* Fix Studio toast close-button positioning
* Use UTF-8 for locale regression test
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden toast close-button positioning
* Limit language menu height
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Single-pass GGUF export for direct outtypes + parallel multi-quant
save_to_gguf defaulted first_conversion to model_dtype before the block
that picks the optimal base conversion, leaving that block dead since it
landed (#3356). Every default export (fast_quantized -> q8_0) therefore
ran two passes: convert HF -> 16-bit GGUF, then llama-quantize -> q8_0,
writing a 2x-size intermediate that the cleanup step deletes again.
- Route single-output exports whose type convert_hf_to_gguf.py emits
directly (f32/f16/bf16/q8_0) through one conversion pass with no
16-bit intermediate. Measured on Qwen2.5-0.5B-Instruct (8-core CPU):
bytes written 1525 MB -> 531 MB (2.9x less), peak extra disk 994 MB
-> 0, wall time neutral on local NVMe (14.8s vs 15.4s). The dequantized
q8_0 tensors are bit-identical to the two-pass output (max diff 0 over
all 290 tensors, same quant-type table). On disk-capped runtimes
(Kaggle 20 GB, Colab) the removed intermediate is the difference
between an export that fits and one that dies - see the Kaggle error
text this file already carries. imatrix runs keep the two-pass route
since only llama-quantize can apply one; explicit first_conversion is
still honored.
- Run independent llama-quantize passes two at a time when several
quant methods are requested (thread budget split between workers,
outputs byte-identical, order preserved). Measured 1.38x wall-clock
on q4_k_m+q5_k_m+q6_k. Sequential under UNSLOTH_ENABLE_LOGGING=1 to
keep subprocess logs readable; kill switch
UNSLOTH_PARALLEL_GGUF_QUANTS=0. Duplicate methods now quantize once.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Guard parallel GGUF quant on Kaggle and make multi-quant failures atomic
Each llama-quantize pass loads the whole model into RAM, so running two at once on Kaggle can OOM a host that succeeded sequentially; skip the parallel path there. On a failed multi-quant export, stop launching queued passes and remove orphaned quant outputs so a failure leaves no partial GGUFs behind, keeping the 16-bit base for retry. Also accept 0/false/no/off/empty for UNSLOTH_PARALLEL_GGUF_QUANTS so a well-meant 'false' actually disables parallelism, and add tests/saving/test_gguf_single_pass_export.py to the CI saving bucket so the new tests run.
* Preserve pre-existing outputs for canceled quant passes on failure
The parallel cleanup unlinked every requested output name, so a failed rerun could delete a valid model.<METHOD>.gguf left by an earlier successful export for a method whose pass was canceled and never ran this session. Skip canceled futures and only remove outputs from passes that actually executed.
* Gate parallel GGUF quant on available memory and preserve prior outputs
Skip the two-worker path when RAM cannot hold two full-model quantizations at once (and on Colab as well as Kaggle), so a multi-quant export that fit sequentially no longer OOMs. On failure, remove only outputs this run newly created, tracked against a pre-launch snapshot, so a rerun into an existing _gguf directory never deletes a valid artifact from an earlier export.
---------
Co-authored-by: djs <dschroers2@gmail.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
RawTextDataLoader.smart_chunk_text takes chunk_size and stride as its own
arguments, so a direct call with stride >= chunk_size bypasses the
constructor validation. In that case `start_idx += chunk_size - stride` is
non-positive, so start_idx never advances past the first window and the
chunking loop never terminates (hangs).
Re-add the chunk_size/stride guard at the top of smart_chunk_text so direct
callers fail fast with a clear ValueError. The constructor keeps its own
guard for the internal callers (defense in depth). Add a regression test
that calls smart_chunk_text directly with stride == chunk_size and
stride > chunk_size and asserts it raises instead of hanging.
Signed-off-by: Anas Khan <83116240+anxkhn@users.noreply.github.com>
* Unsloth: appearance palettes, customization options, and control restyle
Adds Standard, Classic, and Minimal color palettes to Appearance settings,
each adapting to light and dark mode. Classic is a neutral enterprise look
that reserves its blue accent for toggles, badges, and focus rings; Minimal
is strictly black, grey, and white.
Adds customization options scoped to the active mode: accent, background,
and foreground colors with an in-app color picker, UI and code fonts with a
searchable dropdown covering bundled, device, and imported fonts, font file
import, UI and code font sizes, contrast, pointer cursors, reduce motion,
font smoothing, and translucent sidebar. Settings persist through the
personalization API with backend validation and sync across devices.
Restyles core controls for a cleaner, flatter look in both modes: bordered
white input fields, fully rounded pills for single-row controls, no drop
shadows, simple straight-line chevrons replacing all rounded arrow icons,
and consistent hover tones in dropdown menus. Popovers now portal into the
open dialog so their lists scroll correctly inside modal dialogs.
Moves Language into General settings and Chat defaults into the Chat tab
above the Canvas section.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Unsloth: appearance follow-ups, font options, and settings search
Neutralizes focus and selection rings across all palettes so highlighted
elements, including typing boxes and the selected palette card, never take
the accent color. The custom accent no longer recolors rings.
Restyles the color controls as filled pills showing the hex value inside,
with text and border contrast picked from the color's luminance. Menus in
popovers now match the app's dropdown menus: rounded-lg corners, tighter
padding, accent hover rows, and a bordered search field. Popovers inside
modal dialogs are modal so their lists scroll with the wheel. Outline
buttons share the same dark fills as dropdown triggers.
Adds heading and chat font options next to the UI and code fonts, each
using the searchable font dropdown and persisting through the
personalization API. Removes the translucent sidebar option end to end.
Adds settings search: a search field at the top of the settings sidebar
that filters setting names across every tab, grouped by tab with icons,
and jumps to the tab on click.
* Unsloth: use the shared accent token for dark hover fills
The settings dialog nav, its close button, the model selector, and the
project switcher hovered with hardcoded blue tinted greys (#3a3d43,
#2d2e32) in dark mode while every menu and sidebar uses --accent. All
hover and active pill fills now use the accent token so dark hovers are
the same everywhere and adapt to the active palette.
* Unsloth: settings search polish and jump to matched setting
Widens the settings dialog to 880px and the sidebar column to 248px so
the search field has more room. The search pill aligns with the left
start of the Settings title, gets more spacing above and below, and its
icon and placeholder sit slightly further left.
Search results now jump to the exact setting: rows and sections expose
their label as a data attribute, and picking a result opens the tab,
scrolls the matched row into view, and flashes it briefly.
* Unsloth: settings search bar spans the full nav pill width
The search field now starts and ends at the same edges as the nav hover
pills instead of being inset to the title text.
* Unsloth: address review findings on motion, sync, and font limits
Reduce motion Off now opts back out of the OS reduced-motion preference
for CSS animations via a force-motion class that the media rules skip,
and forcing reduce motion On keeps the loader exceptions (spinners,
loading dots, progress bars) animating.
When the color scheme follows the system, the resolved mode is now part
of the theme store snapshot, so an OS scheme flip re-renders consumers
and reapplies per-mode custom colors instead of leaving stale inline
variables from the previous mode.
Imported fonts get an aggregate size cap (4.4M characters) on both the
frontend sanitizer and the backend model so the persisted store always
fits browser localStorage quotas, with a clear error toast when an
import would exceed it. Backend validation also tightens imported font
names (rejects CSS delimiter characters) and requires strict base64
font data URLs, matching the frontend patterns.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Unsloth: profile toggle to hide the sloth in the chat greeting
Adds a Show greeting sloth switch to Settings > Profile. The chat welcome
hides the mascot when it is off. The preference persists locally and
through the personalization API, with backend validation and tests, and
the row is reachable from settings search in all four locales.
