swap's activation rename and its
rollback both fail: reading the pin restores it when no swap holds the
reservation, so latest-tier models are not permanently broken.
Resolve the latest tier in the parent for export loads and for explicitly
16-bit training runs, not only 4-bit ones: tier resolution self-heals an
incomplete sidecar, and repairs are parent-only, so those paths could not
recover before. Sidecar integrity and quantization are independent.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Revert the parent-side latest-tier repair probe on training and export loads
The probe ran before the route freed VRAM, so a resident chat or export worker
made _workers_active_for_repair() refuse the parent-only repair; the route then
tore that worker down and spawned a child that also cannot repair, so an
incomplete sidecar still failed to load. Repairing correctly requires running the
repair between the worker teardown and the child spawn, decoupled from VRAM
sizing, which is a larger change tracked separately. Restore the prior behavior
so these paths match the reviewed form and do not partially attempt a repair that
cannot complete while workers are resident.
* Honor failed worker shutdowns on load and revalidate the cached latest mapping
The fresh-load paths spawned a new worker straight after _shutdown_subprocess
without checking its result, so a worker that outlived terminate/kill (a wedged
CUDA syscall) had its handle overwritten by the replacement while it still held
GPU memory, and is_worker_alive/the pre-swap guard could no longer see it. Both
the inference load and the export checkpoint load now abort when the old worker
did not exit, so the load can be retried once it does.
_config_model_types returned a cached latest mapping without re-checking the
sidecar, so a sidecar deleted or broken in-process after its first parse was
never re-validated: routing kept sending latest-only models to the stale latest
tier while activation failed. The cached latest mapping is now dropped and
re-resolved (self-healing) when the sidecar is no longer intact.
* Drop cached latest mapping when the pin is gone; keep 4-bit for custom-code fallback
_latest_sidecar_intact now returns False when the pin marker itself is gone, not
just when a pinned package is missing. Otherwise a cached latest mapping outlived
a deleted pin: _config_model_types kept returning it, so routing sent latest-only
models to a tier whose worker activation then failed (no pinned version) until
restart. It now drops the cache and re-resolves to no latest tier. The
_overlay_transformers_dir caller already gates on a present pin, so it is
unaffected.
validate_model forced 16-bit sizing whenever a PyPI upgrade was merely offered,
even for a model that can fall back to its own auto_map code. /load loads such a
model 4-bit without the install, and the install route refuses while training is
active, so 16-bit sizing here returned a VRAM 409 for the only viable 4-bit path.
The offered-upgrade flip is now gated on the absence of a custom-code fallback;
an already-active latest sidecar still always sizes 16-bit.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: expose Windows drive roots in the folder browser
The model-selection folder browser bounds navigation to the roots returned
by _build_browse_allowlist(), which exposed Linux removable-media mounts via
linux_run_media_mount_roots() but had no Windows analog. As a result a user
on C: could not browse to D:/E: to pick a model directory.
Add windows_drive_roots(), a Windows-only companion to
linux_run_media_mount_roots() that lists readable logical drive roots, and
wire it into both browse-allowlist builders and their suggestion chips so
other drives are both navigable and offered as quick-picks. The helper is a
no-op on Linux/macOS, so existing platforms are unaffected.
Closes#6368
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: cover the Windows drive-root browse wiring with an integration test
Add an allowlist integration test mirroring the Linux side's
test_legacy_browse_allowlist_includes_linux_run_media_mounts: it extracts
_build_browse_allowlist from routes/models.py, stubs external_media so
windows_drive_roots() yields a fake drive root, and asserts that root becomes
browsable through the built allowlist. Proves the wiring, not just the helper.
* Studio: skip inactive drives via GetLogicalDrives before probing
Resolve active logical drives from GetLogicalDrives() before probing each
letter with os.path.isdir. Probing a drive letter mapped to a disconnected
network share can otherwise block the async backend for tens of seconds per
letter. The call degrades gracefully (falls back to probing all letters) when
ctypes/windll is unavailable, so behavior is unchanged on Linux/macOS. Tests
override the bitmask source to stay deterministic on real Windows hosts.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: allow browsing descendants of a drive-root allowlist entry
routes/models.py _is_path_inside_allowlist() checked descendants with
startswith(root_real + os.sep). A drive root ("D:\") already ends in a
separator, so the prefix became "D:\\" and a child like "D:\models" was
rejected with 403 after the browser opened the drive root. Only append a
separator when the root does not already end in one. folder_browser.py already
uses commonpath and was unaffected. Adds a regression test covering the
separator-terminated-root descendant case.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: enforce the system-directory denylist during folder browsing
Exposing whole Windows drive roots (and any legacy-registered filesystem root)
widened the browse allowlist above system directories, but the browse
resolvers only re-applied the credential/config denylist, not the
_denied_path_prefixes() system-dir denylist that scan-folder registration
enforces. That let browse-folders enumerate C:\Windows, C:\Program Files,
/etc and /proc.
- Add is_denied_system_path() to both storage modules and enforce it in both
browse resolvers (legacy routes/models.py and hub folder_browser.py), on each
resolved child and on the final target, keeping the /run/media carve-out.
- Rework the legacy _is_path_inside_allowlist to use splitdrive + commonpath so
a Windows drive root authorizes its descendants while a bare POSIX / does not,
and to compare case-insensitively like the hub browser.
- Reject the filesystem root in the legacy add_scan_folder, matching the hub.
- Hide denied system dirs from browse listings and suggestion chips.
- Add tests/test_browse_denylist.py and update the external-media path tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: make browse-denylist tests OS-portable
The browse-time denylist tests used real /etc and tmp_path locations; on macOS
tmp lives under the (legitimately denied) /private/var and /etc resolves to
/private/etc, so three tests failed there. Pin the platform / use a tmp-based
denied prefix so they assert the same behavior on Linux, macOS and Windows.
* Studio: apply the bare POSIX-root guard to the hub folder browser too
The _is_path_inside_allowlist guard that stops a legacy-registered '/' scan
folder from authorizing every absolute path lived only in the legacy browser.
The hub browser used commonpath without it, so a stale '/' row let it descend
into /var, /root, /home -- which the system-directory denylist (/proc /sys /dev
/etc /boot /run) does not cover, while the legacy browser blocked them. Mirror
the legacy guard so both browsers treat '/' identically.
Also resolve each directory entry before the denylist check in both listing
loops, so a symlink or junction pointing into a denied dir is hidden instead of
rendered as a row that 403s on descent. Adds legacy-vs-hub parity tests.
* Studio: bound Windows drive probing so a disconnected mapping can't stall the browser
GetLogicalDrives includes mapped network drives, so a disconnected but still
mapped drive (e.g. Z: -> \\nas\share) stays set in the bitmask and reaches
os.path.isdir, which can block for tens of seconds while Windows tries to
reconnect. Because windows_drive_roots() runs synchronously while building both
folder-browser responses, one stale mapping stalled every browse request.
Probe each surviving drive in a daemon thread bounded by a short timeout and
skip it if it does not answer in time, so a hung mapping is dropped instead of
blocking the caller. Connected drives (local or network) still respond well
within the timeout, so drive discovery is unchanged. Corrects the
GetLogicalDrives docstring, which claimed the bitmask alone prevented the stall.
* Studio: probe drive/media roots once per browse request, not twice
Both folder browsers called windows_drive_roots() (and
linux_run_media_mount_roots()) twice per browse request: once to seed the
allowlist in _build_browse_allowlist() and again to build the suggestion chips.
With the bounded drive probe, a disconnected mapped network drive then paid the
timeout twice per folder click. Probe both once in the request handler and pass
the results into _build_browse_allowlist(), reusing them for the chips, in both
the legacy and hub browsers. Adds a test asserting the roots are reused, not
re-probed.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: run the legacy browse endpoint in the threadpool, fix its stale test
Two follow-ups from review of the drive-probe changes:
- browse_folders was 'async def' but does only blocking filesystem I/O (the
timeout-bounded drive probe, iterdir, realpath). On the event loop a
disconnected mapped drive waiting out its probe timeout stalled every other
request. Declare it sync 'def' so FastAPI runs it in the threadpool, matching
the hub browse endpoint. No await was used in the body.
