unsloth/studio/setup.sh
Michael Han d5cf96d628
Studio: add local speech-to-text dictation engine (#7095)
* 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

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* 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.

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* 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

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* 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

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* 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

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* 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

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* 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

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* 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.

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* 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.

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* 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.

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* 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>
2026-07-23 01:39:03 -07:00

2109 lines
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Executable file

#!/usr/bin/env bash
# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
REPO_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
RULE=$(printf '\342\224\200%.0s' {1..52})
# ── Parse flags ──
# --local: install from the local repo checkout (overlays unsloth as editable
# and unsloth-zoo from git main). Mirrors install.sh --local for the Colab
# path that runs setup.sh directly without going through install.sh.
if [ "$#" -gt 0 ]; then
for _arg in "$@"; do
case "$_arg" in
--local)
export STUDIO_LOCAL_INSTALL=1
export STUDIO_LOCAL_REPO="$REPO_ROOT"
;;
esac
done
fi
# ── Maintainer-editable defaults ──────────────────────────────────────────
# Change these in the GitHub-hosted script so all users get updated defaults.
# User environment variables always override these baked-in values.
#
# _DEFAULT_LLAMA_PR_FORCE : PR number to build by default ("" = normal path)
# _DEFAULT_LLAMA_SOURCE : git clone URL for source builds
# _DEFAULT_LLAMA_TAG : llama.cpp ref to build ("latest" = newest release,
# "master" = bleeding-edge, "bNNNN" = specific tag)
# Prefer "latest" over "master" -- "master" bypasses
# the prebuilt resolver (no matching GitHub release),
# forces a source build, and causes HTTP 422 errors.
# Only use "master" temporarily when the latest release
# is missing support for a new model architecture.
#
# UNSLOTH_LLAMA_CPP_BACKEND : "auto" (default) or "cpu". When "cpu", forces
# the CPU-only prebuilt bundle on GPU hosts.
# Fixes Intel iGPU Vulkan crashes (#7213).
# ──────────────────────────────────────────────────────────────────────────
_DEFAULT_LLAMA_PR_FORCE=""
_DEFAULT_LLAMA_SOURCE="https://github.com/ggml-org/llama.cpp"
_DEFAULT_LLAMA_TAG="latest"
_DEFAULT_LLAMA_FORCE_COMPILE_REF="master"
# ── Colors (same palette as startup_banner / install_python_stack) ──
if [ -n "${NO_COLOR:-}" ]; then
C_TITLE= C_DIM= C_OK= C_WARN= C_ERR= C_RST=
elif [ -t 1 ] || [ -n "${FORCE_COLOR:-}" ]; then
C_TITLE=$'\033[38;5;150m'
C_DIM=$'\033[38;5;245m'
C_OK=$'\033[38;5;108m'
C_WARN=$'\033[38;5;136m'
C_ERR=$'\033[91m'
C_RST=$'\033[0m'
else
C_TITLE= C_DIM= C_OK= C_WARN= C_ERR= C_RST=
fi
# ── Output helpers ──
# Consistent column layout: 2-space indent, 15-char label (fits llama-quantize), then value.
# Usage: step <label> <message> [color] (color defaults to C_OK)
# Usage: substep <message> [color] (color defaults to C_DIM)
step() { printf " ${C_DIM}%-15.15s${C_RST}${3:-$C_OK}%s${C_RST}\n" "$1" "$2"; }
substep() { printf " %-15s${2:-$C_DIM}%s${C_RST}\n" "" "$1"; }
_is_verbose() {
[ "${UNSLOTH_VERBOSE:-0}" = "1" ]
}
verbose_substep() {
if _is_verbose; then
substep "$1"
fi
return 0
}
_remove_agent_instruction_files() {
local _root
for _root in "$@"; do
[ -d "$_root" ] || continue
[ -L "$_root" ] && continue
find "$_root" \( -type f -o -type l \) \( -name 'AGENTS.md' -o -name 'CLAUDE.md' \) \
-exec rm -f {} + 2>/dev/null || true
done
}
# ── Corporate-mirror / proxy escape hatch for the frontend npm/bun install (#6491) ──
# studio/frontend/.npmrc pins registry=https://registry.npmjs.org/ as a supply-chain
# lock. A project-level pin overrides a corporate user's ~/.npmrc proxy, so the install
# hits npmjs.org directly and a firewall returns 403. UNSLOTH_NPM_REGISTRY is a
# deliberate opt-in: when set we thread it as `--registry <url>` into every npm/bun
# install. `--registry` is the highest-precedence override for BOTH tools and leaves
# min-release-age / save-exact in force. Empty array (the default) expands to nothing
# under `set -u`, so normal installs are unchanged.
_NPM_REGISTRY_ARGS=()
if [ -n "${UNSLOTH_NPM_REGISTRY:-}" ]; then
_NPM_REGISTRY_ARGS=(--registry "$UNSLOTH_NPM_REGISTRY")
fi
# Failure-path capture log consumed by _suggest_npm_registry. Set to a temp file
# around the npm/bun installs; "" elsewhere so unrelated run_quiet calls don't capture.
_CAPTURE_LOG=""
# Print actionable guidance when a frontend/OXC npm/bun install fails and the registry
# lock is the likely cause (corporate firewall/proxy). No-op once the user has opted in
# via UNSLOTH_NPM_REGISTRY. We never switch registries automatically -- we only guide.
# $1 = path to a captured install log (may be empty/missing).
_suggest_npm_registry() {
[ -n "${UNSLOTH_NPM_REGISTRY:-}" ] && return 0
local _log="${1:-}"
# If we captured output and it does NOT look like a registry/network problem, stay
# quiet -- the raw error already shown is more useful than a misleading hint.
if [ -n "$_log" ] && [ -s "$_log" ] \
&& ! grep -Eqi '40[13]|ENOTFOUND|ECONNREFUSED|ECONNRESET|ETIMEDOUT|EAI_AGAIN|ConnectionRefused|failed to resolve|registry\.npmjs\.org|getaddrinfo|tunneling socket|network|proxy|self.?signed|unable to (get|verify)' "$_log"; then
return 0
fi
# Best-effort: surface a mirror the user already configured (env or ~/.npmrc).
# Read npm config from / (a dir with no project .npmrc) so the frontend's pinned
# registry= does not mask the user's ~/.npmrc / global mirror -- the caller is
# still inside studio/frontend when this runs.
local _mirror="${NPM_CONFIG_REGISTRY:-${npm_config_registry:-}}"
if [ -z "$_mirror" ] && command -v npm >/dev/null 2>&1; then
_mirror="$( (cd / 2>/dev/null && npm config get registry) 2>/dev/null || true )"
fi
case "$_mirror" in
""|undefined|null|https://registry.npmjs.org|https://registry.npmjs.org/) _mirror="" ;;
esac
printf '\n' >&2
step "frontend" "registry.npmjs.org looks blocked (corporate firewall/proxy?)" "$C_WARN" >&2
if [ -n "$_mirror" ]; then
substep "Unsloth pins the public npm registry; your mirror is being ignored." >&2
substep "Detected a registry in your npm config:" >&2
substep " $_mirror" >&2
substep "Re-run pointing Unsloth at it:" >&2
substep " UNSLOTH_NPM_REGISTRY=$_mirror ./install.sh --local" >&2
else
substep "If you use a private mirror/proxy, point Unsloth at it and re-run:" >&2
substep " UNSLOTH_NPM_REGISTRY=https://your-mirror.example/api/npm/ ./install.sh --local" >&2
fi
substep "(min-release-age and save-exact stay enforced.)" >&2
return 0
}
run_maybe_quiet() {
if _is_verbose; then
"$@"
else
"$@" > /dev/null 2>&1
fi
}
# ── Helper: run command quietly, show output only on failure ──
_run_quiet() {
local on_fail=$1
local label=$2
shift 2
if _is_verbose; then
local exit_code
"$@" && return 0
exit_code=$?
step "error" "$label failed (exit code $exit_code)" "$C_ERR" >&2
if [ "$on_fail" = "exit" ]; then
exit "$exit_code"
else
return "$exit_code"
fi
fi
local tmplog
tmplog=$(mktemp) || {
step "error" "Failed to create temporary file" "$C_ERR" >&2
[ "$on_fail" = "exit" ] && exit 1 || return 1
}
if "$@" >"$tmplog" 2>&1; then
rm -f "$tmplog"
return 0
else
local exit_code=$?
step "error" "$label failed (exit code $exit_code)" "$C_ERR" >&2
cat "$tmplog" >&2
if [ -n "${_CAPTURE_LOG:-}" ]; then cat "$tmplog" >> "$_CAPTURE_LOG" 2>/dev/null || true; fi
rm -f "$tmplog"
if [ "$on_fail" = "exit" ]; then
exit "$exit_code"
else
return "$exit_code"
fi
fi
}
run_quiet() {
_run_quiet exit "$@"
}
run_quiet_no_exit() {
_run_quiet return "$@"
}
_nvcc_meets_llama_minimum() {
# Echo "ok|too_old|unknown" then the parsed "X.Y" version, one per line.
# llama.cpp needs CUDA toolkit >= 12.4 (#4437; setup.ps1 aborts via #4517).
_nvcc_bin=$1
[ -n "$_nvcc_bin" ] || { echo "unknown"; echo ""; return 0; }
_raw=$("$_nvcc_bin" --version 2>/dev/null \
| sed -n 's/.*release \([0-9][0-9]*\.[0-9][0-9]*\).*/\1/p' \
| head -1)
if [ -z "$_raw" ]; then
echo "unknown"; echo ""; return 0
fi
_maj=${_raw%%.*}
_min_raw=${_raw#*.}
_min=${_min_raw%%.*}
if [ "$_maj" -lt 12 ] 2>/dev/null; then
echo "too_old"
elif [ "$_maj" -eq 12 ] && [ "$_min" -lt 4 ] 2>/dev/null; then
echo "too_old"
else
echo "ok"
fi
echo "$_raw"
}
# Echo a ';'-separated CUDA arch list (e.g. "86;120"). Override ($2,
# UNSLOTH_LLAMA_CUDA_ARCHS) wins verbatim; else parse+dedupe compute_cap text
# ($1). Empty means "no arch detected", so the caller builds CPU instead of a
# PTX-only binary that fails on an old driver (#5854).
_resolve_cuda_archs() {
local _raw_caps=$1
local _arch_override=$2
if [ -n "$_arch_override" ]; then
printf '%s' "$_arch_override"
return 0
fi
local _archs="" _cap _arch
while IFS= read -r _cap; do
_cap=$(printf '%s' "$_cap" | tr -d '[:space:]')
if [[ "$_cap" =~ ^([0-9]+)\.([0-9]+)$ ]]; then
_arch="${BASH_REMATCH[1]}${BASH_REMATCH[2]}"
case ";$_archs;" in
*";$_arch;"*) ;;
*) _archs="${_archs:+$_archs;}$_arch" ;;
esac
fi
done <<< "$_raw_caps"
printf '%s' "$_archs"
}
# Run a GPU probe under a 10s timeout when `timeout` is available so a wedged
# NVIDIA driver cannot hang setup; fall back to a bare call where it is not.
_setup_run_smi() {
if command -v timeout >/dev/null 2>&1; then
timeout 10 "$@"
else
"$@"
fi
}
# Returns 0 when CUDA_VISIBLE_DEVICES is set to "" or "-1", i.e. every NVIDIA
# device is deliberately hidden (mixed AMD+NVIDIA hosts steering work to the
# AMD card). Unset means all devices visible. nvidia-smi ignores this env var,
# so the probes below cannot see the distinction on their own.
_setup_cvd_hides_nvidia() {
[ "${CUDA_VISIBLE_DEVICES+set}" = "set" ] || return 1
_setup_cvd_trim=$(printf '%s' "$CUDA_VISIBLE_DEVICES" | tr -d '[:space:]')
[ -z "$_setup_cvd_trim" ] || [ "$_setup_cvd_trim" = "-1" ]
