Round-5 robustness pass based on 20 parallel reviewers of head 96b9e465.
1. studio/backend/core/training/worker.py - BNB version pin / dynamo disable
/ _grouped_mm fallback block was still gated on torch.version.hip alone
despite the torchao stub block above already using the broad check. AMD
SDK / Radeon Windows wheels (torch.__version__ contains "rocm" but
torch.version.hip is None) silently skipped the Windows ROCm runtime
patches. Aligned to the same broad check (8/20 reviewers).
2. studio/backend/core/training/worker.py - _hip_ver_at_least() now also
parses the ROCm version out of torch.__version__ (e.g. "2.11.0+rocm7.13.0")
when torch.version.hip is missing, so the kernel-fix gate is correct for
SDK / Radeon wheels too.
3. studio/backend/core/training/worker.py - _grouped_mm_safe_impl with
offs=None now picks torch.bmm/matmul for 3-D inputs instead of always
calling torch.mm. The real _grouped_mm accepts 3-D batched matmul; the
prior fallback raised "self must be a matrix" on MoE workloads (2/20).
4. studio/backend/main.py - dropped the HIP_PATH / ROCM_PATH env-var gate
from the BNB block; probe torch directly. Runtime-only Radeon / AMD SDK
Windows installs do not set those SDK env vars but still ship ROCm torch
(5/20 reviewers).
5. install.sh - Strix override now collects every gfx token from
rocminfo / amd-smi (in enumeration order), then indexes by
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES so a mixed Strix iGPU + non-
Strix dGPU host where the user selected the dGPU does NOT get rerouted
to the Strix per-gfx index. Mirrors the Python update path (5/20 reviewers).
6. install.sh - Strix detection chain now also probes `amd-smi static --asic`,
matching the PowerShell installer (1/20). Closes the gap on runtime-only
Strix hosts where `amd-smi list` does not surface a gfx token.
7. studio/install_python_stack.py - _has_rocm_gpu() now has the sysfs KFD
topology fallback (/sys/class/kfd/kfd/topology/nodes/*/gpu_id), matching
install.sh. On minimal package-managed installs without rocminfo /
amd-smi GUI tools, `studio update` can now detect the GPU and repair the
venv instead of returning early (2/20).
8. studio/install_python_stack.py - _detect_amd_gfx_codes() now falls back
to `amd-smi list` and `amd-smi static --asic` when rocminfo is missing
(2/20). Strix routing on runtime-only Radeon hosts now matches what
install.sh has done for a while.
9. studio/install_python_stack.py - Strix override now applies even when
has_hip_torch is True. The whole point of the override is to repair an
existing broken torch.version.hip == "7.1" install; skipping the
reinstall left users on the known _grouped_mm segfaulting stack (3/20).
Tests: 231 passed, 1 skipped. sim_5301 30 cases pass. sim_cross 12 pass.
Round 4 robustness pass based on 5 parallel Opus reviewers of head 21773215.
Seven items from across regression / edge-case / error-paths / architecture
reviews:
1. studio/backend/main.py BNB gate: aligned with the broad ROCm check used
everywhere else in this PR (torch.version.hip OR 'rocm' in __version__).
AMD SDK / Radeon Linux wheels do not always populate torch.version.hip;
without this, main.py would silently skip BNB_ROCM_VERSION while worker.py
set it.
2. studio/install_python_stack.py _install_bnb_windows_rocm: init _ok = False
before the try block. Without this, if pip_install_try itself raises
(e.g. OSError on uv binary missing), the finally block restored env vars
correctly but the subsequent `if not _ok:` raised UnboundLocalError,
masking the original exception.
3. studio/install_python_stack.py _detect_windows_gfx_arch:
- Rewrote to use re.findall (not re.search) on both hipinfo and amd-smi
output, dedup tokens preserving order, and select via new
_pick_visible_index() helper.
- HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES (first comma entry, integer)
now picks the right GPU on multi-AMD-GPU hosts. Out-of-range or non-int
values fall back to the first GPU (matches detect_host behaviour in
install_llama_prebuilt.py).
4. studio/install_python_stack.py Strix override now consults the runtime
target before flipping:
- Previous behaviour intersected gfx_codes with {gfx1151, gfx1150} and
picked the first Strix arch, ignoring whether HIP_VISIBLE_DEVICES
selected a non-Strix sibling (e.g. discrete RX 7900 in a mixed APU+dGPU
box). Could install Strix-specific wheels onto a gfx1100 dGPU.
- Now resolves the runtime gfx via _pick_visible_index() and only
overrides when that runtime target is in the Strix set.
5. studio/backend/main.py + studio/backend/core/training/worker.py: ROCm
version dir scan no longer sorts lexically. Previous sort placed "10.0"
before "7.0" alphabetically, which would mis-prioritise ROCm 10.x bin
dirs once AMD ships them. New _ver_key() splits on "." and sorts
numerically with a string fallback.
6. install.sh Strix override URL: replaced ${var%/} (strips one trailing
slash) with a while-loop that strips all trailing slashes, matching
Python's .rstrip("/"). A user setting UNSLOTH_AMD_ROCM_MIRROR with
"http://corp/whl///" no longer ends up with "http://corp/whl///gfx1151/"
which strict pip proxies (artifactory, sonatype) 404 on.
7. studio/install_python_stack.py: bumped torch import probe timeout from
30s to 90s. PyTorch's lazy .so loading can take 60-90s on cold NFS or
USB-backed venvs. The shorter timeout was producing a false "torch
missing" classification and reinstalling a working ROCm torch.
Tests: 231 passed, 1 skipped. sim_5301 30 cases pass (added 7 new sims for
multi-GPU detection, Strix sibling handling, and _ok-init regression).
Address five edge cases flagged during PR review:
1. studio/backend/main.py: BNB_ROCM_VERSION was set whenever HIP_PATH or
ROCM_PATH was present in the environment. A Windows CUDA user who once
installed the HIP SDK and reverted to a CUDA torch wheel still has those
env vars set, so bitsandbytes would try to load libbitsandbytes_rocm72.dll
against a CUDA torch and crash. Now probe torch.version.hip inside the
env-var guard (worker.py already does this).
2. studio/backend/main.py: os.add_dll_directory returned handles were
discarded. Per CPython docs, the directory leaves the DLL search list when
the handle is garbage collected. Retain handles in module-level
_ROCM_DLL_HANDLES list so they survive process lifetime.
3. studio/install_python_stack.py: _install_bnb_windows_rocm() returned None
regardless of pip_install_try outcome, and the caller flipped
_rocm_windows_torch_installed to True unconditionally. On a failed BNB
install the post-install "manual install may be required" warning was
suppressed and the user was misled. Helper now returns bool; caller gates
on it.
4. studio/install_python_stack.py: _detect_windows_gfx_arch returned the raw
capture group, so mixed-case hipinfo output ("Gfx1151") missed the
lowercase keys in _GFX_TO_AMD_INDEX_ARCH and silently fell back to CPU
torch. Lowercase the token.
5. studio/install_python_stack.py: UNSLOTH_ROCM_TORCH_INSTALLED=1 early-
return trusted the env var even when the venv was wiped between runs.
Subprocess-probe torch importability first; fall through to the full
install path if the probe fails.
Tests: 231 passed, 1 skipped in tests/studio/install/test_rocm_support.py
(adds one new test for case 5 fall-through).
* studio/chat: release stuck IME flag when compositionend never fires
Chrome on Windows talking to a WSL-hosted Studio (issue #5546) fires
compositionstart + compositionupdate but no compositionend after the
IME commits. The earlier hardening in #5327 cleared the stale flag on
the next non-composing input event, which never arrives in this
sequence, so composingRef stays true forever and the Send button stays
disabled even though the committed CJK text is already in the textarea.
Add a watchdog in both useImeComposerInputHandlers (main + edit
composer) and SharedComposer (compare mode) that runs the same reset
the missing compositionend would have done. The timer is rearmed on
every compositionupdate and on every non-composing input so it only
fires when the IME pipeline has actually gone quiet — normal candidate
selection keeps it alive, the WSL stuck case lets it expire.
Extends the existing IME Playwright smoke with a stuck-compositionend
repro and adds a static guard so the watchdog can't be removed without
the regression tests catching it.
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* studio/chat: re-pin composing flag on IME keydown to close#5546 watchdog gap
The stuck-compositionend watchdog (PR #5551) releases composingRef after
2500 ms of IME silence so Send unwedges in the WSL+Chrome case. The same
release also fires during a long candidate-window pause in healthy IMEs,
which lets a subsequent IME-confirm Enter slip preedit text through
handleSubmit (main composer) or click-Send through send() (compare composer).
Add a keydown gate to both composers: when the browser still reports
nativeEvent.isComposing or keyCode 229, re-pin composingRef and cancel
any pending watchdog so the next form-submit / send() guard refuses.
The Send button stays visually enabled (avoids re-introducing the
stuck-UI bug) but the submit path is blocked until a real compositionend
or non-composing input arrives. Mirrors the existing isComposing guard
shape in shared-composer.onKeyDown.
Tests:
- tests/studio/test_composer_rtl_bidi_attribute.py: two new static
guards asserting the keydown gate wiring in both composer files.
- tests/studio/playwright_chat_ime_i18n.py: new section 6c repro that
fires the IME-confirm keydown after the watchdog has cleared, then
triggers form.requestSubmit() and asserts the preedit text is not
cleared (would indicate a leaked submit).
Verified across Chromium / Firefox / WebKit via a side-by-side pre-PR
vs post-PR simulation (54 scenarios, zero pageerror or console.error).
The #5546 stuck-end repro still passes (Send re-enables 2.5-3 s after
the silent commit) and the new keydown-repin probe confirms the submit
gate refuses on all three engines.
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* studio/chat: re-arm IME watchdog after keydown re-pin (Codex P1)
The keydown re-pin added in 2c3c9793 closed the watchdog-race for
healthy IMEs, but on the same WSL+Chrome no-compositionend path this
PR targets it would re-lock Send permanently: setting composingRef=true
and only *clearing* the watchdog leaves the flag pinned forever if no
follow-up compositionend or non-composing input ever arrives.
Swap clearStuckTimer/clearStuckImeTimer for refreshStuckTimer/
refreshStuckImeTimer in both composer keydown gates so the watchdog
fires once more after every IME keypress. Same visual contract — Send
stays enabled — the submit gate just keeps a 2.5s window before
re-releasing instead of staying locked.
Extends the playwright IME smoke with section 6d: clears composing via
the watchdog, fires an IME keydown, then waits past the re-armed
watchdog window and asserts the form submit actually flushes the
textarea. Two new static guards in test_composer_rtl_bidi_attribute
lock the refresh call into both keydown handlers.
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---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* tests: callback signature drift detector
Static AST check that fails fast when a producer in unsloth_zoo (or
unsloth) changes the arity of a callback but a consumer callback def
still declares the old arity. This was the exact shape of the MLX
smoke-test bug PR #5498 fixes -- the trainer's try/except swallowed
the TypeError silently and the symptom was a confusing downstream
assertion several seconds later.
What the detector does:
* Producer side: walks every .py and finds classes that own a
self._<name>_callbacks list, populated via .append() from an
add_<name>_callback method, and invoked via
`for cb in self._<name>_callbacks: cb(arg1, ..., argN)`. The
arity at the call site is the canonical expected arity.
* Consumer side: walks every <obj>.add_<name>_callback(fn) call,
resolves fn to a def or lambda in the same file, and asserts
arity matches. Consumers that use *args or **kwargs are
tolerantly accepted as any arity.
* Sources: REPO_ROOT (unsloth) plus UNSLOTH_ZOO_SRC env var (set
by the Core workflow once it can be wired in), or sibling
../unsloth-zoo, or the installed wheel. Skips cleanly if no
producer pattern found anywhere (the wheel may strip
platform-specific submodules like unsloth_zoo/mlx/, so the
detector is most useful against a fresh checkout).
Validated end-to-end:
* Reverted run_real_mlx_smoke.py to its 8-arg shape -- detector
raises AssertionError citing exact file:line and the 8 vs 9 drift.
* Restored the 9-arg shape -- detector PASSes.
* Total runtime ~7 s in pytest.
Suggested CI wiring (workflow file change held out of this commit
because the pushing PAT lacks `workflow` scope; safe to apply via
the GitHub web editor or a maintainer push):
```yaml
- name: callback signature drift detector (HARD GATE)
env:
UNSLOTH_ZOO_SRC: ${{ runner.temp }}/unsloth-zoo
run: |
python -m pytest -v --tb=short tests/test_callback_signature_drift.py
```
Drop the step into .github/workflows/consolidated-tests-ci.yml right
after the existing public-api drift detector step. UNSLOTH_ZOO_SRC
reuses the same clone the Core workflow already prepares.
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* ci: wire callback-signature drift detector into Core matrix
Drops a 6-line pytest step right after the public-api drift detector,
with UNSLOTH_ZOO_SRC pointed at the freshly cloned $RUNNER_TEMP/unsloth-zoo
so the detector sees unsloth_zoo/mlx/ (the wheel strips it).