* Unsloth: control restyle, dropdown scrolling, and palette consistency
Settings sidebar puts search on top with the tab list under a small
Settings label. Combobox popups scroll with the wheel inside dialogs by
falling back to manual list scrolling while a dialog scroll lock is
active, and the local model selector popover became modal for the same
reason. Number inputs swap native spinners for a shared grey stepper
that clamps to min, max, and step. Run settings fields in light mode use
the same white fill and border as the settings dialog. Selection and
focus rings derive from each palette's border color instead of near
black, hover borders soften the same way, the Classic sidebar stays
white like Standard, decorative greens follow the palette accent, and
meaning-carrying marks like the hub verified badge keep the brand green
in every palette.
* Unsloth: palette card selection keyed off the palette attribute
Switching palettes restyles the whole page the moment data-palette lands
on the html element, but the React re-render that moves the selection
classes arrives later, so the ring and check briefly stayed on the
previous card with the new palette's colors. The active ring and check
now key off html[data-palette] in CSS, so they swap in the same style
pass that swaps the tokens. Also adds breathing room around the settings
search bar and under the Settings label, shortens the greeting sloth
description, and renames the avatar section to Or pick a sloth profile
picture in all locales.
* Unsloth: restore neutral rings, drop the palette check, sidebar spacing
Puts the ring tokens back to their fixed per palette values and removes
the hover border darkening, undoing the derived border experiment. The
selected palette card no longer shows a check since the ring already
marks it. The settings sidebar search bar, nav pills, and search results
get a little side padding, and the Settings label lines up with the pill
text.
* Unsloth: indicator restyle, sidebar menu customization, edge fade toggle
- Derive focus and selection rings from the border color so indicators
stay 1px and adapt to every theme and palette
- Suppress mouse focus rings except on pressed controls to remove the
selection flash on the avatar and palette pickers
- Defer settings panel rendering so the active nav pill updates instantly
- Customizable sidebar user menu with drag to reorder and shortcuts to
the settings tabs
- Grey hover for the standard light palette instead of green
- Borderless controls in dark mode with fill based focus states
- Profile picture: no picture option, pencil edit icon, atomic selection
- Font dropdowns: narrower triggers and the resolved default shown as
Inter Variable (Default)
- System prompt border darkens on focus
- New appearance setting to swap edge fades for thin divider lines
- Move the theme bootstrap to an external script to satisfy CSP
* Unsloth: harden theme boot and Firefox scroll container focus
- Guard the theme and palette storage reads separately so a blocked
localStorage (private browsing) still resolves a mode from the OS
preference instead of skipping the boot entirely
- Firefox makes scrollable containers keyboard focusable and drew its
3px UA outline on them; swap it for the app's soft 1px indicator
* Unsloth: make the UI and code font settings reach the font utilities
The theme block declared the sans and mono stacks as literals, so
Tailwind inlined them into every font-sans and font-mono utility at
build time and the runtime overrides from Settings > Appearance never
applied. Reference the :root tokens instead, matching how the color
tokens already work.
* Unsloth: in-dropdown font upload, accent meters and avatar, naming cleanup
- Move font importing into each font dropdown: Upload and Select folder
sit side by side under the list, imported fonts get an inline remove,
and the standalone Import font row is gone
- Uploads reuse fonts the user already has (bundled, imported, or
installed, matched by file name with style suffixes stripped) instead
of embedding a duplicate copy; only new fonts are embedded
- Folder scan lists font files from a picked folder in every dropdown
for the session; picking one imports it through the same path
- Fallback avatar uses the control accent with a readable foreground
instead of the neutral primary that rendered black outside standard
- Monitor bars, progress defaults, sliders, and usage meters use the
control accent; warning and danger tiers stay amber and red
- User facing strings that called the app just Studio now say Unsloth
in all four locales, keeping Unsloth Studio and LM Studio intact
* Unsloth: left align the font upload actions and divide them
Upload and Select folder now read from the left like the list items,
with a short vertical rule between the two.
* Unsloth: keep sliders neutral and the chat greeting on Hellix
- Sliders are controls, not meters, so their fill goes back to the
neutral primary instead of the palette accent
- The base h1 rule reads --font-heading with !important and the chat
thread root resets that variable to the sans stack, which pulled the
greeting off Hellix; restore the stack on the greeting element
* Unsloth: move the None avatar cell last and keep footer actions on one line
- None sits after the sloth pictures instead of leading the grid
- Upload shrinks to its label so Select folder no longer wraps
* Unsloth: size the folder action to its label
Both footer actions now hug their content so the hover pill does not
stretch across the leftover row width.
* Unsloth: separators only between unrelated settings clusters
Rows inside a titled section are related, so the per row divide-y is
gone from SettingsSection. A SettingsGroupDivider marks the two real
boundaries in the theme section (colors to fonts, fonts to contrast)
and the Clear all chats row gets its destructive border back now that
divide-y no longer draws one for it.
* Unsloth: balance the two font upload actions
Both actions share the footer row evenly again; nowrap keeps Select
folder on one line at the narrower width.
* Unsloth: drop the theme section dividers and split the chat menu groups
The colors, fonts, and contrast rows read fine without rules, and the
chat menu gains its one real boundary between the pin toggles and the
disclaimer rows.
* Unsloth: normalize oversized sidebar menus and reject newline font data URLs
Two backend validation fixes in PersonalizationCustomization:
- sidebarMenu refused any list longer than the number of distinct ids
because Field(max_length) is enforced before the dedupe validator runs.
A stale or duplicated payload that would normalize to one entry per id
was rejected outright, defeating the normalizer that exists for exactly
that case. Cap the incoming list at a generous multiple so it reaches
the validator; a pathologically long list is still refused.
- The imported font dataUrl validator used re.match on a pattern ending
in $, which also matches just before a trailing newline, so
"data:font/woff2;base64,AAAA\n" passed even though the frontend JS
pattern rejects it. Use re.fullmatch for parity.
Adds covering tests for both.
* Unsloth: preview fonts in their own typeface and slim the color pills
- Every font dropdown entry, the default item, and the closed trigger
render in the font they name, falling back to the UI stack for
families the browser cannot resolve
- Color swatch pills drop from 36px to 28px so they sit closer to the
row label height
* Unsloth: drop the font row and theme section descriptions
The labels carry the meaning on their own; the mode switching note in
particular read long and confusing.
* Unsloth: let the chat greeting follow the heading font setting
The greeting stays on Hellix by default but adopts a chosen heading
font through a --custom-heading-font variable the applier sets only
while an override exists, so the thread root's sans reset for chat
prose no longer hides the user's pick from the greeting.
* Unsloth: divide the theme section clusters and align the color pill height
Separators return between colors and fonts and between fonts and
contrast, and the color pills share the 32px height of the font
dropdown triggers.
* Unsloth: color pills at half the dropdown width
Fixed w-24 against the w-48 font triggers, with tighter padding so the
hex value still fits.
* Studio: update dep-removal test after next-themes was replaced
The frontend no longer declares next-themes or imports it in src (it was
replaced by the custom theme store and boot script), so the checker now
reports its removal as a safe no-op. The C1 and C8 fixtures in
test_frontend_dep_removal.py still asserted next-themes was a used
dependency, which fails the studio frontend CI dependency-removal safety
check. Update C1 to expect a no-op PASS and drop next-themes from the C8
expected failures so the suite matches the checker's correct output.
* Studio: remove unused ageLabel and exportCollectionJsonl helpers
* Studio: fix blocked-storage theme desync, search jump race, font validation
- theme-store.ts: keep an in-memory currentTheme/currentPalette so a selected
value survives when localStorage is blocked (private browsing). The snapshots
previously re-read empty storage and reverted React state to the default while
the DOM already changed. The matchMedia handler no longer re-reads storage, so
it cannot clobber the in-memory choice; cross-tab storage events still adopt.