}
# Returns 0 when an NVIDIA GPU is present and usable. Primary probe is
# `nvidia-smi -L` (timeout-bounded). Fallback is /proc/driver/nvidia/gpus,
# which the driver populates per GPU regardless of nvidia-smi state -- handles
# PATH gaps and driver init races. Mirrors install.sh _has_usable_nvidia_gpu
# (PR 6174) so setup routes the same way as the torch installer. A GPU hidden
# via CUDA_VISIBLE_DEVICES=""/-1 counts as NOT usable (matches
# install_llama_prebuilt.py has_usable_nvidia), so the AMD probes still run
# and a mixed host steered to its AMD card keeps the ROCm route.
_setup_has_usable_nvidia_gpu() {
if _setup_cvd_hides_nvidia; then
return 1
fi
_setup_nvsmi=""
if command -v nvidia-smi >/dev/null 2>&1; then
_setup_nvsmi="nvidia-smi"
elif [ -x "/usr/bin/nvidia-smi" ]; then
_setup_nvsmi="/usr/bin/nvidia-smi"
fi
if [ -n "$_setup_nvsmi" ]; then
if _setup_run_smi "$_setup_nvsmi" -L 2>/dev/null \
| awk '/^GPU[[:space:]]+[0-9]+:/{found=1} END{exit !found}'; then
return 0
fi
fi
if [ -d /proc/driver/nvidia/gpus ] && \
[ -n "$(ls -A /proc/driver/nvidia/gpus 2>/dev/null)" ]; then
return 0
fi
return 1
}
_cuda_driver_max_version() {
command -v nvidia-smi >/dev/null 2>&1 || return 0
_setup_run_smi nvidia-smi 2>/dev/null \
| sed -nE 's/.*CUDA( UMD)? Version:[[:space:]]*([0-9]+)\.([0-9]+).*/\2.\3/p' \
| head -1 || true
}
_cuda_version_gt() {
local _left=${1:-}
local _right=${2:-}
if ! [[ "$_left" =~ ^([0-9]+)\.([0-9]+)$ ]]; then
return 1
fi
local _left_major=$((10#${BASH_REMATCH[1]}))
local _left_minor=$((10#${BASH_REMATCH[2]}))
if ! [[ "$_right" =~ ^([0-9]+)\.([0-9]+)$ ]]; then
return 1
fi
local _right_major=$((10#${BASH_REMATCH[1]}))
local _right_minor=$((10#${BASH_REMATCH[2]}))
if [ "$_left_major" -gt "$_right_major" ]; then
return 0
fi
if [ "$_left_major" -eq "$_right_major" ] && [ "$_left_minor" -gt "$_right_minor" ]; then
return 0
fi
return 1
}
_cuda_toolkit_major_gt_driver() {
local _toolkit_version=${1:-}
local _driver_version=${2:-}
if ! [[ "$_toolkit_version" =~ ^([0-9]+)\.([0-9]+)$ ]]; then
return 1
fi
local _toolkit_major=$((10#${BASH_REMATCH[1]}))
if ! [[ "$_driver_version" =~ ^([0-9]+)\.([0-9]+)$ ]]; then
return 1
fi
local _driver_major=$((10#${BASH_REMATCH[1]}))
[ "$_toolkit_major" -gt "$_driver_major" ]
}
_cuda_nvcc_candidate_paths() {
if command -v nvcc >/dev/null 2>&1; then
command -v nvcc
fi
if [ -x /usr/local/cuda/bin/nvcc ]; then
printf '%s\n' "/usr/local/cuda/bin/nvcc"
fi
ls -d /usr/local/cuda-*/bin/nvcc 2>/dev/null | sort -V -r 2>/dev/null || true
}
_cuda_find_compatible_nvcc_for_driver() {
local _driver_version=$1
local _exclude_path=${2:-}
local _candidate _seen _check _status _version
local _best_path="" _best_version=""
_seen="
"
while IFS= read -r _candidate; do
[ -n "$_candidate" ] || continue
[ "$_candidate" != "$_exclude_path" ] || continue
[ -x "$_candidate" ] || continue
case "$_seen" in
*"
$_candidate
"*) continue ;;
esac
_seen="${_seen}${_candidate}
"
_check="$(_nvcc_meets_llama_minimum "$_candidate")"
_status="$(printf '%s\n' "$_check" | sed -n '1p')"
_version="$(printf '%s\n' "$_check" | sed -n '2p')"
[ "$_status" = "ok" ] || continue
[ -n "$_version" ] || continue
if _cuda_toolkit_major_gt_driver "$_version" "$_driver_version"; then
continue
fi
if [ -z "$_best_version" ] || _cuda_version_gt "$_version" "$_best_version"; then
_best_path="$_candidate"
_best_version="$_version"
fi
done <<EOF
$(_cuda_nvcc_candidate_paths)
EOF
[ -n "$_best_path" ] || return 1
printf '%s\n%s\n' "$_best_path" "$_best_version"
}
_print_cuda_driver_toolkit_mismatch() {
local _toolkit_version=$1
local _driver_version=$2
local _toolkit_major=${_toolkit_version%%.*}
local _driver_major=${_driver_version%%.*}
substep "CUDA Toolkit $_toolkit_version is a major-version mismatch: toolkit major $_toolkit_major exceeds driver CUDA major $_driver_major ($_driver_version)." "$C_WARN"
substep "Update the NVIDIA GPU driver to run CUDA Toolkit $_toolkit_version, or install a CUDA $_driver_major.x toolkit." "$C_WARN"
substep "Or let Unsloth use the prebuilt CUDA bundle; it does not need the local toolkit." "$C_WARN"
}
print_llama_error_log() {
local log_file=$1
[ -s "$log_file" ] || return 0
substep "llama.cpp diagnostics (last 120 lines):"
tail -n 120 "$log_file" | sed 's/^/ | /' >&2
}
installed_llama_prebuilt_release() {
local install_dir=${1:-}
local metadata_path="$install_dir/UNSLOTH_PREBUILT_INFO.json"
[ -f "$metadata_path" ] || return 0
python - "$metadata_path" <<'PY' 2>/dev/null || true
import json
import sys
from pathlib import Path
try:
payload = json.loads(Path(sys.argv[1]).read_text(encoding="utf-8"))
except Exception:
raise SystemExit(0)
if not isinstance(payload, dict):
raise SystemExit(0)
repo = str(payload.get("published_repo") or "").strip()
release_tag = str(payload.get("release_tag") or "").strip()
llama_tag = str(payload.get("tag") or "").strip()
source = str(payload.get("source") or "").strip()
binary_repo = str(payload.get("binary_repo") or "").strip()
binary_tag = str(payload.get("binary_release_tag") or "").strip()
if not repo or not release_tag:
raise SystemExit(0)
# For non-fork sources (e.g. ggml-org upstream prebuilts) the published_repo/
# release_tag refer to the unsloth source tree while the actual binaries came
# from a different repo. Show both so the log is unambiguous.
if source and source != "upstream" and binary_repo and binary_tag and binary_repo != repo:
message = f"installed release: {repo}@{release_tag} + {source}@{binary_tag}"
else:
message = f"installed release: {repo}@{release_tag}"
if llama_tag and llama_tag != release_tag:
message += f" (tag {llama_tag})"
print(message)
PY
}
print_installed_llama_prebuilt_release() {
local install_dir=${1:-}
local installed_release
installed_release="$(installed_llama_prebuilt_release "$install_dir")"
if [ -n "$installed_release" ]; then
substep "$installed_release"
fi
}
# ── Banner ──
echo ""
printf " ${C_TITLE}%s${C_RST}\n" "🦥 Unsloth Studio Setup"
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
verbose_substep "verbose diagnostics enabled"
_LLAMA_ONLY="${UNSLOTH_STUDIO_LLAMA_ONLY:-0}"
if [ "$_LLAMA_ONLY" = "1" ]; then
substep "llama.cpp only mode"
fi
if [ "${STUDIO_LOCAL_INSTALL:-0}" = "1" ]; then
substep "local mode: overlaying $REPO_ROOT (editable) + unsloth-zoo from git main"
fi
# ── Clean up stale caches ──
rm -rf "$REPO_ROOT/unsloth_compiled_cache"
rm -rf "$SCRIPT_DIR/backend/unsloth_compiled_cache"
rm -rf "$SCRIPT_DIR/tmp/unsloth_compiled_cache"
# ── Detect Colab ──
IS_COLAB=false
keynames=$'\n'$(printenv | cut -d= -f1)
if [[ "$keynames" == *$'\nCOLAB_'* ]]; then
IS_COLAB=true
fi
# Resolve studio home + ownership marker before the llama-only split: the
# llama.cpp section needs STUDIO_HOME / _STUDIO_HOME_IS_CUSTOM, but
# UNSLOTH_STUDIO_LLAMA_ONLY=1 ('unsloth studio update') skips the base install.
# UNSLOTH_STUDIO_HOME (or STUDIO_HOME alias) overrides the install root
# (mirrors install.sh). UNSLOTH_STUDIO_HOME wins when both are set.
_studio_override_var=""
_studio_override="${UNSLOTH_STUDIO_HOME:-}"
if [ -n "$_studio_override" ]; then
_studio_override_var="UNSLOTH_STUDIO_HOME"
else
_studio_override="${STUDIO_HOME:-}"
[ -n "$_studio_override" ] && _studio_override_var="STUDIO_HOME"
fi
# Strip whitespace so " " is treated as unset (matches Python .strip()).
_studio_override=$(printf '%s' "$_studio_override" | sed -e 's/^[[:space:]]*//' -e 's/[[:space:]]*$//')
case "$_studio_override" in
"~") _studio_override="$HOME" ;;
"~/"*) _studio_override="$HOME/${_studio_override#'~/'}" ;;
esac
if [ -n "$_studio_override" ]; then
# setup.sh runs against an existing install (via 'unsloth studio update');
# a typo in the override must fail fast instead of materializing an
# empty workspace dir. Mirrors setup.ps1 behavior.
if [ ! -d "$_studio_override" ]; then
echo "ERROR: $_studio_override_var=$_studio_override does not exist." >&2
echo " Run install.sh to create the install root before 'unsloth studio update'." >&2
exit 1
fi
[ -w "$_studio_override" ] || { echo "ERROR: $_studio_override_var=$_studio_override is not writable." >&2; exit 1; }
STUDIO_HOME="$(CDPATH= cd -P -- "$_studio_override" && pwd -P)" || exit 1
else
STUDIO_HOME="$HOME/.unsloth/studio"
fi
VENV_DIR="$STUDIO_HOME/unsloth_studio"
VENV_T5_530_DIR="$STUDIO_HOME/.venv_t5_530"
VENV_T5_550_DIR="$STUDIO_HOME/.venv_t5_550"
VENV_T5_510_DIR="$STUDIO_HOME/.venv_t5_510"
_STUDIO_OWNED_MARKER=".unsloth-studio-owned"
_LEGACY_STUDIO_HOME="$HOME/.unsloth/studio"
_studio_home_canon="$STUDIO_HOME"
if [ -d "$_studio_home_canon" ]; then
_studio_home_canon=$(CDPATH= cd -P -- "$_studio_home_canon" 2>/dev/null && pwd -P) \
|| _studio_home_canon="$STUDIO_HOME"
fi
if [ -d "$_LEGACY_STUDIO_HOME" ]; then
_LEGACY_STUDIO_HOME=$(CDPATH= cd -P -- "$_LEGACY_STUDIO_HOME" 2>/dev/null && pwd -P) \
|| _LEGACY_STUDIO_HOME="$HOME/.unsloth/studio"
fi
_STUDIO_HOME_IS_CUSTOM=false
if [ "$_studio_home_canon" != "$_LEGACY_STUDIO_HOME" ]; then
_STUDIO_HOME_IS_CUSTOM=true
fi
# Directory-local evidence Unsloth created "$1": only prebuilt-installer metadata
# counts (UNSLOTH_PREBUILT_INFO.json for llama.cpp, UNSLOTH_NODE_PREBUILT_INFO.json
# for Node, UNSLOTH_WHISPER_PREBUILT_INFO.json for whisper.cpp), all written only
# by our installers. Mirrors the setup.ps1 Node guard. A markerless source build
# stays strict since this runs right before an rm -rf.
_studio_owned_adoptable() {
[ -f "$1/UNSLOTH_PREBUILT_INFO.json" ] && return 0
[ -f "$1/UNSLOTH_NODE_PREBUILT_INFO.json" ] && return 0
[ -f "$1/UNSLOTH_WHISPER_PREBUILT_INFO.json" ] && return 0
return 1
}
_assert_studio_owned_or_absent() {
_aso_dir="$1"
_aso_label="$2"
[ -d "$_aso_dir" ] || return 0
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ] && [ ! -f "$_aso_dir/$_STUDIO_OWNED_MARKER" ]; then
if _studio_owned_adoptable "$_aso_dir"; then
: > "$_aso_dir/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
return 0
fi
echo "ERROR: $_aso_dir already exists and is not marked as an Unsloth-owned $_aso_label." >&2
echo " Move it aside or choose an empty UNSLOTH_STUDIO_HOME before re-running." >&2
exit 1
fi
}
if [ "$_LLAMA_ONLY" != "1" ]; then
# ── Detect whether frontend needs building ──
# Skip if SKIP_STUDIO_FRONTEND=1 (Tauri desktop app bundles its own frontend),
# or if dist/ exists AND no tracked input is newer than dist/.
if [ "${SKIP_STUDIO_FRONTEND:-0}" = "1" ]; then
_NEED_FRONTEND_BUILD=false
step "frontend" "bundled (Tauri)"
else
_NEED_FRONTEND_BUILD=true
if [ -d "$SCRIPT_DIR/frontend/dist" ]; then
_changed=$(find "$SCRIPT_DIR/frontend" -maxdepth 1 -type f \
! -name 'bun.lock' \
-newer "$SCRIPT_DIR/frontend/dist" -print -quit 2>/dev/null)
if [ -z "$_changed" ]; then
_changed=$(find "$SCRIPT_DIR/frontend/src" "$SCRIPT_DIR/frontend/public" \
-type f -newer "$SCRIPT_DIR/frontend/dist" -print -quit 2>/dev/null) || true
fi
[ -z "$_changed" ] && _NEED_FRONTEND_BUILD=false
fi
fi # end SKIP_STUDIO_FRONTEND guard
# OXC validator runtime (below) needs node/npm whenever its dir exists, regardless
# of dist staleness; provision Node when the frontend builds OR the OXC dir exists.
_OXC_DIR="$SCRIPT_DIR/backend/core/data_recipe/oxc-validator"
if [ "$_NEED_FRONTEND_BUILD" = false ] && [ ! -d "$_OXC_DIR" ]; then
step "frontend" "up to date"
verbose_substep "frontend dist is newer than source inputs"
else
# ── Node (isolated; never touches the system Node/npm) ──
# Unsloth's frontend (Vite 8) needs Node ^20.19 || >=22.12 || >=23 and npm >= 11.
# Three sources:
# system -- system Node + npm already satisfy both; used read-only.
# bundled -- install a pinned isolated Node under $UNSLOTH_HOME/node, build-only.
# skip -- UNSLOTH_SKIP_NODE_INSTALL=1 and system unsuitable; print manual fix.
# decide_node_source(node_v, npm_v, skip_flag) -> system | bundled | skip
# (pure; unit-tested in tests/sh/test_node_decision.sh).
decide_node_source() {
_dns_node="${1#v}"
_dns_npm="$2"
_dns_skip="$3"
# Treat empty or non-numeric versions as "missing".
case "$_dns_node" in ''|*[!0-9.]*) _dns_node='' ;; esac
case "$_dns_npm" in ''|*[!0-9.]*) _dns_npm='' ;; esac
if [ -n "$_dns_node" ] && [ -n "$_dns_npm" ]; then
_dns_nmaj="${_dns_node%%.*}"
case "$_dns_node" in
*.*) _dns_rest="${_dns_node#*.}"; _dns_nmin="${_dns_rest%%.*}" ;;
*) _dns_nmin=0 ;;
esac
case "$_dns_nmin" in ''|*[!0-9]*) _dns_nmin=0 ;; esac
_dns_pmaj="${_dns_npm%%.*}"
_dns_ok=false
if [ "$_dns_nmaj" -eq 20 ] && [ "$_dns_nmin" -ge 19 ]; then _dns_ok=true; fi
if [ "$_dns_nmaj" -eq 22 ] && [ "$_dns_nmin" -ge 12 ]; then _dns_ok=true; fi
if [ "$_dns_nmaj" -ge 23 ]; then _dns_ok=true; fi
if [ "$_dns_ok" = true ] && [ "$_dns_pmaj" -ge 11 ]; then
echo system
return 0
fi
fi
if [ "$_dns_skip" = "1" ]; then
echo skip
return 0
fi
echo bundled
}
# Mirror the llama.cpp UNSLOTH_HOME derivation; the frontend build runs first.
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
_NODE_PARENT="$STUDIO_HOME"
else
_NODE_PARENT="$HOME/.unsloth"
fi
NODE_DIR="$_NODE_PARENT/node"
_SYS_NODE_VER="$(node -v 2>/dev/null || true)"
_SYS_NPM_VER="$(npm -v 2>/dev/null || true)"
NODE_SOURCE="$(decide_node_source "$_SYS_NODE_VER" "$_SYS_NPM_VER" "${UNSLOTH_SKIP_NODE_INSTALL:-0}")"
_FRONTEND_SKIP=false
if [ "$NODE_SOURCE" = system ]; then
step "node" "$(node -v) | npm $(npm -v) (system)"
elif [ "$NODE_SOURCE" = bundled ]; then
mkdir -p "$_NODE_PARENT"
# install_node_prebuilt.py uses os.replace(); guard a custom-home dir so we
# never displace a user-owned $UNSLOTH_STUDIO_HOME/node.
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
_assert_studio_owned_or_absent "$NODE_DIR" "Node install"
fi
substep "installing isolated Node (system Node/npm left untouched)..."