- test_browse_folders_hides_sensitive_dirs monkeypatched _build_browse_allowlist
with a zero-arg lambda; the once-per-request refactor now calls it with
(media_roots, drive_roots), so the lambda raised TypeError. Accept and ignore
the args.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: probe Windows drive roots concurrently so multiple dead mappings don't stack timeouts
windows_drive_roots() probed each candidate serially, so N disconnected-but-mapped network drives each paid the full per-drive timeout in turn (e.g. four stale mappings added ~8s to every folder-browser request). Collect the candidate roots first, then probe them all at once under a single overall deadline, so the added delay stays at ~one timeout regardless of how many drives are disconnected. _readable_dir_within stays as a thin single-path wrapper for its existing callers/tests.
* Studio: tighten comments in the folder-browser drive-root changes
Condense the comments and docstrings added by the Windows drive-root and
system-directory denylist work to be shorter and clearer while keeping the
security and correctness rationale intact. Comment and docstring text only;
no code changes.
* Studio: iterate the input, not the results dict, when collecting readable drive probes
_readable_dirs_within returned {path for path, ok in results.items()...}, but a probe thread that exceeded the join deadline is still alive and can insert its key into results during that iteration, raising 'dictionary changed size during iteration' -- reachable exactly in the disconnected-mapped-drive case the probe exists for. Iterate the fixed input list and read results.get(path) (an atomic read) instead.
* Studio: keep the browse-route containment tests denylist-inert so they pass on macOS
test_browse_folders_route.py exercises allowlist containment and the file-vs-directory guard, not the system-directory denylist. On macOS pytest tmp_path resolves under /private/var, a denied prefix, so _resolve_browse_target 403s the fixture dirs before the containment logic runs (4 failures). Add an autouse fixture that makes is_denied_system_path inert in this file; the denylist keeps its own coverage in test_browse_denylist.py.
* Studio: keep the hub browse tests denylist-inert so they pass on macOS
* Studio: register a UNC share root; only reject local filesystem roots
* Studio: reject device drive roots and browse a registered UNC share root
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: treat device-namespace volume GUID roots as local filesystem roots
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Studio: pin llama.cpp update apply to the release the banner offered
* Document the pinned walk-back trade-off and cover win32
* Trim the pin comments
* Studio: verify a pinned llama.cpp update landed on the pinned release
The pin passes --published-release-tag so the installer resolves exactly the offered release. Also verify the result: if the post-install marker stays on the pinned repo but reports a different tag, the installer ignored the pin, so fail with a retryable error instead of a false success. A Vulkan/Intel host legitimately reroutes fork to upstream and drops the pin, so the check is scoped to the pinned repo.
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* Fix broken manual response-template markers in Studio's fallback table
Six template families in TEMPLATE_TO_RESPONSES_MAPPER shipped markers that
never match what their chat templates actually render, so the manual
train_on_completions path masked every assistant token and the run died on
the all-labels-masked safety net:
- mistral, llama: '[INST] ' / ' [/INST]' - the surrounding spaces fold into
the neighbouring tokens ('[INST]'/'[/INST]' are single special tokens in
Mistral v0.3, SentencePiece pieces in Llama-2), so the padded strings
never match. Now '[INST]' / '[/INST]'.
- starling: trailing space after 'GPT4 Correct Assistant:' folds into the
next content token. Now no trailing space.
- glm: '[gMASK]<sop>' renders once at text start, never before later user
turns, and '<think>' is generation scaffolding rendered as a lone
'</think>' on non-final turns. Now '<|user|>' / '<|assistant|>'.
- qwen3-thinking: '<think>' is stripped from non-final assistant turns
(Qwen3-Thinking-2507) and never rendered by QwQ. Now the bare assistant
header, matching the other qwen entries.
- zephyr: role tags are plain text and SentencePiece tokenizes them
differently at text start than after '</s>' + newline mid-conversation;
the markers need the leading newline anchor. Now '\n<|user|>\n' /
'\n<|assistant|>\n'.
Validated token-level on each family's representative tokenizer with a
two-turn fixture plus system message: user and system content fully masked,
every assistant turn trained, and the final EOS label never -100. The
fixed mistral, llama, starling and glm markers produce labels identical to
zoo auto-detection; qwen3-thinking differs only in one turn-separator
newline token. All 22 unchanged entries produce byte-identical labels to
before this change.
Adds tests/test_response_template_markers.py pinning the fixed and key
unchanged marker literals (dependency-free) plus token-level masking checks
that skip when tokenizers or unsloth_zoo are unavailable offline.
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* Close tokenizer config handle and read it as UTF-8
Chat templates in tokenizer_config.json are rarely ASCII-only, so the
default locale codec could fail the GLM fallback loader on Windows.
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* Tighten comments
* Anchor the llama marker on <s> and harden the marker test
On transformers 5.x llama-2 tokenizes [INST] after <s> as a bare left
bracket while the standalone encoding gives the space-prefixed piece, so
the unanchored marker missed every turn boundary and later user turns
leaked into training; 4.57 masked this. Anchoring on <s>[INST] matches
both tokenizations, verified token-level under 4.57.6 and 5.5.0.
The test now unwraps the BatchEncoding that apply_chat_template returns
on 5.x before indexing, and the latent trailing spaces in the unreachable
unsloth and vicuna entries are dropped for table consistency.
---------
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* Auto-detect completion masking markers with template table fallback
Studio's train_on_completions previously relied only on the hardcoded
MODEL_TO_TEMPLATE_MAPPER / TEMPLATE_TO_RESPONSES_MAPPER tables and
silently disabled masking when a model was not in the table, so unmapped
models (LFM2-8B-A1B, DeepSeek, and others) trained on full sequences
without telling the user. Several mapped templates (glm, mistral, llama,
starling, zephyr, qwen3-thinking) also carried markers that mask every
assistant token, which made every row drop in the post-masking filter.
Both training callsites (CUDA trainer.py and MLX worker.py) now share
utils.datasets.completion_masking.apply_completion_masking:
- Try unsloth_zoo chat template auto-detection first; it raises loudly
when the template cannot be parsed and never masks the EOS token.
- gpt-oss models keep their manual markers so non-final assistant
<|end|> tokens stay trained, matching current behavior.
- If auto-detection raises, fall back to the template table exactly as
before.
- If the table also misses, emit an explicit user-visible warning that
completion masking could not be applied and full-sequence training
will occur, instead of a quiet log line.
The >30 percent dropped-rows safety net in trainer.py now guards the
auto path as well. Table consumers for inference and chat templates are
unchanged. Validated against one representative tokenizer for every
template in TEMPLATE_TO_RESPONSES_MAPPER plus the unmapped models:
no template regresses; unit tests cover the four decision paths.
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* Restrict masking fallback to marker detection failures
The auto branch wrapped the whole train_on_responses_only call, so a real
failure while applying the masking (dataset map, tokenization) was treated
as a detection miss and training silently proceeded on full sequences.
Detect markers separately via get_chat_template_parts (test seam via
detect_fn), then apply them with errors propagating, matching the manual
path. Tokenizers with preset unsloth marker attrs skip detection and call
bare so zoo reuses the stored parts.
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* Fail the run when applying completion masking raises
The helper already falls back internally on detection failures and returns
applied=False on a double miss, so an exception reaching the callsites is a
real failure applying the masking. Remove the callsite catches that
downgraded it to full-sequence training; the run now fails visibly instead.
Also use the explicit re-export alias form in utils/datasets/__init__.py for
the two new names, satisfying the import-hoist source lint.
* Import completion masking from its submodule
The import-hoist source lint counts only real name loads, so package-level
re-exports of the two new names cannot satisfy it. Import
apply_completion_masking from utils.datasets.completion_masking directly at
both callsites and leave utils/datasets/__init__.py untouched.
* Completion masking: gpt-oss renames and MLX raw/alpaca parity
Renamed or private gpt-oss checkpoints are name-detected as gpt-oss but miss
the exact-name table; default them to the gpt-oss template markers instead of
falling through to full-sequence training.
Gate the MLX masking call on not raw_text_mode and format_type != alpaca,
mirroring the CUDA path: raw/CPT text has no chat turns to mask and
Alpaca-rendered text lacks the tokenizer's chat markers.