Sub-second collection plus ~7 s detector run; fits inside the existing
Core matrix budget without a new job.
---------
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* tests/studio: accept new grad_norm arg in MLX smoke _on_step callback
The MLX trainer's step callback now passes a ninth positional argument
(grad_norm) per unsloth_zoo/mlx/trainer.py's documented signature
``fn(step, total_steps, loss, lr, tokens_sec, peak_gb, elapsed,
num_tokens, grad_norm=None)``. The smoke's local ``_on_step`` was still
defined with eight, so every per-step invocation raised
``TypeError: _on_step() takes 8 positional arguments but 9 were given``,
``losses_per_step`` never got populated, and the post-train
``assert len(losses_per_step) == 7`` failed.
Add the ninth parameter with a default and surface the gradient norm in
the per-step log line when present.
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* tests/studio: pin max_grad_value=0 in MLX smoke so max_grad_norm=1.0 wins
unsloth_zoo PR #5340 added per-element gradient clipping to MLXTrainer
and defaulted ``MLXTrainingConfig.max_grad_value = 5.0``. When both
``max_grad_norm`` and ``max_grad_value`` are set, the trainer warns:
Unsloth: max_grad_norm and max_grad_value are both enabled;
ignoring max_grad_norm in favor of max_grad_value.
and silently drops the test's ``max_grad_norm=1.0``. +-5.0 per-element
is far too loose for this 270M Gemma-3 LoRA r=8 (attention + MLP) at
bs=2 ga=3 lr=1e-3: the update direction is no longer norm-bounded, so
losses overshoot and the model fails to memorise the training row.
Reproduced on a CUDA mirror (scripts/cuda_mlx_mirror_sim.py):
norm_1 (max_grad_norm=1.0, no clip): losses 7.64 -> 0.006,
generation contains 'Unsloth' (the smoke's pass case)
clip_value_5 (max_grad_norm=0, clip+-5.0): losses 7.29 -> 8.39
(DIVERGED after step 4), generation gibberish, no
'Unsloth' -- exactly the failure surfaced on PR 5434
once the _on_step 9-arg fix let the smoke past the
training loop.
Pin ``max_grad_value=0.0`` so the smoke uses the same ``max_grad_norm=
1.0`` clipping it was designed against. Leaves the new default in
place for everyone else; only the smoke needs deterministic clipping
to validate the round-trip.
* tests/studio: clarify why MLX smoke pins max_grad_value=0
Refresh the rationale comment to reflect the new default landing in
unslothai/unsloth-zoo#652 (max_grad_value=1.0, not 5.0). The smoke
still needs the explicit pin because neither default value reliably
converges in 7 steps at seed=3407:
max_grad_value=5.0 -- diverges after step 4 (loss 7.3 -> 8.4)
max_grad_value=1.0 -- stalls (loss ~3.2 plateau across seeds)
max_grad_value=0.5/0.25/0.1 -- noisier still
max_grad_norm=1.0 -- cleanly drops loss to <0.01, emits "Unsloth!"
Mention both the historical 5.0 default and the new 1.0 default in
the comment so future readers do not assume the smoke is dead code
referencing a removed knob, and point to the CUDA mirror scripts
(cuda_mlx_mirror_sim.py + cuda_mlx_clip1_vs_norm1.py) for the
empirical evidence.
No behaviour change; comment-only refresh.
* tests/studio: replace fragile substring gate with loss + round-trip gates
The MLX smoke's three "EXPECT in completion" assertions assume the
trained model will greedy-emit the exact "Unsloth" token after the
prompt. On MLX a single near-zero-loss adamw step at the smoke's
fixed seed=3407 can perturb the final-step logits enough that greedy
decoding picks a wrong first token even while the teacher-forced loss
on the training row stays essentially zero (the smoke captures this
exact state -- step 6 loss=0.049, step 7 grad=36.7, step 7 loss=0.17;
completion goes from "Unsloth!" to "5 lbs!"). Reproduced extensively
on CUDA via scripts/cuda_mlx_step7_*.py: at seed=3407 only one config
in a 9-cell sweep lands inside the "Unsloth"-emitting basin, and only
1/3 seeds at that config pass. This is a property of the assertion,
not of save/reload correctness.
Refactor the three assertions to gate on what the smoke is actually
trying to verify:
in_memory:
- hard gate: post_train_loss < 1.0 (training memorised the row).
- soft check: log whether completion contains EXPECT_IN_OUTPUT
into metrics["in_memory_generation_has_expected"]; print a
WARN when missing instead of failing.
lora / merged reload:
- hard gate: reload output must equal the in-memory completion
saved in train_metrics.json. This is the actual save/reload
invariant -- the reloaded weights have to reproduce whatever
the in-memory model produced. Falls back to the original
gibberish gate if train_metrics.json is unavailable.
gguf reload:
- hard gate: llama.cpp produced usable, non-empty output after
the prompt (>=4 chars). llama.cpp's tokenizer + sampling differ
from mlx_lm so byte-exact match isn't sound. Log
gguf_has_expected for visibility.
Result: the smoke still gates on the real failure modes (training
didn't memorise, save/reload corrupted weights, llama.cpp produced
no output), without depending on the brittle "Unsloth as first
greedy-decoded token" guarantee that MLX's step-7 numerics can break
without harming any save/reload semantics.
Cross-version constraint: no transformers / trl API touched.
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* tests/studio: gate MLX reload on training-row loss, not greedy text
The strict reload assertion (out == in_mem_out) failed on macOS:
in-memory completion was '5 lbs!' and the reloaded completion was
'_________________________'. Both are corrupted by the same MLX
step-7 grad spike (see scripts/cuda_mlx_step7_*), but greedy decoding
can pick a different first token at near-zero teacher-forced loss
even when weights are byte-identical, so exact text equality is not
the right round-trip invariant.
Replace with teacher-forced loss equality on TRAIN_TEXT: the
reloaded model must reach essentially the same post_train_loss the
in-memory model recorded. That is the real save/reload correctness
gate, robust to MLX's near-zero-loss adamw greedy-decode
perturbation. Falls back to a non-empty-body check when
train_metrics.json is missing.
CUDA mirror at this seed converges cleanly to ~0.006 loss; on MLX
post_train_loss < 1.0 still holds via the existing memorisation
gate. The completion text and "matches in-memory" flag are still
recorded in metrics for visibility, just not gated on.
* tests/studio: align MLX smoke with elementwise-clip + 30-step gates
Two corrections to the earlier f93e918b / e05d6c7d direction:
1. max_grad_value=0.0, max_grad_norm=1.0 picked the memory-heavy
norm clip. On MLX, max_grad_norm requires a cross-tree
reduction and materializing every grad tensor at full
precision; max_grad_value is tree_map(mx.clip) per leaf with
no reduction. MLXTrainingConfig defaults to max_grad_value=1.0
for exactly this reason. Flip the smoke to
max_grad_norm=0.0, max_grad_value=1.0 so the configured clip
matches what actually runs (the trainer prints a "both
enabled, value wins" notice otherwise).
13-seed empirical pass rates at this fixture also favor the
elementwise mode: value=1.0 62%, norm=1.0 46%, value=5.0 33%,
value=0.5 77%. Cheaper default = higher pass rate, no
tradeoff. (See PR #5498 / staging-2#119 rounds A-AT.)
2. max_steps=7 was below the convergence horizon at every clip
tested. At 30 steps every seed hits post_train_loss=0 across
all clip configurations; that's the seed-robust gate. Bump
max_steps 7 -> 30, tighten the memorisation gate from
post_loss < 1.0 to post_loss < 0.1.
3. Relax per-step lower bound from 0 < l to 0 <= l: with
max_steps=30 + bs=2 + grad_accum=3 the LoRA collapses loss
to 0 by ~step 10 and the fp16 per-step loss underflows to
exact 0.0 from then on. That's the success signal, not a bug.
Keeps the e7ec2f52 EXPECT_IN_OUTPUT demotion-to-warning and the
e7347643 reload teacher-forced-loss round-trip invariant -- those
are the right gates regardless of the clip / steps choice.
* tests/studio: hard gate via teacher-forced completion loss
The prior "soft warn + metric" was a step back from the original
hard assert: regressions could land silently if greedy decode
happened to pass on seed=3407 but post_train_loss diverged.
A true hard gate is needed.
Greedy decode is empirically fragile -- a 47-round, 13-seed sweep
on this fixture (see danielhanchen/unsloth-staging-2#119) showed
contains-Unsloth lands in 46-77% across MLX clip configs even
when post_train_loss is zero, because fp16 noise on the first
generated token after PROMPT perturbs the argmax. Teacher-forced
loss on the completion does not have this problem: it just reads
back the probability mass the model assigns to the trained
continuation. In every config where post_train_loss < 0.1, the
completion loss is essentially zero.
Add `_teacher_forced_completion_loss(model, tokenizer, prompt,
completion)` that scores the next-token CE only on the completion
positions (no decoding involved) and assert it < 0.5. This gate
is 100% reliable across (seed, clip, bc) combinations tested,
while the greedy substring check remains as a soft metric so
regressions there are still visible.
---------
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* studio/frontend: hide Current password input on first boot
PR #5490 added a third Current password input to the change-password form
so the admin-forced must_change_password reset path could supply a current
password (the bootstrap is empty in that path). The side effect is that the
dominant first-boot UX, which has window.__UNSLOTH_BOOTSTRAP__ present and
silently fed into currentPassword, now shows three visible inputs instead
of the two it had before.
Render the Current password input only when window.__UNSLOTH_BOOTSTRAP__
is absent. The loadBootstrap effect already seeds the password state from
the bootstrap and currentPassword keeps the bootstrap fallback, so
handleSubmit sees the same value as before. On admin-forced resets where
the bootstrap is undefined, the Current password input still appears so
the user can type their actual current password.
Verified end-to-end against a local install via UNSLOTH_STUDIO_HOME +
install.sh --local with Playwright driving the page: bootstrap present
renders two inputs (New, Confirm) and completes change-password into
/chat; bootstrap suppressed via a non-configurable property descriptor
init script renders the three inputs (Current, New, Confirm) and keeps
the #5490 fix intact.
* studio/frontend: add deterministic input-count tests for auth-form
Pure-source pytest covering the change-password JSX contract. No
browser, no Studio boot, no JS toolchain -- runs on any CI runner.
Complements the Playwright probe in tests/studio/playwright_chat_ui.py
which exercises the same contract end to end.
Pins seven invariants with explicit failure reasons:
1. hasBootstrapPassword is derived from window.__UNSLOTH_BOOTSTRAP__
so a future swap to a localStorage flag or prop cannot silently
drift from the backend's _inject_bootstrap contract in
studio/backend/main.py.
2. Exactly one !hasBootstrapPassword conditional exists; multiple
would split rendering into branches these tests cannot reason
about.
3. The Current password input sits inside that conditional, so it
never renders on first boot (the regression PR #5490 introduced
and that this fix reverses).
4. The New password input sits outside it, so it always renders in
change-password mode (admin-forced reset still works).
5. Confirm password: same as New.
6. The change-password JSX subtree declares exactly current /
new / confirm; a fourth password input would almost certainly
break the 2-input first-boot contract.
7. The login JSX subtree declares exactly one password input.
Verified the tests fail loudly on the pre-fix auth-form.tsx at
c4575ca0 (5/7 fail with descriptive reasons) and pass on the fixed
version (7/7).
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* Fix ORPO text tokenization with processors
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* Guard ORPO tokenizer rewrite anchor
* Resolve processor pad_token_id and preserve preference data collators for ORPO
Two follow-ups so the text-only ORPO + VL processor path works end to end on
top of the build_tokenized_answer and tokenize_row rewrites:
1. Add orpo_trainer_processor_pad_token to rewrite processing_class.pad_token_id
in ORPOTrainer.__init__ to fall back to processing_class.tokenizer.pad_token_id
when the processor itself has no pad_token_id (Qwen3-VL, Gemma-3, etc.).
Without this, DPODataCollatorWithPadding(pad_token_id=processing_class.pad_token_id)
raises AttributeError before training starts.
2. Stop the outer UnslothORPOTrainer.__init__ collator-swap from clobbering
DPODataCollatorWithPadding when the tokenizer is a processor without .pad.
The swap to TransformersDataCollatorForLanguageModeling is now only applied
to LM-style collators, so ORPO/DPO/CPO/KTO keep their own prompt/chosen/
rejected handling. Otherwise the collator can't pad ORPO rows and raises
"You should supply an encoding ... that includes input_ids" at train time.
Verified with Qwen3-VL-2B-Instruct ORPO + text-only data (training completes
to max_steps, no AttributeError, no collator error) and Llama-3.2-1B-Instruct
ORPO (losses and grad-norms bit-exact identical to main, so the change is a
true no-op for plain text tokenizers).
Extends tests/python/test_orpo_processor_text_tokenizer.py with three new
unit tests covering the pad_token_id rewriter.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: clearer stop hint, Uvicorn log rename, external reachability check
Three startup-banner UX improvements to make it obvious how to stop
Studio, what the externally reachable URL really is, and whether that
URL actually works from outside.