- settings-dialog.tsx: the search jump waited a single fixed 60ms for the
deferred tab panel to render, then silently missed under render lag. Retry
across animation frames until the target row exists, then scroll and flash.
- settings.py: apply the font-name character check to the four selected-font
fields (uiFont/headingFont/chatFont/codeFont), and forbid backslash, comma,
slash and control characters so a name cannot escape the quoted CSS
font-family or smuggle extra fallbacks. Adds covering tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix appearance customization edge cases for PR #7077
- Reset all local preferences now also clears palette and appearance customization
- Number input wrapper keeps full width so fields fill their flex/grid cell, and the stepper stays pinned to the field edge
- Number stepper snaps to the min anchored step grid like the native spinner instead of leaving a step-invalid value
- Code font now applies to chat code fences and inline code via a dedicated token
- Reduce motion (on/off) is honored by onboarding/tour confetti and the theme toggle view transition
- Re-importing a font under the same name with new bytes now swaps the FontFace
- Keep local customization when a synced record predates the customization field, and re-push it
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Align client font name sanitization with server validation for PR #7077
sanitizeFont now strips the same characters the backend _FONT_NAME_FORBIDDEN
rejects (backslash, slash, comma, backtick) plus control chars, so a locally
chosen font name can no longer pass the client but fail the personalization PUT
and silently stall appearance sync.
* Address follow-up review items for PR #7077
- Number input wrapper carries React Flow interaction classes (nodrag/nopan/nowheel) so clicking the stepper arrows increments instead of dragging the node
- Preserve local palette and greeting-sloth toggle when the synced record predates those fields, and re-push them, mirroring the customization handling (new paletteSaved and greetingSlothSaved response flags)
- Add settings-search scroll targets (data-settings-label) for the Profile title, description, display name, nickname, and avatar shape rows
* Preserve absent personalization fields on PUT for PR #7077
A stale client that omits palette or customization previously had those
defaults materialized by model_dump() and persisted, which flipped
paletteSaved/customizationSaved to true and defeated the legacy detection.
The PUT now dumps only the request's set fields and merges them onto the
stored record, so omitted fields keep whatever was already stored.
* Persist theme and palette via a fixed allow-list for PR #7077
The theme/palette values reach setTheme/setPalette from the authenticated
personalization sync, which made the CodeQL clear-text-storage query treat
writing them to localStorage as storing sensitive data. Store a re-derived
literal from a constant map instead, so a plain UI preference is not tracked
as sensitive; behavior is unchanged.
* Harden imported-font handling for PR #7077
- syncImportedFonts: a rejected FontFace.load() only clears the registry entry
if it still points at that face, so a same-name re-import while the old load
was pending is no longer untracked/leaked.
- Cap imported-font names to the backend length (100) so an over-long name can
no longer pass the client but fail the personalization PUT and stall sync.
- Add a backend test that a stale PUT preserves an existing stored palette and
customization (not just that absent fields stay absent).
* Return the merged personalization record from PUT
The PUT /personalization handler returned the request payload, which
Pydantic had already filled with defaults for any field the client
omitted. A partial or stale write (for example a client sending only
theme) therefore got back a response that contradicted both storage and
the next GET: preserved fields like palette and the custom font showed
their defaults instead of the stored values.
Return model_validate(merged) so the response mirrors what was stored.
The stored record is still the full merged dict, so legacy fields the
model does not know about are preserved as before.
* Fix small UI and keyboard-focus defects in appearance settings
- Settings search now scrolls to the result within its destination tab
instead of a same-named row in the previously rendered deferred tab
(for example "Storage" and "Models folder" appear in both General and
Resources).
- The reduce-motion segmented control honors its own Off/On/System choice
by reading useReducedMotionConfig instead of the OS-only useReducedMotion.
- The color picker saturation/value area is operable by keyboard, so the
role="slider" surface responds to the arrow keys it advertises.
- Profile avatars and palette cards show a visible keyboard focus ring
again.
- Guard the persisted appearance-customization write so a blocked or full
localStorage does not throw out of a store action, matching the theme
store.
- Import the appearance store symbols from the settings feature barrel.
* Tighten appearance fix comments
---------
Co-authored-by: shimmyshimmer <shimmyshimmer@users.noreply.github.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: resolve llama.cpp prebuilts via the release-assets CDN to avoid GitHub API rate limits
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: resolve manifest-named prebuilt assets on the download-host fast path
Add tag-pinned CDN URLs for any manifest artifact whose hash is keyed under an
upstream-tag alias in the checksum asset, so the fast path resolves the same
assets the API path does. Cover the resolve body directly (only download_bytes
stubbed) and soften the doc's validation-equivalence wording.
* Studio: pin llama.cpp fast path to the releases/latest redirect tag
Derive the authoritative latest tag from GitHub's /releases/latest redirect
target instead of trusting the checksum asset's self-reported release_tag, so the
existing release_tag cross-check in parse_approved_release_checksums is a real
check again: a stale or mis-tagged checksum asset now falls back to the API. Pin
every fast-path URL to that tag. Fall back to the API on a manifest 404 as well,
since an in-progress release can publish the checksum asset before the manifest,
matching the sha256 404 handling. Document the releases/latest (created_at /
make_latest) versus published_at ordering divergence and why it is an accepted,
mitigated tradeoff.
* Studio: drop the llama.cpp prebuilt-resolution doc
Remove studio/docs/llama-cpp-prebuilt-resolution.md and the docstring pointer to
it; the resolution rationale (the created_at/make_latest vs published_at ordering
nuance) stays inline in _download_host_latest_release_tag.
* Studio: tighten llama.cpp download-host fast-path comments
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Handle linked instruction files in Bash cleanup
* Limit instruction cleanup to managed dependencies
* Make Bash cleanup test portable
* Run junction cleanup regression on Windows
* Keep instruction cleanup CI focused
* Studio: remove AGENTS.md from install artifacts
* Studio: prune CLAUDE.md from install artifacts
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix Studio instruction cleanup edge cases
* Trim Studio cleanup comments
* Make Studio cleanup safe on PowerShell 5.1
* Fix Studio cleanup ownership boundaries
* Simplify Windows link detection
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix SyntheticDataKit.chunk_data emitting chunks over max_tokens
The multi-chunk path built boundaries from np.linspace(..., n_chunks), but
pairing boundaries[:-1] with boundaries[1:] turns N points into N-1 ranges,
so it produced one fewer, oversized chunk: every chunk exceeded max_tokens
and a document just over the threshold came back as a single unsplit chunk.
Use n_chunks + 1 points so exactly n_chunks ranges are emitted, each within
max_tokens.
Also base n_chunks on the non-overlapped span: consecutive chunks overlap by
overlap, so covering length needs ceil((length - overlap) / stride) chunks, not
ceil(length / stride). The looser count over-counted by one just past a stride
multiple (a 673-token doc became 3 chunks of ~267 instead of 2 of ~369),
emitting an extra redundant chunk. Coverage and overlap are unchanged and every
chunk still stays within max_tokens.
* Condense chunk_data comments and clarify over-split test for PR #7073
---------
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Restore dropped FP8 weight_scale_inv tensors on load
Some block-scale FP8 checkpoints (for example Qwen3.6-27B-FP8, issue #6200) load
with transformers leaving an mlp.gate_proj as a plain bf16 Linear instead of an
fp8 module. Its raw quantized values are read into the bf16 weight and the
weight_scale_inv is dropped as an unexpected key, so the weight is used un-scaled
and the base model is garbage (perplexity around 2 million).
After load, for every checkpoint weight_scale_inv whose live weight is not fp8,
dequantize the orphaned weight in place using the block scale from the checkpoint
index. Modules that were converted correctly keep an fp8 weight and are skipped,
so healthy checkpoints and single-file checkpoints are a no-op.