# Runs before the venv is activated, so bare `python` may be absent; resolve
# venv python, then python3, then python.
if [ -x "$VENV_DIR/bin/python" ]; then
_NODE_PY="$VENV_DIR/bin/python"
elif command -v python3 >/dev/null 2>&1; then
_NODE_PY="python3"
else
_NODE_PY="python"
fi
_NODE_LOG="$(mktemp)"
set +e
if _is_verbose; then
"$_NODE_PY" "$SCRIPT_DIR/install_node_prebuilt.py" --install-dir "$NODE_DIR" 2>&1 | tee "$_NODE_LOG"
_NODE_STATUS=${PIPESTATUS[0]}
else
"$_NODE_PY" "$SCRIPT_DIR/install_node_prebuilt.py" --install-dir "$NODE_DIR" >"$_NODE_LOG" 2>&1
_NODE_STATUS=$?
fi
set -e
if [ "$_NODE_STATUS" -eq 3 ]; then
step "node" "install blocked by another active Unsloth install" "$C_ERR"
sed 's/^/ | /' "$_NODE_LOG" >&2; rm -f "$_NODE_LOG"
substep "close other Unsloth installs and retry"
exit 3
elif [ "$_NODE_STATUS" -ne 0 ]; then
step "node" "isolated Node install failed" "$C_ERR"
sed 's/^/ | /' "$_NODE_LOG" >&2; rm -f "$_NODE_LOG"
substep "install Node >= 20.19 (with npm >= 11) yourself and re-run, or check your network"
exit 1
fi
grep -Fq "already matches" "$_NODE_LOG" && verbose_substep "isolated Node already up to date"
rm -f "$_NODE_LOG"
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ] && [ -d "$NODE_DIR" ]; then
: > "$NODE_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
fi
# Prepend the isolated bin (this process only) so node/npm/bun resolve here.
export PATH="$NODE_DIR/bin:$PATH"
# Keep npm and module resolution inside the isolated Node.
export NPM_CONFIG_PREFIX="$NODE_DIR"
export npm_config_prefix="$NODE_DIR"
unset NODE_PATH
hash -r 2>/dev/null || true
step "node" "$(node -v) | npm $(npm -v) (isolated)"
else
_FRONTEND_SKIP=true
step "frontend" "skipped (no suitable Node; system left untouched)" "$C_WARN"
substep "found Node='${_SYS_NODE_VER:-none}' npm='${_SYS_NPM_VER:-none}'; Unsloth needs Node >=20.19/22.12/23 and npm >= 11"
substep "install a suitable Node + npm, or unset UNSLOTH_SKIP_NODE_INSTALL to let Unsloth manage an isolated Node"
fi
verbose_substep "node source: $NODE_SOURCE (sys node=${_SYS_NODE_VER:-none} npm=${_SYS_NPM_VER:-none}) dir=$NODE_DIR"
if [ "$_FRONTEND_SKIP" = true ]; then
: # no suitable Node (skip source): message already shown above; nothing to build
elif [ "$_NEED_FRONTEND_BUILD" = false ]; then
# Node was provisioned only for the OXC runtime; the dist is already current.
step "frontend" "up to date"
verbose_substep "frontend dist is newer than source inputs"
else
# ── Install bun (optional, faster package installs) ──
# Install bun via npm only when we manage the isolated Node (npm -g lands in the
# isolated prefix); on a system Node we install nothing global. Build falls back to npm.
if command -v bun &>/dev/null; then
substep "bun already installed ($(bun --version))"
elif [ "$NODE_SOURCE" = bundled ]; then
substep "installing bun..."
# --allow-scripts=bun: npm >=11.16 gates install scripts and bun's
# postinstall fetches its binary; without it the install is a broken stub.
if run_maybe_quiet npm install -g bun --allow-scripts=bun "${_NPM_REGISTRY_ARGS[@]+"${_NPM_REGISTRY_ARGS[@]}"}" && command -v bun &>/dev/null; then
substep "bun installed ($(bun --version))"
else
substep "bun install skipped (npm will be used instead)"
fi
else
verbose_substep "skipping global bun install on system Node (npm will be used)"
fi
# ── Build frontend ──
substep "building frontend..."
cd "$SCRIPT_DIR/frontend"
_HIDDEN_GITIGNORES=()
_dir="$(pwd)"
while [ "$_dir" != "/" ]; do
_dir="$(dirname "$_dir")"
if [ -f "$_dir/.gitignore" ] && grep -qx '\*' "$_dir/.gitignore" 2>/dev/null; then
mv "$_dir/.gitignore" "$_dir/.gitignore._twbuild"
_HIDDEN_GITIGNORES+=("$_dir/.gitignore")
fi
done
_restore_gitignores() {
for _gi in "${_HIDDEN_GITIGNORES[@]+"${_HIDDEN_GITIGNORES[@]}"}"; do
mv "${_gi}._twbuild" "$_gi" 2>/dev/null || true
done
}
trap _restore_gitignores EXIT
# Use bun for install if available (faster), fall back to npm.
# Build always uses npm (Node runtime -- avoids bun runtime issues on some platforms).
# NOTE: We intentionally avoid run_quiet for the bun install attempt because
# run_quiet calls exit on failure, which would kill the script before the npm
# fallback can run. Instead we capture output manually and only show it on failure.
#
# IMPORTANT: bun's package cache can become corrupt -- packages get stored
# with only metadata (package.json, README) but no actual content (bin/,
# lib/). When this happens bun install exits 0 but leaves binaries missing.
# We verify critical binaries after install. If missing, we clear the cache
# and retry once before falling back to npm.
_try_bun_install() {
local _log _exit_code=0
_log=$(mktemp)
bun install "${_NPM_REGISTRY_ARGS[@]+"${_NPM_REGISTRY_ARGS[@]}"}" >"$_log" 2>&1 || _exit_code=$?
# bun may create .exe shims on Windows (Git Bash / MSYS2) instead of plain scripts
if [ "$_exit_code" -eq 0 ] \
&& { [ -x node_modules/.bin/tsc ] || [ -f node_modules/.bin/tsc.exe ] || [ -f node_modules/.bin/tsc.bunx ]; } \
&& { [ -x node_modules/.bin/vite ] || [ -f node_modules/.bin/vite.exe ] || [ -f node_modules/.bin/vite.bunx ]; }; then
rm -f "$_log"
return 0
fi
# Either bun install failed or it exited 0 but left packages missing
if [ "$_exit_code" -ne 0 ]; then
echo " bun install failed (exit code $_exit_code):"
else
echo " bun install exited 0 but critical binaries are missing:"
fi
sed 's/^/ | /' "$_log" >&2
if [ -n "${_CAPTURE_LOG:-}" ]; then cat "$_log" >> "$_CAPTURE_LOG" 2>/dev/null || true; fi
rm -f "$_log"
rm -rf node_modules
return 1
}
# Capture install output (bun + npm fallback) so we can detect a registry block.
_FRONTEND_INSTALL_LOG=$(mktemp)
_CAPTURE_LOG="$_FRONTEND_INSTALL_LOG"
_bun_install_ok=false
if command -v bun &>/dev/null; then
substep "using bun for package install (faster)"
if _try_bun_install; then
_bun_install_ok=true
else
# First attempt failed, likely due to corrupt cache entries.
# Clear the cache and retry once.
echo " Clearing bun cache and retrying..."
run_maybe_quiet bun pm cache rm || true
if _try_bun_install; then
_bun_install_ok=true
fi
fi
fi
if [ "$_bun_install_ok" = false ]; then
# `|| _npm_install_rc=$?` keeps this off `set -e`'s exit path (run_quiet_no_exit
# returns non-zero on failure) so the hint branch is reachable; it also captures
# the exact exit code. Mirrors the `|| BUILD_OK=false` idiom used below.
_npm_install_rc=0
run_quiet_no_exit "npm install" npm install --no-fund --no-audit --loglevel=error "${_NPM_REGISTRY_ARGS[@]+"${_NPM_REGISTRY_ARGS[@]}"}" || _npm_install_rc=$?
if [ "$_npm_install_rc" -ne 0 ]; then
_suggest_npm_registry "$_FRONTEND_INSTALL_LOG"
rm -f "$_FRONTEND_INSTALL_LOG"
exit "$_npm_install_rc"
fi
fi
_CAPTURE_LOG=""
rm -f "$_FRONTEND_INSTALL_LOG"
run_quiet "npm run build" npm run build
_restore_gitignores
trap - EXIT
_MAX_CSS=$(find "$SCRIPT_DIR/frontend/dist/assets" -name '*.css' -exec wc -c {} + 2>/dev/null | sort -n | tail -1 | awk '{print $1}')
if [ -z "$_MAX_CSS" ]; then
step "frontend" "built (warning: no CSS emitted)" "$C_WARN"
elif [ "$_MAX_CSS" -lt 100000 ]; then
step "frontend" "built (warning: CSS may be truncated)" "$C_WARN"
else
step "frontend" "built"
fi
cd "$SCRIPT_DIR"
fi # end _FRONTEND_SKIP guard (Node available: system or isolated)
fi # end frontend build check
# ── oxc-validator runtime ──
# Skip when the user opted out of Node (NODE_SOURCE=skip): there is no suitable
# Node, so do not run npm install against an unsuitable/absent system Node.
if [ -d "$_OXC_DIR" ] && [ "${NODE_SOURCE:-}" != skip ] && command -v npm &>/dev/null; then
cd "$_OXC_DIR"
_OXC_INSTALL_LOG=$(mktemp)
_CAPTURE_LOG="$_OXC_INSTALL_LOG"
# `|| _oxc_install_rc=$?` keeps this off `set -e`'s exit path so the hint branch
# below is reachable; it also captures the exact exit code.
_oxc_install_rc=0
run_quiet_no_exit "npm install (oxc validator runtime)" npm install --no-fund --no-audit --loglevel=error "${_NPM_REGISTRY_ARGS[@]+"${_NPM_REGISTRY_ARGS[@]}"}" || _oxc_install_rc=$?
_CAPTURE_LOG=""
if [ "$_oxc_install_rc" -ne 0 ]; then
_suggest_npm_registry "$_OXC_INSTALL_LOG"
rm -f "$_OXC_INSTALL_LOG"
exit "$_oxc_install_rc"
fi
rm -f "$_OXC_INSTALL_LOG"
cd "$SCRIPT_DIR"
elif [ -d "$_OXC_DIR" ] && [ "${NODE_SOURCE:-}" != skip ]; then
# No npm on PATH: skip rather than abort; the backend Node resolver degrades
# the validator gracefully. Mirrors setup.ps1's elseif on this block.
substep "OXC validator runtime skipped (no npm found); code validation degrades until Node is available" "$C_WARN"
fi
_remove_agent_instruction_files \
"$SCRIPT_DIR/frontend/node_modules" \
"$_OXC_DIR/node_modules"
# ── Python venv + deps ──
[ -d "$REPO_ROOT/.venv" ] && rm -rf "$REPO_ROOT/.venv"
[ -d "$REPO_ROOT/.venv_overlay" ] && rm -rf "$REPO_ROOT/.venv_overlay"
[ -d "$REPO_ROOT/.venv_t5" ] && rm -rf "$REPO_ROOT/.venv_t5"
[ -d "$REPO_ROOT/.venv_t5_530" ] && rm -rf "$REPO_ROOT/.venv_t5_530"
[ -d "$REPO_ROOT/.venv_t5_550" ] && rm -rf "$REPO_ROOT/.venv_t5_550"
# Note: do NOT delete $STUDIO_HOME/.venv here — install.sh handles migration
_COLAB_NO_VENV=false
if [ ! -x "$VENV_DIR/bin/python" ]; then
if [ "$IS_COLAB" = true ]; then
# On Colab there is no Unsloth venv -- install backend deps into system Python.
# Strip all version constraints so pip keeps Colab's pre-installed
# packages (huggingface-hub, datasets, transformers) and only pulls
# in genuinely missing ones (structlog, fastapi, etc.).
substep "Colab detected, installing Unsloth backend dependencies..."