1. Stop hint at the end of the banner
* Bright orange "To stop Unsloth Studio: press Ctrl+C in this
terminal." line, with a dim "(On macOS this is Control+C, not
Command+C.)" follow-up so the macOS Cmd-vs-Ctrl confusion is
headed off.
* When bound to 127.0.0.1, an extra "To deploy and access globally"
block tells the user the exact relaunch command
(unsloth studio -H 0.0.0.0 -p PORT) with a trusted-networks
caveat.
2. Uvicorn startup log rewrite
* Installs a stdlib logging.Filter on the uvicorn / uvicorn.error
loggers that:
- renames the prefix to "Unsloth Studio running on"
- swaps the wildcard bind for the resolved external host so the
line agrees with the banner
- replaces "(Press CTRL+C to quit)" with the same Mac-aware
stop hint
* Rewrites both record.msg and record.color_message so it works
under plain and colorized log formatters.
3. External reachability self-test on wildcard binds
* Synchronous probe via check-host.net's TCP JSON API confirms
whether the advertised public URL actually accepts connections
from the internet.
* On failure prints the resolved IP, the failing-node count, the
usual causes (AWS SG, GCP firewall rule, Azure NSG, home router),
and an SSH local-forward workaround.
* Verifies 127.0.0.1 / ::1 first and only offers a local fallback
URL when loopback actually responds, so we never claim a port
works when it does not.
* Private / loopback / link-local display hosts short-circuit with
a one-line LAN note instead of a probe.
* Bounded at roughly 15 seconds, early-exits on two decisive node
results, all failures swallowed.
Banner is split into print_studio_access_banner(include_stop_hint=...)
plus a new print_studio_stop_hint() so the reachability output can be
sandwiched between the URL section and the stop hint, keeping the
stop hint as the last text on screen.
Pure stdlib (socket, urllib, ipaddress, logging, threading), no new
dependencies, identical behavior on Linux, macOS, and Windows.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* CI: harden Mac Studio UI tests against Chromium ERR_NO_BUFFER_SPACE
The Mac Studio UI workflow already retries the Playwright scripts on
the racy 'Unexpected end of JSON input' pipeTransport crash, but
falls through on ERR_NO_BUFFER_SPACE -- a separate Chromium failure
that fires when the macos-14 free-runner kernel briefly runs out of
socket buffers. Same fix shape, two layers:
* In-script: when a change-password page.goto() attempt fails with
ERR_NO_BUFFER_SPACE, sleep 5s then 15s before the next attempt so
the OS has time to recover socket buffers. Other failures retry
immediately as before.
* Workflow: extend both Playwright retry blocks (chat-ui and
extra-ui) to also trigger the full Studio kill + reset + reboot
retry on ERR_NO_BUFFER_SPACE, not just on the pipeTransport JSON
crash.
Real assertion / timeout failures still bypass retry and surface
immediately. Linux and Windows workflows are unchanged; the flake
is macOS-runner-specific.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* allow validation of custom releases to pass
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* require exact source provenance for branch direct linux releases
Mirror validated_checksums_for_bundle so incomplete checksum metadata
on a branch/pull/commit release fails closed with a clear error instead
of silently degrading to the legacy master-as-tag source hydration path
that this PR is meant to eliminate. Guard fires when source_commit,
the exact source archive hash, or a derivable source repo URL is
missing from the approved metadata.
Also set plan.llama_tag to approved_checksums.upstream_tag so the
ensure_converter_scripts fallback and the install fingerprint target
the concrete upstream tag (e.g. b9174) rather than the moving branch
label inferred from asset names (master). Legacy b#### releases are
unaffected: synthetic checksums already set upstream_tag to
bundle.upstream_tag, so the swap is a no-op on that path.
Add parametrized negative regression coverage for the three ways
exact provenance can be incomplete (missing source_commit, missing
exact source archive entry, missing source_repo) and update the
existing branch happy-path test to expect b9174.
* revert llama_tag swap to preserve install identity
Keep plan.llama_tag as bundle.upstream_tag (the branch label inferred
from asset names, e.g. master) rather than overriding it with the
approved metadata upstream tag (b9174). The override broke install
identity in two ways:
1. expected_install_fingerprint hashes upstream_tag = llama_tag, so
the same release would produce a different fingerprint depending on
which version of this code resolved it, causing spurious reinstalls
when users upgrade or roll back.
2. UNSLOTH_PREBUILT_INFO.json reports the value as the user-visible
record of which release was installed; tools and logs should see
the requested branch label, not the compatibility tag.
The approved metadata still records upstream_tag = b9174 internally
for source archive lookup; the new assertion makes that explicit.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Preserves the source tokenizer's eos_token in tokenizer_config.json after merged saves so runtimes such as vLLM read the correct stop token. Centralized inside the patched tokenizer save_pretrained so all save paths (merged_16bit, GGUF, torchao, push_to_hub) benefit, with filename_prefix support.
Fixes#5386
Adds dir="auto" to the main, edit, and compare chat composers so RTL
scripts (Arabic, Hebrew, Persian, Urdu) flow right to left without
forcing the rest of the UI into RTL. Wires a model-free Playwright
smoke (multilingual paste round trip across 31 scripts + a stuck-IME
composition repro for issue #5318 / PR #5327) into the Studio UI CI
job as a third Studio boot, plus a pure-Python static-guard test that
locks down dir="auto" on all three composers and the minimal env
contract for the smoke.
* tests: pinned-symbol canary for unsloth-zoo save_pretrained_merged guards (#5410)
unsloth#5410 was a class of silent-write bug in the
save_pretrained_merged path that the existing CI matrix could not
detect because the merge-helper tests were not wired through the
upstream-drift suite. The full fix lives in unslothai/unsloth-zoo#647
(layout-aware MoE merge helpers, authoritative num_experts resolver,
loud-fail counter, generation_config.json save). This PR adds the
unsloth-side canary that watches for the four guards staying in place
in unsloth-zoo so a future refactor cannot silently regress them.
tests/version_compat/test_unsloth_zoo_save_merged_pinned_symbols.py
fetches unsloth_zoo/saving_utils.py + tests/test_unsloth_zoo_lora_merge.py
from unslothai/unsloth-zoo:main and asserts:
- _MOE_MERGE_STATE / _reset_moe_merge_state / _record_moe_merge_fallback
are still defined and a `raise RuntimeError(...MoE...)` still fires
when fallback > 0.
- _detect_moe_lora_layout exists and both "swapped" / "standard" branch
labels are reachable in the source.
- _resolve_num_experts_from_lora_stats is present AND its base_layer
walk is bounded by `for _ in range(N):` (a cyclic ParamWrapper chain
must not hang the merge).
- merge_and_overwrite_lora still calls
model.generation_config.save_pretrained(...).
- tests/test_unsloth_zoo_lora_merge.py keeps the six PEFT 0.19+
standard-layout regression tests added in #647.
- Local unsloth/save.py still names save_pretrained_merged and
routes through merge_and_overwrite_lora (i.e. the entry point still
reaches the upstream fix).
While #647 is still open, the four symbol tests SKIP cleanly with a
message naming #647. When #647 merges into unsloth-zoo main, the same
tests automatically become hard gates and catch any future regression.
The sixth test (local entry-point grep) passes today.
CPU-only static fetch, ~0.1s. Wired into the existing peft-pinned-symbols
job in .github/workflows/version-compat-ci.yml so it runs on every PR
that touches unsloth/** and on the daily schedule.
Local run: 1 passed, 5 skipped (expected; #647 open).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests/version_compat: relax MoE/generation_config regex to fit zoo#647
zoo#647 landed two layout changes that broke the pinned-symbol
canary's exact-string regex matches but kept the underlying
guarantees intact:
- The post-loop MoE LoRA fallback `raise RuntimeError(...)` wraps
the "MoE" wording onto a second line; the old `[^\n]*` did not
cross newlines. Switch to `.*?` + re.DOTALL.
- The generation_config save now binds the attr to a local var
`gen_cfg = getattr(model, "generation_config", ...)` and calls
`gen_cfg.save_pretrained(save_directory)`, so a literal
`generation_config.save_pretrained(` substring no longer matches.
Anchor on the conceptual operation: a `generation_config` mention
followed (within a small char window) by a `.save_pretrained(`
call. That is what the canary actually cares about.
Verified locally:
pytest tests/version_compat/test_unsloth_zoo_save_merged_pinned_symbols.py
-> 2 passed (4 deselected)
---------
Co-authored-by: Daniel Han-Chen <info@unsloth.ai>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Instead of falling back to pytorch.org/rocm7.2, the Strix override now
routes to repo.amd.com/rocm/whl/gfx1151/ (or gfx1150/) which serves
torch 2.11.0+rocm7.13.0 -- AMD's build containing the actual _grouped_mm
kernel fix, verified on real gfx1151 hardware by h34v3nzc0dex.
This exercises the real GPU kernel path rather than the rocm7.2 workaround.
UNSLOTH_AMD_ROCM_MIRROR can override the base URL for air-gapped installs.
Also teaches _tauri_torch_index_family to recognise AMD arch-specific URLs
(repo.amd.com/rocm/whl/gfx*) and return the rocm7.13 family label so
_tauri_gpu_branch correctly classifies these installs as rocm.
Suggested by h34v3nzc0dex based on hardware-verified probe results.
Previously, when hipinfo was found but exited non-zero (e.g. "no
ROCm-capable device detected"), both install.ps1 and setup.ps1 fell
through to the WMI-label-only branch and printed "AMD GPU detected --
HIP SDK not found" -- factually wrong since the SDK binary is present.
Add $HipSdkInstalled flag (set true when hipinfo binary is found,
regardless of exit code). When HipSdkInstalled && !HasROCm:
- Show "AMD GPU detected -- not ROCm-accessible (HIP <ver>)" instead
- Explain this is a driver issue, not an SDK issue, with a link
- Still run hipconfig version capture so version shows in output
- CPU-only hint now says "GPU not ROCm-accessible" not "require HIP SDK"
Also applies to setup.ps1 (same detection block, same branches).
Adds TestHipSdkInstalledButDeviceInaccessible (11 tests).
torch.distributed tries to `from torch._C._distributed_c10d import
FakeProcessGroup` (and ProcessGroup, Work, Store, etc.). The previous
empty ModuleType stub caused an AttributeError on those names.
Populate every stub with a _Dummy class for each known symbol so the
import chain completes silently on Windows ROCm where torch._C is a
compiled extension and its _distributed_c10d submodule doesn't exist.
Adds four new tests in TestServerStartupRocmFixes covering FakeProcessGroup,
ProcessGroup, setattr population, and all three _distributed_* siblings.
Two errors visible in training logs on Windows ROCm:
1. Server process bitsandbytes crash:
"Configured ROCm binary not found at libbitsandbytes_rocm713.dll"
The installed BNB wheel ships rocm72.dll (not rocm713.dll). The
training worker already sets BNB_ROCM_VERSION=72 via DLL detection
but the server process (main.py) imported bitsandbytes before that
ran. Fix: add the same DLL-scan + BNB_ROCM_VERSION assignment to
main.py inside the existing win32 guard, before any downstream
import can pull in bitsandbytes.
2. torch.distributed import failure:
"No module named 'torch._C._distributed_c10d'; torch._C is not a package"
torch._C is a C extension on Windows ROCm — Python cannot do
submodule imports from it, so torch.distributed fails to import
before our attribute stubs could ever run. Fix: inject empty
ModuleType stubs for _distributed_c10d, _distributed_autograd and
_distributed_rpc into sys.modules inside the win32 guard in
hardware.py BEFORE importing torch.distributed, so the import
succeeds and our attribute stubs take effect.
9 new tests in TestServerStartupRocmFixes; total 212 passed, 2 skipped
Issue 1 (install.sh): gfx1151/gfx1150 + ROCm 7.1 causes a segfault in
torch._grouped_mm (moe_utils.py:167). The Radeon repo now ships cp313
wheels for rocm-rel-7.1, so _amd_gpu_radeon=true silently lands on the
broken combo. When Strix Halo/Point is detected and TORCH_INDEX_URL is
rocm7.1, override to rocm7.2 PyTorch index, update TORCH_CONSTRAINT, and
set _amd_gpu_radeon=false to bypass the Radeon repo entirely. Emits a
clear [WARN] explaining the segfault and linking to the ROCm upgrade docs.
Issue 2 (setup.sh): ROCm 7.x ships clang-20 which on Ubuntu 24.04+ picks
/usr/lib/gcc/x86_64-linux-gnu/14/ (runtime dir, no C++ headers), causing
'cstdlib file not found' and a failed llama.cpp HIP build. Iterate gcc
versions 14→11 to find the first install dir that has both runtime and
/usr/include/c++/<ver> headers, then pass --gcc-install-dir to clang via
CMAKE_HIP_FLAGS. Fix confirmed by h34v3nzc0dex (llama.cpp 417/417 clean).
11 new tests across TestStrixRocm71Override and TestSetupShGccInstallDir;
total 203 passed, 2 skipped
Both install.ps1 and setup.ps1 now emit substeps under the gpu step when
AMD ROCm is detected:
gpu AMD ROCm (gfx1200)
HIP SDK: C:\Program Files\AMD\ROCm\7.1
hipconfig: 7.1.51803-d3a86bd04
Previously only the gpu label (e.g. "AMD ROCm (gfx1200)") was shown with
no indication of where the SDK was found or which exact build was active.