Verified on Qwen3.6-27B-FP8: 64 gate_proj scales restored, perplexity 2028902 to
8.9. No-op on Qwen3-8B-FP8 (all scales already live).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden FP8 weight_scale_inv restore from review
- Skip restore when the model has no fp8 weights, so an intentionally
dequantized load (load_in_16bit) is never re-scaled and corrupted.
- Thread revision, subfolder and cache_dir through the index and shard
downloads so scales come from the same snapshot as the weights.
- Cover unsharded single-file model.safetensors checkpoints (no index).
- Handle transposed block-scale layouts and skip on a true grid mismatch
instead of applying a wrong scale.
- Match text-only VLM loads where the language_model prefix was stripped.
- Restore on the FastLanguageModel text path too, not only vision.
- Handle a scalar weight_block_size; per-tensor error handling so one bad
tensor cannot abort the rest or hide a partial mutation.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address second review round on FP8 scale restore
- Bound peak memory: dequantize block views in place with the fp32 scale
broadcast instead of materializing a full expanded scale and fp32 copy,
so a near-VRAM-limit load is not pushed into OOM by the repair.
- Restore on the sequence-classification load path too.
- Cover more VLM key remappings (language_model.model.* to
model.language_model.*) when matching modules.
- Skip the restore for variant loads (variant=...) rather than risk
applying default-checkpoint scales to variant weights.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Align FP8 scale restore revision with the loaded weights and warn on disk-offloaded layers
In llama.py the CausalLM/SequenceClassification weight loads resolve model_name on its
default branch (revision is not forwarded there), so read the dropped weight_scale_inv
tensors from the same default branch instead of the requested revision, avoiding rescaling
default-branch weights with scales from another revision.
In loader_utils.py a disk-offloaded layer keeps its weight on the meta device until the
offload hook materializes it, so the scale cannot be applied in place. Skip such layers
explicitly and print a warning rather than silently leaving them unscaled.
* Tighten comments in the FP8 scale restore path
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
parse_direct_linux_release_bundle and direct_linux_release_plan are no
longer reached by any live code path. Fork Linux installs resolve through
_fork_manifest_release_plans -> _linux_published_attempts, and the upstream
(ggml-org) path uses direct_upstream_release_plan. The dead parser also
called _resolve_linux_bundle_profile, which no longer exists, so its CUDA
branch would raise NameError if ever executed.
Drop both functions and the obsolete TestDirectLinuxNvidiaCpuGate; its live
equivalent TestLinuxPublishedAttemptsNvidiaCpuGate already covers the
NVIDIA no-silent-CPU behaviour.
* Keep native RoPE scaling when extending context; carry rope_theta for linear
When max_seq_length exceeds a model's native window, the loader overwrote the
model's rope_scaling with linear scaling. For models that already ship a scaled
RoPE (llama3/yarn/longrope) that is far worse for long context, and on
transformers v5 the linear dict omitted rope_theta (v5 keeps it under
rope_parameters), so the rotary base fell back to 10000 and broke past ~8K tokens.
Keep the native scaling and just widen the window; only synthesize linear for
plain-RoPE models, and carry rope_theta so v5 keeps the real base.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Only preserve native llama3 when extending context; keep linear fallback otherwise
The patched attention constructor (patch_llama_rope_scaling) rebuilds only linear,
llama3 and longrope and its longrope branch reads a top-level
original_max_position_embeddings, so preserving yarn or a nested-only longrope config
would raise during construction on transformers <= 4.47.1. Keep only llama3 native;
yarn/longrope/other types fall back to the linear override, still carrying rope_theta.
* Correct long-context extension comment to match llama3-only preservation
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix fast_gemv crash on compressed-tensors FP8 models
Loading a compressed-tensors FP8 checkpoint (for example
unsloth/Llama-3.2-1B-Instruct-FP8-Block) with fast_inference=False and
running a forward crashed with 'Parameter object has no attribute absmax'
inside fast_gemv.
A compressed-tensors CompressedLinear exposes an already dequantized bf16
weight at forward time while keeping a weight_scale Parameter. The quant
state resolution in get_lora_parameters/get_lora_parameters_bias fell back
to that weight_scale, so a bf16 weight was routed into the bitsandbytes
fast_gemv/fast_dequantize path, which expects a bitsandbytes QuantState
with an absmax attribute.
Only fall back to weight_scale_inv/weight_scale when the weight is still
fp8. A decompressed bf16 weight then resolves to no quant state and flows
through the normal bf16 path, which already handles bias and the LoRA
backward. Real fp8 and bitsandbytes 4bit weights are unchanged.
* Skip the fast_gemv dispatch test before importing unsloth when bitsandbytes is absent
* Fix Windows installer torch index override
* Clear inherited uv index env vars for pinned installs in studio/setup.ps1 (#6898)
* Harden setup.ps1 index-var clearing to truly remove vars (#6898)
* Apply UV_DEFAULT_INDEX torch index fix to Linux/Mac install.sh (#6898)
* Neutralize all uv index env vars for pinned torch installs (#6898)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix FastSentenceTransformer Qwen embedding preprocessing
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Document Transformer.load embedding modality fix for #6881
* Harden #6881 fix and add forwards/backwards-compatible regression tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fall back to Transformer constructor on legacy sentence-transformers without Hub-capable load
* Mirror legacy sentence-transformers fallback in embedding-parity tripwire test
* Tighten #6881 comments and docstrings
* Skip embedding-parity test on CPU-only runners since FastSentenceTransformer requires CUDA
* Honor the transformer module's saved subfolder when loading
modules.json records a path for the Transformer module (root for
decoder embedders like Qwen3-Embedding, 0_Transformer for the classic
layout). Pooling/Normalize already load from their saved path; thread the
same path into Transformer.load as subfolder so config and tokenizer
resolve like stock ST. stays a no-op, so single-module models are
unchanged.
* Make embedding-parity test bf16-aware
fp16 overflows to NaN on bf16-native embedders such as EmbeddingGemma
(Gemma3), producing a false parity failure. Prefer bf16 when the GPU
supports it so the tripwire can guard the full documented embedding
matrix (Qwen3-Embedding, EmbeddingGemma, BGE-M3, all-MiniLM, GTE-ModernBERT),
not just fp16-safe models.
---------
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Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Retry the Studio UI shutdown re-login on transient goto timeout
The Chat UI Playwright smoke intermittently failed at the pre-shutdown
re-login: page.goto('/login') can hit a 60s TimeoutError on a slow runner
even while the server is healthy, and the surrounding except only tolerated
ERR_ABORTED / interrupted-navigation, so a plain timeout hard-failed the job.
Wrap the re-login goto/wait/fill/submit in the same 3-attempt retry the
change-password step already uses (recover_or_replace_page between tries,
per-attempt fail screenshots, wait_for_health pre-gate). The composer wait
stays outside the loop so a retry never re-navigates after login has set
tokens (which would redirect to /chat via the guest guard); it remains the
authoritative confirmation, so a genuinely broken login still fails.
* Catch transient login-request failures and preserve error listeners on recovery
Wait on the /api/auth/login POST inside the retry (via click_and_wait_for_response)
so a transient 4xx/5xx is retried in-loop instead of surfacing only at the
out-of-loop composer wait, matching the change-password step. When
recover_or_replace_page swaps in a fresh page, re-attach the pageerror/console
listeners so error tracking survives the replacement.
* Stabilize floating monitor drag
* Restore floating monitor exit animation
* Harden Windows Studio smoke checks
* Keep API menu badge removed
* Apply no-build-tools env overrides in-script
The runner does not apply step-level env keys containing parentheses,
so ProgramFiles(x86) kept its real value and Find-VsBuildTools still
detected VS through vswhere. Set the overrides inside each pwsh step
instead; child processes inherit them. The resolver step moves to pwsh
because bash cannot export a variable named ProgramFiles(x86).