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* Define raw_text_mode outside the MLX feature-detect block
With an older zoo lacking the append_eos config field, the masking
gate referenced raw_text_mode before assignment. Hoist the assignment
above the feature detection so both consumers see it.
* Gate MLX masking on the formatter's resolved format
format_type auto can resolve to alpaca or raw text; the masking skip
checked only the requested value, so auto-detected Alpaca data got
chat-template markers applied to rendered prompt text. Track the
final_format returned by format_and_template_dataset and gate on it,
matching the CUDA path.
* Unwrap the mlx-lm TokenizerWrapper before marker checks
The wrapper delegates plain reads to the wrapped HF tokenizer but hides
underscore attrs, so preset unsloth markers were invisible and detection
relied on the loader's call patch. Unwrap to the real tokenizer first,
as the zoo MLX resolver does.
* Tighten masking comments
* gpt-oss: auto-detect markers first like every other template
The quantized and BF16 gpt-oss checkpoints ship a chat template without
the channel final header, so the pinned manual markers match nothing
there and masking trained zero tokens. Auto-detection derives markers
from whichever template the checkpoint ships and keeps the final
terminator trained; the manual gpt-oss markers remain the detection
failure fallback, including for renamed checkpoints.
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* Tighten comments
---------
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* Studio: route models by CONFIG_MAPPING_NAMES instead of hardcoded tables
A model whose model_type is absent from an overlay's transformers cannot load
there, so a new MoE arch not yet in the tier tables gets routed to default and
fails (e.g. lfm2_moe, deepseek_v4). Add a static resolver that parses each
overlay's CONFIG_MAPPING_NAMES straight from source (AST only, no import, no
network, no trust_remote_code) and picks the lowest tier that ships the
model_type. Runs after the existing checks and only ever upgrades default, so
no existing routing changes and new archs no longer need a table edit.
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* Studio router: harden the CONFIG_MAPPING_NAMES resolver
- Resolve the default tier map from the base install, skipping any .venv_t5_*
sidecar on sys.path, so an in-process 5.x activation cannot make a 5.x-only
model look loadable by 4.x.
- Do not cache an overlay whose sidecar dir is absent, so a later call re-reads
it once provisioned instead of serving a stale empty map.
- Also collect model types added via CONFIG_MAPPING_NAMES.update({...}) and
**{...} unpacking, not just the literal assignment (5.10 uses both).
- Wrap the AST walk in the try/except so a malformed source can never crash tier
resolution.
- Feed the mapping fallback from _load_config_json so a config served from the
hub cache during a transient outage still routes new architectures.
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* Tighten comments
---------
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LFM2-8B-A1B and any other lfm2_moe checkpoint were missing from the
transformers tier tables, so they fell through to the default 4.57.x
sidecar, which does not register lfm2_moe and errors with
"not supported yet in transformers==4.57.6". Only lfm2_vl was listed.
Add Lfm2MoeForCausalLM / lfm2_moe to the 5.3.0 tier (lfm2_moe is
registered in transformers 5.3.0). get_transformers_tier now returns
530 for LFM2-8B-A1B and the model loads and trains as expected.
* Studio: add Vulkan llama.cpp support
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address gemini's feedback
* Studio: move the Vulkan VRAM probe into a standalone script
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Improve Vulkan probe error reporting
* Resolve llama-server symlink so Vulkan build is detected
* Drop unreachable Vulkan fallback in GPU free-memory dispatcher
* Skip the Intel GPU probe when NVIDIA or ROCm is present
* Reserve host RAM headroom for Vulkan integrated GPUs
* Add a `UNSLOTH_FORCE_VULKAN` environment variable
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Honor GGML_VK_VISIBLE_DEVICES, reserve discrete Vulkan VRAM headroom, and clear Intel GPU on --cpu-fallback
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Route Intel and forced-Vulkan hosts to the upstream Vulkan prebuilt, add arm64 Vulkan, keep Vulkan out of RAG auto-detect
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Clear the fork release pin when routing a Vulkan host to the upstream repo
* Gate auto-Vulkan routing on no physical NVIDIA so hidden CUDA devices aren't used
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Pin Vulkan launches with --device Vulkan<i> instead of the raw GGML_VK_VISIBLE_DEVICES index space
* Let user --device override the Vulkan pin, and gate direct Vulkan asset picks on no physical NVIDIA
* Update RAG auto-backend test mocks for the _resolve_auto binary and Vulkan probes
* Keep the add_dll_directory handle alive through the Vulkan probe DLL loads
* Revert RAG auto Vulkan guard, guard multi-backend Vulkan detection, and preserve forced Vulkan across updates
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* Use getattr for RTLD_GLOBAL in the Vulkan probe CDLL mode
* Skip CUDA/ROCm APU and datacenter GPU tuning on Vulkan builds
On a Vulkan llama.cpp build gpu_indices are ggml compact ordinals, not
CUDA/ROCm physical ids, so _amd_apu_wants_unified_memory and
_apply_datacenter_env were reading the wrong device. On a mixed AMD APU
plus discrete GPU host that could raise a spurious system-RAM shortfall
and block a valid discrete-GPU load. Gate all three call sites on
not is_vulkan_backend; the Vulkan path already reserves iGPU host
headroom and the backend ignores GGML_CUDA_* anyway.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Tighten Vulkan-guard comment in load_model
* Reduce comments in Vulkan support to be more succinct
* Resolve shell-wrapper llama-server entrypoint to the real lib dir
create_exec_entrypoint falls back to a #!/bin/sh wrapper at the install
root when it cannot symlink into build/bin. _find_llama_server_binary
returns that root entrypoint, but Path.resolve() does not follow a shell
wrapper, so _llama_lib_dir returned the install root and _is_vulkan_backend
missed libggml-vulkan.so -- silently skipping the Vulkan probe and --device
pin on an otherwise valid Vulkan install. Follow the wrapper's exec target
to build/bin. Regression test: test_shell_wrapper_entrypoint_resolves_to_real_lib_dir.
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Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* fix: Remove moot has_blackwell_gpu() function
Fixes unslothai/unsloth#6961. This function skipped flash-attn on Blackwell GPUs because no prebuilt wheel existed;
Dao-AILab now ships one and url_exists() already gates resolution.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix: use torchao 0.17.0 for Blackwell
Fixes#6961. Torchao 0.16.0's cpp extensions are built against CUDA 12, so on a CUDA-13
torch (cu130 / Blackwell) they fail to load with "libcudart.so.12: cannot
open shared object file". Select 0.17.0 there instead: its cpp targets torch
2.11, so it is skipped cleanly rather than crashing. CUDA-12 / ROCm / CPU
torch 2.10 keeps 0.16.0 and its working kernels.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Condense torchao version-selection comments (no behavior change)
* Support torch 2.11 in the Studio installer via the torch2.10 prebuilt wheels
Map torch 2.11 to the torch2.10 prebuilt wheels for flash-attn, causal-conv1d,
and mamba through wheel_utils.prebuilt_wheel_torch_mm, applied in direct_wheel_url
(filename) and flash_attn_wheel_url (version). Those torch2.10 CUDA wheels load and
pass each project's own test suite on torch 2.11 (verified on B200), so a torch 2.11
environment gets the prebuilt accelerators instead of skipping or building from source.
Raise _CUDA_TORCH_PKG_SPEC to <2.12.0 (torchvision <0.27.0, torchaudio <2.12.0) so
the CUDA torch repair path can install torch 2.11, where torchao 0.17's cpp kernels
load cleanly. Add tests for the mapping.
* Keep has_blackwell_gpu as a False stub for future arch gating
* Restore has_blackwell_gpu as a return-False probe kept for future arch gating
Keep the nvidia-smi compute_cap detection and its two call sites, but short-circuit
with return False at the top so flash-attn is no longer skipped on Blackwell (sm_100+
now has prebuilt wheels and url_exists gates resolution). Drop the early return to
re-enable arch-based detection later.