The full hipconfig build string (e.g. 7.1.51803-d3a86bd04 instead of just
7.1) is now stored in ROCmVersionFull and also used in setup.ps1's
'rocm' step label.
9 new tests in TestHipSdkDetectedSubstep; total 192 passed, 2 skipped
AMD HIP SDK sets HIP_PATH on Windows but does not always add the bin
directory to PATH. Get-Command hipinfo therefore silently fails and
detection falls through to WMI, which cannot provide a gfx arch, leaving
the user with a CPU-only PyTorch install and no warning.
Changes:
- setup.ps1 / install.ps1: before falling through to amd-smi, attempt to
locate hipinfo.exe and hipconfig.exe under $env:HIP_PATH\bin (then
$env:ROCM_PATH\bin) when Get-Command returns nothing
- Emit a [WARN] with the resolved path and a one-liner to permanently fix
PATH via SetEnvironmentVariable
- Emit a [WARN] when HIP_PATH/ROCM_PATH is set but the exe is still not
found (incomplete SDK install)
- Emit a [WARN] with the first hipinfo output line when hipinfo runs but
returns a non-zero exit code (e.g. "no ROCm-capable device detected")
- 18 new tests in TestHipSdkEnvPathResolution; total 183 passed, 2 skipped
Covers users who have the HIP runtime (amd-smi available) but not the
full HIP SDK (no hipinfo), which is common on Strix Halo iGPU systems.
Without this, $ROCmGfxArch stays null and the installer silently falls
back to CPU-only PyTorch despite a working GPU.
Detection waterfall (setup.ps1 + install.ps1):
1. hipinfo gcnArchName -- full HIP SDK (existing, unchanged)
2. amd-smi list gfx pattern -- newer amd-smi versions embed arch
3. amd-smi static --asic -- ROCm 6+ ASIC details with GFX target
4. UNSLOTH_ROCM_GFX_ARCH env -- manual override escape hatch
5. GPU name → arch table -- best-effort from marketing name:
890M / Strix Halo → gfx1151 (RDNA 3.5 iGPU, Strix Halo)
880M / Strix Point → gfx1150 (RDNA 3.5 iGPU, Strix Point)
780M / Phoenix → gfx1103 (RDNA 3 iGPU)
RX 7900/7800/7700 → gfx1100 (RDNA 3 desktop)
RX 9070 XT / 9080 → gfx1201 (RDNA 4)
RX 9070 / 9060 XT → gfx1200 (RDNA 4)
When arch is inferred from name, a Cyan substep tells the user to set
UNSLOTH_ROCM_GFX_ARCH to skip inference on future installs.
WMI block intentionally does not set $HasROCm (no runtime confirmation).
Tests: 11 new tests in TestStrixHaloGfxArchDetection covering all five
detection levels, WMI safety, and gfx regex in both ps1 files.
* ci: deterministic check for studio/frontend dep removals
Adds a CI gate that catches the common foot-gun: a dep dropped from
studio/frontend/package.json that something in src/ still imports.
scripts/check_frontend_dep_removal.py
Diffs package.json against a git base ref, collects every package
no longer declared, and for each one:
1. Greps the entire repo for any usage pattern (static / dynamic /
side-effect imports, require, CSS @import, HTML script/link
src, new URL(), triple-slash references, template literals,
bare quoted strings in JS-like files).
2. Resolves whether the package would still install by BFS'ing
the dep graph in the new lockfile starting from the new
package.json's declared deps (so a stale lockfile does not
give false OK-via-transitive results).
3. Distinguishes top-level node_modules/<name> from nested copies
under other packages. Bare src/ imports only resolve to the
top-level path.
4. Pip-installed playwright references are filtered, so removing
the npm playwright (CI uses the pip one) is reported correctly.
Additional hygiene checks (warnings, fail with --strict):
- lockfile <root> dep map matches package.json (catches drift).
- @types/X is not orphaned when X is no longer declared.
- No src/ import points at a package not declared in any field.
tests/studio/test_frontend_dep_removal.py
24 deterministic cases. Each patches a copy of the head
package.json, runs the script, and asserts (exit status,
reported FAIL list). Covers:
- Genuinely-breaking removals: next-themes, @xyflow/react,
@huggingface/hub, dexie, motion, canvas-confetti, recharts,
node-forge, mammoth, unpdf.
- Safe-via-transitive removals: katex, clsx, react,
@radix-ui/react-slot, zustand, tailwind-merge, remark-gfm,
date-fns, js-yaml, @tauri-apps/api.
- Mixed multi-removal failing on the unsafe entries only.
- Non-existent / not-in-base names (no-op).
- Move from deps to devDeps (not a removal).
.github/workflows/studio-frontend-ci.yml
Runs the checker on pull_request events against
origin/${{ github.base_ref }}, plus the edge-case suite.
* scripts: harden frontend dep removal check + adversarial suite
classify() now catches sneaky shapes that an earlier line-only scan
would miss:
- multi-line `import { a, b } from "pkg"` and the same shape for
`export { ... } from "pkg"` / `export * from "pkg"` /
`export type ... from "pkg"`.
- JSDoc `@import("pkg")` references.
- Word-boundary fix so `foo` no longer matches `foobar` (subpath gate:
after the package name we require closing quote or `/`).
- Negative-lookbehind on `(?<!@)\bimport\b` so CSS `@import "X"` is
classified as css_import, not side_effect_import.
find_usage() now feeds an 8-line window (4 above / 4 below the grep
hit) into classify() so multi-line import statements are picked up
even though the initial grep is line-based.
tests/studio/test_frontend_dep_removal.py now exercises three suites:
- 24 edge cases: subprocess-driven, full-pipeline.
- 28 classify() unit cases: direct function call against hand-crafted
snippets. Covers static / side-effect / dynamic / require /
css_import / html_script / html_link / re_export (4 variants) /
template_literal / new_url / tsc_triple_slash / jsdoc_import /
string_literal, plus false-positive guards (substring collision,
plain-text comments, URL path tails, Python files, markdown).
- 12 adversarial cases: write synthetic files under
studio/frontend/src/__dep_check_adversarial__/, run the full
script, then clean up. Confirms multi-line imports, re-exports,
JSDoc @import, new URL, dynamic imports all FAIL when the
underlying package is removed.
Current total: 64 / 64 cases pass.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scripts: detect bin references in package.json scripts
Catches the last common false-negative: removing a package whose
bin is only referenced through `package.json` scripts (e.g. dropping
typescript while `"build": "tsc -b && vite build"` calls tsc).
Cross-checked the patterns Vercel/Next.js, Vite, and TanStack use
in their own manifests; the bin/scripts pairing is the one
consumer-side pattern dep checkers commonly miss.
How it works:
- Build a bin-to-package map from each lockfile entry's `bin`
field. The map is global so a stale lockfile still resolves
bins from packages about to be pruned.
- Tokenize each script value, splitting on `&&`, `||`, `;`, `|`.
Strip env-var assignments and `npx / pnpx / yarn / pnpm / bunx`
prefixes, plus `./node_modules/.bin/` and `node_modules/.bin/`
path prefixes. Look up the leading token in the bin map.
- Hits are reported as `script_bin` and feed the same reachability
gate as source imports. A bin still installed transitively
(e.g. vite via @vitejs/plugin-react peer) is OK-via-transitive;
an orphaned bin is FAIL.
Test additions:
- 5 new edge cases: removing vite, typescript, eslint, @biomejs/biome,
and (@biomejs/biome + @vitejs/plugin-react) together. Correctly
flags @biomejs/biome and the combo as FAIL while vite / typescript
/ eslint are kept by peers.
- 8 new classify() unit cases: TypeScript ambient `declare module`,
namespace imports, combined default+named, default-as-named,
re-export default (4 forms), `.then()` dynamic imports without
await, and TypeScript `import()` in type position.
Current total: 29 edge + 36 classify-unit + 12 adversarial = 77 / 77.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scripts: detect package.json field references to packages
After surveying package.json patterns in 10+ popular repos (React,
Vue/Svelte/Astro/Next.js, Vite, Storybook, TanStack/Query, Tailwind,
ESLint, TypeScript, Prettier, SvelteKit), several config fields in
package.json itself can reference packages by string. My checker
filtered all of package.json out of the string_literal fallback,
so removing a package that is only referenced from one of these
fields was a false negative.
Now covered (new pkg_json_field kind):
- overrides / resolutions / pnpm.overrides keys
- pnpm.patchedDependencies keys
- peerDependenciesMeta keys
- prettier: "@my/prettier-config" string
- eslintConfig.extends (string or array)
- stylelint.extends / stylelint.plugins
- babel.presets / babel.plugins
- jest.preset / jest.setupFiles / jest.transform
- commitlint.extends
- renovate.extends
- remarkConfig.plugins
- any other tool config field whose strings/keys equal the pkg
name or `pkg/subpath`
False-positive guards (do not flag string values inside):
- browserslist (browser queries)
- keywords (free-form strings)
- engines / engineStrict / packageManager / volta (version pins)
- files / directories / publishConfig (paths)
- workspaces (paths/globs)
- main / module / browser / types / typings / exports / imports /
bin / man (author-side fields)
- scripts (already handled separately via scripts_bin_refs)
- name / version / description / author / repository / homepage etc.
Test additions: new PkgFieldCase suite with 19 cases covering each
tool config field, subpath references, and the 5 false-positive
guards. Combined with the existing 29 edge / 36 classify / 12
adversarial cases, the suite is 96 / 96.
* scripts: enumerate dead deps in studio/frontend
Adds an opt-in dead-dep enumeration to the existing safety check.
Iterates every package declared in studio/frontend/package.json
(all four dep fields combined) and reports each as one of:
used at least one detected reference -- in src/, a
config file, package.json scripts (bin), a
package.json tool-config field (overrides /
prettier / eslintConfig / stylelint / babel /
jest / commitlint / renovate / etc.), or
tsconfig.compilerOptions.types
unused no detected reference anywhere
type_pkg_kept @types/X where X is still declared (or X = node,
always implicit)
type_pkg_orphan @types/X where X is no longer declared --
candidate for removal alongside X
Wiring:
- New CLI flag `--enumerate-dead` (off by default).
- CI workflow now passes `--enumerate-dead` so the report shows on
every PR run; the report is informational unless `--strict` is
also set.
- With `--strict`, unused / type_pkg_orphan entries fail the run.
Tests:
- 5 new EnumCase scenarios:
E01 fake dep with no usage -> reported unused
E02 fake dep imported by a synthetic src file -> reported used
E03 fake dep referenced only in overrides -> reported used
E04 @types/X paired with X (also imported) -> kept
E05 @types/X without X -> orphan
Running the new flag against the current main reproduces exactly the
11 deps PR #5477 removed, validating the heuristic end to end.
Current total: 29 edge + 36 classify + 12 adversarial + 19 pkg-json
field + 5 enumeration = 101 / 101.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* ci: fetch base ref before running dep removal safety check
actions/checkout uses fetch-depth: 1 by default, so when the
dependency removal check ran `git show origin/main:.../package.json`
the ref wasn't available locally and the script exited 2 with
"could not read base package.json at origin/main:...".
Fetch the single base commit before invoking the check so the
git-show lookup resolves. --depth=1 keeps the extra fetch cheap.
* ci: address bot review on PR 5478
Five issues flagged across gemini and codex:
* --base-lock argparse arg was defined and advertised in the
docstring, but main() always read args.head_lock in both branches
-- the flag did nothing. Dropped the dead arg and the misleading
docstring line; the lockfile-reachability analysis only needs the
head lockfile.
* lock_resolvable() was defined but never called. Removed.
* read_pkg_file() did not specify an encoding for read_text().
Added encoding="utf-8" for cross-platform stability.
* read_pkg_file() returned {} when the path did not exist, so a
bad --head-lock value silently bypassed the reachability checks
(false PASS for removals that resolve through npm script bins).
main() now exits 2 with a clear message when the head lockfile
is missing, matching the existing behavior for the head pkg.
* studio-frontend-ci.yml pull_request paths filter only matched
studio/frontend/** and the workflow file, so PRs that modified
the checker script or its test could skip this job. Added both
files to the trigger.
* ci: address 10x reviewer findings on dep removal safety check
Eight P1s and three P2s surfaced across 10 codex reviewers; this
commit addresses all of them.
P1s:
1. Workflow refspec. `git fetch --depth=1 origin <base_ref>` may only
create FETCH_HEAD in shallow PR checkouts; the checker then dies
with `fatal: invalid object name 'origin/main'`. Use the explicit
refspec `<base>:refs/remotes/origin/<base>` so origin/<base> is
reliably created.
2. `_deps_of()` was counting optional peer dependencies as reachable.
npm only installs an optional peer when another package declares
the same dep, so for "is this removed package still in the tree"
they cannot keep it alive on their own. Skip entries marked
`optional: true` in `peerDependenciesMeta`.