_COLAB_REQS_TMP="$(mktemp)"
sed 's/[><=!~;].*//' "$SCRIPT_DIR/backend/requirements/studio.txt" \
| grep -v '^#' | grep -v '^$' > "$_COLAB_REQS_TMP"
if [ -s "$_COLAB_REQS_TMP" ]; then
if ! run_quiet_no_exit "install Colab backend deps" pip install -q -r "$_COLAB_REQS_TMP"; then
rm -f "$_COLAB_REQS_TMP"
step "python" "Colab backend dependency install failed" "$C_ERR"
exit 1
fi
else
step "python" "no Colab backend dependencies resolved from requirements file" "$C_WARN"
fi
rm -f "$_COLAB_REQS_TMP"
_COLAB_NO_VENV=true
else
step "python" "venv not found at $VENV_DIR" "$C_ERR"
substep "Run install.sh first to create the environment:"
substep "curl -fsSL https://unsloth.ai/install.sh | sh"
exit 1
fi
else
source "$VENV_DIR/bin/activate"
fi
install_python_stack() {
python "$SCRIPT_DIR/install_python_stack.py"
}
USE_UV=false
if command -v uv &>/dev/null; then
USE_UV=true
elif {
if _is_verbose; then
curl -LsSf https://astral.sh/uv/install.sh | sh
else
curl -LsSf https://astral.sh/uv/install.sh | sh > /dev/null 2>&1
fi
}; then
export PATH="$HOME/.local/bin:$PATH"
command -v uv &>/dev/null && USE_UV=true
fi
fast_install() {
if [ "$USE_UV" = true ]; then
uv pip install --python "$(command -v python)" "$@" && return 0
fi
python -m pip install "$@"
}
cd "$SCRIPT_DIR"
# On Colab without a venv, skip venv-dependent Python deps sections but
# continue to llama.cpp install so GGUF inference is available.
if [ "$_COLAB_NO_VENV" = true ]; then
step "python" "backend deps installed into system Python"
substep "continuing to llama.cpp install for GGUF inference support"
fi
# ── Check if Python deps need updating ──
# Compare installed package version against PyPI latest.
# Skip all Python dependency work if versions match (fast update path).
# On Colab (no venv), skip this version check (it needs $VENV_DIR/bin/python)
# but still run install_python_stack below (it uses sys.executable).
_SKIP_PYTHON_DEPS=false
_SKIP_VERSION_CHECK=false
if [ "$_COLAB_NO_VENV" = true ]; then
_SKIP_VERSION_CHECK=true
fi
_PKG_NAME="${STUDIO_PACKAGE_NAME:-unsloth}"
if [ "$_SKIP_VERSION_CHECK" != true ] && [ "${SKIP_STUDIO_BASE:-0}" != "1" ] && [ "${STUDIO_LOCAL_INSTALL:-0}" != "1" ]; then
# Only check when NOT called from install.sh (which just installed the package)
INSTALLED_VER=$("$VENV_DIR/bin/python" -c "
import sys; from importlib.metadata import version
print(version(sys.argv[1]))
" "$_PKG_NAME" 2>/dev/null || echo "")
LATEST_VER=$(curl -fsSL --max-time 5 "https://pypi.org/pypi/$_PKG_NAME/json" 2>/dev/null \
| "$VENV_DIR/bin/python" -c "import sys,json; print(json.load(sys.stdin)['info']['version'])" 2>/dev/null \
|| echo "")
if [ -n "$INSTALLED_VER" ] && [ -n "$LATEST_VER" ] && [ "$INSTALLED_VER" = "$LATEST_VER" ]; then
step "python" "$_PKG_NAME $INSTALLED_VER is up to date"
_SKIP_PYTHON_DEPS=true
# A pre-#6483-fix install can be stuck on anyio>=4.14 even though
# $_PKG_NAME itself is current; the fast path above would otherwise
# never reach install_python_stack's anyio repair (#6797).
if "$VENV_DIR/bin/python" -c "
import re, sys
from importlib.metadata import version, PackageNotFoundError
try:
parts = version('anyio').split('.')
major = int(parts[0])
minor = int(re.sub(r'[^0-9].*', '', parts[1])) if len(parts) > 1 else 0
except (PackageNotFoundError, ValueError, IndexError):
sys.exit(1)
sys.exit(0 if (major, minor) >= (4, 14) else 1)
" 2>/dev/null; then
substep "anyio >=4.14 found (#6483) -- forcing dependency pass to repair..."
_SKIP_PYTHON_DEPS=false
fi
elif [ -n "$INSTALLED_VER" ] && [ -n "$LATEST_VER" ]; then
substep "$_PKG_NAME $INSTALLED_VER -> $LATEST_VER available, updating..."
elif [ -z "$LATEST_VER" ]; then
substep "could not reach PyPI, updating to be safe..."
fi
fi
if [ "$_SKIP_PYTHON_DEPS" = false ]; then
install_python_stack
else
step "python" "dependencies up to date"
verbose_substep "python deps check: installed=$_PKG_NAME@${INSTALLED_VER:-unknown} latest=${LATEST_VER:-unknown}"
fi
# ── 6b. Pre-install transformers 5.x into .venv_t5_530/, .venv_t5_550/, and .venv_t5_510/ ──
# Models like GLM-4.7-Flash, Qwen3 MoE need transformers>=5.3.0.
# Gemma 4 models need transformers>=5.5.0; Gemma 4 Unified needs 5.10.x.
# Pre-install into separate directories to avoid runtime pip overhead.
# The training subprocess prepends the appropriate dir to sys.path.
_target_has_pkg_version() {
_thpv_dir="$1"
_thpv_pkg="$2"
_thpv_version="$3"
[ -d "$_thpv_dir" ] || return 1
_thpv_pkg_norm=$(printf '%s' "$_thpv_pkg" | tr '-' '_')
for _thpv_metadata in \
"$_thpv_dir"/"$_thpv_pkg_norm"-*.dist-info/METADATA \
"$_thpv_dir"/"$_thpv_pkg"-*.dist-info/METADATA
do
[ -f "$_thpv_metadata" ] || continue
grep -qx "Version: $_thpv_version" "$_thpv_metadata" && return 0
done
return 1
}
_NEED_T5_INSTALL=false
if [ -d "$STUDIO_HOME/.venv_t5" ]; then
# Legacy layout — migrate
_assert_studio_owned_or_absent "$STUDIO_HOME/.venv_t5" "legacy transformers sidecar venv"
rm -rf "$STUDIO_HOME/.venv_t5"
_NEED_T5_INSTALL=true
fi
[ ! -d "$VENV_T5_530_DIR" ] && _NEED_T5_INSTALL=true
[ ! -d "$VENV_T5_550_DIR" ] && _NEED_T5_INSTALL=true
[ ! -d "$VENV_T5_510_DIR" ] && _NEED_T5_INSTALL=true
_target_has_pkg_version "$VENV_T5_530_DIR" "transformers" "5.3.0" || _NEED_T5_INSTALL=true
_target_has_pkg_version "$VENV_T5_550_DIR" "transformers" "5.5.0" || _NEED_T5_INSTALL=true
_target_has_pkg_version "$VENV_T5_510_DIR" "transformers" "5.10.2" || _NEED_T5_INSTALL=true
# Also reinstall when python deps were updated (packages may need rebuild)
[ "$_SKIP_PYTHON_DEPS" = false ] && _NEED_T5_INSTALL=true
if [ "$_NEED_T5_INSTALL" = true ]; then
_assert_studio_owned_or_absent "$VENV_T5_530_DIR" "transformers 5.3 sidecar venv"
[ -d "$VENV_T5_530_DIR" ] && rm -rf "$VENV_T5_530_DIR"
mkdir -p "$VENV_T5_530_DIR"
: > "$VENV_T5_530_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
run_quiet "install transformers 5.3.0" fast_install --target "$VENV_T5_530_DIR" --no-deps "transformers==5.3.0"
run_quiet "install huggingface_hub for t5_530" fast_install --target "$VENV_T5_530_DIR" --no-deps "huggingface_hub==1.8.0"
run_quiet "install hf_xet for t5_530" fast_install --target "$VENV_T5_530_DIR" --no-deps "hf_xet==1.4.2"
run_quiet "install tiktoken for t5_530" fast_install --target "$VENV_T5_530_DIR" "tiktoken"
step "transformers" "5.3.0 pre-installed"
_assert_studio_owned_or_absent "$VENV_T5_550_DIR" "transformers 5.5 sidecar venv"
[ -d "$VENV_T5_550_DIR" ] && rm -rf "$VENV_T5_550_DIR"
mkdir -p "$VENV_T5_550_DIR"
: > "$VENV_T5_550_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
run_quiet "install transformers 5.5.0" fast_install --target "$VENV_T5_550_DIR" --no-deps "transformers==5.5.0"
run_quiet "install huggingface_hub for t5_550" fast_install --target "$VENV_T5_550_DIR" --no-deps "huggingface_hub==1.8.0"
run_quiet "install hf_xet for t5_550" fast_install --target "$VENV_T5_550_DIR" --no-deps "hf_xet==1.4.2"
run_quiet "install tiktoken for t5_550" fast_install --target "$VENV_T5_550_DIR" "tiktoken"
step "transformers" "5.5.0 pre-installed"
_assert_studio_owned_or_absent "$VENV_T5_510_DIR" "transformers 5.10 sidecar venv"
[ -d "$VENV_T5_510_DIR" ] && rm -rf "$VENV_T5_510_DIR"
mkdir -p "$VENV_T5_510_DIR"
: > "$VENV_T5_510_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
run_quiet "install transformers 5.10.2" fast_install --target "$VENV_T5_510_DIR" --no-deps "transformers==5.10.2"
run_quiet "install huggingface_hub for t5_510" fast_install --target "$VENV_T5_510_DIR" --no-deps "huggingface_hub==1.8.0"
run_quiet "install hf_xet for t5_510" fast_install --target "$VENV_T5_510_DIR" --no-deps "hf_xet==1.4.2"
run_quiet "install tiktoken for t5_510" fast_install --target "$VENV_T5_510_DIR" "tiktoken"
step "transformers" "5.10.2 pre-installed"
fi
fi
# ── GPU detection summary (mirrors setup.ps1 step "gpu" block) ──
# WSL2 ROCDXG: the system rocminfo enumerates the GPU over /dev/dxg only when
# HSA_ENABLE_DXG_DETECTION=1 (a no-op on bare metal), and /opt/rocm/bin can be
# off PATH outside login shells (the profile.d drop-in). Seed both before the
# probes or a ROCDXG WSL host is misdetected as CPU-only.
export HSA_ENABLE_DXG_DETECTION="${HSA_ENABLE_DXG_DETECTION:-1}"
if ! command -v rocminfo >/dev/null 2>&1 && [ -x /opt/rocm/bin/rocminfo ]; then
PATH="$PATH:/opt/rocm/bin"
fi
_setup_amd_detected=false
_setup_nvidia_usable=false
_setup_gfx_all=""
_setup_mkt=""
# NVIDIA priority: classify NVIDIA first and skip the AMD probes entirely on
# a usable-NVIDIA host (mirrors _has_rocm_gpu in install_python_stack.py).
# This also keeps a wedged rocminfo/amd-smi from hanging setup before the
# host is classified; the AMD probes themselves run under _setup_run_smi.
if _setup_has_usable_nvidia_gpu; then
_setup_nvidia_usable=true
fi
if [ "$_setup_nvidia_usable" != true ]; then
if command -v rocminfo >/dev/null 2>&1 && \
_setup_run_smi rocminfo 2>/dev/null | awk '/Name:[[:space:]]*gfx[1-9][0-9]/{found=1} END{exit !found}'; then
_setup_amd_detected=true
_setup_gfx_all=$(_setup_run_smi rocminfo 2>/dev/null | grep -oE 'gfx[1-9][0-9a-z]{2,3}' || true)
_setup_mkt=$(_setup_run_smi rocminfo 2>/dev/null | awk -F': ' \
'/Marketing Name:/{gsub(/^[[:space:]]+|[[:space:]]+$/,"", $2); if($2){print $2; exit}}' || true)
elif command -v amd-smi >/dev/null 2>&1 && \
_setup_run_smi amd-smi list 2>/dev/null | awk '/^GPU[[:space:]]*[:\[][[:space:]]*[0-9]/{ found=1 } END{ exit !found }'; then
_setup_amd_detected=true
_setup_gfx_all=$(_setup_run_smi amd-smi list 2>/dev/null | grep -oE 'gfx[1-9][0-9a-z]{2,3}' || true)
[ -z "$_setup_gfx_all" ] && \
_setup_gfx_all=$(_setup_run_smi amd-smi static --asic 2>/dev/null | grep -oE 'gfx[1-9][0-9a-z]{2,3}' || true)
_setup_mkt=$(_setup_run_smi amd-smi static --asic 2>/dev/null | awk -F'[:|]' \
'/[Mm]arket.?[Nn]ame/{gsub(/^[[:space:]]+|[[:space:]]+$/,"", $2); if($2){print $2; exit}}' || true)
elif [ -e /dev/kfd ] && \
awk '/vendor_id/ && $2 == 4098 { found = 1 } END { exit !found }' \
/sys/class/kfd/kfd/topology/nodes/*/properties 2>/dev/null; then
# KFD sysfs fallback, AMD vendor_id 4098 only (mirrors install.sh
# _has_amd_rocm_gpu): covers AMD hosts where rocminfo/amd-smi are
# missing but the kernel exposes the GPU, so the source-build gate
# below does not drop them to a CPU llama.cpp build. No gfx arch is
# available from this path; name-based inference handles it.
_setup_amd_detected=true
fi
fi
if [ "$_setup_nvidia_usable" = true ]; then
step "gpu" "NVIDIA GPU detected"
elif [ "$_setup_amd_detected" = true ]; then
_setup_vis="${HIP_VISIBLE_DEVICES:-${ROCR_VISIBLE_DEVICES:-}}"
_setup_vis_idx=0
if [ -n "$_setup_vis" ] && [ "$_setup_vis" != "-1" ]; then
_setup_first="${_setup_vis%%,*}"
case "$_setup_first" in ''|*[!0-9]*) ;; *) _setup_vis_idx=$_setup_first ;; esac
fi
_setup_gfx=$(printf '%s\n' "$_setup_gfx_all" | awk -v idx="$_setup_vis_idx" \
'NF && !seen[$0]++ { a[n++]=$0 } END { if(idx>=n) idx=0; if(n>0) print a[idx] }')
# UNSLOTH_ROCM_GFX_ARCH env override (mirrors setup.ps1)
if [ -n "${UNSLOTH_ROCM_GFX_ARCH:-}" ]; then
_setup_gfx="${UNSLOTH_ROCM_GFX_ARCH}"
substep "gfx arch from UNSLOTH_ROCM_GFX_ARCH env override: $_setup_gfx"