* Reset chat UI session without a second browser context
macOS runs Chromium with --single-process, where closing the last
context tears down the whole browser, so the shutdown re-login died
with TargetClosedError on new_page. Clear cookies and swap pages
inside the same context instead, opening the replacement page before
closing the old one.
* Keep the no-build-tools Path filtered across session refreshes
install.ps1's Refresh-SessionPath and setup.ps1's Refresh-Environment
rebuild the session Path from the Machine and User registry scopes, so
the process-level filter could be undone mid-install and re-expose
CMake. Filter those scopes in the Prepare step with normalized dir
matching and restore them in cleanup.
* Drop stale localStorage auth tokens before re-login
Auth tokens live in localStorage, not cookies, and the login guest
guard redirects on their mere presence. Remove them during the session
reset so the /login navigation is deterministic instead of relying on
the tolerated redirect bounce.
* 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: source CPU llama.cpp prebuilts from the unslothai fork
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: reject unknown Linux CPU arches and keep ROCm-tooling hosts off the CPU prebuilt
* Studio: extend the resolve-prebuilt ROCm-tooling guard to Windows
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: let ROCm-SDK-only CPU hosts take the fork CPU prebuilt
* Studio: accept windows-arm64 prebuilt kind and refresh stale fork-routing comments
* Studio: correct stale fork-routing comments and --resolve-prebuilt help
* Refresh stale ggml-org routing 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>
* 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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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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---------
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Co-authored-by: danielhanchen <danielhanchen@gmail.com>
Co-authored-by: wasimysaid <112766706+wasimysaid@users.noreply.github.com>
* version-compat CI: fake CPU training runs for SFT/GRPO/DPO
Adds a runtime layer on top of the patch-run canary: actually runs
trainer.train() for a couple of steps on a CPU-only runner under the CUDA
spoof, wrapping a plain tiny HF model in the Unsloth-patched trainer. Exercises
the real train() loop (collation, generation, the injected
_get_per_token_logps_and_entropies, loss, backward, optimizer) so a TRL or
transformers change that breaks the loop at runtime -- not just the source
structure -- surfaces here. No GPU, no meaningful numerics.
Needs a chain of small CPU shims (eager torch.compile, dynamo suppress, cuda
tensor-alloc redirect to CPU, model.for_training/for_inference equivalents)
documented inline. Does not exercise Unsloth's Triton/GPU kernels (CPU can't).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* cpu fake-train: force adamw_torch + disable dynamo for CPU runner
On a real CPU-build torch runner (GitHub CI) two things bit that a CUDA-build
torch with GPUs hidden masked locally:
- The default optimizer is adamw_8bit (bitsandbytes), whose is_on_gpu() check
dies on CPU tensors. Force optim=adamw_torch in all three configs.
- import unsloth reinstalls the real torch.compile over the eager passthrough,
so the GRPO hot path (chunked_selective_log_softmax) actually compiles and
inductor picks the spoofed CUDA device, crashing on device props
(gcnArchName). Re-apply the eager passthrough after import and flip
torch._dynamo.config.disable so every @torch.compile runs eager at call time.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* cpu fake-train: write checkpoints under pytest tmp_path
Use pytest's tmp_path for each trainer's output_dir instead of a hardcoded
relative temp/ci_* path, so a local pytest run does not leave untracked dirs in
the repo tree and the tests are CWD-independent.
* version-compat CI: disable dynamo at process level for the fake-run job
Set TORCHDYNAMO_DISABLE / TORCH_COMPILE_DISABLE in the fake-run step env so
dynamo/inductor is off before conftest.py's early import unsloth, not only via
the per-test runtime shim. Defense in depth on the GPU-less runner: the GRPO
hot path never compiles regardless of when its functions were decorated.
---------
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* Fix PEFT replacement for TRL >= 1.7.0, add missing compute_aux_loss for TRL 1.7.0
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix GRPO for TRL >= 1.7.0: PEFT ref-adapter removal and return arity
rl.py: for trl >= 1.7.0, scope the PEFT removal regex to the ref-adapter
block only by anchoring the end on ref_param.data.copy_(param.data), so it
no longer also deletes the following gradient-checkpointing
enable_input_require_grads() block. Neutralize TRL 1.7.0's
`if _is_quantized_model:` bf16 cast the same way the existing
is_loaded_in_4bit cast is handled.
rl_replacements.py: initialize _extra_moe_kwargs before use (it was
referenced before assignment whenever compute_aux_loss was passed) and only
request output_router_logits when the aux loss is actually wanted.
rl_replacements.py: _get_per_token_logps_and_entropies now returns a 3-tuple
(logps, entropies, aux_loss) for trl >= 1.7.0 and a 2-tuple for older TRL,
matching how every TRL call site unpacks the result. Without this, TRL 1.7.x
_generate_and_score_completions unpacks 3 values from a 2-tuple and raises
"not enough values to unpack (expected 3, got 2)".
* Return zero aux_loss placeholder and drop inference-mode aux collection
* GRPO TRL >= 1.7.0: reject router aux-loss opt-in at init; drop zero aux placeholder
Unsloth's optimized GRPO forward cannot compute the MoE router auxiliary loss.
Previously an explicit opt-in (router_aux_loss_coef > 0) returned a fabricated
zero, silently training without the requested load-balancing penalty. Now reject
it at trainer init with a clear NotImplementedError, and return None (not zero)
for the aux slot of TRL's 3-tuple. Default stays off (coef 0), so the common
path is unaffected.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* GRPO hidden-states fallback: free ModelOutput before chunked log-softmax
The old/ref logprob fallback binds the full ModelOutput (which holds every
layer's hidden_states when output_hidden_states=True) and kept it alive across
chunked_hidden_states_selective_log_softmax, an avoidable OOM on large models.
Extract logits then del outputs in both the text and VLM branches.
* Version-compat CI: proactively catch TRL GRPO breakage
The existing TRL canary is a static symbol/source grep: it verifies symbols
exist but is blind to structural changes (TRL 1.7.0's 2->3-tuple per-token-logps
return arity and restructured PEFT ref-adapter block, which the fix in this PR
addresses, both slipped past it because the methods still existed).
Two additions:
- test_trl_grpo_pinned_symbols.py: extend TRL_TAGS to 1.5/1.6/1.7 and pin the
exact source-string contracts the rl.py / rl_replacements.py transforms depend
on for TRL >= 1.7.0 (PEFT elif ref-adapter block + enable_input_require_grads
survival, if _is_quantized_model, aux_loss_enabled anchor, compute_aux_loss
arity). A future TRL change fails on main a few days before the PyPI release.
- test_trl_grpo_fake_run.py + a version-compat-ci job: fake-CUDA run that drives
the real GRPO/SFT/DPO source-transform patchers against latest + main TRL on a
CPU-only runner (no training) and asserts the generated Unsloth trainer still
satisfies the transform contracts. Catches behavioral regressions the grep
cannot see.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fake-run test: use a normal Version import for the aux gate
* version-compat CI: fix fake-run job gate + torch-absent collection
- Drop the invalid job-level matrix if (matrix is not available in
jobs.<id>.if -> 'Unrecognized named-value: matrix' fails the whole
workflow). Use a single job that runs vs TRL latest always and re-runs
vs TRL main only on schedule/dispatch via a step-level github.event_name
guard. Validated with actionlint.
- Module-level skip the fake-run test when torch is absent so
daily-fresh-fetch (pytest-only, collects tests/version_compat/) does not
crash on the top-level spoof import.