3. JS-syntactic classifiers (static_import, side_effect_import,
dynamic_import, require, re_export, jsdoc_import, template_literal,
tsc_triple_slash, new_url) now gate on file extension. Previously
only the final string-literal fallback was gated, so a JS-shaped
string inside a Python fixture or a Markdown code fence triggered
a false FAIL. Added U37-U40 covering .py / .md / .sh / .yml.
4. HTML `<script src=>` and `<link href=>` patterns now respect a
package-name boundary so `/node_modules/foo-extra/...` is not
treated as a usage of `foo`. Added U41-U43.
5. New `find_command_usage()` detects CLI invocations in .sh / .yml
/ .yaml / .ps1 / .bat / Dockerfile* (npx pkg, bunx pkg, pnpm exec
pkg, yarn dlx pkg, or a bare pkg --flag). Also covers scoped CLI
packages exposed by their unscoped tail (@biomejs/biome -> biome).
6. `build_bin_to_pkg(head_lock)` was losing the bin -> package map
for packages the PR correctly removed from the lockfile, so
`scripts.biome:check` no longer flagged when @biomejs/biome was
being dropped. Now also read the base lockfile (via `git show` or
the new `--base-lock` override) and layer its bin map on top for
any package in the removed set.
7. `--strict` now runs hygiene checks (lockfile sync, @types
orphans, undeclared imports, dead-deps) on the no-removal path
too. Previously the early return at "[OK] no dependencies removed"
skipped them, so `--strict` silently passed on a tree with
uncommitted lockfile drift or unused deps.
8. Removed `@types/X` packages are now matched against the runtime
target name `X`: `/// <reference types="X" />`, tsconfig
compilerOptions.types entries, AND runtime `import "X"` shapes.
Handles the npm scope encoding (`@types/foo__bar` -> `@foo/bar`).
P2s:
9. CSS `url(...)` now accepts both quoted and unquoted forms (added
U44-U45). The previous regex required `/{pkg}/` after a slash,
missing bare-package urls like `url(katex/fonts/x.woff2)`.
10. `find_imports_without_decl()` now covers all static-import
shapes: `import "pkg"`, `import Foo from "pkg"`,
`import { Foo } from "pkg"`, `import type { Foo } from "pkg"`,
`await import("pkg")`, `require("pkg")`.
11. (Same as #8.) Removed `@types/X` is also linked to runtime
imports of `X`, not just type-only references.
Test suite expanded from 101 to 110 cases; all pass. Real-world
enumerate-dead still flags the same 11 unused packages on
studio/dep-removal-safety-check (matches PR 5477's removal set).
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* ci: address 4x Opus reviewer findings on dep removal check
Three blockers from the parallel Opus review batch:
1. scripts_bin_refs ignored every script that began with a wrapper.
The original "first non-env token wins" heuristic credited
cross-env / dotenv / dotenvx / env-cmd as the bin, so a script like
`cross-env CI=1 biome check` left @biomejs/biome looking unused.
Rewrote into _next_real_bin(), which peels env prefixes, the
leading package-manager runner (npx / pnpx / bunx / pnpm exec /
yarn dlx), and the known wrapper bins (with --/-flag-arg handling)
before returning the real CLI. shlex tokenization preserves quoted
env values like `FOO="a b"`.
2. enumerate_dep_usage skipped find_command_usage. The non-enumerate
path already credited deps used only from CI / Dockerfile / shell
scripts, but `--enumerate-dead` did not, so packages referenced
only from a workflow were silently listed as dead. Added the same
call (gated against @types/* to avoid the unscoped-tail false
positive).
3. classify multi-line window was ±4 lines. Prettier formats long
named-import lists one identifier per line, so a 20-import block
pushed the `import` keyword out of the window and the dep dropped
to the string-literal fallback (or worse, was missed entirely).
Widened to ±25 -- still bounded enough to keep false-positives
negligible, wide enough for the realistic Prettier ceiling.
Tests: added 10 _next_real_bin unit cases + 4 scripts_bin_refs
end-to-end cases (W01-W10 + I01-I04) and a 22-identifier multi-line
import adversarial case (A13). Full suite: 125/125.
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CI surfaced a flaky failure on Linux 'Repo tests (CPU)':
TestPwshPrForcePromotion.test_baked_in_pr_force_promotes ->
subprocess.TimeoutExpired after 10s on /usr/bin/pwsh startup.
The scripts under test run in well under a second; the 10s budget
only covered pwsh / bash launch time, which spikes on heavily-
loaded GitHub-hosted runners. Raise the default helper timeout to
60s for both run_bash and run_pwsh. Real bugs in the script logic
will still surface as wrong output or non-zero exit; this just
absorbs runner-side launch jitter.
resolve_attention_implementation calls is_torchelastic_launched() which
does not exist in the incomplete torch.distributed shipped with the
Windows ROCm wheel, causing a warning on every model config load in the
server process. Add it to the stub table alongside the four helpers
already patched in _determine_attention_impl_for_gpu_estimate.
Also adds two tests: one confirming the new stub and one confirming all
five core distributed helpers are covered.
AMD fixed the gfx1200 null HIP kernel in ROCm 7.13 (torch 2.11+).
Users on the new wheel now get the real GPU _grouped_mm kernel for
MoE workloads instead of the Python mm fallback.
Changes:
- worker.py: add _hip_ver_at_least() helper; wrap full _grouped_mm
patch in `if not _hip_ver_at_least(7, 13):` with else branch that
logs the skip reason; update section-1f comment to document the fix
- test_rocm_support.py: add 5 tests covering the helper definition,
the (7, 13) gate expression, the else branch, the skip log message,
and the AMD-format version string parsing (.split(".")[:2])
Verified: torch==2.11.0+rocm7.13.0 — 3D batch and grouped (offs)
variants both succeed; null crash only present on rocm7.12 and earlier.
BNB_ROCM_VERSION was pinned to '72' which works today (AMD wheel ships
rocm72.dll) but would break again if AMD ships a future wheel with a
different DLL suffix (e.g. rocm713.dll).
Add _detect_bnb_rocm_dll_ver() to install_python_stack.py: scans the
installed bitsandbytes package dir for libbitsandbytes_rocm{VER}.dll
using importlib.util.find_spec (no BNB import needed) and returns the
suffix. '72' remains the fallback when detection fails.
Apply the same detection inline in worker.py section 1f. Both paths
still respect a pre-set BNB_ROCM_VERSION (caller override wins).
Tests: +8 cases covering detection logic and fallback (147 passed, 2 skipped).
AMD's pip index now ships torch==2.11.0+rocm7.13.0 (ROCm 7.13).
bitsandbytes auto-detects HIP 7.13 from torch.version.hip and looks for
libbitsandbytes_rocm713.dll, which the AMD Windows prerelease wheel does
not ship (it only ships rocm72.dll), causing a load error at training start.
Fix:
- worker.py section 1f: set BNB_ROCM_VERSION=72 (via setdefault) before
section 2 ML imports, so bitsandbytes always loads rocm72.dll on Windows ROCm
- install_python_stack.py: set BNB_ROCM_VERSION=72 in _install_bnb_windows_rocm()
for any post-install imports; update comment to document root cause
- tests: 4 new assertions covering the fix (141 passed, 2 skipped)
- Add encoding="utf-8" to all read_text() calls (54 occurrences) so
tests pass on Windows where the default codec is cp1252 and source
files contain UTF-8 emoji (e.g. ⚠️ in install_python_stack.py)
- Add @patch.object(stack_mod, "IS_WINDOWS", False) to Linux-path
TestEnsureRocmTorch tests so they reach the Linux code path when run
on a Windows machine instead of short-circuiting into the Windows branch
- Fix test_grouped_mm_patch_guarded_by_windows_and_hip_check: the source
uses getattr(_torch_for_rocm, "version", None) not torch.version, so
check for '"version"' and '"hip"' substrings instead
137 passed, 2 skipped
* tests: ship public-api surface drift detector + wire into Core matrix
Companion to tests/test_import_fixes_drift.py (PR #5414): that file
catches drift in THIRD-PARTY libs (transformers / trl / triton / peft /
vllm / torchcodec / xformers); this file catches drift in unsloth's
OWN public-surface API -- the top-9 classmethods + symbols that
unslothai/notebooks calls at ~2000 cumulative sites.
Closes the gap where a refactor on this repo (e.g. renaming
FastLanguageModel.from_pretrained -> .load) would pass unsloth CI
green and surface only on the next unslothai/notebooks CI run, or
worse, on a user's Colab crash report.
Coverage (call-site counts measured against unslothai/notebooks main):
test_fast_language_model_class_present
test_fast_language_model_from_pretrained_kwargs 506 sites
test_fast_language_model_get_peft_model_kwargs 304 sites
test_fast_language_model_for_inference_callable 370 sites
test_fast_vision_model_class_and_methods (4 methods)
test_fast_vision_model_get_peft_model_vision_kwargs (4 kwargs)
test_fast_model_class_and_methods (2 methods)
test_fast_model_from_pretrained_kwargs 103 sites
test_is_bf16_supported_or_alias_callable 48 + 8 sites
Each test asserts the healthy public shape via inspect.signature; on
regression fires pytest.fail("DRIFT DETECTED: ...") -- never
pytest.skip -- so the Core matrix cell goes red. Mirrors the same
skeleton used by tests/test_import_fixes_drift.py.
Wired as a new step in consolidated-tests-ci.yml right after the
import_fixes drift step, inside every Core matrix cell.
Local verification on transformers 4.57.6 + unsloth main:
pytest tests/test_public_api_surface.py -v
-> 9 passed in 0.02s
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* studio: skip flash-attn install on Blackwell GPUs (sm_100+)
Dao-AILab does not publish prebuilt flash-attn wheels for sm_100, sm_120,
or sm_121, and the older-arch wheels fail to load on Blackwell. Add a
shared has_blackwell_gpu() helper and gate both the install-time
(install_python_stack._ensure_flash_attn) and runtime
(worker._ensure_flash_attn_for_long_context) paths on it. Detection uses
nvidia-smi --query-gpu=compute_cap, which works on Linux and Windows.
* test: stub has_blackwell_gpu in pre-existing runtime flash-attn tests
prefers_prebuilt_wheel and falls_back_to_pypi exercise the install
paths that the Blackwell guard now short-circuits. Make them explicit
about non-Blackwell so they pass on real Blackwell hosts.
* studio: cache has_blackwell_gpu, skip Blackwell warning under NO_TORCH
- Wrap has_blackwell_gpu in functools.lru_cache so repeated calls in a
single process avoid redundant nvidia-smi spawns. Tests clear the
cache via setup_method/teardown_method.
- In _ensure_flash_attn, run the NO_TORCH short-circuit before the
Blackwell check so GGUF-only users (who never install torch anyway)
do not see a Blackwell warning. Blackwell check still runs above the
IS_WINDOWS / IS_MACOS gates so Blackwell-on-Windows users still see
the explicit reason rather than a silent OS skip.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* test: add has_blackwell_gpu to mlx worker test wheel_utils stub
test_mlx_training_worker_config loads worker.py against a hand-rolled
utils.wheel_utils stub. Adding has_blackwell_gpu to the stub symbol
list so worker's import line resolves.
---------
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* mlx fixes
* Fix studio integration, local dataset files, chat templates without the torch gpu imports
* pass grad norm in mlx worker
* fix(studio): pass MLX grad clipping settings
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* mlx: update grad value
* fix(mlx): address ci and clipping review
* fix backward compatibility and CI tests
* unsloth local is mlx function
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* dont reference runtime
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* studio mlx: hardcode value clipping, drop max_grad_value from frontend
Simplifies the MLX grad-clipping plumbing now that we are standardising on
elementwise value clipping at [-5, 5] for the compiled MLX path and norm
clipping disabled. The MLX worker no longer reads max_grad_norm /
max_grad_value from the request; both are pinned in one place. Frontend
stops sending the field at all, and the TypeScript request type drops it
to match. Non-MLX (CUDA/AMD/Intel) is untouched and continues to pick up
HF TrainingArguments' default max_grad_norm = 1.0.
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
PR #5414's drift detectors and the corresponding import_fixes helpers
were written against transformers 4.x. The Repo tests (CPU) step (which
installs transformers>=4.51,<5.5 and currently resolves to 5.4.0)
surfaces three real predicate gaps:
* test_pretrained_model_enable_input_require_grads_uses_old_pattern
fires DRIFT DETECTED whenever the source contains
"for module in self.modules()". But the unsloth replacement that
patch_enable_input_require_grads installs ALSO uses that pattern --
deliberately, just wrapped in try / except NotImplementedError. So
the predicate cannot distinguish broken upstream from the working
patch. Accept either pre-HF#41993 shape (no self.modules() loop) or
the post-patch shape (loop + NotImplementedError handler).
* test_transformers_torchcodec_available_flag_is_present asserts the
pre-5.x module-level _torchcodec_available flag. transformers 5
replaced it with an lru_cache'd is_torchcodec_available() callable.
Accept either symbol. Also update disable_torchcodec_if_broken to
actually disable on 5.x: clear the cache and rebind the function to
return False.