# Name-based arch inference when tools don't report gfx (mirrors setup.ps1 nameArchTable)
elif [ -z "$_setup_gfx" ] && [ -n "$_setup_mkt" ]; then
# Kept in sync with the table in install.sh (and the PS nameArchTable).
# gfx1102 matched BEFORE gfx1100 so the spaceless "RX 7700S" lands on
# gfx1102 (bash case has no negative lookahead like the PS tables).
case "$_setup_mkt" in
*"9070 XT"*|*9080*) _setup_gfx="gfx1201" ;; # RDNA 4
*9070*|*9060*) _setup_gfx="gfx1200" ;; # RDNA 4
*"8065S"*|*"8060S"*|*"8050S"*|*"8040S"*|*"Strix Halo"*|*"Ryzen AI Max"*|*"AI Max"*) _setup_gfx="gfx1151" ;; # RDNA 3.5 (Strix Halo + Gorgon Halo: Radeon 8065S/8060S/8050S/8040S iGPU, Ryzen AI Max / Max+)
*"890M"*|*"880M"*|*"860M"*|*"840M"*|*"Strix Point"*|*"Krackan"*|*"HX 37"*|*"AI 9 HX"*|*"AI 9 36"*|*"AI 7 35"*|*"AI 5 34"*|*"AI 7 PRO 35"*|*"AI 5 33"*) _setup_gfx="gfx1150" ;; # RDNA 3.5 (Strix/Krackan Point: Radeon 890M/880M iGPU, Ryzen AI 9 HX 370/375)
*"RX 7600"*|*"RX 7700S"*|*"RX 7650"*|*"PRO W7600"*|*"PRO W7500"*|*"PRO V710"*) _setup_gfx="gfx1102" ;; # RDNA 3 (Navi 33)
*"RX 7900"*|*"RX 7800"*|*"RX 7700"*|*"PRO W7900"*|*"PRO W7800"*|*"PRO W7700"*) _setup_gfx="gfx1100" ;; # RDNA 3 desktop / workstation (Navi 31)
*"780M"*|*"760M"*|*"740M"*|*"Phoenix"*|*"Hawk Point"*|*"Z1 Extreme"*|*"Z2 Extreme"*) _setup_gfx="gfx1103" ;; # RDNA 3 iGPU (Phoenix / Hawk Point)
*"RX 6900"*|*"RX 6800"*|*"RX 6750"*|*"RX 6700"*|*"PRO W6800"*|*"PRO W6900"*) _setup_gfx="gfx1030" ;; # RDNA 2 (Navi 21)
*"RX 6650"*|*"RX 6600"*|*"PRO W6600"*|*"PRO W6650"*) _setup_gfx="gfx1032" ;; # RDNA 2 (Navi 23)
*"RX 6500"*|*"RX 6400"*|*"RX 6300"*|*"PRO W6400"*|*"PRO W6500"*) _setup_gfx="gfx1034" ;; # RDNA 2 (Navi 24)
esac
if [ -n "$_setup_gfx" ]; then
substep "gfx arch inferred from GPU name: $_setup_gfx"
substep "Tip: set UNSLOTH_ROCM_GFX_ARCH=$_setup_gfx to skip inference next time"
fi
fi
# ROCm version via hipconfig, then amd-smi
_setup_rocm_ver=""
if command -v hipconfig >/dev/null 2>&1; then
_setup_rocm_ver=$(hipconfig --version 2>/dev/null | awk 'NR==1 && /^[0-9]/{print; exit}' || true)
fi
if [ -z "$_setup_rocm_ver" ] && command -v amd-smi >/dev/null 2>&1; then
_setup_rocm_ver=$(amd-smi version 2>/dev/null | awk -F'ROCm version: ' \
'NF>1{gsub(/[[:space:]]/,"", $2); print $2; exit}' || true)
fi
if [ -n "$_setup_gfx" ]; then
step "gpu" "AMD ROCm ($_setup_gfx)"
else
step "gpu" "AMD ROCm"
fi
_setup_rocm_root="${ROCM_PATH:-${HIP_PATH:-/opt/rocm}}"
substep "ROCm: $_setup_rocm_root"
[ -n "$_setup_rocm_ver" ] && substep "hipconfig: $_setup_rocm_ver"
[ -n "$_setup_mkt" ] && [ -n "$_setup_gfx" ] && substep "GPU: $_setup_mkt"
elif [ "$(uname -s 2>/dev/null)" = "Darwin" ] && [ "$(uname -m 2>/dev/null)" = "arm64" ]; then
# Apple Silicon: llama.cpp builds with Metal over unified memory, so not a CPU-only host.
step "gpu" "Apple Silicon (Metal, unified memory)"
else
step "gpu" "none (chat-only / GGUF)" "$C_WARN"
substep "Training and GPU inference require an NVIDIA or AMD ROCm GPU."
fi
# ── 7. Prefer prebuilt llama.cpp bundles before any source build path ──
# Nest llama.cpp under $STUDIO_HOME only for real env-overrides; legacy
# default keeps ~/.unsloth/llama.cpp so pre-PR builds are still discovered.
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
UNSLOTH_HOME="$STUDIO_HOME"
else
UNSLOTH_HOME="$HOME/.unsloth"
fi
mkdir -p "$UNSLOTH_HOME"
LLAMA_CPP_DIR="$UNSLOTH_HOME/llama.cpp"
LLAMA_SERVER_BIN="$LLAMA_CPP_DIR/build/bin/llama-server"
_NEED_LLAMA_SOURCE_BUILD=false
_LLAMA_CPP_DEGRADED=false
_LLAMA_FORCE_COMPILE="${UNSLOTH_LLAMA_FORCE_COMPILE:-0}"
_REQUESTED_LLAMA_TAG="${UNSLOTH_LLAMA_TAG:-${_DEFAULT_LLAMA_TAG}}"
_HOST_SYSTEM="$(uname -s 2>/dev/null || true)"
_HOST_MACHINE="$(uname -m 2>/dev/null || true)"
# Pick the release repo install_llama_prebuilt.py plans against. Every host this
# installer supports now pulls its llama.cpp prebuilt from the unslothai fork: it
# ships the CUDA (Linux x64/arm64, Windows), ROCm (Linux/Windows) and macOS
# bundles, plus the CPU bundles for Linux/Windows on both x86_64 and arm64.
# ggml-org artifacts are no longer used by default.
_HELPER_RELEASE_REPO="unslothai/llama.cpp"
# UNSLOTH_ROCM_GFX_ARCH may be set on a host where no probe fired, so the override
# nested in the AMD-detected branch above never ran and _setup_gfx is still empty.
# Honour it here so the --rocm-gfx forwarding below still sees it
# (install_llama_prebuilt.py reads the same env var as the --rocm-gfx default).
if [ "${_setup_nvidia_usable:-}" != true ] && [ -z "${_setup_gfx:-}" ] && [ -n "${UNSLOTH_ROCM_GFX_ARCH:-}" ]; then
_setup_gfx="${UNSLOTH_ROCM_GFX_ARCH}"
fi
_LLAMA_PR="${UNSLOTH_LLAMA_PR:-}"
_SKIP_PREBUILT_INSTALL=false
_LLAMA_PR_FORCE="${UNSLOTH_LLAMA_PR_FORCE:-${_DEFAULT_LLAMA_PR_FORCE}}"
_LLAMA_SOURCE="${_DEFAULT_LLAMA_SOURCE}"
_LLAMA_SOURCE="${_LLAMA_SOURCE%.git}" # normalize: strip trailing .git
_RESOLVED_SOURCE_URL="$_LLAMA_SOURCE"
_RESOLVED_SOURCE_REF="$_REQUESTED_LLAMA_TAG"
_RESOLVED_SOURCE_REF_KIND="tag"
_RESOLVED_LLAMA_TAG="$_REQUESTED_LLAMA_TAG"
if [ "$_LLAMA_FORCE_COMPILE" = "1" ]; then
_NEED_LLAMA_SOURCE_BUILD=true
_SKIP_PREBUILT_INSTALL=true
fi
# Baked-in PR_FORCE promotes to _LLAMA_PR when user hasn't set one.
if [ -z "$_LLAMA_PR" ] && [ -n "$_LLAMA_PR_FORCE" ] && \
[[ "$_LLAMA_PR_FORCE" =~ ^[0-9]+$ ]] && [ "$_LLAMA_PR_FORCE" -gt 0 ]; then
_LLAMA_PR="$_LLAMA_PR_FORCE"
step "llama.cpp" "baked-in PR_FORCE=$_LLAMA_PR_FORCE" "$C_WARN"
fi
if [ -n "$_LLAMA_PR" ]; then
if ! [[ "$_LLAMA_PR" =~ ^[0-9]+$ ]] || [ "$_LLAMA_PR" -le 0 ]; then
step "llama.cpp" "UNSLOTH_LLAMA_PR=$_LLAMA_PR is not a valid PR number" "$C_ERR"
exit 1
fi
step "llama.cpp" "UNSLOTH_LLAMA_PR=$_LLAMA_PR -- will build from PR head" "$C_WARN"
_RESOLVED_LLAMA_TAG="pr-$_LLAMA_PR"
_RESOLVED_SOURCE_URL="$_LLAMA_SOURCE"
_RESOLVED_SOURCE_REF="pr-$_LLAMA_PR"
_RESOLVED_SOURCE_REF_KIND="pull"
_NEED_LLAMA_SOURCE_BUILD=true
_SKIP_PREBUILT_INSTALL=true
fi
verbose_substep "requested llama.cpp tag: $_REQUESTED_LLAMA_TAG (repo: $_HELPER_RELEASE_REPO)"
# GGUF export's check_llama_cpp() looks for a llama-quantize shim at the root of
# the install dir, but a source build keeps the binary under build/bin/. Mirror
# the source-build-reuse step and create the shim when the reused tree has one
# but no root shim yet. Best-effort: the tree may be read-only (shared/CI cache),
# and under `set -e` a failed ln would otherwise abort an good reuse.
_link_local_llama_quantize_shim() {
if [ -x "$1/build/bin/llama-quantize" ] && [ ! -e "$1/llama-quantize" ]; then
ln -sf build/bin/llama-quantize "$1/llama-quantize" 2>/dev/null || \
substep "could not create llama-quantize shim in linked dir (read-only?); GGUF export may be unavailable"
fi
}
# Accept any layout LlamaCppBackend._layout_candidates() resolves so the flag
# never rejects a tree Unsloth could actually run: a root-level llama-server (a
# `make` build or a flat-extracted release) or the CMake build/bin/llama-server.
_has_local_llama_server() {
[ -x "$1/llama-server" ] || [ -x "$1/build/bin/llama-server" ]
}
_LOCAL_LLAMA_CPP_LINKED=false
if [ -n "${UNSLOTH_LOCAL_LLAMA_CPP_DIR:-}" ]; then
if [ ! -d "$UNSLOTH_LOCAL_LLAMA_CPP_DIR" ]; then
step "llama.cpp" "UNSLOTH_LOCAL_LLAMA_CPP_DIR does not exist: $UNSLOTH_LOCAL_LLAMA_CPP_DIR" "$C_ERR"
exit 1
fi
_RESOLVED_LOCAL="$(CDPATH= cd -P -- "$UNSLOTH_LOCAL_LLAMA_CPP_DIR" && pwd -P)"
# Canonicalize the install path the same way before comparing: _RESOLVED_LOCAL
# is fully resolved, but LLAMA_CPP_DIR is textual ($UNSLOTH_HOME/llama.cpp). If
# $HOME (or UNSLOTH_HOME) contains a symlink, the two never match even when the
# user pointed the flag at the canonical install itself -- and the rm -rf below
# would then wipe the very tree they asked to reuse. Resolve via the parent so
# this works whether or not the leaf currently exists.
_CANON_LLAMA_CPP_DIR="$LLAMA_CPP_DIR"
_LLAMA_CPP_PARENT="$(dirname "$LLAMA_CPP_DIR")"
if [ -d "$_LLAMA_CPP_PARENT" ]; then
_CANON_LLAMA_CPP_DIR="$(CDPATH= cd -P -- "$_LLAMA_CPP_PARENT" && pwd -P)/$(basename "$LLAMA_CPP_DIR")"
fi
if [ "$_RESOLVED_LOCAL" = "$_CANON_LLAMA_CPP_DIR" ]; then
# Points at the canonical install location itself: never delete-then-link
# it onto itself. If a usable build is already there, reuse it and skip
# both the prebuilt download and the source build -- the prebuilt installer
# uses os.replace() and would otherwise clobber an existing source build at
# this path. If nothing is built there yet, fall through to the normal
# install so it gets built in place exactly as it would without the flag.
if _has_local_llama_server "$LLAMA_CPP_DIR"; then
substep "UNSLOTH_LOCAL_LLAMA_CPP_DIR is the canonical install location and already holds a build; reusing it"
_link_local_llama_quantize_shim "$LLAMA_CPP_DIR"
_LOCAL_LLAMA_CPP_LINKED=true
_NEED_LLAMA_SOURCE_BUILD=false
_SKIP_PREBUILT_INSTALL=true
else
substep "UNSLOTH_LOCAL_LLAMA_CPP_DIR points to the canonical install location with nothing built there yet; running the normal install"
fi
else
# Reusing disables BOTH the prebuilt download and the source build, so the
# linked tree must already contain a runnable llama-server in one of the
# layouts the backend resolves (root-level or build/bin/). Fail clearly
# rather than link an unbuilt or wrong-platform checkout and leave Unsloth
# with no usable binary.
if ! _has_local_llama_server "$_RESOLVED_LOCAL"; then
step "llama.cpp" "no llama-server under $_RESOLVED_LOCAL (looked for ./llama-server and ./build/bin/llama-server) -- build llama.cpp there first, or drop --with-llama-cpp-dir" "$C_ERR"
exit 1
fi
# A stale link from a previous --with-llama-cpp-dir run isn't Unsloth-owned
# content; drop it before the ownership check so re-runs stay idempotent
# for a custom UNSLOTH_STUDIO_HOME (the assert would otherwise follow the
# link into the user's dir and reject it as unowned).
[ -L "$LLAMA_CPP_DIR" ] && rm -f "$LLAMA_CPP_DIR"
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
_assert_studio_owned_or_absent "$LLAMA_CPP_DIR" "llama.cpp install"
fi
rm -rf "$LLAMA_CPP_DIR"
ln -sfn "$_RESOLVED_LOCAL" "$LLAMA_CPP_DIR"
_link_local_llama_quantize_shim "$LLAMA_CPP_DIR"
step "llama.cpp" "linked local directory: $_RESOLVED_LOCAL"
_LOCAL_LLAMA_CPP_LINKED=true
_NEED_LLAMA_SOURCE_BUILD=false
_SKIP_PREBUILT_INSTALL=true
fi
fi
if [ "$_LOCAL_LLAMA_CPP_LINKED" = true ]; then
: # local directory linked above; skip prebuilt install
elif [ "$_LLAMA_FORCE_COMPILE" = "1" ]; then
step "llama.cpp" "UNSLOTH_LLAMA_FORCE_COMPILE=1 -- skipping prebuilt" "$C_WARN"
_NEED_LLAMA_SOURCE_BUILD=true
elif [ "${_SKIP_PREBUILT_INSTALL:-false}" = true ]; then
substep "prebuilt install skipped -- falling back to source build"
else
substep "installing prebuilt llama.cpp..."