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: keep transformers off sys.modules until the training worker activates the sidecar
The training worker (core/training/worker.py:run_training_process) decides the per-worker
Xet env flip during preflight by importing utils/hf_xet_fallback.py, which eagerly imported
unsloth_zoo at module load. unsloth_zoo's __init__ imports transformers, so the default
transformers 4.57.x was cached in sys.modules before activate_transformers_for_subprocess
prepended the 5.x sidecar to sys.path. Since activation only edits sys.path, the already
cached module won, and 5.x models failed to load their tokenizer or config:
- Qwen3.5 / GLM-4.7 (tokenizer_class TokenizersBackend): "Tokenizer class TokenizersBackend
does not exist or is not currently imported."
- gemma-4: "... is not supported yet in transformers==4.57.6."
Fix: load the shared unsloth_zoo backend lazily (only when a heavy download helper is first
used, which is after activation). child_should_disable_xet and the DEFAULT_* constants are
defined locally so importing the shim stays light. The download wrappers, the DownloadStallError
class, start_watchdog and get_hf_download_state resolve the shared backend on first use, and the
degraded no-unsloth_zoo fallback is preserved.
Tests:
- test_hf_xet_fallback.py: existing suite kept green via the restored _shared_* seam; the
GPU-init retry test now triggers the lazy load explicitly; new guard asserts importing
child_should_disable_xet does not import transformers/unsloth_zoo.
- test_training_worker_import_discipline.py: new invariant test that the worker preflight
imports leave transformers unimported, so this class of regression cannot return silently.
Runs in studio-backend-ci (CPU only, no network/GPU/weights).
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* Studio: CPU-only guard that activation switches transformers to the model's sidecar version
Adds test_worker_activates_correct_transformers.py: runs the real worker preflight
(from utils.hf_xet_fallback import child_should_disable_xet) plus the real tier
detection and activate_transformers_for_subprocess for a transformers-5.x model
(Qwen3.5, tier 530), then asserts the in-process transformers actually switched to
the 5.x sidecar. A stale pre-activation import leaves 4.57.x pinned and fails the
assertion, which is exactly the TokenizersBackend regression (#6951).
Self-contained CUDA spoof (mirrors tests/_zoo_aggressive_cuda_spoof.py) forces
unsloth_zoo down its full, transformers-importing init path on a GPU-less runner;
without it unsloth_zoo degrades and never preloads transformers, masking the bug.
A one-line stub sidecar stands in for the 5.x venv, so no GPU, network, weights, or
real sidecar are needed. Passes on this fix, fails on buggy main.
* Studio: load the repo's canonical CUDA spoof in the correct-version guard
Load tests/_zoo_aggressive_cuda_spoof.py (the committed spoof the consolidated CI
already relies on) as the single source of truth so the guard matches CI and stays
robust on a CPU-only torch wheel, where a partial hand-rolled spoof could miss a
torch.cuda call and let the unsloth_zoo import raise (masking the bug). Falls back to
a minimal inline spoof for a standalone studio checkout. Verified: passes on this fix,
fails on buggy main, and the fallback path passes when the spoof file is absent.
* Studio: declare the lazily-resolved xet names so ruff F822 stays green
DownloadStallError, start_watchdog and get_hf_download_state are provided via the
module __getattr__ (PEP 562), so ruff F822 flagged them as undefined names in __all__
and the Source-lint / pre-commit checks went red. Add annotation-only declarations
(no value bound, so __getattr__ still resolves them lazily to the shared unsloth_zoo
backend) to mark them defined for the linter while keeping F822 active for the rest
of __all__.
* Studio: tighten comments on the sidecar-activation fix and its tests
* Studio: mirror the new MLX-dispatch preflight import in the import-discipline guard
The worker preflight now also runs 'from core.training.training import
is_apple_silicon_training_platform, should_use_mlx_training_backend' before it
activates the transformers sidecar. Add that import (guarded) to the guard's
preflight snippet so the invariant test stays a faithful mirror: a future change
that makes core.training.training pull transformers/unsloth_zoo eagerly would then
be caught too. Verified clean on the current tree (no leak).
---------
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Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* feat: detect installed coding agent CLIs in Studio settings
The API-keys panel only ever showed the "claude" flavor of the
`unsloth start` command, so anyone using Codex, OpenCode, OpenClaw,
Hermes, or Pi had to manually rewrite the copied command by hand.
Add a backend check that looks for each agent's CLI binary on PATH
(shutil.which, mirroring the pattern already used elsewhere in
studio/backend/utils) and expose it as GET /api/settings/coding-agents.
The API-keys panel now renders a picker for all six supported agents,
marks the ones it finds installed, and defaults to one of those instead
of always falling back to claude.
Includes unit tests for the detection helper.
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* address review feedback on coding-agent detection
Three fixes from PR review:
- detect_installed_coding_agents now treats a PATH lookup failure as
"not installed" instead of letting it bubble up and break the
settings endpoint; added a regression test for it.
- CodingAgentsResponse.agents is now typed as an immutable tuple
instead of a list built from one, matching CODING_AGENTS itself.
- Fixed a race in the API-keys panel: picking an agent while the
installed-CLI check is still in flight could get silently overwritten
once that check resolved. A ref now tracks whether the user has made
a manual choice, so the auto-detected default only applies before
that happens.
* Address Codex feedback: GGUF gating and remote-detection scope
- codex refuses to launch against a non-GGUF (transformers-backed) model
(unsloth_cli's _require_gguf_for_codex), so auto-defaulting to it produced
a copy-pasteable command that fails immediately whenever the loaded model
isn't GGUF. Add useActiveModelIsGguf() (looks up the active checkpoint in
the chat runtime store) and a correction effect that steers the auto-pick
away from codex unless the loaded model qualifies, without ever touching a
choice the user made by hand.
- Detection runs via shutil.which on the Studio backend host, which isn't
the same machine as the browser in a tunnel/remote session. Reword the
'installed'/'detected' copy to say so explicitly when the tunnel URL is
in use, instead of implying the check ran on the viewer's own device.
* Rework auto-default per review: loopback gating + inline GGUF check
Replaces the previous approach with the exact shape discussed on the PR:
- Export isLoopbackHost/normalizeHost from agent-command.ts. The detection
endpoint runs shutil.which on the Studio backend, which only describes the
browser's own machine when the base this panel targets resolves to
loopback. For a LAN or tunnel/remote base, gate the whole thing off --
don't mark anything as "detected" and don't let it drive the default --
instead of just relabeling the copy.
- Drop the separate GGUF-correction effect and useActiveModelIsGguf hook.
Read useChatRuntimeStore.getState().activeGgufVariant inline inside the
existing detection effect's .then() (so it doesn't need to sit in the
effect's deps), and pick the first detected agent that isn't codex unless
the loaded model is GGUF, leaving the existing default untouched when no
compatible agent is detected.
Verified both branches (loopback vs LAN/tunnel base, gguf vs non-gguf,
manual pick preserved, no-compatible-agent fallback) with a standalone
port of the .then() logic.
* Address latest Codex findings: stale detection, model swap, cache
- Clear detectedAgents (and skip the network call entirely) when the panel
leaves a loopback base, instead of leaving a previous loopback detection
result marked 'installed' for a command that now targets a LAN/tunnel/
remote host.
- Add a separate, network-free correction effect keyed on the live
activeGgufVariant: if codex was auto-picked while a GGUF model was loaded
and the user then switches to a transformers-backed model while this panel
stays mounted, steer away from codex instead of leaving a command that
unsloth_cli's _require_gguf_for_codex will now reject. Never touches a
manual pick.
- Drop coding-agents.ts's module-lifetime cache. Installed-CLI detection is
environment state, not a persisted setting, so a stale positive/negative
from before the user installed something (or reopened the tab) is worse
than one extra cheap local API call per mount; keep only the in-flight
de-dupe for concurrent callers.
Verified the correction-effect logic (gguf->non-gguf swap with/without a
fallback, still-gguf no-op, manual pick never overridden) with a standalone
port of the effect.
* Make the codex/GGUF auto-pick symmetric in both directions
The correction effect only steered away from codex when the model stopped
being GGUF; it never steered back toward codex if the model became GGUF
*after* a non-GGUF-gated fallback had already picked something else (e.g.
codex is the only detected CLI, a transformers model is loaded so the
selection correctly falls back to the claude default, then the user loads a
GGUF model while the panel stays mounted -- codex never gets reconsidered).