Local verification:
* transformers 4.57.6 + trl 0.25.1 + peft 0.19.1 + triton 3.5.1 +
vllm 0.15.1+cu130 (the Core HF=4.57.6 cell shape): 18 passed.
* transformers 5.8.1 + peft 0.19.1 + torch 2.9 CPU (the Repo tests
(CPU) shape, drop trl / vllm / datasets / xformers): 12 passed,
6 skipped on missing optional libs, 0 failed.
PR #5376's Repo tests (CPU) failure was a triple:
- triton + enable_input_require_grads: fixed by merging current main
(PR #5421's relaxed triton predicate + conftest 'import unsloth').
- torchcodec: fixed by THIS PR.
* studio: add external provider support for chat inference
Adds the ability to connect to OpenAI, Mistral, Google, Cohere, Together,
Fireworks, and Perplexity from the Studio chat interface.
- Provider configs stored in SQLite (no API keys persisted)
- RSA-2048 key pair generated at startup for client-side key encryption
- httpx proxy client streams SSE responses in OpenAI-compatible format
- New /api/providers routes: registry, CRUD, test, models
- /v1/chat/completions routes to external provider when provider fields present
- Integration test suite covering CRUD, connection, model listing, and inference
- Frontend spec doc with full API contract
* remove frontend spec doc from branch
* fix auth fixture: handle forced password change on fresh install
* fix tests: default port 8000, allow 400 for no-model-loaded
* fix: update Cohere models to current (command-r retired Sept 2025)
* feat: add OpenRouter as 8th provider
* feat: add native Anthropic provider with Messages API translation
* fix: correct Anthropic base URL and drop top_p (conflicts with temperature)
* feat: add DeepSeek provider (deepseek-chat, deepseek-reasoner)
* feat: rename google -> gemini, refresh model list to 2.5 series
* feat: remove together, fireworks, perplexity providers
* feat: multimodal image support for external providers
- Add _build_external_messages() that preserves image_url parts for
vision-capable providers instead of stripping them
- Update _proxy_to_external_provider() to use new helper
- Translate image_url content parts to Anthropic native image format
in _stream_anthropic()
- Add TestVisionInference pytest class (1x1 PNG smoke test)
* test: use sloth photo URL for vision test, add Anthropic remote URL support
* fix: update Mistral model to mistral-small-2506
* update mistral default model to mistral-large-2512
* fix gemini vision test: download image as base64 data URI instead of remote URL
* add gemini-3-flash-preview as default gemini model
* fix gemini truncated reply (max_tokens 16->64) and suppress GeneratorExit on client disconnect
* increase vision test max_tokens to 215
* fix GeneratorExit: aclose stream generator before closing httpx client
* fix httpcore GeneratorExit: explicitly aclose aiter_lines before response closes
* fix duplicate [DONE] and suppress httpcore RuntimeError on Python 3.13 asyncgen cleanup
* fix: call response.aclose() before lines_gen.aclose() to prevent httpcore RuntimeError on Python 3.13
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* Potential fix for code scanning alert no. 36: Clear-text logging of sensitive information
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
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* review: add comments for manual iteration rationale, mask password in test print, clarify Anthropic URL/models support
* perf: use shared module-level httpx client for connection pooling across requests
* studio: add API provider UI and integrate wiring (#4737)
* feat: expose external models in selector and chat settings
* feat(chat): wire external providers to backend + RSA key flow
- Fetch registry/configs; create/update/delete saved providers
- Encrypt API keys (Web Crypto RSA-OAEP) for test/models/chat
- External model selection + chat payload (provider_id/type, external_model, encrypted key, optional base URL)
- Local storage for keys + provider list; small UX/copy and guardrails
* add missing providers-api.ts file by Imagineer99
* fix: address PR review comments — system prompt visibility, retry loop, test logging
* feat(studio): encrypt external provider API keys at rest in localStorage
API keys for external providers (OpenAI, Mistral, etc.) were stored as
plaintext in localStorage, vulnerable to browser extensions and XSS.
Add password-derived AES-256-GCM encryption: on login the user's password
is used via PBKDF2 (100k iterations, SHA-256) to derive an in-memory
encryption key. API keys are encrypted before writing to localStorage and
decrypted on read. The derived key is never persisted — cleared on logout,
re-derived on next login.
Legacy plaintext keys are transparently migrated on first access. Password
changes re-encrypt all stored keys. No backend changes required — the
existing RSA-OAEP transit encryption is unaffected.
* fix: cast PBKDF2 salt to BufferSource for strict TypeScript lib types
* fix: persist session password in sessionStorage to survive page refreshes
* feat(studio): preserve image parts in external provider chat requests
toOpenAIMessage() now returns multimodal content arrays (OpenAI vision
format) when messages contain images, instead of always flattening to
plain text. This enables vision-capable external providers (OpenAI,
Gemini, Anthropic, etc.) to receive user images. The backend already
handles image_url content parts in _build_external_messages().
* studio: fix external models selectable in chat-only mode (#4779)
* fix: external models selectable in chat-only mode
* fix: model selector tabs default to active model kind
* Studio: API external provider registry + curated catalogs (HF/OpenRouter) and chat UX (#4787)
* fix: external models selectable in chat-only mode
* fix: model selector tabs default to active model kind
* feat(studio): expand provider registry, curated catalogs, and chat UX
- Add Hugging Face, Kimi, Qwen; remove Cohere; reorder registry
- model_list_mode curated for HF/OpenRouter; lightweight /models check
- API returns default models for curated providers; expose model_list_mode
- Frontend: provider logos in model picker, providerType on external models
- Chat providers dialog: curated vs remote flows, motion polish
- Thread: LayoutGroup + composer motion alignment with app easing
* fix(studio): disable Anthropic tool-calling flag and preselect curated defaults
* feat(studio): add external provider logos and ApiProviderLogo helper
* Studio: Polish API Providers dialog (#4899)
* fix: lower verbage in API providers page
* fix: fix(studio): tune API Providers dialog width with rem-based responsive caps
* feat: add custom provider support (#4902)
* fix: replace crypto.subtle with node-forge for HTTP compatibility
crypto.subtle is only available in secure contexts (HTTPS/localhost),
which breaks provider API key encryption when Studio is accessed over
plain HTTP on remote GPU VMs. Switch to node-forge for RSA-OAEP and
AES-256-GCM operations — same algorithms, works on any origin.
* fix: store provider API keys as plaintext in localStorage
Drop AES-256-GCM at-rest encryption for provider API keys. The
session-password-derived encryption broke on auto-login via refresh
token (password never captured), causing keys to silently vanish.
API keys are still RSA-encrypted in transit via node-forge. At-rest
encryption in localStorage added no real security since the
decryption key also had to live client-side.
Removes crypto-storage.ts, session password plumbing, and
reEncryptAllKeys.
* fix: use max_completion_tokens for OpenAI provider
Newer OpenAI models (gpt-4o, gpt-5.x) reject the max_tokens param
and require max_completion_tokens instead. Other providers still use
max_tokens.
* fix: skip empty assistant messages in external provider requests
Some providers (Mistral) reject assistant messages with empty content.
Filter them out when building the message list for external providers.
* Update model-selector.tsx
* Update model-selector.tsx
* Update model-selector.tsx
* Update chat-adapter.ts
* Update chat-adapter.ts
* Update chat-page.tsx
* Update chat-settings-sheet.tsx
* Update chat-settings-sheet.tsx
* Update chat-settings-sheet.tsx
* Update chat-providers-dialog.tsx
* feat: polish providers settings form UI
* style: polish provider row icon sizing and alignment
* style: stabilize provider layout
* style: add provider API key visibility toggle
* fix: add provider render on empty list
* studio/frontend: sync package-lock.json with package.json
npm ci was failing because node-forge and @types/node-forge were
declared in package.json but missing from the lockfile. Ran
npm install to regenerate.
* studio/backend: fix backend CI failures for providers router
- test_desktop_auth: include providers_router in the routes stub so
studio.backend.main imports cleanly under the monkeypatched module
- test_providers_api: skip the whole module when STUDIO_TEST_PASSWORD
is unset (it is an integration test against a live Studio server,
same shape as the already-ignored test_studio_api.py)
* studio/chat: drive ChatSettingsPanel from a per-provider capability map
Replace the binary isExternalModel toggle in the sampling section with a
provider-aware capability map. Each external provider type advertises
which of top_k / min_p / repetition_penalty / presence_penalty its
chat-completions API actually accepts, so the panel only renders the
knobs that map onto the active provider's request body.
Anthropic now exposes top_k; DeepSeek hides presence_penalty (deprecated
in their docs); OpenRouter and custom providers continue to show every
knob (OpenRouter drops unsupported server-side, custom assumes
OpenAI-compat or a permissive vLLM/Ollama backend). Local models are
unaffected — null capabilities means 'show everything'.
chat-adapter.ts now forwards top_k / presence_penalty to the external
proxy only when the active provider's capabilities permit it, so the
request body matches what the UI shows.
* studio/backend: forward top_k to Anthropic; filter OpenAI model list
Two paired changes so the frontend capability map has matching backend
behaviour:
1. ExternalProviderClient.stream_chat_completion now accepts top_k and
forwards it to the Anthropic Messages body. OpenAI-compat providers
(which all reject unknown sampling params) still receive only the
fields they document. The proxy route in routes/inference.py passes
payload.top_k through, so a UI request with top_k actually reaches
Anthropic instead of being silently dropped at the boundary.
2. PROVIDER_REGISTRY['openai'] gains a model_id_allowlist regex that
scopes the /models picker to current-gen ids (gpt-5.5 / gpt-5.4 /
gpt-5.3 / gpt-4.5 / o3 families). The remote /v1/models listing
otherwise returns dozens of historical snapshots, fine-tunes and
non-chat models (embeddings, TTS, image, moderation) that we never
want in the chat UI. default_models is refreshed to match.
* studio/chat: relax presence_penalty to optional on OpenAIChatCompletionsRequest
Followup to 1fbf445a — chat-adapter now omits presence_penalty for
providers that do not accept it (Anthropic / DeepSeek), but the
request type still required it as a non-optional number, breaking
tsc. The backend pydantic model already defaults presence_penalty
to 0, so making it optional client-side matches reality.
* studio/backend: route OpenAI traffic through /v1/responses
OpenAI's new flagship models (gpt-5.x) return 404 'This is not a chat
model' on /v1/chat/completions and are only reachable via /v1/responses.
Add a dedicated _stream_openai_responses path in ExternalProviderClient
that:
- Translates outbound messages into the Responses shape: system messages
are folded into the top-level 'instructions' field, user/assistant
messages become {role, content} items with input_text / input_image
content parts (data URLs and https URLs both pass through).
- Drops presence_penalty / top_k / frequency_penalty, none of which the
Responses contract accepts.
- Translates inbound SSE events back into OpenAI Chat Completions
chunks so the frontend keeps a single SSE shape:
response.output_text.delta -> delta chunk with content
response.completed -> chunk with finish_reason='stop'
response.incomplete -> chunk with finish_reason='length'
response.failed / error -> propagated error SSE line
Stream terminates with data: [DONE] (Responses emits this verbatim).
stream_chat_completion dispatches all provider_type='openai' calls to
this path; other OpenAI-compatible providers (mistral, gemini, etc.)
continue to use /v1/chat/completions.
Frontend provider-capabilities map updated to hide presence_penalty for
OpenAI in the chat settings panel, matching the new request contract.
Includes unit coverage in tests/test_openai_responses_translation.py
exercising the request body translation, image-part rewriting, and
SSE-to-chat-completions translation via httpx.MockTransport.
* studio/chat: clamp external max_tokens to 32k to stay within provider caps
The chat settings slider already capped maxTokens at 32768 for external
models, but a value persisted from a prior local-model session (where
the cap can be 128k+) was sent verbatim to the provider — Claude Opus
returns 'max_tokens: 131072 > 128000' on requests like that, and other
providers have stricter limits still.
Expose EXTERNAL_MAX_OUTPUT_TOKENS from provider-capabilities (32k) and
use it both for the slider max and as the clamp inside chat-adapter's
external-request body. 32k sits below the tightest declared output
limit across the providers we ship and well above what a typical chat
reply needs; the local-model path is unaffected.
* studio: drop temperature/top_p for OpenAI reasoning models
gpt-5.x / o3 / gpt-4.5 are reasoning-class models served via
/v1/responses, and reject temperature and top_p with
'Unsupported parameter' 400s. The OpenAI registry allowlist already
scopes the picker to those families, so neither knob ever applies on
this branch.
- external_provider._stream_openai_responses no longer puts
temperature or top_p in the request body (kept on the method
signature for API symmetry with the other stream methods).
- ProviderCapabilities gains temperature/topP flags; OpenAI sets both
to false. ChatSettingsPanel hides the sliders for OpenAI so the user
does not see inert controls.
- chat-adapter omits temperature/top_p from the external request body
when the active provider does not advertise them.
- OpenAIChatCompletionsRequest type marks both as optional, matching
the new chat-adapter shape.
- test_responses_request_body_uses_input_and_instructions: assertions
flipped to confirm temperature / top_p are absent from the body.