if [ -d "$LLAMA_CPP_DIR" ]; then
substep "existing install detected -- validating update"
fi
# why: install_llama_prebuilt.py uses os.replace(), which would displace
# an unrelated $UNSLOTH_STUDIO_HOME/llama.cpp before the source-build
# ownership check below ever runs.
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
_assert_studio_owned_or_absent "$LLAMA_CPP_DIR" "llama.cpp install"
fi
_PREBUILT_CMD=(
python "$SCRIPT_DIR/install_llama_prebuilt.py"
--install-dir "$LLAMA_CPP_DIR"
--llama-tag "$_REQUESTED_LLAMA_TAG"
--published-repo "$_HELPER_RELEASE_REPO"
)
if [ -n "${UNSLOTH_LLAMA_RELEASE_TAG:-}" ]; then
_PREBUILT_CMD+=(--published-release-tag "$UNSLOTH_LLAMA_RELEASE_TAG")
fi
# Forward the gfx arch resolved above so the per-gfx ROCm prebuilt is picked
# even when the installer's own probe cannot report it (amd-smi-only hosts,
# name-inferred arch). Implies --has-rocm on the installer side.
if [ -n "${_setup_gfx:-}" ]; then
_PREBUILT_CMD+=(--rocm-gfx "$_setup_gfx")
elif [ "$_setup_amd_detected" = true ] && \
{ command -v hipcc >/dev/null 2>&1 || [ -x /opt/rocm/bin/hipcc ] || \
ls /opt/rocm-*/bin/hipcc >/dev/null 2>&1; }; then
# AMD detected but gfx unknown (KFD-only host): forward --has-rocm only when
# hipcc can actually build llama.cpp (incl. a versioned /opt/rocm-*/bin, the
# same paths the source build uses). With no gfx the prebuilt resolver finds
# no ROCm bundle and the source build would fail, so without hipcc fall
# through to the CPU prebuilt instead of breaking the install.
_PREBUILT_CMD+=(--has-rocm)
fi
# UNSLOTH_LLAMA_CPP_BACKEND=cpu (case-insensitive, trimmed) forces the CPU-only
# prebuilt via --force-cpu, bypassing Vulkan/CUDA/ROCm. Fixes Intel iGPU crash (#7213).
# No effect on macOS: the universal bundle already runs on CPU (Metal is a runtime
# -ngl choice), so warn instead of writing a misleading forced-CPU marker.
_llama_backend="$(printf '%s' "${UNSLOTH_LLAMA_CPP_BACKEND:-auto}" | awk '{$1=$1; print tolower($0)}')"
case "$_llama_backend" in
cpu)
if [ "$_HOST_SYSTEM" = "Darwin" ]; then
step "llama.cpp" "UNSLOTH_LLAMA_CPP_BACKEND=cpu has no effect on macOS (universal build; use -ngl 0 at runtime for CPU-only)" "$C_WARN" >&2
else
_PREBUILT_CMD+=(--force-cpu)
fi
;;
""|auto) ;;
*) step "llama.cpp" "Ignoring UNSLOTH_LLAMA_CPP_BACKEND='$UNSLOTH_LLAMA_CPP_BACKEND' (expected 'auto' or 'cpu')" "$C_WARN" >&2 ;;
esac
_PREBUILT_LOG="$(mktemp)"
set +e
if _is_verbose; then
"${_PREBUILT_CMD[@]}" 2>&1 | tee "$_PREBUILT_LOG"
_PREBUILT_STATUS=${PIPESTATUS[0]}
else
"${_PREBUILT_CMD[@]}" >"$_PREBUILT_LOG" 2>&1
_PREBUILT_STATUS=$?
fi
set -e
if [ "$_PREBUILT_STATUS" -eq 0 ]; then
if grep -Fq "already matches" "$_PREBUILT_LOG"; then
step "llama.cpp" "prebuilt up to date and validated"
else
step "llama.cpp" "prebuilt installed and validated"
fi
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ] && [ -d "$LLAMA_CPP_DIR" ]; then
: > "$LLAMA_CPP_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
fi
print_installed_llama_prebuilt_release "$LLAMA_CPP_DIR"
verbose_substep "llama.cpp install dir: $LLAMA_CPP_DIR"
rm -f "$_PREBUILT_LOG"
elif [ "$_PREBUILT_STATUS" -eq 3 ]; then
step "llama.cpp" "install blocked by active llama.cpp process" "$C_WARN"
print_llama_error_log "$_PREBUILT_LOG"
rm -f "$_PREBUILT_LOG"
if [ -d "$LLAMA_CPP_DIR" ]; then
substep "existing install was restored"
fi
substep "close Unsloth or other llama.cpp users and retry"
exit 3
else
step "llama.cpp" "prebuilt install failed (continuing)" "$C_WARN"
print_llama_error_log "$_PREBUILT_LOG"
rm -f "$_PREBUILT_LOG"
if [ -d "$LLAMA_CPP_DIR" ]; then
substep "prebuilt update failed; existing install restored"
fi
substep "falling back to source build"
_NEED_LLAMA_SOURCE_BUILD=true
fi
fi
# Source-built llama.cpp installs do not have the prebuilt metadata used above
# for exact release matching. Reuse a complete local source build unless the
# caller explicitly requested a rebuild or a PR-specific llama.cpp checkout.
if [ "$_NEED_LLAMA_SOURCE_BUILD" = true ] && \
[ "$_LLAMA_FORCE_COMPILE" != "1" ] && \
[ -z "$_LLAMA_PR" ] && \
[ -x "$LLAMA_CPP_DIR/build/bin/llama-server" ] && \
[ -x "$LLAMA_CPP_DIR/build/bin/llama-quantize" ]; then
step "llama.cpp" "existing source build found; skipping rebuild"
ln -sf build/bin/llama-quantize "$LLAMA_CPP_DIR/llama-quantize"
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
: > "$LLAMA_CPP_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
fi
_NEED_LLAMA_SOURCE_BUILD=false
fi
# ── 8. WSL: pre-install GGUF build dependencies for fallback source builds ──
# On WSL, sudo requires a password and can't be entered during GGUF export
# (runs in a non-interactive subprocess). Install build deps here instead.
if [ "$_NEED_LLAMA_SOURCE_BUILD" = true ] && grep -qi microsoft /proc/version 2>/dev/null; then
_GGUF_DEPS="pciutils build-essential cmake curl git libcurl4-openssl-dev"
apt-get update -y >/dev/null 2>&1 || true
apt-get install -y $_GGUF_DEPS >/dev/null 2>&1 || true
_STILL_MISSING=""
for _pkg in $_GGUF_DEPS; do
case "$_pkg" in
build-essential) command -v gcc >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
pciutils) command -v lspci >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
libcurl4-openssl-dev) command -v curl-config >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
*) command -v "$_pkg" >/dev/null 2>&1 || _STILL_MISSING="$_STILL_MISSING $_pkg" ;;
esac
done
_STILL_MISSING=$(echo "$_STILL_MISSING" | sed 's/^ *//')
if [ -z "$_STILL_MISSING" ]; then
step "gguf deps" "installed"
elif command -v sudo >/dev/null 2>&1; then
step "gguf deps" "sudo required for: $_STILL_MISSING" "$C_WARN"
printf " %-15s" ""
printf "accept? [Y/n] "
if [ -r /dev/tty ]; then
read -r REPLY </dev/tty || REPLY="y"
else
REPLY="y"
fi
case "$REPLY" in
[nN]*)
substep "skipped -- run manually:"
substep "sudo apt-get install -y $_STILL_MISSING"
_SKIP_GGUF_BUILD=true
;;
*)
sudo apt-get update -y
sudo apt-get install -y $_STILL_MISSING
step "gguf deps" "installed"
;;
esac
else
step "gguf deps" "missing (no sudo) -- install manually:" "$C_WARN"
substep "apt-get install -y $_STILL_MISSING"
_SKIP_GGUF_BUILD=true
fi
fi
# ── 9. Build llama.cpp binaries for GGUF inference + export when prebuilt install fails ──
# Builds at ~/.unsloth/llama.cpp — a single shared location under the user's
# home directory. This is used by both the inference server and the GGUF
# export pipeline (unsloth-zoo).
# - llama-server: for GGUF model inference
# - llama-quantize: for GGUF export quantization (symlinked to root for check_llama_cpp())
if [ "$_NEED_LLAMA_SOURCE_BUILD" = false ]; then
:
elif [ "${_SKIP_GGUF_BUILD:-}" = true ]; then
step "llama.cpp" "skipped (missing build deps)" "$C_WARN"
[ -f "$LLAMA_SERVER_BIN" ] || _LLAMA_CPP_DEGRADED=true
else
{
if ! command -v cmake &>/dev/null; then
step "llama.cpp" "skipped (cmake not found)" "$C_WARN"
[ -f "$LLAMA_SERVER_BIN" ] || _LLAMA_CPP_DEGRADED=true
elif ! command -v git &>/dev/null; then
step "llama.cpp" "skipped (git not found)" "$C_WARN"
[ -f "$LLAMA_SERVER_BIN" ] || _LLAMA_CPP_DEGRADED=true
else
if [ -z "$_LLAMA_PR" ]; then
_RESOLVED_SOURCE_URL="$_LLAMA_SOURCE"
if [ "$_LLAMA_FORCE_COMPILE" = "1" ]; then
if [ "$_REQUESTED_LLAMA_TAG" = "latest" ]; then
_RESOLVED_SOURCE_REF="${UNSLOTH_LLAMA_FORCE_COMPILE_REF:-${_DEFAULT_LLAMA_FORCE_COMPILE_REF}}"
_RESOLVED_SOURCE_REF_KIND="branch"
else
_RESOLVED_SOURCE_REF="$_REQUESTED_LLAMA_TAG"
_RESOLVED_SOURCE_REF_KIND="tag"
fi
elif [ "$_REQUESTED_LLAMA_TAG" = "latest" ]; then
_RESOLVE_TAG_ARGS=(--resolve-llama-tag latest --published-repo "ggml-org/llama.cpp" --output-format json)
set +e
_RESOLVE_TAG_JSON="$(python "$SCRIPT_DIR/install_llama_prebuilt.py" "${_RESOLVE_TAG_ARGS[@]}" 2>/dev/null)"
_RESOLVE_TAG_STATUS=$?
set -e
if [ "$_RESOLVE_TAG_STATUS" -eq 0 ] && [ -n "${_RESOLVE_TAG_JSON:-}" ]; then
_RESOLVED_SOURCE_REF="$(
printf '%s' "$_RESOLVE_TAG_JSON" | python -c 'import json,sys; print(json.load(sys.stdin).get("llama_tag",""))' 2>/dev/null || true
)"
else
_RESOLVED_SOURCE_REF=""
fi
if [ -z "$_RESOLVED_SOURCE_REF" ]; then
_RESOLVED_SOURCE_REF="latest"
fi
_RESOLVED_SOURCE_REF_KIND="tag"
else
_RESOLVED_SOURCE_REF="$_REQUESTED_LLAMA_TAG"
_RESOLVED_SOURCE_REF_KIND="tag"
fi
if [ -z "$_RESOLVED_SOURCE_URL" ]; then
_RESOLVED_SOURCE_URL="$_LLAMA_SOURCE"
fi
if [ -z "$_RESOLVED_SOURCE_REF" ]; then
_RESOLVED_SOURCE_REF="$_REQUESTED_LLAMA_TAG"
fi
fi
verbose_substep "source build repo: $_RESOLVED_SOURCE_URL"
verbose_substep "source build ref: ${_RESOLVED_SOURCE_REF:-latest} (${_RESOLVED_SOURCE_REF_KIND})"
BUILD_OK=true
mkdir -p "$(dirname "$LLAMA_CPP_DIR")"
_BUILD_TMP="${LLAMA_CPP_DIR}.build.$$"
rm -rf "$_BUILD_TMP"
if [ -n "$_LLAMA_PR" ]; then
run_quiet_no_exit "clone llama.cpp" \
git clone --depth 1 "${_LLAMA_SOURCE}.git" "$_BUILD_TMP" || BUILD_OK=false
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "fetch PR #$_LLAMA_PR" \
git -C "$_BUILD_TMP" fetch --depth 1 origin "pull/$_LLAMA_PR/head:pr-$_LLAMA_PR" || BUILD_OK=false
fi
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "checkout PR #$_LLAMA_PR" \
git -C "$_BUILD_TMP" checkout "pr-$_LLAMA_PR" || BUILD_OK=false
fi
elif [ "$_RESOLVED_SOURCE_REF_KIND" = "pull" ] && [ -n "$_RESOLVED_SOURCE_REF" ]; then
run_quiet_no_exit "clone llama.cpp" \
git clone --depth 1 "${_RESOLVED_SOURCE_URL}.git" "$_BUILD_TMP" || BUILD_OK=false
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "fetch source PR ref" \
git -C "$_BUILD_TMP" fetch --depth 1 origin "$_RESOLVED_SOURCE_REF" || BUILD_OK=false
fi
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "checkout source PR ref" \
git -C "$_BUILD_TMP" checkout -B unsloth-llama-build FETCH_HEAD || BUILD_OK=false
fi
elif [ "$_RESOLVED_SOURCE_REF_KIND" = "commit" ] && [ -n "$_RESOLVED_SOURCE_REF" ]; then
run_quiet_no_exit "clone llama.cpp" \
git clone --depth 1 "${_RESOLVED_SOURCE_URL}.git" "$_BUILD_TMP" || BUILD_OK=false
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "fetch source commit" \
git -C "$_BUILD_TMP" fetch --depth 1 origin "$_RESOLVED_SOURCE_REF" || BUILD_OK=false
fi
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "checkout source commit" \
git -C "$_BUILD_TMP" checkout -B unsloth-llama-build FETCH_HEAD || BUILD_OK=false
fi
else
_CLONE_ARGS=(git clone --depth 1)
if [ "$_RESOLVED_SOURCE_REF" != "latest" ] && [ -n "$_RESOLVED_SOURCE_REF" ]; then
_CLONE_ARGS+=(--branch "$_RESOLVED_SOURCE_REF")
fi
_CLONE_ARGS+=("${_RESOLVED_SOURCE_URL}.git" "$_BUILD_TMP")