Consolidate into one effect that re-derives the preferred detected agent
from scratch whenever detectedAgents or activeGgufVariant changes, in either
direction, instead of only reacting to the codex-specific downgrade case.
The fetch effect now only populates detectedAgents/availableAgents; this
effect is the single source of truth for what gets auto-picked from that
list. Never overrides a manual choice.
Verified both transition directions plus the manual-pick-survives and
initial-detection cases with a standalone port of the derivation logic.
* Reset the auto-pick to the default when it stops being trustworthy
Two more real gaps from the latest Codex pass on d988f52:
- The unified derivation effect only handled the case where a *different*
detected agent could take over. If codex was the only detected agent and
auto-picked while a GGUF model was loaded, then the model stopped being
GGUF, 'preferred' came back undefined and the effect silently left the
selection on codex -- exactly the command unsloth_cli's
_require_gguf_for_codex now rejects. Fall back to DEFAULT_AGENT in that
case instead of leaving it untouched.
- Leaving a loopback base cleared detectedAgents (so the 'installed' badges
correctly disappear) but left whatever agent had been auto-picked from
that now-stale, server-side-only detection still selected. Reset to
DEFAULT_AGENT there too, unless the user picked by hand.
Introduces a shared DEFAULT_AGENT constant instead of repeating the "claude"
literal at each reset site. Verified all five cases (both new resets, both
manual-pick-survives variants, and the existing multi-detected-agent
fallback still preferring another compatible agent over resetting) with a
standalone port of the effects.
* Derive GGUF-ness from the actual loaded state, not just the variant string
activeGgufVariant only covers an HF-repo GGUF pick (a specific quant
variant string). A direct local .gguf file -- custom folder, LM
Studio, or drag-drop -- is just as much a GGUF the codex preflight
(unsloth_cli's _require_gguf_for_codex) would accept, but it never has
a "variant" to report, so it read as non-GGUF here even though
/api/inference/status correctly reports is_gguf: true for it. That
mismatch could leave a Codex-only install not auto-selected, or reset
an auto-picked Codex, for a model that actually supports it.
Combined activeGgufVariant with activeNativePathToken (covers the
drag-drop/picked-file case) and ggufContextLength (only ever populated
when the backend last reported is_gguf: true for the active model, see
applyActiveModelStatusToStore) so all three paths a model can be GGUF
through are covered, matching the same is_gguf-or-equivalent check
hasGgufSource already applies to a staged pick elsewhere in this
codebase.
* Clear stale native-path token on a non-GGUF status refresh
When a native (drag-dropped or picked) GGUF was loaded and the backend later
switches to a transformers model outside the UI load path, refresh() adopts the
new /api/inference/status via setCheckpoint and applyActiveModelStatusToStore.
Those reset activeGgufVariant and ggufContextLength but never clear
activeNativePathToken, so the isGguf OR stays true after the switch and a
Codex-only detection auto-selects unsloth start codex for a non-GGUF model its
preflight rejects.
Drop activeNativePathToken in applyActiveModelStatusToStore whenever the status
is non-GGUF. A real GGUF load reports is_gguf: true, so its token is preserved
(the load path owns it); only a non-GGUF status clears it.
* Add the AGPL-3.0 header to the new studio contract test
* Fix/adjust agent detection for PR #6909
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: handle case-variant GGUF cache hits for unsloth start
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* gguf cache: keep split shards co-located and isolate cache tests properly
When a cached main shard was reused from an older snapshot, the extra shards
were resolved independently and could come from a different snapshot dir (or a
fresh download into the current ref), leaving llama.cpp unable to load a
multi-shard GGUF whose pieces are split across directories. Only reuse a cached
main shard when every sibling shard sits in the same snapshot; otherwise fetch
the whole set together so they stay co-located.
Also patch huggingface_hub.constants.HF_HUB_CACHE (not just the HF_HUB_CACHE env
var) in the two cache tests that seeded a temp cache: the snapshot lookup reads
the module constant, so the env-only override let the real cache leak in and
skip an asserted download.
* Do not let a companion-only cache snapshot shadow real GGUF variants
When listing GGUF variants from the local HF cache, a newer snapshot may
contain only a companion file (for example a vision projector fetched on
demand) while the actual quant files live in an older snapshot. The prior
scan returned the first snapshot whose vision flag was set, yielding an
empty variant list and hiding the real quants. Keep scanning older
snapshots for actual variants and carry the vision flag across snapshots.
Also record the disk-space fallback variant's size in expected_sizes so
the later cache-reuse probe can size-verify the fallback main shard
instead of only checking for its existence.
* Propagate cached repo casing to companions and preflight split co-location
Two fixes to the case-variant GGUF cache reuse:
- Resolve the requested repo id to its cached canonical casing once in
load_model, up front, and pass it to the main GGUF and its companions
(mmproj / MTP drafter). Previously only _download_gguf resolved the
casing internally, so a case-variant request loaded the main file from
the canonical cache dir while the companions kept the requested casing
and missed the cached vision projector / drafter offline. Extracted the
resolution into a shared _resolve_repo_id_casing helper.
- Apply the split-shard co-location check in the disk-space preflight. When
a split GGUF's shards are cached across different snapshots the whole set
is refetched later, so counting them as cached made the preflight read 0
bytes to download, skip the smaller-variant fallback, and then fail the
full download on a low-disk machine.
* Reuse a co-located split GGUF snapshot and fix split fallback size probe
- When reusing a cached split GGUF, scan snapshots for one that holds the
whole set co-located instead of taking the newest snapshot's first shard.
A newer snapshot with only the first shard no longer shadows an older
complete snapshot, so an already-cached split model is reused rather than
refetched (which would fail offline).
- The disk-space fallback records its size in expected_sizes only for a
single-file fallback. _find_smallest_fitting_variant returns the whole
variant size, so using it as the first shard's expected size rejected a
valid cached first shard of a split fallback and forced a re-download.
* Scan for a complete split snapshot in the preflight; require a loaded catalog hit
- The disk-space preflight now uses the same co-located snapshot scan as the
download path (_cached_colocated_split_main) instead of the newest-snapshot
probe, so a newer snapshot holding only the first shard no longer masks an
older complete one and trips the smaller-variant fallback for a fully cached
split model.
- _resolve_model only attaches to a /v1/models entry that is actually loaded
(loaded != False). /v1/models also lists cached-but-unloaded catalog entries,
and matching one by case skipped /api/inference/load and left the agent
pointed at a model that is not resident.
* Restrict cross-snapshot GGUF cache reuse to offline
Reusing a same-name blob from an older or case-variant snapshot bypasses the
Hub revision/etag check, so a repo that updates a GGUF in place could serve
stale weights online. Gate the cross-snapshot and case-variant reuse (both the
disk-space preflight accounting and the download path) on HF_HUB_OFFLINE.
Online, hf_hub_download fetches the current revision and resumes a partial
download, so the reuse is unnecessary there; offline it remains the resilience
fallback. Marked the two reuse regression tests as the offline scenarios they
represent and added an online test asserting a fresh fetch.
* Harden offline cache reuse and hub-id detection
Three follow-ups on the case-variant GGUF cache path:
- Honor every truthy HF_HUB_OFFLINE spelling (1/true/yes/on), not just "1", when
gating the cross-snapshot and case-variant cache reuse. With HF_HUB_OFFLINE=true
the Hub calls are already offline, so the reuse must trigger or the cached GGUF
fails to load; route both the preflight accounting and the download path through
the same offline parse the rest of the backend uses.
- Resolve mmproj/MTP companions from the actual cached snapshot when offline.
resolve_cached_repo_id_case can keep a partial lower-case spelling when any dir
exists under the requested casing, so an hf_hub_download on that casing misses the
canonical companion; scan every case-variant snapshot and return the cached path.
- Restrict the case-insensitive model-id match to syntactically valid hub ids
(a single namespace/name over the HF charset). A server-side relative path such
as models/Llama/Foo.gguf is no longer treated as a hub id, so it cannot
casefold-match a differently cased path on a case-sensitive filesystem. This is
host independent, unlike the local-existence probe which cannot see a server path.