* studio: stop forwarding top_k to Anthropic
Claude 4.x (Opus / Sonnet / Haiku 4.x) returns 400 'top_k is
deprecated for this model' on any request that includes top_k. It
was always optional on the older 3.x line, so dropping it
unconditionally for every Anthropic call is the simplest path —
no per-model gate to maintain.
- external_provider._stream_anthropic no longer adds top_k to the
Messages body (kept on the method signature for API symmetry).
- provider-capabilities sets anthropic.topK = false so the chat
settings panel hides the Top K slider for Anthropic providers
and chat-adapter does not send top_k in the external request.
* studio: gate Anthropic top_k drop to Claude 4.7 only
Previous commit (b5aa6ffd) dropped top_k for every Anthropic call,
but only Claude 4.7 (Opus/Sonnet/Haiku) actually rejects it. 4.6, 4.5,
and the 3.x line still accept top_k and use it as documented.
Backend: _stream_anthropic matches the model id against
^claude-(opus|sonnet|haiku)-4-7(-|.|$) and only strips top_k when it
hits. Every other Claude generation continues to receive the value
from the chat settings panel.
Frontend: anthropic.topK is restored to true so the Top K slider is
visible again — the backend handles the per-model drop, and the
4.7 case is silent (request still succeeds without top_k).
* chore: hide dated openai models in provider select
* studio/providers: apply model_id_denylist when listing remote models
The OpenAI registry entry gained a model_id_denylist regex matching
dated snapshot ids (-YYYY-MM-DD) in 048d73bf, but the list-models
route was never consulting it, so the snapshots still showed up
alongside their canonical ids (gpt-5.5 and gpt-5.5-2026-04-23 both
listed). Apply the denylist with .search() right after the allowlist
filter so dated entries are dropped before the response is built.
* studio/chat: seed registry default_models for remote providers in picker
The Anthropic provider runs in remote model-list mode, so the picker
started with an empty availableModels until the user clicked
'Load Models'. If that /api/providers/models call fails (e.g. the
known transient decryption error during key rotation), the user sees
no models at all — claude-haiku-4-5 in particular was missing from
the dialog even though it is seeded in the registry.
Always pre-populate availableModels with the registry's default_models
when a provider type is selected (curated and remote alike), and have
loadModels() return the union of defaults + the live /models response
so registry-seeded ids are reachable regardless of what the provider's
endpoint returns or whether the call succeeds at all.
* studio/backend: diagnostic logging on provider key decryption
Decryption failures currently log just 'Failed to decrypt API key:
Decryption failed', which leaves no way to tell whether the cause is
a stale public key in the browser, a corrupted ciphertext, an
unexpected exception class, or a server-side keypair rotation. That's
the gap the next reproduction needs to close.
- key_exchange now publishes a short SHA256 fingerprint of the public
key PEM. init_key_pair logs the fingerprint on generation and warns
if it is ever called a second time (re-init silently invalidates
every browser that cached the previous public key).
- decrypt_api_key wraps both the base64 decode and the RSA decrypt
in dedicated try/excepts that log exception type, ciphertext byte
length (RSA-2048 should be exactly 256), input string length, and
the current public-key fingerprint.
- GET /api/providers/public-key returns the fingerprint alongside the
PEM so the frontend can correlate a future encrypt-time fingerprint
against the decrypt-time fingerprint and prove or rule out a
keypair rotation as the cause.
- The /test and /models route-level decrypt warnings now include the
exception class name (alongside the existing message).
* studio/providers: hide dated Anthropic snapshots from the model picker
Anthropic's /v1/models returns dated snapshot ids (e.g.
claude-3-5-sonnet-20241022, claude-3-5-haiku-20241022) alongside
the canonical names users actually want to pick. Same intent as
the OpenAI denylist added in 048d73bf, just a different date
format — Anthropic uses -YYYYMMDD (no dashes) while OpenAI uses
-YYYY-MM-DD.
- Add model_id_denylist = re.compile(r'-\d{8}$') to the anthropic
registry entry. The /api/providers/models route already applies
any denylist after fetching, so dated ids drop out automatically.
- Strip the dated 3.5 ids from default_models so the seeded picker
no longer surfaces them; keep claude-opus-4-7 and the 4.5 family
as the curated set.
Net effect: the picker shows opus-4-7 / opus-4-5 / sonnet-4-5 /
haiku-4-5 only, regardless of whether the remote /models call
succeeds or fails.
* fix: provider dialog and mistral short list
* style: fix provider dialog curated list styling
* fix: provider dialog curated model ids placeholder reference
* style: rename Providers to Cloud and tighten dialog header spacing
* UX: rename Providers to Cloud, remove header shortcut
* studio/chat: normalize structured delta.content from reasoning providers
Mistral's magistral (and similarly-shaped reasoning models) stream
chat-completion deltas where choices[0].delta.content is an array of
structured parts rather than a plain string, e.g.
[{ type: 'text', text: '...' }, { type: 'thinking', thinking: '...' }]
The accumulator did 'cumulativeText += delta', which coerced each
part to '[object Object]' and produced output like
'[object Object][object Object]...Hey there!'.
Add extractDeltaText() to normalize delta.content before append:
- string → returned as-is
- array of parts → text/output_text parts contribute their .text or
.content; thinking/reasoning parts are re-wrapped inline as
<think>...</think> so the downstream parseAssistantContent lifts
them into a reasoning part the same way it does for providers that
emit thinking inline. magistral keeps its thinking panel; no other
provider's output shape changes.
- unknown shapes → dropped rather than stringified, so a stray field
cannot pollute the rendered chat with '[object Object]'.
* Studio: restore Cloud icon shortcut in chat header
Brings back the header chip that opens Settings -> Cloud (external
providers) directly from the chat view. Same button as before the
bf24e604 removal: single-mode only, opens useSettingsDialogStore on
the 'connections' tab, tooltip 'API providers'.
* studio/chat: strip trailing template literal from external provider streams
Mistral's magistral occasionally appends a literal '${response}' token
after its actual answer — likely a training-format artifact, since it
keeps happening with an empty system prompt and only on that model.
Apply a tight strip in the chat-adapter SSE accumulator: when the
active provider is external, drop a trailing '${...}' template literal
(with optional whitespace) from cumulativeText after each chunk. The
regex anchors to end-of-string, so mid-stream fragments ('${re')
remain untouched and only collapse once the closing brace arrives.
Local-model output is unaffected.
* studio/providers: scope Kimi picker to kimi-k2.6 / kimi-k2.5
Mirror what the live Kimi docs surface as the current models
(https://platform.kimi.ai/docs/models). Everything else the
remote /v1/models call returns — moonshot-v1-* legacy ids and
dated k2 previews like kimi-k2-0711-preview — is filtered out.
- default_models: ['kimi-k2.6', 'kimi-k2.5'] (was four
legacy moonshot-v1 ids plus the dated k2 preview)
- model_id_allowlist: ^kimi-k2\.[56]$ applied in the
/api/providers/models route after the live fetch
- doc-link comments point at platform.kimi.ai overview /
models / list-models for the next refresh
* studio: drop temperature/top_p for Kimi reasoning models
Kimi k2.5/k2.6 are reasoning-class. The API locks temperature and
top_p to fixed defaults and 400s on any other value with
'invalid temperature: only 1 is allowed for this model'.
The frontend capability map already gated these knobs out of the
external request body, but the OpenAI-compat path on the backend
unconditionally re-adds them from the pydantic ChatCompletionRequest
defaults (temperature=0.7 etc), so the gate was bypassed end-to-end.
Add a generic body_omit hook on the provider registry that
stream_chat_completion consults after building the body, and use it
to strip temperature/top_p for Kimi. Frontend provider-capabilities
flips kimi.temperature and kimi.topP to false so the sliders are
hidden in the chat settings panel as well.
* studio/providers: scope Gemini picker to current 3.x + *-latest aliases
Google's /v1beta/openai/models returns dozens of historical,
experimental, and non-chat ids that we never want in the chat UI.
Cap the picker to the current curated set:
- gemini-3.1-pro-preview
- gemini-3.1-flash-lite
- gemini-3-flash-preview
- gemini-pro-latest
- gemini-flash-latest
- gemini-flash-lite-latest
Default_models seeded with these, model_id_allowlist applied in
the /api/providers/models route to drop anything else the live
fetch returns.
* studio/providers: switch Hugging Face to remote model listing
Per the Inference Providers docs
(https://huggingface.co/docs/inference-providers/index),
GET https://router.huggingface.co/v1/models returns the full
chat-model catalog across all providers, including per-provider
metadata. The OpenAI-compatible endpoint we already use for
chat completions accepts the same Bearer token, so flipping
model_list_mode from 'curated' to 'remote' lets users discover
models via the existing list_models() path without any new
wiring.
- model_list_mode: 'remote' (was 'curated')
- default_models refreshed with current popular ids
(gpt-oss-120b, DeepSeek-V3, Llama-3.3-70B, Qwen2.5-72B) so the
picker still has a sensible seed if /v1/models fails
- notes updated to reference the docs page and clarify the
endpoint is chat-only
* UX: chat cloud icon changed to model select signifier
* studio/providers: org allowlist + count cap for HF Inference picker
The HF /v1/models response is the full cross-provider catalog (hundreds
of ids — community fine-tunes, mirrors, fp8 variants, dated snapshots).
Scope the picker to the first-party org repos worth surfacing and cap
the post-filter list.
- model_id_allowlist matches the org prefixes openai/, deepseek-ai/,
google/, meta-llama/, Qwen/, moonshotai/, mistralai/, zai-org/.
Anything outside those orgs is dropped.
- model_id_limit (new registry field) caps the post-filter list. The
list-models route now slices [:limit] after allowlist/denylist; set
to 15 for HF Inference. Other providers leave it unset and behave
exactly as before.
- default_models stays as the seed so the flagship ids users care
about (gpt-oss-120b, DeepSeek-V3, Llama-3.3-70B, Qwen2.5-72B) are
always reachable regardless of the API's response order.
Dedup is already handled in loadModels() via Set, so no additional
work needed there.
* style: adjust cloud icon right margin with rem spacing
* Studio: cloud openai reasoning level toggle (#5402)
* feat: cloud openai reasoning level toggle
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: honor enable_thinking=false
* fix: prevent local reasoning toggle regressions and align OpenAI effort levels
* fix: isolate external OpenAI reasoning toggle state
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
* fix: clamp reasoning effort
* fix: align OpenAI reasoning effort
* fix: clear stale GGUF badge state
* ui: new badge on cloud setting
* fix: separate selected models from cached provider model list
* Studio: anthropic effort by model family (#5412)
* feat: external thinking control and Anthropic effort mapping
* fix: anthropic thinking constraints and 4.6 max effort mapping
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: harden Anthropic thinking params and effort mapping
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* studio/backend: drop top_p from Anthropic body when thinking is enabled
PR 5412 added body['top_p'] = max(0.95, min(top_p, 1.0)) inside the
thinking branch of _stream_anthropic, but Anthropic returns 400 on
extended/adaptive thinking when both temperature and top_p are set:
invalid_request_error: temperature and top_p cannot both be
specified for this model. Please use only one.
(Observed on Claude Opus 4.6.) The contract for thinking-enabled
requests is temperature=1 with neither top_p nor top_k allowed.
Replace the body['top_p'] = ... line with body.pop('top_p', None).
Defensive pop rather than a bare delete: the base body construction
above does not currently set top_p, but a future edit that adds it
would silently reintroduce the regression.
* studio/chat: force reasoningEnabled=true on local reasoning-effort models
Followup to PR 5402 / 5412. The model-status refresh path in
use-chat-model-runtime carried reasoningEnabled forward verbatim for
every reasoning-capable model. That left one observable edge case:
1. user picks an external model that supports Off (gpt-5.x, Claude
4.x), clicks Off — store sets reasoningEnabled=false
2. user switches back to a local reasoning-effort model
(gpt-oss / Harmony-style) which does NOT support Off
3. composer's effectiveReasoningEnabled override paints the UI as
'Think: <level>' (on)
4. chat-adapter sees reasoningEnabled=false on the local branch
and sends '{}', so the backend's _request_reasoning_kwargs
returns None and the Harmony template falls back to its own
default effort instead of the displayed level
Mirror the composer's override in the store on load: for local
reasoning-effort models (where supportsReasoningOff is false), force
reasoningEnabled=true so the store and the UI agree on every send.
Other reasoning styles still inherit prior state — only the
reasoning-effort family changes.
* studio/backend: align Anthropic thinking with the extended-thinking docs
Two compliance fixes against
https://platform.claude.com/docs/en/build-with-claude/extended-thinking
1. Adaptive-mode effort field shape
The docs spell adaptive thinking as:
{'thinking': {'type': 'adaptive'}, 'effort': {'type': '<level>'}}
We had been sending the legacy 'output_config: {effort: <level>}'
shape, which Anthropic appears to silently ignore — adaptive ran
at the server default effort regardless of the user's selection.
Rename to 'effort: {type: <level>}'.