run_quiet_no_exit "clone llama.cpp" \
"${_CLONE_ARGS[@]}" || BUILD_OK=false
fi
if [ "$BUILD_OK" = true ]; then
# Set Release explicitly (llama.cpp only defaults to it on non-MSVC/Xcode).
CMAKE_ARGS="-DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF -DLLAMA_BUILD_EXAMPLES=OFF -DLLAMA_BUILD_SERVER=ON -DGGML_NATIVE=ON"
_TRY_METAL_CPU_FALLBACK=false
_HOST_SYSTEM="$(uname -s 2>/dev/null || true)"
_HOST_MACHINE="$(uname -m 2>/dev/null || true)"
_IS_MACOS_ARM64=false
if [ "$_HOST_SYSTEM" = "Darwin" ] && { [ "$_HOST_MACHINE" = "arm64" ] || [ "$_HOST_MACHINE" = "aarch64" ]; }; then
_IS_MACOS_ARM64=true
fi
# macOS: pin a low deployment target so the source build loads on
# older macOS too (else a macOS 26 host stamps minos=26). Set before
# CPU_FALLBACK_CMAKE_ARGS copies CMAKE_ARGS so both paths inherit it.
if [ "$_HOST_SYSTEM" = "Darwin" ]; then
_MACOS_DEPLOYMENT_TARGET="${UNSLOTH_MACOS_DEPLOYMENT_TARGET:-13.3}"
CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_OSX_DEPLOYMENT_TARGET=${_MACOS_DEPLOYMENT_TARGET}"
export MACOSX_DEPLOYMENT_TARGET="${_MACOS_DEPLOYMENT_TARGET}"
fi
if command -v ccache &>/dev/null; then
CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache -DCMAKE_CUDA_COMPILER_LAUNCHER=ccache"
fi
CPU_FALLBACK_CMAKE_ARGS="$CMAKE_ARGS"
GPU_BACKEND=""
NVCC_PATH=""
# Gate the CUDA toolkit search on an actually-usable NVIDIA GPU
# (_setup_nvidia_usable, computed in the GPU summary block above;
# already false when hidden via CUDA_VISIBLE_DEVICES=""/-1).
# A CUDA toolkit alone (CPU-only build container, leftover packages)
# is not proof of a GPU: building with -DGGML_CUDA=ON there yields a
# binary that fails at runtime, so fall through to the CPU build.
if [ "$_setup_nvidia_usable" = true ]; then
if command -v nvcc &>/dev/null; then
NVCC_PATH="$(command -v nvcc)"
GPU_BACKEND="cuda"
elif [ -x /usr/local/cuda/bin/nvcc ]; then
NVCC_PATH="/usr/local/cuda/bin/nvcc"
export PATH="/usr/local/cuda/bin:$PATH"
GPU_BACKEND="cuda"
elif ls /usr/local/cuda-*/bin/nvcc &>/dev/null 2>&1; then
# Pick the newest cuda-XX.X directory
NVCC_PATH="$(ls -d /usr/local/cuda-*/bin/nvcc 2>/dev/null | sort -V | tail -1)"
export PATH="$(dirname "$NVCC_PATH"):$PATH"
GPU_BACKEND="cuda"
fi
fi
# Check for ROCm (AMD) only if CUDA was not already selected, and
# only when an AMD GPU was actually detected (_setup_amd_detected).
# hipcc presence alone (HIP SDK, no GPU) must not select a HIP build.
# NVIDIA-usable hosts never build HIP (defense in depth: the AMD
# probes above are already skipped when NVIDIA is usable).
ROCM_HIPCC=""
if [ -z "$GPU_BACKEND" ] && [ "$_setup_nvidia_usable" != true ] && [ "$_setup_amd_detected" = true ]; then
if command -v hipcc &>/dev/null; then
ROCM_HIPCC="$(command -v hipcc)"
GPU_BACKEND="rocm"
elif [ -x /opt/rocm/bin/hipcc ]; then
ROCM_HIPCC="/opt/rocm/bin/hipcc"
export PATH="/opt/rocm/bin:$PATH"
GPU_BACKEND="rocm"
elif ls /opt/rocm-*/bin/hipcc &>/dev/null 2>&1; then
ROCM_HIPCC="$(ls -d /opt/rocm-*/bin/hipcc 2>/dev/null | sort -V | tail -1)"
export PATH="$(dirname "$ROCM_HIPCC"):$PATH"
GPU_BACKEND="rocm"
fi
fi
_BUILD_DESC="building"
if [ "$_IS_MACOS_ARM64" = true ]; then
# Metal takes precedence on Apple Silicon (CUDA/ROCm not functional on macOS)
_BUILD_DESC="building (Metal)"
CMAKE_ARGS="$CMAKE_ARGS -DGGML_METAL=ON -DGGML_METAL_EMBED_LIBRARY=ON -DGGML_METAL_USE_BF16=ON -DCMAKE_INSTALL_RPATH=@loader_path -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON"
CPU_FALLBACK_CMAKE_ARGS="$CPU_FALLBACK_CMAKE_ARGS -DGGML_METAL=OFF"
_TRY_METAL_CPU_FALLBACK=true
elif [ -n "$NVCC_PATH" ]; then
# Returns "ok|too_old|unknown\nX.Y" on stdout.
_NVCC_CHECK="$(_nvcc_meets_llama_minimum "$NVCC_PATH")"
_NVCC_STATUS="$(printf '%s\n' "$_NVCC_CHECK" | sed -n '1p')"
_NVCC_VER="$(printf '%s\n' "$_NVCC_CHECK" | sed -n '2p')"
if [ "$_NVCC_STATUS" = "too_old" ]; then
substep "CUDA toolkit $_NVCC_VER is below llama.cpp minimum (12.4)." "$C_ERR"
substep "install a newer CUDA toolkit: https://developer.nvidia.com/cuda-toolkit-archive" "$C_WARN"
substep "falling back to CPU llama.cpp build for this run." "$C_WARN"
NVCC_PATH=""
GPU_BACKEND=""
_BUILD_DESC="building (CPU, CUDA toolkit < 12.4)"
else
_DRIVER_MAX_CUDA="$(_cuda_driver_max_version)"
_CUDA_TOOLKIT_ALLOWED=true
if [ -n "$_NVCC_VER" ] && [ -n "$_DRIVER_MAX_CUDA" ] && \
_cuda_toolkit_major_gt_driver "$_NVCC_VER" "$_DRIVER_MAX_CUDA"; then
_BLOCKED_NVCC_VER="$_NVCC_VER"
if _ALT_NVCC_CHECK="$(_cuda_find_compatible_nvcc_for_driver "$_DRIVER_MAX_CUDA" "$NVCC_PATH")"; then
NVCC_PATH="$(printf '%s\n' "$_ALT_NVCC_CHECK" | sed -n '1p')"
_NVCC_VER="$(printf '%s\n' "$_ALT_NVCC_CHECK" | sed -n '2p')"
GPU_BACKEND="cuda"
export PATH="$(dirname "$NVCC_PATH"):$PATH"
substep "CUDA Toolkit $_BLOCKED_NVCC_VER is a major-version mismatch with driver CUDA $_DRIVER_MAX_CUDA; using compatible CUDA Toolkit $_NVCC_VER at $NVCC_PATH." "$C_WARN"
else
_print_cuda_driver_toolkit_mismatch "$_NVCC_VER" "$_DRIVER_MAX_CUDA"
substep "falling back to CPU llama.cpp build for this run." "$C_WARN"
NVCC_PATH=""
GPU_BACKEND=""
_BUILD_DESC="building (CPU, CUDA toolkit major > driver)"
_CUDA_TOOLKIT_ALLOWED=false
fi
fi
if [ "$_CUDA_TOOLKIT_ALLOWED" = true ]; then
# Resolve the arch list before committing to a CUDA build;
# an empty list means CPU instead of a PTX-only binary (#5854).
_raw_caps=""
# Resolve nvidia-smi as _setup_has_usable_nvidia_gpu does
# (PATH, then /usr/bin); `command -v` alone would miss an
# off-PATH binary and wrongly drop a CUDA host to CPU.
_smi_bin=""
if command -v nvidia-smi >/dev/null 2>&1; then
_smi_bin="nvidia-smi"
elif [ -x "/usr/bin/nvidia-smi" ]; then
_smi_bin="/usr/bin/nvidia-smi"
fi
if [ -n "$_smi_bin" ]; then
_raw_caps=$(_setup_run_smi "$_smi_bin" --query-gpu=compute_cap --format=csv,noheader 2>/dev/null || true)
fi
CUDA_ARCHS="$(_resolve_cuda_archs "$_raw_caps" "${UNSLOTH_LLAMA_CUDA_ARCHS:-}")"
if [ -n "$CUDA_ARCHS" ]; then
CMAKE_ARGS="$CMAKE_ARGS -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCHS}"
CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_CUDA_FLAGS=--threads=0"
_BUILD_DESC="building (CUDA, sm_${CUDA_ARCHS//;/+sm_})"
# Allow a host gcc/clang newer than nvcc's whitelist (else a fresh
# toolkit aborts with "unsupported GNU version"); via env to avoid word-splitting.
export NVCC_PREPEND_FLAGS="${NVCC_PREPEND_FLAGS:+$NVCC_PREPEND_FLAGS }-allow-unsupported-compiler"
else
# No detectable arch: build CPU (CMAKE_ARGS has no
# -DGGML_CUDA=ON yet, so clearing GPU_BACKEND yields CPU).
substep "could not detect a CUDA compute capability; building CPU llama.cpp instead of a PTX-only binary (set UNSLOTH_LLAMA_CUDA_ARCHS, e.g. \"120\", to force a CUDA build)." "$C_WARN"
GPU_BACKEND=""
_BUILD_DESC="building (CPU, CUDA arch undetectable)"
fi
fi
fi
elif [ "$GPU_BACKEND" = "rocm" ]; then
# Resolve hipcc symlinks to find the real ROCm root
_HIPCC_REAL="$(readlink -f "$ROCM_HIPCC" 2>/dev/null || printf '%s' "$ROCM_HIPCC")"
ROCM_ROOT=""
if command -v hipconfig &>/dev/null; then
ROCM_ROOT="$(hipconfig -R 2>/dev/null || true)"
fi
if [ -z "$ROCM_ROOT" ]; then
ROCM_ROOT="$(cd "$(dirname "$_HIPCC_REAL")/.." 2>/dev/null && pwd)"
fi
_BUILD_DESC="building (ROCm)"
CMAKE_ARGS="$CMAKE_ARGS -DGGML_HIP=ON"
# ROCm 7.x ships clang-20 which on Ubuntu 24.04+ defaults to the
# highest-numbered gcc lib dir (/usr/lib/gcc/x86_64-linux-gnu/14/)
# which contains runtime objects but NOT C++ headers, causing:
# fatal error: 'cstdlib' file not found
# Find the newest gcc install dir that actually has both the
# runtime dir AND /usr/include/c++/<ver> headers, then pass it
# to clang via --gcc-install-dir so HIP builds succeed.
_GCC_INSTALL_DIR=""
_gcc_pm="$(gcc -print-multiarch 2>/dev/null)"
case "$_gcc_pm" in
*-linux-gnu*) _GCC_MULTIARCH="$_gcc_pm" ;;
*) _GCC_MULTIARCH="$(uname -m)-linux-gnu" ;;
esac
for _gcc_ver in 14 13 12 11; do
if [ -d "/usr/lib/gcc/$_GCC_MULTIARCH/$_gcc_ver/include" ] && \
[ -d "/usr/include/c++/$_gcc_ver" ]; then
_GCC_INSTALL_DIR="/usr/lib/gcc/$_GCC_MULTIARCH/$_gcc_ver"
break
fi
done
if [ -n "$_GCC_INSTALL_DIR" ]; then
CMAKE_ARGS="$CMAKE_ARGS -DCMAKE_HIP_FLAGS=--gcc-install-dir=\"$_GCC_INSTALL_DIR\""
substep "ROCm HIP gcc install dir: $_GCC_INSTALL_DIR"
fi
export ROCM_PATH="$ROCM_ROOT"
export HIP_PATH="$ROCM_ROOT"
# Use upstream-recommended HIP compiler (not legacy hipcc-as-CXX)
if command -v hipconfig &>/dev/null; then
_HIP_CLANG_DIR="$(hipconfig -l 2>/dev/null || true)"
[ -n "$_HIP_CLANG_DIR" ] && export HIPCXX="$_HIP_CLANG_DIR/clang"
fi
# Detect AMD GPU architecture (gfx target)
GPU_TARGETS=""
if command -v rocminfo &>/dev/null; then
_gfx_list=$(rocminfo 2>/dev/null | grep -oE 'gfx[0-9]{2,4}[a-z]?' | sort -u || true)
_valid_gfx=""
for _gfx in $_gfx_list; do
if [[ "$_gfx" =~ ^gfx[0-9]{2,4}[a-z]?$ ]]; then
# Drop bare family-level targets (gfx10, gfx11, gfx12, ...)
# when a specific sibling is present in the same list.
# rocminfo on ROCm 6.1+ emits both the specific GPU and
# the LLVM generic family line (e.g. gfx1100 alongside
# gfx11-generic), and the outer grep above captures the
# bare family prefix from the generic line. Passing that
# bare prefix to -DGPU_TARGETS breaks the HIP/llama.cpp
# build because clang only accepts specific gfxNNN ids.
# No real AMD GPU has a 2-digit gfx id, so this filter
# can only ever drop family prefixes, never real targets.
if [[ "$_gfx" =~ ^gfx[0-9]{2}$ ]] \
&& echo "$_gfx_list" | grep -qE "^${_gfx}[0-9][0-9a-z]?$"; then
continue
fi
_valid_gfx="${_valid_gfx}${_valid_gfx:+;}$_gfx"
fi
done
[ -n "$_valid_gfx" ] && GPU_TARGETS="$_valid_gfx"
fi
if [ -n "$GPU_TARGETS" ]; then
CMAKE_ARGS="$CMAKE_ARGS -DGPU_TARGETS=${GPU_TARGETS}"
_BUILD_DESC="building (ROCm, ${GPU_TARGETS//;/+})"
fi
elif [ -d /usr/local/cuda ] || _setup_run_smi nvidia-smi &>/dev/null; then
_BUILD_DESC="building (CPU, CUDA driver found but nvcc missing)"
elif [ -d /opt/rocm ] || command -v rocm-smi &>/dev/null; then
_BUILD_DESC="building (CPU, ROCm driver found but hipcc missing)"
else
_BUILD_DESC="building (CPU)"
fi
substep "$_BUILD_DESC..."