* fake-run test: do not skip on import failure
unsloth/trl are installed in the grpo-fake-run job, so a failing import is the
import-time drift this canary must catch. Keep only the not-installed find_spec
skips; let a real import error fail the test.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* GRPO arity gate: regex downgrade + fail loud + CI coverage
The TRL < 1.7.0 per-token-logps return downgrade was an exact-string replace
anchored on the full return line incl. its comment, so a reformat (e.g.
pre-commit) could silently no-op it and ship a 3-tuple to older TRL. Switch to
a regex tolerant of comment/whitespace drift, and raise if the anchor stops
matching (re.subn count != 1) instead of failing silently. Add a monkeypatched
trl_version unit test asserting both arities, since CI only installs TRL >= 1.7.0
and never exercised the downgrade otherwise.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fake-run: give SFT/DPO a real contract, not just ast-parse
The SFT/DPO fake patch runs only checked the generated trainer parses. Also
assert the shared QLoRA _is_quantized_model bf16 cast is neutralized (TRL 1.7's
spelling, present in both sft_trainer and dpo_trainer), so a structural TRL
change to that block is caught for SFT/DPO too, not just GRPO.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* GRPO PEFT ref-adapter removal: lower gate to the TRL 1.4.0 floor
The elif is_peft_model(model) and args.beta != 0.0: ref-adapter block was
introduced in TRL 1.4.0 and is unchanged through 1.7.x, but the removal was
gated at >= 1.7.0, so for 1.4 <= TRL < 1.7 the transform fell through to the
0.27 branch (which matches the older if is_peft_available()... form) and
silently no-oped: a PEFT + beta != 0 GRPO run then computed the KL reference
from the copied ref adapter instead of the base model. Lower the gate to
1.4.0 and keep the 1.7.0-only router aux-loss fail-fast nested. Widen the
pinned-symbol contract test to run from 1.4.0 so the covered versions are
actually exercised.
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Move New badge to System settings tab
Show the "New" badge on the System tab and drop it from Connections.
* Stabilize refresh revocation UI test
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---------
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* Studio: add assistant response details panel
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Hide model badge by default, show on hover/focus
Wrap MessageResponseModelBadge in a span with hidden/group-hover visibility classes to reduce visual clutter. The badge now only displays when hovering or focusing on the assistant message, improving the UI presentation. Updated corresponding tests to verify the new CSS classes.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Add RDNA 2/3/4 ROCm routing tests via a CPU-only torch spoof
Introduces tests/_zoo_rocm_spoof.py, the ROCm sibling of _zoo_aggressive_cuda_spoof.py: it reuses the CUDA spoof's torch.cuda no-op machinery and overlays an AMD Radeon identity (torch.version.hip, gcnArchName, capability) for any RDNA 2/3/4 gfx target, so hip code paths run on CPU-only CI with no AMD hardware.
tests/studio/install/test_rocm_rdna_routing.py then asserts unsloth_zoo routes every RDNA arch (gfx1030/1031/1032/1034, gfx1100/1101/1102, gfx1150/1151, gfx1200/1201) correctly: device_type resolves to hip, llama.cpp target resolves to (rocm, gfx), and the per-family ROCm bundle suffix (gfx103X/gfx110X/gfx120X, or self for gfx1150/1151) is picked. The torch-facing checks run in a subprocess so the spoof never leaks into sibling tests and DEVICE_TYPE (cached at import) resolves from a clean process; the pure gfx-family mapping runs in-process. Guarded by importorskip so it runs where torch and unsloth_zoo are installed (the Repo tests CPU job) and skips elsewhere.
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---------
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* Guard RoPE scaling against the transformers v5 buffer blank; honor extended factor
Add a family-agnostic guard that builds each rotary from a scaled config,
blanks its non-persistent buffers (what transformers v5 does on load), runs
loader._fix_rope_inv_freq, and asserts every buffer is restored to its scaled
value (llama3 and longrope). This catches the whole bug class, not just the
one call site, and is validated to fail on the pre-fix repair.
Also make LlamaExtendedRotaryEmbedding read the llama3 factor from the config
instead of hardcoding 8 (wrong for Llama-3.2, factor 32), falling back to the
Llama-3.1 defaults when built without a config.
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* Pass config into extended rotary codegen; skip v5 round-trip on transformers 4.x
- patch_llama_rope_scaling now builds the llama3 extended rotary with
config=self.config so it reads the real factor (32 for Llama-3.2) instead
of falling back to 8; the template already references self.config.
- test_v5_blank_repair_roundtrip now skips when loader._NEEDS_ROPE_FIX is
False, since _fix_rope_inv_freq is a no-op on transformers 4.x and cannot
restore the blanked buffers there.
* Raise stream deadlock-guard timeouts from 0.2s to 5.0s in passthrough tests
These asyncio.wait_for guards bound test setup and cross-task event
signaling that complete near-instantly on success; the 0.2s budget is a
latency assertion in disguise and times out under CI scheduling load
(seen on the 3.11 matrix leg while 3.10/3.12/3.13 pass the same commit).
5.0s matches the timeout used elsewhere in the suite and still fails fast
on a real hang. No test relies on the guard expiring.
* Extended rotary reads rope_parameters as well as rope_scaling
transformers v5 stores llama3 scaling under config.rope_parameters and
exposes rope_scaling only as a back-compat property. Reading that property
works on 5.0-5.13 (verified: factor resolves to 32 for Llama-3.2), but a
future release may drop the shim, after which the subclass path would fall
back to factor 8. Read either field so the factor survives the rename.
Adds test_extended_rotary_reads_rope_parameters_v5 (fails on the old
single-field read: rope_parameters-only config resolves to 8, not 32).
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---------
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* WSL ROCm: generalize ROCm-on-WSL bootstrap from Strix-only to any RDNA arch
install_rocm_wsl_strixhalo.sh hardcoded gfx1151, so its verify step died on
discrete Radeon cards even though the ROCm + librocdxg setup is arch-agnostic.
Auto-detect the GPU arch from rocminfo (override via UNSLOTH_WSL_GFX), verify any
GPU agent enumerates over DXG, and map the arch to AMD's per-arch wheel family for
the optional smoke test (injecting librocdxg into torch/lib so torch's bundled
ROCr finds the DXG bridge). Verified on gfx1200 (Radeon RX 9060 XT) in WSL2 +
Ubuntu 24.04 -- torch.cuda now enumerates the GPU.
* WSL ROCm: trigger the ROCm-on-WSL bootstrap for discrete Radeon GPUs too
_maybe_bootstrap_rocm_wsl only fired for Strix APUs (matched via /proc/cpuinfo,
which discrete cards don't appear in). Add _wsl_amd_gpu_name() -- queries the
Windows host via WMI -- and broaden the trigger gate plus the 'already-usable
ROCm' rocminfo check from gfx1151-only to any real GPU agent (gfxNNNN, excluding
the gfx11-generic fallback ISA). The generalized bootstrap then auto-detects the
arch. Enables 'curl install.sh | sh' to set up ROCm-on-WSL on discrete Radeon RX
7000/9000 in WSL2 + Ubuntu 24.04, not just Strix Halo/Point.
* WSL ROCm: address review -- filter generic ISA in bootstrap, bound the host GPU query
- install_rocm_wsl_strixhalo.sh: exclude the gfx11-generic fallback ISA in arch
detection (grep -v generic), matching install.sh's rocminfo check, so a generic
agent listed before the real one can't be picked as the arch.
- install.sh: wrap the powershell.exe Win32_VideoController query in _run_bounded
(10s timeout) so an unstable WSL-interop / busy host can't hang the installer.
* WSL ROCm: harden arch-detect + librocdxg copy under set -eo pipefail (review)
- _detected_gfx: append '|| true' so a no-GPU rocminfo (empty pipeline, non-zero
under pipefail) doesn't abort the assignment before the '[ -z ]' branch prints
the diagnostic + die message.
- smoke-test librocdxg copy: gate on '[ -d "$_tlib" ]' instead of '[ -n ]' so a
non-directory value can't make cp rename librocdxg to 'lib'.