* Only casefold-match model ids against a loopback Studio
A two-segment string like Models/Foo is indistinguishable from a hub id, and the
local Path.exists() probe in _is_hub_model_id cannot see a path that exists only
on a remote Studio host. So against a remote server, casefolding could attach to
a distinct server-side path (Models/Foo vs models/foo) on a case-sensitive
filesystem. Gate the case-insensitive match on is_loopback_url(base): only a
local Studio, where the existence probe is authoritative, casefolds. For a remote
Studio the match is exact and a case-mismatched request falls through to
/api/inference/load, whose already-loaded dedup resolves it correctly.
---------
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Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.com>
* fix: match qwen3-thinking chat template double-newline in response pattern
The Qwen3-thinking chat template generates `<think>\n\n` (double newline)
after the think tag, but `train_on_responses_only` was looking for
`<think>\n` (single newline).
`\n\n` is token 271 while `\n` is token 198 -- different tokens, so the
pattern match in `train_on_responses_only` fails, masking ALL tokens and
dropping 100% of training samples.
Update the response pattern from `<think>\n` to `<think>\n\n` to match
what the actual qwen3-thinking template generates.
Fixes#6919
* fix qwen3 thinking response marker
---------
Co-authored-by: Ayushman Paul <ayushman@HP>
Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
* Studio: exclude mlx-lm 0.31.3 (broke gemma4/qwen3_5 QK-norm load)
mlx-lm 0.31.3 regressed the QK-norm archs: its strict load_weights rejects the
q_norm/k_norm tensors with "Received N parameters not in model", so gemma4 and
qwen3_5 checkpoints fail to load. Studio installs the MLX stack unpinned at
latest, which pulls 0.31.3. Verified on a real macos-14 runner: gemma4 fails to
load on 0.31.3 but loads and generates coherently on 0.31.2 and on git-main
(future 0.31.4). See mlx-lm #1242.
Exclude just that release (!=0.31.3) in the installer and the self-heal floor so
--upgrade still resolves to the newest good build, and treat an already-installed
0.31.3 as unsatisfied so the self-heal replaces it.
* Studio MLX: cover fresh-install path + robust bad-version compare
Address PR review:
- Fresh install.sh (Apple Silicon) runs the base 'uv pip install unsloth' with
SKIP_STUDIO_BASE=1, skipping the guarded MLX-stack step, so transitive
resolution could still pull mlx-lm 0.31.3. install.sh already exports
UV_OVERRIDE -> overrides-darwin-arm64.txt before that install, so exclude
mlx-lm 0.31.3 there too; this also strengthens the self-heal (same override).
- Match the known-bad version with parsed packaging.Version so 0.31.3 == 0.31.3.0
(trailing-zero normalization) instead of raw string equality.
* Studio: exclude mlx-lm 0.31.3 on the fresh Apple Silicon install too
The overrides file only applies via UV_OVERRIDE when it exists relative to the
script, which is not true for a curl-piped install, and the guarded MLX step in
install_python_stack.py is skipped there (SKIP_STUDIO_BASE=1). So the base
install could still resolve the transitive mlx-lm to the broken 0.31.3. Append
mlx-lm!=0.31.3 to the base install on Apple Silicon (empty elsewhere), so the
fresh path pins away from 0.31.3 without waiting for the runtime self-heal.
* Studio: exclude mlx-lm 0.31.3 on the migrated install; keep the >=0.22.0 floor
The with-deps migrated install did not append ${_MLX_LM_EXCLUDE_ARG:-}, so a
curl-piped Apple Silicon migration (no repo overrides file, UV_OVERRIDE unset)
could resolve mlx-lm 0.31.3 transitively. Append the exclusion there, matching
the fresh install path. The no-torch migration is left alone since --no-deps
never resolves mlx-lm (same as the fresh no-torch path).
Also restore the >=0.22.0 floor in overrides-darwin-arm64.txt: a uv override
replaces the transitive constraint, so a bare !=0.31.3 could let the resolver
drop below the supported minimum that mlx_repair.py enforces at runtime.
* Triage huggingface_hub 1.22.0 / fastapi / multiprocess scanner false positives
The scan-packages gate red-failed on all three shards after transitive deps
bumped. Every new CRITICAL is a benign false positive, verified against upstream:
- huggingface_hub 1.22.0 added _sandbox.py for the remote HF sandbox feature.
Its job-startup bootstrap string (fetch sbx-server into the container /tmp and
exec it) and the SandboxPool host-reservation loop trip the staged-dropper and
C2-loop heuristics; that script runs inside a remote HF container, not on the
user machine. The bump also re-hashed the already-reviewed benign polling loops
in hf_api.py and utils/_http.py. The PyPI artifact is byte-identical to the
official v1.22.0 tag.
- fastapi 0.139.0 routing.py re-hashed the websocket keepalive while-True loop;
byte-identical to upstream 0.139.0.
- multiprocess 0.70.19 forkserver.py and tests/__init__.py re-hashed the AF_UNIX
fork-server IPC and fd-inheritance tests; genuine uqfoundation release, local
IPC not network.
Added 7 reviewed allowlist entries (no blind regenerate). All three shards
(hf-stack, studio, extras) exit 0 locally.
* Tighten mlx-lm 0.31.3 exclusion comments
* Trim mlx-lm 0.31.3 exclusion comments
* Fix external drive custom folder selection
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Update studio/backend/tests/test_linux_external_media_paths.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Keep legacy media scan validation strict
* Apply sensitive-dir denylist to legacy folder browser for PR #6799
The legacy /api/models browse endpoint gained the new /run/media mount
roots in its allowlist but not the credential/config guard that scan-folder
registration and the Hub browser already enforce. Filter sensitive names
during enumeration and reject them in _resolve_browse_target so .ssh, .aws,
.config, etc. under allowlisted roots stay unbrowseable, matching the Hub
browser. Add a public contains_sensitive_path_component helper and cover the
legacy resolver with a regression test.
* Trim redundant comments in PR #6799 changes
* Skip sensitive Linux media roots
* Reject sensitive dirs at exact browse roots for PR #6799
Both _resolve_browse_target functions only checked contains_sensitive_path_component
while walking descendant parts, so requesting an allowlisted root itself (empty
relative path) returned it unchecked. A pre-existing scan-folder row under ~/.ssh,
~/.aws, ~/.config, etc. (registerable before the denylist was added) is re-added to
the allowlist on upgrade and could then be browsed. Check the resolved target once
before returning in both the legacy and Hub browsers, and cover the root case in
both test suites.
* fix: avoid unused path helper reexports
* fix: import sensitive path helpers directly
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Co-authored-by: imagineer99 <samleejackson0@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* Studio: expose full compressed-tensors scheme set in an export formats dropdown
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: multi-select export formats, portable torchao FP8/INT8, GGUF LoRA, source parity
Export page overhaul on top of the formats dropdown:
- Unify merged precision into one sorted multi-select list (16-bit first, then
8-bit, then 4-bit). Drop "vLLM" from labels, add INT8 (W8A8), INT8 (W8A16),
INT4 (W4A16), MXFP4, MXFP8. Quick formats render as toggle pills; the rest live
in a multi-select "More formats" dropdown, so several formats export in one run.
- Add a portable torchao FP8/INT8 save path (Float8WeightOnlyConfig /
Int8WeightOnlyConfig) that needs no NVIDIA GPU to produce and loads in vLLM.
FP8 serializes to safetensors, INT8 to .bin. Wired into save_pretrained_merged
and push_to_hub_merged via a TORCHAO_EXPORT_SCHEMES registry and
_unsloth_save_torchao, parallel to the compressed-tensors path.
- Hide NVIDIA-only compressed-tensors formats when no NVIDIA GPU is present; keep
16-bit and portable FP8/INT8. The backend also rejects a compressed request on
non-NVIDIA hardware so it stays authoritative.
- Relax merged export to non-PEFT models so Local Model and Hugging Face sources
get the same 16-bit / compressed / portable options.
- GGUF: send the whole quant list in one call (merge once, quantize many).
- LoRA: add a GGUF adapter option (convert_lora_to_gguf.py) with an outtype
select (f16/bf16/f32/q8_0/auto), alongside the safetensors adapter.