NCPU=$(nproc 2>/dev/null || sysctl -n hw.ncpu 2>/dev/null || echo 4)
CMAKE_GENERATOR_ARGS=""
if command -v ninja &>/dev/null; then
CMAKE_GENERATOR_ARGS="-G Ninja"
fi
# GPU label for the CPU-fallback message: Metal, else GPU_BACKEND
# (cuda/rocm). Empty on a bare CPU build (nothing to fall back from).
_gpu_fallback_label() {
if [ "$_TRY_METAL_CPU_FALLBACK" = true ]; then
echo "Metal"
elif [ -n "$GPU_BACKEND" ]; then
printf '%s' "$GPU_BACKEND" | tr '[:lower:]' '[:upper:]'
fi
}
if ! run_quiet_no_exit "cmake llama.cpp" cmake $CMAKE_GENERATOR_ARGS -S "$_BUILD_TMP" -B "$_BUILD_TMP/build" $CMAKE_ARGS; then
_FB_LABEL="$(_gpu_fallback_label)"
if [ -n "$_FB_LABEL" ]; then
_TRY_METAL_CPU_FALLBACK=false
substep "$_FB_LABEL configure failed; retrying CPU build..." "$C_WARN"
rm -rf "$_BUILD_TMP/build"
if run_quiet_no_exit "cmake llama.cpp (cpu fallback)" cmake $CMAKE_GENERATOR_ARGS -S "$_BUILD_TMP" -B "$_BUILD_TMP/build" $CPU_FALLBACK_CMAKE_ARGS; then
_BUILD_DESC="building (CPU fallback after $_FB_LABEL configure failed)"
# Now configured for CPU; clear GPU_BACKEND so a later
# build-step failure won't re-enter fallback on this config.
GPU_BACKEND=""
else
BUILD_OK=false
fi
else
BUILD_OK=false
fi
fi
fi
if [ "$BUILD_OK" = true ]; then
if ! run_quiet_no_exit "build llama-server" cmake --build "$_BUILD_TMP/build" --config Release --target llama-server -j"$NCPU"; then
_FB_LABEL="$(_gpu_fallback_label)"
if [ -n "$_FB_LABEL" ]; then
_TRY_METAL_CPU_FALLBACK=false
substep "$_FB_LABEL build failed; retrying CPU build..." "$C_WARN"
rm -rf "$_BUILD_TMP/build"
if run_quiet_no_exit "cmake llama.cpp (cpu fallback)" cmake $CMAKE_GENERATOR_ARGS -S "$_BUILD_TMP" -B "$_BUILD_TMP/build" $CPU_FALLBACK_CMAKE_ARGS; then
_BUILD_DESC="building (CPU fallback after $_FB_LABEL build failed)"
GPU_BACKEND=""
run_quiet_no_exit "build llama-server (cpu fallback)" cmake --build "$_BUILD_TMP/build" --config Release --target llama-server -j"$NCPU" || BUILD_OK=false
else
BUILD_OK=false
fi
else
BUILD_OK=false
fi
fi
fi
if [ "$BUILD_OK" = true ]; then
run_quiet_no_exit "build llama-quantize" cmake --build "$_BUILD_TMP/build" --config Release --target llama-quantize -j"$NCPU" || true
# Best-effort: the DiffusionGemma visual server (an example target, present
# on llama.cpp PR #24423). No-op when the diffusion example is not configured.
run_quiet_no_exit "build diffusion visual server" cmake --build "$_BUILD_TMP/build" --config Release --target llama-diffusion-gemma-visual-server -j"$NCPU" || true
fi
# Swap only after build succeeds -- preserves existing install on failure
if [ "$BUILD_OK" = true ]; then
_assert_studio_owned_or_absent "$LLAMA_CPP_DIR" "llama.cpp install"
rm -rf "$LLAMA_CPP_DIR"
mv "$_BUILD_TMP" "$LLAMA_CPP_DIR"
: > "$LLAMA_CPP_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
# Symlink to llama.cpp root -- check_llama_cpp() looks for the binary there
QUANTIZE_BIN="$LLAMA_CPP_DIR/build/bin/llama-quantize"
if [ -f "$QUANTIZE_BIN" ]; then
ln -sf build/bin/llama-quantize "$LLAMA_CPP_DIR/llama-quantize"
fi
# DiffusionGemma visual server, if it was built (PR #24423): link next to
# llama-server so Unsloth serves DiffusionGemma GGUFs without DG_VISUAL_BIN.
if [ -f "$LLAMA_CPP_DIR/build/bin/llama-diffusion-gemma-visual-server" ]; then
ln -sf build/bin/llama-diffusion-gemma-visual-server "$LLAMA_CPP_DIR/llama-diffusion-gemma-visual-server"
fi
else
rm -rf "$_BUILD_TMP"
fi
if [ "$BUILD_OK" = true ] && [ -f "$LLAMA_SERVER_BIN" ]; then
step "llama.cpp" "built"
[ -f "$LLAMA_CPP_DIR/llama-quantize" ] && step "llama-quantize" "built"
elif [ "$BUILD_OK" = true ]; then
step "llama.cpp" "binary not found after build" "$C_WARN"
_LLAMA_CPP_DEGRADED=true
else
step "llama.cpp" "build failed" "$C_ERR"
[ -f "$LLAMA_SERVER_BIN" ] || _LLAMA_CPP_DEGRADED=true
fi
fi
}
fi # end _SKIP_GGUF_BUILD check
# ── arm64 Linux GPU: CPU prebuilt as a last resort ──
# An arm64 Linux GPU host source-builds for the GPU above. If that produced no
# binary, install the fork's arm64 CPU prebuilt (app-<tag>-linux-arm64-cpu.tar.gz)
# instead of leaving the host without llama.cpp. --cpu-fallback drops the GPU
# attributes so the CPU bundle is selected rather than re-attempting CUDA.
if [ "$_LLAMA_CPP_DEGRADED" = true ] \
&& [ "$_HOST_SYSTEM" = "Linux" ] \
&& { [ "$_HOST_MACHINE" = "aarch64" ] || [ "$_HOST_MACHINE" = "arm64" ]; }; then
substep "GPU source build unavailable; trying arm64 CPU prebuilt..."
_ARM64_CPU_CMD=(
python "$SCRIPT_DIR/install_llama_prebuilt.py"
--install-dir "$LLAMA_CPP_DIR"
--llama-tag "$_REQUESTED_LLAMA_TAG"
--published-repo "unslothai/llama.cpp"
--cpu-fallback
)
# Trust the installer's exit code: it validates the server before exiting 0,
# the same signal the primary prebuilt path above relies on.
if run_quiet_no_exit "arm64 CPU prebuilt" "${_ARM64_CPU_CMD[@]}"; then
step "llama.cpp" "arm64 CPU prebuilt installed (GPU build unavailable)" "$C_WARN"
_LLAMA_CPP_DEGRADED=false
print_installed_llama_prebuilt_release "$LLAMA_CPP_DIR"
fi
fi
if [ ! -L "$LLAMA_CPP_DIR" ] && {
[ "$_STUDIO_HOME_IS_CUSTOM" != true ] ||
[ -f "$LLAMA_CPP_DIR/$_STUDIO_OWNED_MARKER" ] ||
_studio_owned_adoptable "$LLAMA_CPP_DIR"
}; then
_remove_agent_instruction_files "$LLAMA_CPP_DIR"
fi
# ── whisper.cpp (local speech-to-text dictation engine) ──
# Optional runtime for local dictation. Fail-open: any failure leaves the
# Transformers STT engine and browser dictation working, so it never aborts
# setup (unlike llama.cpp). Runs in 'unsloth studio update' too so the runtime
# installs/refreshes without a compiler. Installs beside llama.cpp under the
# same managed home the sidecar's _managed_whisper_cpp_dir() resolves.
WHISPER_CPP_DIR="$UNSLOTH_HOME/whisper.cpp"
if [ -n "${WHISPER_SERVER_PATH:-}" ] || [ -n "${UNSLOTH_WHISPER_CPP_PATH:-}" ]; then
verbose_substep "whisper.cpp: using a user-configured binary/dir; skipping managed install"
elif [ "${UNSLOTH_SKIP_WHISPER_INSTALL:-0}" = "1" ]; then
verbose_substep "whisper.cpp: install skipped (UNSLOTH_SKIP_WHISPER_INSTALL=1)"
else
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ]; then
_assert_studio_owned_or_absent "$WHISPER_CPP_DIR" "whisper.cpp install"
fi
_WHISPER_CMD=(python "$SCRIPT_DIR/install_whisper_prebuilt.py" --install-dir "$WHISPER_CPP_DIR")
if [ -n "${UNSLOTH_WHISPER_RELEASE_TAG:-}" ]; then
_WHISPER_CMD+=(--published-release-tag "$UNSLOTH_WHISPER_RELEASE_TAG")
fi
if [ -n "${_setup_gfx:-}" ]; then
_WHISPER_CMD+=(--rocm-gfx "$_setup_gfx")
elif [ "$_setup_amd_detected" = true ]; then
_WHISPER_CMD+=(--has-rocm)
fi
_WHISPER_LOG="$(mktemp)"
set +e
if _is_verbose; then
"${_WHISPER_CMD[@]}" 2>&1 | tee "$_WHISPER_LOG"
_WHISPER_STATUS=${PIPESTATUS[0]}
else
"${_WHISPER_CMD[@]}" >"$_WHISPER_LOG" 2>&1
_WHISPER_STATUS=$?
fi
set -e
if [ "$_WHISPER_STATUS" -eq 0 ]; then
if grep -Fq "already matches" "$_WHISPER_LOG"; then
step "whisper.cpp" "prebuilt up to date"
else
step "whisper.cpp" "prebuilt installed"
fi
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ] && [ -d "$WHISPER_CPP_DIR" ]; then
: > "$WHISPER_CPP_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
fi
rm -f "$_WHISPER_LOG"
elif [ "$_WHISPER_STATUS" -eq 3 ]; then
# A warm dictation server holds the binary; keep the old install.
step "whisper.cpp" "install busy; keeping existing runtime" "$C_WARN"
rm -f "$_WHISPER_LOG"
else
# A source build is opt-in. Keep the installer log until fallback has
# finished so setup can distinguish release skew from an operational
# installer failure and report the exact pairing when available.
_WHISPER_RECOVERED=false
_WHISPER_BUILD="$SCRIPT_DIR/../scripts/build_whisper_cpp.sh"
if [ "${UNSLOTH_WHISPER_FORCE_COMPILE:-0}" = "1" ] && [ -f "$_WHISPER_BUILD" ] \
&& command -v cmake >/dev/null 2>&1 && command -v git >/dev/null 2>&1; then
substep "whisper.cpp prebuilt unavailable; building from source (UNSLOTH_WHISPER_FORCE_COMPILE=1)..."
# The source build overwrites whisper-server in the managed dir but
# knows nothing about the prebuilt marker; a stale marker would make
# a later setup run report "already matches" and skip repairing the
# prebuilt over the source binary. Drop it before building.
rm -f "$WHISPER_CPP_DIR/UNSLOTH_WHISPER_PREBUILT_INFO.json" 2>/dev/null || true
if run_quiet_no_exit "whisper.cpp source build" sh "$_WHISPER_BUILD"; then
_WHISPER_RECOVERED=true
step "whisper.cpp" "source build installed"
if [ "$_STUDIO_HOME_IS_CUSTOM" = true ] && [ -d "$WHISPER_CPP_DIR" ]; then
: > "$WHISPER_CPP_DIR/$_STUDIO_OWNED_MARKER" 2>/dev/null || true
fi
else
:
fi
fi
if [ "$_WHISPER_RECOVERED" != true ]; then
if [ "$_WHISPER_STATUS" -eq 2 ]; then
_WHISPER_REQUIRED_TAG="$(sed -n 's/.*slim bundle requires llama\.cpp \([^; ]*\).*/\1/p' "$_WHISPER_LOG" | tail -n 1)"
_WHISPER_INSTALLED_TAG="$(python - "$UNSLOTH_HOME/llama.cpp/UNSLOTH_PREBUILT_INFO.json" <<'PY' 2>/dev/null || true
import json, sys
try:
print(json.load(open(sys.argv[1], encoding="utf-8")).get("release_tag", ""))
except Exception:
pass
PY
)"
_WHISPER_PAIRING="installed llama.cpp ${_WHISPER_INSTALLED_TAG:-unknown}; whisper requires ${_WHISPER_REQUIRED_TAG:-unknown}"
step "whisper.cpp" "no compatible prebuilt ($_WHISPER_PAIRING); curated whisper.cpp dictation is unavailable; publish the paired releases in llama.cpp then whisper.cpp order; browser and Transformers dictation remain available" "$C_WARN"
else
step "whisper.cpp" "prebuilt install failed; curated whisper.cpp dictation is unavailable; retry setup or inspect verbose output; browser and Transformers dictation remain available" "$C_WARN"
fi
fi
rm -f "$_WHISPER_LOG"
fi
fi
# ── Footer ──
if [ "$_LLAMA_ONLY" = "1" ]; then
echo ""
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
if [ "$_LLAMA_CPP_DEGRADED" = true ]; then
printf " ${C_WARN}%s${C_RST}\n" "llama.cpp update finished (limited: llama.cpp unavailable)"
else
printf " ${C_TITLE}%s${C_RST}\n" "llama.cpp update finished"
fi
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
elif [ "$IS_COLAB" = true ]; then
echo ""
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
if [ "$_LLAMA_CPP_DEGRADED" = true ]; then
printf " ${C_WARN}%s${C_RST}\n" "Unsloth Studio Setup Complete (limited: llama.cpp unavailable)"
else
printf " ${C_TITLE}%s${C_RST}\n" "Unsloth Studio Setup Complete"
fi
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
substep "from colab import start"
substep "start()"
else
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
if [ "$_LLAMA_CPP_DEGRADED" = true ]; then
printf " ${C_WARN}%s${C_RST}\n" "Unsloth Studio Installed (limited: llama.cpp unavailable)"
else
printf " ${C_TITLE}%s${C_RST}\n" "Unsloth Studio Installed"
fi
printf " ${C_DIM}%s${C_RST}\n" "$RULE"
if [ "$_LLAMA_CPP_DEGRADED" = true ]; then
printf " ${C_DIM}%-15s${C_WARN}%s${C_RST}\n" "launch" "unsloth studio -p 8888"
else
printf " ${C_DIM}%-15s${C_OK}%s${C_RST}\n" "launch" "unsloth studio -p 8888"
fi
printf " ${C_DIM}%-15s%s${C_RST}\n" "" "(add -H 0.0.0.0 for LAN / cloud access; exposes the raw port only, not a public URL)"
printf " ${C_DIM}%-15s%s${C_RST}\n" "" "(add -H 0.0.0.0 --cloudflare for a public Cloudflare HTTPS link, or --secure to keep the raw port private; anyone with the API key can run code)"
fi
echo ""
# When called from install.sh (SKIP_STUDIO_BASE=1), exit non-zero so the
# installer can report the GGUF failure after finishing PATH/shortcut setup.
# When called directly via 'unsloth studio update', keep the install
# successful -- the footer above already reports the limitation and Unsloth
# is still usable for non-GGUF workflows.
if [ "$_LLAMA_CPP_DEGRADED" = true ] && [ "${SKIP_STUDIO_BASE:-0}" = "1" ]; then
exit 1
fi