* WSL ROCm: address Codex review (gfx000, 24.04 reroute for discrete, test locator)
- Exclude gfx000 (the CPU agent) from the WSL 'usable ROCm' check and the bootstrap
arch-detect: match gfx[1-9] (nonzero arch), so a partial ROCm install that only
reports the CPU ISA no longer short-circuits the librocdxg setup. (P2)
- Reuse the Ubuntu-24.04 reroute for discrete Radeon: broaden
_maybe_reroute_strixhalo_to_2404's gate with the same _wsl_amd_gpu_name (WMI)
fallback, so a discrete card on 26.04 reroutes to a 24.04 distro like Strix does
instead of falling to CPU. Moved _wsl_amd_gpu_name above the reroute and made it
self-contained + 10s-bounded (it runs before _run_bounded is defined). (P2)
- Update TestInstallShDropinPersistence to locate the gate by its unique
'!/generic/' clause now that the gfx1151 literal is gone. (P1)
* Condense ROCm-on-WSL comments in install.sh and bootstrap helper
* Guard WSL reroute from NVIDIA hybrid hosts and fix GFX-override pipefail check
* Honor CUDA_VISIBLE_DEVICES-hidden NVIDIA in the WSL reroute guard
* Reuse _has_usable_nvidia_gpu in the WSL reroute guard
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Fix repeated base model downloads across checkpoint exports (#6890)
Pre-warm the HF hub cache with the 16bit base weights before
merge_and_overwrite_lora runs. The merge fetches shards with
hf_hub_download(local_dir=...), which never populates the hub cache, so
temporary merge directories (GGUF checkpoint exports) forced a full
re-download of the base model for every checkpoint. The first export now
downloads once into the cache and later exports copy from it.
Skips itself when already cached, offline, on Kaggle/Colab, for local or
nf4/fp4 bases, non-downloading save methods, or low disk. Opt out with
UNSLOTH_PREWARM_HUB_CACHE=0.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Show MB for small base models in the pre-warm download message
* Harden pre-warm: getattr for model config, abspath for relative HF_HUB_CACHE
- Read config._name_or_path via getattr so a model without a config skips
cleanly instead of taking the outer error path.
- abspath the cache probe so a relative HF_HUB_CACHE walks up to a real root
rather than "", which would zero the free-space check and skip pre-warm.
Both from PR review; each covered by a test that fails without the fix.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Pre-warm the live-env HF cache so runtime redirects still hit (#6890)
Resolve the hub cache the same way the merge does (unsloth_zoo _active_caches,
live env) instead of huggingface_hub's import-time-frozen constants.HF_HUB_CACHE,
and pass it as cache_dir to the cached probe, disk check and snapshot_download.
Without this, a runtime HF_HOME/HF_HUB_CACHE redirect (unsloth_zoo
redirect_hf_cache_if_readonly on a read-only default cache, or Studio) makes the
pre-warm populate a different directory than the one the merge reads, so the
cache-copy fast path misses and the base re-downloads on every export anyway.
Adds 3 regression tests covering the cache_dir threading and the redirect case.
* Apply ruff-format kwarg spacing to the pre-warm cache-dir changes
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* Pre-warm the 16bit sibling for FP8 bases so their merged_16bit exports reuse the cache too
For a merged_16bit export of an FP8 base with an existing 16bit sibling, the merge
swaps to the sibling and downloads that (unsloth_zoo _resolve_fp8_16bit_sibling), so
pre-warming the FP8 repo missed the cache and re-downloaded the sibling every export.
Mirror the swap and pre-warm the sibling. No sibling still caches the FP8 repo for the
in-place dequant path. Adds 2 regression tests.
* Filter pre-warm shards through the safetensors index like the merge does
Repos that ship a leftover shard set the index does not reference (e.g. granite-3.2)
made the disk gate over-count and snapshot_download fetch shards the merge never reads.
Mirror the merge: on the download path, keep only index-referenced shards. Runs after
the already-cached check so the cached fast path stays network-free. Adds 2 tests.
* Tighten pre-warm comments
---------
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* Fix llama3 RoPE scaling dropped on transformers v5
transformers v5 loads on meta then blanks non-persistent buffers, so
_fix_rope_inv_freq rebuilds inv_freq after load. It recomputed a vanilla
inv_freq and applied _apply_inv_freq_scaling, a no-op on the base
LlamaRotaryEmbedding used by the config/llama3 path, so inv_freq ended up
divided by 1 instead of the config factor (8 for Llama 3.1, 32 for Llama
3.2). This corrupts long-range positions and inflates long-context loss
about 3-5x. transformers 4.x was unaffected.
Route __init__ and the v5 repair through one _unsloth_recompute_inv_freq
so they cannot diverge, and stash the config on the rotary module so the
repair can rebuild the same scaled value.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add test for llama3 RoPE scaling under the transformers v5 repair
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Update RoPE drift guard for the recompute refactor and guard the v5 repair
The drift guard's AST tripwire asserted the config-scaling call lived in the
if config is not None branch of LlamaRotaryEmbedding.__init__. The fix moved
that into _unsloth_recompute_inv_freq, so follow it there (with a fallback to
the old inline branch) and add a guard that loader._fix_rope_inv_freq rebuilds
inv_freq through the same helper. Also add a CPU functional check of the helper
and drop the redundant standalone test.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Honor an explicit sdpa or flex_attention request when flash is disabled
When flash attention is disabled for a model, the fallback selection could
downgrade a caller who explicitly passed attn_implementation='sdpa' or
'flex_attention' to a different backend, because the disable reason is
flash-specific. Keep an explicit non-flash request as-is; flash requests
still fall back as before.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Tighten comments
* Gate honor-explicit attention on provenance and flex support
Only honor an explicit non-flash attention request when it comes from the
caller argument, not from a config value the loaders synthesize (the language
path seeds attn_implementation=sdpa). Honor explicit flex_attention only when
supports_flex_attention is True so excluded/broken configs (e.g. gpt_oss) fall
back instead of selecting a known-broken backend. Explicit sdpa stays honored.
* Honor explicit sdpa through the resolver guard
* Keep SDPA exclusions when honoring an explicit sdpa request
An explicit attn_implementation="sdpa" was re-enabling sdpa for models in
_SDPA_EXCLUDED_MODELS (e.g. gpt_oss) where sdpa is known-broken: the helper
honored the request and the resolver's final not-supports_sdpa guard skipped
the eager downgrade for any explicit request. Honor an explicit sdpa only when
the model is not sdpa-excluded, mirroring the flex guard that already falls
back for _FLEX_EXCLUDED_MODELS via supports_flex_attention. Conservative
supports_sdpa=False (large head dim / attention-sink models) still honors an
explicit sdpa; a synthesized/default sdpa still downgrades to eager.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Honor DISABLE_SDPA_MODEL_NAMES when honoring explicit sdpa
The honor-explicit-sdpa guard only skipped the sdpa->eager downgrade for models
in _SDPA_EXCLUDED_MODELS (gpt_oss). Gemma3/Gemma3Text disable SDPA through the
loader's DISABLE_SDPA_MODEL_NAMES (their bundled SDPA modules are wrong), so an
explicit sdpa request bypassed the downgrade and re-enabled a known-wrong path.
Extend _is_sdpa_excluded to also treat DISABLE_SDPA_MODEL_NAMES membership as
excluded, replicating the loader's trailing-comma substring match so gemma3 and
gemma3_text match but gemma3n does not. Move the constant into _utils.py (single
source of truth, re-exported from loader.py) to avoid a loader -> _utils cycle.