- Thread the new fields through models, routes, orchestrator, and worker; extend
the export tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: gate export by accelerator with a torch-aware reason; fix export save dir naming
Export runs through Unsloth, which requires a compute accelerator (NVIDIA/AMD/Intel
GPU or Apple MLX) and has no CPU code path, so a bare-CPU host cannot export even
with PyTorch installed. Add export_capability() in utils/hardware that reports
export_supported plus a precise reason so the UI stops showing a generic "no GPU":
- pytorch_not_installed: a --no-torch install (even a physical GPU is unusable)
- no_accelerator: PyTorch present but no supported accelerator (bare CPU)
- mlx_unavailable: Apple Silicon where the MLX stack is missing or too old
Expose the fields on /api/system/hardware and /api/system, and guard the mutating
export routes (load-checkpoint, export/merged|base|gguf|lora) with HTTP 400 and the
reason, leaving read-only endpoints usable so the Export page still renders.
Make core/export/export.py import without PyTorch and without a usable accelerator
(the Unsloth import is caught) so the export worker degrades to a clear message
instead of crashing at import.
Frontend: keep /export reachable on chat-only hosts and gray out the method and
format options with the backend reason (Alert plus disabled MethodPicker) instead
of silently redirecting to /chat, so users see why export is unavailable.
Also fix the export save directory producing "model/null" for Local Model and
Hugging Face sources that have no run/checkpoint, naming the folder from the model id.
* CI: validate Studio export capability gating on Linux, Windows and macOS
Add a small pytest matrix that runs studio/backend/tests/test_export_capability.py
on ubuntu-latest, windows-latest and macos-latest. It confirms, on each real OS,
that hardware.export_capability() reports the right decision and reason
(pytorch_not_installed, no_accelerator, or mlx_unavailable) and that the export
backend imports without PyTorch and degrades to a clear message instead of crashing.
Hosted runners have no GPU/MLX, so this covers the "export unavailable, here is why"
path a Mac/Windows user without an accelerator sees; a real accelerator export is
validated separately. The job installs only a CPU PyTorch plus the backend import
deps (no unsloth, triton, or llama.cpp), so it runs in seconds with no GPU.
* Studio export: address Codex review (source-aware gating, GGUF LoRA token/MLX/guard)
Frontend (export-page):
- Gate LoRA and quantized-model restrictions on the active source. isAdapter /
isQuantized come from the selected checkpoint; in Local Model / Hugging Face
("model") source mode they were stale, so LoRA stayed wrongly enabled for a
direct base model (backend then rejects "No adapter to export") and a stale
"quantized" flag disabled every method for an unrelated, exportable model. Add
effectiveIsAdapter / effectiveIsQuantized (false outside checkpoint mode) and use
them in the method-reset effect and the MethodPicker disabled state.
- Hide the GGUF LoRA option on a macOS/MLX host (the backend rejects GGUF LoRA on
MLX), so users no longer pick it, wait through the load, and always fail. Disable
the "GGUF adapter" button on a Mac host and never send loraGguf there.
Backend (core/export/export.py):
- Pass the HF token into the GGUF LoRA conversion (save_pretrained_gguf), so a
gated/private base model's config fetch in convert_lora_to_gguf.py is
authenticated; without it the load can succeed but the conversion fails.
- Guard the save_pretrained_gguf capability check with getattr so an older Unsloth
model that lacks the method returns the clean "not supported" message instead of
an AttributeError that surfaces as a generic 500.
* Studio export: address 2nd Codex review (CI index, empty merged, test import)
- studio-export-capability-ci.yml: add --extra-index-url https://pypi.org/simple to
the torch install so torch's transitive deps still resolve; --index-url alone
replaces PyPI with only the CPU wheel index, which does not serve all of them.
- export-page handleStart: reject an empty merged selection (mirrors canExport), so
clicking the panel's Start button with every precision pill deselected no longer
submits mergedSelections: [] and launches an unintended default 16-bit export.
- test_export_imatrix_compressed: the torchao-registry test now reads unsloth/save.py
as text (like the other ast/string checks) instead of `import unsloth.save`, which
raised ModuleNotFoundError in the CPU studio-backend suite that has no unsloth
installed.
* Studio export: make comments succinct across the export changes
* Studio export: use load token for local GGUF LoRA export of gated bases
* Studio export: harden portable torchao path and gate multi-format Hub push
torchao (_unsloth_save_torchao):
- merge to an isolated temp staging dir so a co-selected 16-bit output at save_directory is not deleted
- narrow VLM detection to vision_config / ForVisionText2Text so T5/BART/Whisper are not misrouted
- forward trust_remote_code (from auto_map) to the reload so custom-code models export
Export UI:
- hide portable torchao formats on macOS/MLX (backend rejects quantized export there)
- restrict a Hub merged export to a single format (each writes to the repo root)
* Studio export: torchao tokenizer remote-code + XPU offload, scale GGUF timeout
torchao (_unsloth_save_torchao):
- honor auto_map in the staged tokenizer/processor configs (not just model.config) when
deriving trust_remote_code, so custom-code tokenizers reload after the merge
- offload single-device XPU models to CPU (and empty the XPU cache) before the reload, matching
the CUDA path, so an Intel GPU that fits the model once does not OOM on the second copy
Export orchestrator:
- scale the GGUF wait timeout by the number of requested quants so a multi-quant list export of a
large model does not time out at a flat 3600s
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* Studio export: show portable torchao formats only on non-NVIDIA (CPU) hosts
Portable torchao FP8/INT8 is the fallback for hosts without the NVIDIA compressed-tensors path.
On an NVIDIA GPU the compressed-tensors FP8/FP4/INT formats are the intended path (llm-compressor
auto-installs), so hide the portable duplicates there; keep them on CPU / non-NVIDIA hosts and
continue hiding them on macOS/MLX.
* Studio export: report all output folders and the exported formats
- Multi-format merged export now collects every sibling output directory (one per selected
precision) instead of only the last; the success banner lists them all.
- Show the selected precision formats in the run summary (a Formats row, like GGUF Quantizations),
so the panel says what is being exported rather than just 'Merged Model'.
- Persist the selected formats in the run summary and seed them on mount, so navigating away and
back (or toggling the export method) restores the selection instead of resetting to 16-bit.
* Studio export: list all output formats, add GGUF LoRA target, default Q8_0, auto-select newest checkpoint
- Progress/summary panel now shows a Formats row with the selected merged
formats, and the success banner lists every output folder a multi-format
merged run creates (one line per format) instead of only the last one.
- Merged format selection is seeded from the active run, so navigating away
and back (or switching method cards) no longer resets it to 16-bit.
- GGUF / Llama.cpp now offers an Export target toggle (Full model or LoRA
adapter) for adapter checkpoints, reusing the LoRA GGUF export path.
- Removed the Auto GGUF LoRA output type and defaulted to Q8_0 in the UI,
the request model, and the backend defaults; the outtype list is now
Q8_0/F16/BF16/F32. Core save.py still accepts auto for external callers.
- When a finetune has no checkpoint selected, auto-select the newest one.
* Studio torchao export: robust reload class + optional VLM import
Two fixes to the portable torchao FP8/INT8 export reload, from review of the
narrowed VLM detection:
- Encoder-decoder seq2seq checkpoints (T5/BART/Whisper) are not causal LMs.
With the narrowed is_vlm test they now correctly skip the image-text class,
but fell through to AutoModelForCausalLM and failed to reload after the merge.
Reload them with their own architecture class from the config instead.
- AutoModelForImageTextToText was imported unconditionally at the top of the
torchao path, so on Transformers builds without that class the import aborted
every torchao export (even text-only). Import it lazily only for a VLM, with
the AutoModelForVision2Seq fallback used elsewhere in Unsloth.
* Studio: enable FP8/FP4 compressed export for newer-transformers models
The shipped llm-compressor 0.10.x pins transformers<=4.57.6, so FP8/FP4 export failed
for models needing a transformers 5.x sidecar (Qwen3.5, Gemma-4, Qwen3-Next): the
quantization subprocess crashed importing the removed TORCH_INIT_FUNCTIONS.
Run the quantization against a dedicated llm-compressor-main "shadow": a --target
package dir (transformers 5.10.2 + llm-compressor main + compressed-tensors) layered
over the existing torch. It installs --no-deps so torch is never touched (works on any
Studio torch build), is provisioned lazily and fingerprint-cached, and can be turned
off with UNSLOTH_DISABLE_LLMCOMPRESSOR_MAIN.