Conservative supports_sdpa=False models not in either list still honor explicit
sdpa.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Scope MoE expert LoRA detection to actual MLP projection targets
_moe_target_set_from_string treated any regex containing the substring mlp
or ffn as targeting the expert MLP projections. Unsloth's auto-generated
attention-only regex lists mlp, ffn and feed_forward as allowed intermediate
path segments while its final group matches only q_proj/k_proj/v_proj/o_proj,
so attention-only finetuning on MoE models silently enabled expert LoRA as
well: the experts were trained and every MoE layer paid the extra expert LoRA
grouped matmuls. Detect expert intent from the projection names themselves
(gate_proj/up_proj/down_proj/gate_up_proj) instead of the mlp substring.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Tighten comments
* Detect MoE expert LoRA via mlp path segment, not proj names
The auto-generated target regex always lists every projection leaf
(q/k/v/o and gate/up/down), so keying detection on a proj name mis-fired:
it enabled expert LoRA for attention-only regexes and dropped the
mlp/ffn path regexes. Key on the mlp/ffn/feed_forward/experts path
segment instead, which is present only when the MLP/experts are actually
targeted. Add a regression test for the attention-only case.
* Scope expert LoRA targets to the leaves a regex names
An mlp path alternative with attention-only leaves, for example
(mlp|self_attn).(q_proj|o_proj), no longer enables expert LoRA, and a
regex naming a single expert leaf such as .*experts.*down_proj now
targets only that projection instead of the whole broad set. Generic
mlp projections (.*mlp.*proj) and the auto regex mlp tag block keep the
broad set for fused-expert models whose leaves are plain Parameters.
* Route explicit leaf list into MoE expert detection
An attention-only explicit target_modules list routed through get_peft_regex
for family scoping (e.g. FastVisionModel with vision layers off) yields a
regex carrying the full mlp|feed_forward|ffn|dense component block even though
its leaf group only names q/k/v/o_proj. Keying expert detection on that regex
trained the experts for a language-only/attention-only request. Use the
caller's original leaf list for detection; only the auto path uses the regex,
where the mlp block is the sole MLP-intent signal on fused-expert models.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Respect finetune_mlp_modules and finetune_language_layers scope for MoE expert detection
When an explicit leaf list that names MLP projections (gate_proj/up_proj/down_proj)
is routed through get_peft_regex under finetune_mlp_modules=False, the scoped regex
correctly drops the MLP leaves, but MoE expert detection was still keyed on the
original list and re-added mlp.experts.* via target_parameters, training the experts
the caller had frozen. Same gap for finetune_language_layers=False on vision-only runs.
Prefer the original list only when MLP and language families are both in scope
(preserving the attention-only fix); otherwise honor the scoped result so the frozen
family is respected. Factored the choice into _select_moe_detection_targets with unit
tests over the full selection matrix.
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Handle odd shapes and non-float scales in FP8BlockQuantLinear
Small fp8 checkpoints (e.g. tiny test models) break the block-quantized
linear in three ways: weight scales stored in a float8 dtype such as
float8_e8m0fnu have no triton dtype mapping; activations whose hidden dim is
not a multiple of the activation quant block fail act_quant's divisibility
assert; and weights whose dims are not multiples of the weight block cannot
be tiled by the triton dequant kernel.
Cast non-float scales to float32 on entry, and when the hidden dim does not
divide into the activation block, dequantize the weight and run a plain
matmul instead of the fp8 block matmul. The dequant goes through a new
shape-safe helper that falls back to a torch-native scale expansion when the
weight does not tile evenly; backward uses the same helper so the gradient
path works for every shape the forward accepts. Full-size checkpoints are
unaffected.
* Add tiny / e8m0 fp8 block-quant regression test
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix FP8 block-quant fallback: real block size in dequant and scalar-scale fast path
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Route rectangular fp8 blocks through torch dequant and keep block_size across e8m0 upcast
The triton weight_dequant kernel uses one BLOCK_SIZE for both axes, so
rectangular blocks (block_size[0] != block_size[1]) mis-index the column
scale and corrupt grad_X. Route those through the torch scale expansion,
which handles each dimension independently, and keep the triton path for
square blocks only.
Also preserve a block_size attribute carried on the scale tensor across the
e8m0 -> float32 upcast so the later lookup no longer falls back to [128, 128].
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix export-time trust_remote_code bypass in FP8/INT8/GGUF-LoRA export
The torchao, compressed-tensors, and LoRA GGUF export paths re-read the merged
checkpoint and used to set trust_remote_code from the checkpoint config's static
auto_map (the torchao path also scanned the staged tokenizer/processor configs).
A model that loads with built-in Transformers classes can carry an auto_map entry,
which skips the load-time remote-code consent scan (that only runs when the load
already requested trust_remote_code) yet flips trust_remote_code on at export,
running unvetted custom code.
Derive the reload trust_remote_code from the approved load decision instead: a new
_loaded_via_remote_code() checks whether the in-memory model / tokenizer was itself
loaded from custom code (its class lives in the transformers_modules package),
walking PEFT / wrapper layers. Built-in-loaded models no longer gain trust from
config metadata; genuine custom-code models (loaded with consent) still reload
correctly. Add CPU-only regression tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Harden _loaded_via_remote_code against a None/missing __module__
Read type(node).__module__ via getattr and require a string before startswith,
so a dynamically created or C-extension class with a None module does not raise
during export. Add a regression test.
* Split model and tokenizer trust for the compressed subprocess, walk processor components
The compressed-tensors export collapsed model and tokenizer trust into
one --trust-remote-code flag, so an approved custom tokenizer would have
let an unapproved model's custom code run inside the quantization
subprocess. The subprocess now takes --trust-remote-code-tokenizer for
the processor load and keeps --trust-remote-code for the model loads,
matching the torchao path's separate model_trust / tok_trust.
_loaded_via_remote_code now also walks processor components (tokenizer,
image_processor, feature_extractor, video_processor), so an approved
custom tokenizer held inside a built-in ProcessorMixin keeps its trust
on the export reload instead of failing with trust_remote_code=False.
The walk is a bounded BFS with a seen set so wrapper cycles terminate.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix: skip fp16/bf16 validation for full finetuning in RL trainers
When doing full finetuning (FFT) of a bfloat16 model, the fp16/bf16
mismatch validation fires before the corrective logic runs, causing a
misleading error even though the code would properly handle it downstream.
Skip the validation when full_finetuning is active.
Fixes#6731
* Fix: auto-correct fp16/bf16 mismatches for full finetuning before validation
Instead of entirely skipping validation (which could let mismatches
through when mixed_precision_dtype is float32), auto-correct explicit
fp16/bf16 settings that conflict with the model's dtype for FFT. This
way the existing validation still catches real mismatches for non-FFT
cases, and the corrective logic below handles the normalized settings.
Fixes the issue raised in Codex review of PR #6813.
* Guard Windows ROCm torchao override skip
Detect installed ROCm torch directly before applying the torchao override so Windows ROCm environments never install the crashing torchao package even if the earlier ROCm-installed flag is missing.
* Update unsloth/models/rl.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Update studio/install_python_stack.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Harden ROCm probe and sync RL precision flags
Tolerate stray stdout noise when probing Windows ROCm torch installs by checking the last non-empty output line, matching the existing torch version probe behavior. Also keep args.fp16 and args.bf16 synchronized with the full-finetuning precision auto-corrections in the RL trainer patch so downstream eval settings see a consistent TrainingArguments state.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add MLX trainer compatibility shims
Patch imported MLXTrainer and MLXTrainingConfig objects to preserve the expected dataclass field ordering and to provide a _train_dataset_for_batches fallback when older trainers or test doubles only expose train_dataset. Also add focused worker tests covering both compatibility paths.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Scope PR to Windows ROCm torchao guard
* Restore PR scope to Windows ROCm guard
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* test: cover Windows ROCm torchao skip behavior
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
Co-authored-by: Ayushman Paul <ayushman@HP>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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>
Co-authored-by: imagineer99 <samleejackson0@gmail.com>
* Fix TrainingArguments silently disabling unsloth gradient checkpointing
* Cover loaded adapters and preserve explicit None in GC restore
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---------
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>