- transformers_version.py: provision + validate .venv_llmcompressor.
- export.py: route all compressed exports through the shadow when available; else keep
the workspace 0.10.x path and fail fast past its transformers ceiling.
- save.py: launch _compressed_quantize.py with a clean PYTHONPATH = shadow.
- _compressed_quantize.py: skip linear_attn / vision tower / MTP modules (matches the
RedHatAI and NVIDIA reference quants, and is required by the grouped schemes).
Verified all four schemes (fp8, w8a8, w4a16, mxfp4) on Qwen3.5-9B and Llama-3.2-1B, and
fp8 on Gemma-4, end to end through Studio.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix GGUF LoRA export tests
* Fix export CI expectations
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: wasimysaid <112766706+wasimysaid@users.noreply.github.com>
Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.com>
* Add --with-llama-cpp-dir flag to install.ps1 and install.sh
Users can now pass --with-llama-cpp-dir /path/to/llama.cpp to the
installer to skip downloading or building llama.cpp and use a local
directory instead. A junction (Windows) or symlink (Linux/macOS) is
created at the canonical install location, bypassing both the prebuilt
download (Phase 3) and source build (Phase 4) steps in setup.ps1/setup.sh.
The path is passed via UNSLOTH_LOCAL_LLAMA_CPP_DIR env var which
setup.ps1 and setup.sh read directly.
Ported from the idea in unslothai/unsloth#4384, reimplemented against
current Studio architecture.
* test: add static wiring test for --with-llama-cpp-dir flag
Cross-checks install.sh, install.ps1, studio/setup.sh and studio/setup.ps1
so the flag's contract (parse -> UNSLOTH_LOCAL_LLAMA_CPP_DIR env var -> link
local dir, skip prebuilt download and source build) can't silently regress.
Wired into studio-backend-ci.yml alongside the other tests/sh installer tests.
* Address review feedback on --with-llama-cpp-dir flag
- setup.ps1: delete an existing junction/symlink via DirectoryInfo.Delete()
instead of a recursive remove, which can traverse the link and wipe the
user's real llama.cpp directory on PowerShell 5.1.
- setup.ps1: short-circuit the build chain when a local dir is linked so CMake
never runs inside the user's checkout when it lacks a Windows-layout binary.
- install.sh / setup.sh: resolve paths with CDPATH= cd -P so a set CDPATH
cannot corrupt the resolved path.
- install.sh: seed _WITH_LLAMA_CPP_DIR from UNSLOTH_LOCAL_LLAMA_CPP_DIR so an
exported env var (piped-install style) is honored instead of being clobbered.
- setup.sh: create the root llama-quantize shim when linking a local source
build so GGUF export's check_llama_cpp() still finds it.
- setup.sh / setup.ps1: drop a stale link before the custom-home ownership
assert so re-runs with the flag stay idempotent.
- test: pin the new linked-dir build short-circuit.
* Harden --with-llama-cpp-dir against Codex/Gemini review findings
- install.sh: error when --with-llama-cpp-dir is the final arg with no path,
matching the existing --package/--python post-loop guards (was a silent
fallback to the normal prebuilt/source install).
- studio/setup.sh: canonicalize LLAMA_CPP_DIR before the self-link no-op
compare. _RESOLVED_LOCAL is fully resolved while LLAMA_CPP_DIR was textual,
so a symlinked $HOME made the guard miss and the rm -rf could wipe the
user's real llama.cpp tree.
- studio/setup.sh: make the llama-quantize shim non-fatal; it writes through
the link into the user's tree, which may be read-only (shared/CI cache),
and under set -e a failed ln aborted an otherwise-good reuse.
- studio/setup.ps1: detect a broken junction via Get-Item -Force instead of
Test-Path so a dangling link from a prior run is removed and mklink can
relink to a new valid directory.
- studio/setup.ps1: use Copy-Item -LiteralPath so a source path containing
[ ] isn't treated as a wildcard in the junction copy fallback.
- tests: update the wiring assertions for the LiteralPath copy and the
canonicalized compare.
* Validate/reuse local llama.cpp tree and guard the in-use case
Addresses the second Codex pass on the --with-llama-cpp-dir flag:
- Validate the linked tree before disabling installs (setup.sh + setup.ps1):
reusing a local dir skips BOTH the prebuilt download and the source build,
so the dir must already contain a runnable llama-server (build/bin on
Linux/macOS, build\bin\Release\llama-server.exe on Windows). Bail out with a
clear message instead of linking an unbuilt/wrong-platform checkout and
leaving Studio with no usable binary.
- Treat a canonical-path target as already linked when it holds a build
(setup.sh + setup.ps1): point the flag at ~/.unsloth/llama.cpp itself and an
existing build is reused (skip prebuilt + source) rather than clobbered by
the staged prebuilt installer (which uses os.replace()/replace). An empty
canonical dir still falls through to the normal in-place install.
- Abort when an in-use llama.cpp can't be removed on Windows (setup.ps1):
Remove-Item -ErrorAction SilentlyContinue can silently leave a locked tree
in place; detect that and stop with the same active-process message + exit 3
the prebuilt path uses, instead of junctioning over a half-present dir.
Left as follow-up (already tracked by the PR author as a non-blocker): the
in-app "Update llama.cpp" updater does not yet recognize a local-link install
as externally managed; that fix belongs in studio/backend/utils/llama_cpp_update.py.
* Accept all backend llama-server layouts in --with-llama-cpp-dir validation
The linked-tree validation only accepted build/bin[/Release]/llama-server, but
LlamaCppBackend._layout_candidates() resolves a root-level llama-server first,
then build/bin, then build/bin/Release on Windows. A `make` build or a flat
release extract (binary at the dir root) was therefore rejected with a hard
installer failure even though Studio would have run it.
Validate the same candidate set the backend uses in both setup scripts, and add
wiring-test assertions so the check can't silently narrow again.
* Treat --with-llama-cpp-dir local links as externally managed
A --with-llama-cpp-dir install junctions/symlinks the canonical llama.cpp dir to
the user's own checkout, but two backend paths still treated it as a Studio-owned
tree:
- The in-app updater (llama_cpp_update) offered and could apply an official
prebuilt over the link, writing through it into the user's checkout (or
failing) and silently dropping the link the flag created.
- Orphan cleanup (LlamaCppBackend._kill_orphaned_servers) resolved the linked
root into its kill allowlist, so a llama-server the user launched from the same
checkout was classified as ours and killed on startup.
Detect the canonical dir being a symlink/junction (reparse point) and treat the
install as unmanaged: get_update_status reports unsupported, start_update refuses
with reason "local_link", and the linked root is left out of the orphan
allowlist. Adds behavioral tests (link vs plain dir, updater refusal, and the
spared-vs-killed orphan control).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add behavioral shell test for --with-llama-cpp-dir linking
The existing tests/sh/test_with_llama_cpp_dir_flag.sh is a static grep of the
scripts. This adds a behavioral test that extracts the real link block from
studio/setup.sh (by content anchors, with a self-validating extraction) and runs
it against hermetic fake dirs, asserting the outcomes that matter:
- an external CMake build links and arms neither the prebuilt download nor the
source build
- a flat / make tree (root-level llama-server, no build/bin) is accepted too
- an unbuilt tree is rejected with a non-zero exit and no link left behind
- relinking over a stale link preserves the target's contents (no data loss)
- pointing at the canonical path is a no-op reuse, not a self-referential link
Symlink-identity checks run only where real symlinks exist (skipped on Windows
git-bash copy-mode); the link/skip/no-data-loss checks run everywhere. Wired into
studio-backend-ci.yml next to the static test.
* Install psutil in backend CI so orphan-cleanup tests run
The new orphan-cleanup tests import psutil for the process scan, but the Backend
CI deps step installed studio.txt plus a fixed extras list that omits it, so the
two tests failed with ModuleNotFoundError. Add psutil to both backend pytest dep
steps (kept in shared shape), and guard the import with pytest.importorskip so a
minimal env without psutil skips these tests instead of erroring.
---------
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Souravrajvi0