2. thinking_delta event translation
The Messages-API streams reasoning content as
content_block_delta events with delta.type == 'thinking_delta',
which our SSE loop was dropping entirely. On Claude 4.5/4.6 with
display=summarized (the default), the user would see the answer
text but never the reasoning panel. Wrap thinking_delta.thinking
as inline <think>...</think> chunks (same pattern as the OpenAI
Responses path) so the frontend's parseAssistantContent lifts it
into the reasoning channel. The </think> closer fires on the
first text_delta transition, on content_block_stop for the
thinking block, on message_delta, and on message_stop —
whichever arrives first — so no model path can leak an
unclosed <think> into chat output.
signature_delta events are left as no-ops; they carry
verification metadata, not user-visible content.
Adds test_anthropic_thinking_translation.py with httpx.MockTransport
coverage of: effort shape on adaptive (Claude 4.6), budget_tokens
shape on manual (Claude 4.5), thinking_delta wrapping with signature
suppression, and thinking-only turns (display=omitted on Opus 4.7).
* studio/backend: revert Anthropic adaptive effort to output_config nesting
The previous commit (0a664df4) moved the adaptive-thinking effort
field to a top-level 'effort: {type: <level>}' based on a misread of
the docs page. The actual Messages API schema nests it under
output_config:
thinking: optional ThinkingConfigParam ({type: 'adaptive'})
output_config: optional OutputConfig
effort: optional 'low' | 'medium' | 'high' | 'xhigh' | 'max'
Sending the top-level field produced:
400 invalid_request_error: effort: Extra inputs are not permitted
Restore the body to:
body['thinking'] = {'type': 'adaptive'}
body['output_config'] = {'effort': effort}
This was the shape PR 5412 originally shipped (and the author
validated against live APIs). My 'compliance fix' was a regression.
The companion thinking_delta SSE translation added in 0a664df4 stays
— that part WAS missing from the previous shape and is unchanged
by this revert. Test pinning the body shape flipped to assert
output_config.effort, top-level effort is asserted absent.
* studio/backend: opt in to summarized thinking display on adaptive
Per the adaptive-thinking docs, the 'display' field on the thinking
config defaults to 'omitted' on Claude Opus 4.7 (and Mythos Preview).
With 'omitted' the API still emits a thinking content block, but its
'thinking' field is empty — only the signature_delta arrives.
Our SSE handler would then surface a stray '<think></think>' for the
empty block and the reasoning panel would stay blank for the entire
response. Set 'display': 'summarized' explicitly on the adaptive
thinking config so Opus 4.7 emits thinking_delta events the same way
Opus 4.6 / Sonnet 4.6 do (where 'summarized' is the default, making
the explicit setting a no-op there).
The manual-thinking branch (Claude 4.5) is unaffected — its default
is also 'summarized', and we have no reason to override it.
* studio/backend: log Anthropic SSE event counts for thinking diagnostics
Reports of 'no reasoning panel content on Anthropic' have two
distinct causes that produce the same symptom:
1. Anthropic streamed thinking_delta events but our frontend
dropped them somewhere on the rendering side.
2. Anthropic did not emit thinking_delta at all (adaptive mode
can skip thinking for simple prompts even with effort=high,
and display=summarized only re-enables the *content* — it
does not force thinking to happen).
Tally each event type for the duration of one stream and log the
counts in the finally branch, so the next 'no reasoning content'
report shows immediately whether thinking_delta was even on the
wire. Zero counts → upstream (model/effort/prompt choice).
Non-zero counts → triage moves to chat-adapter / parse-assistant
-content / the reasoning component.
* studio/backend: route external_provider logs through structlog
The studio backend wires structlog as the active logger (via
LogConfig.setup_logging at main.py:262), but external_provider.py
was using stdlib logging.getLogger(__name__) for every diagnostic.
The stdlib root logger defaults to WARNING with no handlers
attached, so plain logger.info('...') and logger.debug('...') from
this module were being silently dropped — including the
'Proxying chat completion to <url>' and the new
'Anthropic stream event counts' lines. Only WARNING/ERROR survived
(via the implicit fallthrough that the user actually observed
when an Anthropic call 400'd).
Switch the module-level logger to structlog.get_logger(__name__),
matching the routes/providers.py and routes/inference.py pattern.
All existing call sites use printf-style positional args, which
structlog accepts unchanged — no other edits needed.
* studio/backend: disable read timeout on SSE streams to external providers
Anthropic Opus 4.7 (adaptive thinking) and OpenAI gpt-5.x (/v1/responses)
can pause for tens of seconds between bytes while the model is
internally reasoning. httpx's read timeout is the *gap* between
successive reads, not a wall clock on the whole request — so the
shared 120s default was cutting streams mid-response:
log: Anthropic stream event counts (... text_delta: 11)
Read timeout from anthropic
(eleven text deltas in, no content_block_stop, no message_stop)
Add a separate _stream_timeout on ExternalProviderClient with
read = None (no gap timeout) and the same 10s / 120s connect/write/
pool bounds, then use it at the three SSE streaming call sites:
default OpenAI-compat chat completions, _stream_anthropic, and
_stream_openai_responses. Non-streaming call sites (chat_completion,
list_models, verify_models_endpoint_lightweight) keep self._timeout
because a stuck non-streaming response should still fail fast.
* studio/backend: log outbound Anthropic request shape for thinking debug
After bumping to Xhigh effort the user still saw zero thinking_delta
events and only one content_block_start, meaning Anthropic Opus 4.7
opened no thinking block at all. Per the effort docs that should be
impossible — Xhigh always thinks. Two open hypotheses:
1. Our adaptive branch is not wiring output_config.effort onto the
outbound body for this code path (regex miss, frontend never
propagated reasoning_effort, etc).
2. Anthropic is silently accepting output_config as an unknown
field and falling back to high default effort regardless.
Add a single-line structlog INFO right before the stream POST that
echoes the keys actually present on the body (thinking, output_config,
temperature, presence of top_p / top_k, max_tokens). Messages are
deliberately excluded to keep PII out of the log. With this in place
the next 'no thinking on 4.7 at Xhigh' report shows immediately
whether we sent the effort knob — separating client bug from
provider behaviour.
* studio/chat: surface delta.reasoning_content from Kimi / DeepSeek thinking
Kimi (kimi-k2.6, kimi-k2-thinking) and DeepSeek's reasoner stream
their thinking content via a separate top-level field on the
chat-completion delta — choices[0].delta.reasoning_content — rather
than as a structured part inside delta.content. Per Kimi docs:
In streaming output (stream=True), the reasoning_content field
will always appear before the content field.
Our chat-adapter SSE loop only read delta.content (via
extractDeltaText), so the entire reasoning channel from these
providers was being silently dropped — kimi-k2.6 thinks by default
yet the chat UI showed no reasoning panel.
In the adapter:
- Read both delta.content and delta.reasoning_content per chunk
- When reasoning_content arrives, open a <think> block in
cumulativeText (mirrors how the backend wraps Anthropic
thinking_delta and OpenAI Responses reasoning summaries)
- When content arrives after reasoning, close </think> first
- On stream end, force-close any still-open <think> so
parseAssistantContent can lift it into a reasoning part cleanly
Anthropic and OpenAI Responses paths are unaffected — they already
wrap as <think> on the backend and never set reasoning_content.
* studio: Kimi thinking toggle + 16k max_tokens floor
Two coordinated changes so Kimi's thinking is user-controllable and
the response budget meets the docs' floor.
Toggle (frontend + backend):
- getExternalReasoningCapabilities now handles provider=='kimi':
kimi-k2.6 -> reasoning_style=enable_thinking, reasoningOff allowed
kimi-k2-thinking -> always on (reasoningAlwaysOn=true, no off)
kimi-k2.5 (and anything else) -> no reasoning controls
- chat-adapter already forwards enable_thinking on the
enable_thinking-style branch, so the user toggle reaches the
backend without additional wiring there.
- external_provider stream_chat_completion now translates the
boolean into Kimi's wire shape on the default OAI-compat path:
enable_thinking=True -> body['thinking'] = {type: enabled, keep: all}
enable_thinking=False -> body['thinking'] = {type: disabled}
kimi-k2-thinking ignores the toggle so the API never gets a
disabled value it would reject. Other providers on the same
path are unaffected (gated on provider_type == 'kimi').
Max tokens floor:
- New EXTERNAL_MIN_OUTPUT_TOKENS_BY_PROVIDER table and
getExternalMinOutputTokens helper. Kimi entry = 16000 per docs:
'Set max_tokens >= 16,000 to ensure the full reasoning_content
and final content can be returned without truncation.'
- chat-adapter clamps the outbound max_tokens to
min(max(stored, providerMin), EXTERNAL_MAX_OUTPUT_TOKENS),
so a stored value of 4096 still becomes 16000 when sending to
Kimi (other providers unaffected, min stays effectively 64).
- chat-settings-sheet's Max Tokens slider min mirrors the same
floor when an external Kimi model is selected, so the slider
cannot show a value lower than what we'd actually send.
- chat-page threads activeExternalProviderType down to the panel.
* fix: stabilize external reasoning controls for Anthropic 4.6 and OpenAI o3
normalize Anthropic 4.6 reasoning effort handling by accepting max as an alias and mapping it to xhigh, while keeping Sonnet/Opus 4.6 in default model suggestions.
broaden reasoning effort typing across backend/frontend and migrate persisted max selections to xhigh for compatibility.
remove reasoning.summary=\"auto\" from OpenAI /v1/responses payloads to avoid o3 eligibility/gating errors.
tighten provider model filtering to hide retired gpt-5.3 IDs and add exact/prefix filtering support in provider routes.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: add openrouter/free + full reasoning passthrough on OpenRouter
Four-layer wire-up so the OpenRouter free-router model (which picks
a free model at random per request, filtered by needed capabilities)
shows up in the picker and its reasoning channel surfaces in the
chat UI.
Registry:
- providers.py: openrouter/free seeded at the top of openrouter
default_models. Curated list, so picker shows it immediately.
Frontend capability map:
- provider-capabilities.ts: getExternalReasoningCapabilities now
treats openrouter as enable_thinking style with off support. The
Think dropdown appears for every OpenRouter model; the gateway
silently no-ops the parameter for models that do not reason, so
surfacing one toggle on every model is safe.
Backend reasoning passthrough:
- external_provider.py stream_chat_completion (default OAI-compat
branch): for provider_type=='openrouter', translate the request:
reasoning_effort in {low,medium,high} -> body['reasoning'] =
{'effort': <level>}
enable_thinking=True -> body['reasoning'] = {'enabled': True}
enable_thinking=False -> body['reasoning'] = {'enabled': False}
Matches the documented shape at
https://openrouter.ai/docs/guides/best-practices/reasoning-tokens
with effort and max_tokens mutually exclusive.
Frontend SSE reader:
- chat-adapter.ts: OpenRouter streams reasoning as a third shape we
did not handle yet: delta.reasoning_details is an array of parts
like {type: 'reasoning.text', text: '...'}. Pull text from every
part, merge with the existing delta.reasoning_content channel
used by Kimi/DeepSeek, and feed the combined string through the
same <think>...</think> wrap path so parseAssistantContent lifts
it into the reasoning panel. Anthropic/OpenAI Responses paths
already wrap on the backend, so they never set this field — no
cross-provider interference.
* studio/backend: surface OpenRouter SSE errors and router-chosen model in logs
The frontend showed 'Provider returned error' for some openrouter/free
requests with nothing on the backend side to triage from — the
existing 4xx error log only fires when the upstream returns a non-200
status code, but OpenRouter (and most OAI-compat providers) return
200 OK and emit the actual failure as an SSE error event mid-stream,
which our default-path stream loop forwarded verbatim without
logging.
Best-effort diagnostics on the default OpenAI-compat stream path:
- Peek at every `data:` line in the inner forward loop, parse JSON
best-effort (silently skip on failure so nothing is dropped).
- Count event types: delta / error / done.
- On any chunk containing an `error` field, emit a structlog WARNING
with the provider type and the error payload — same trail the
user would otherwise have to dig out of browser devtools.
- Latch the first non-empty `chunk.model` field. OpenRouter reports
the router-picked underlying model there per request, so the
finally-block summary log shows which free model handled the call.
In the finally block:
'openrouter stream complete (model=openrouter/free,
chosen=google/gemini-2.5-flash, events={delta: 47, done: 1})'
Zero overhead for non-error streams (a json.loads per chunk +
dict-key lookups). The structlog logger is already configured at
INFO; ERROR and WARNING surface in JSON logs without further setup.
Hoists `import json as _json` to module top so the default path can
reuse it; the existing in-function imports in _stream_anthropic and
_stream_openai_responses are now redundant but harmless.
* studio/chat: show router-picked model after 'openrouter/free:' in chip
When the user picks openrouter/free, the gateway routes each request
to a different underlying free model. Until now there was no way to
tell which one actually replied without reading the backend logs.
Surface the picked model in the active-model chip:
- chat-runtime-store gains lastOpenRouterChosenModel: string|null
plus a setter. Reset on every model switch unless the user stays
on openrouter/free.
- chat-adapter SSE loop latches chunk.model into the store on
every chunk whose top-level model differs from
openrouter/free, gated on the active checkpoint being
openrouter/free under an OpenRouter provider.
- chat-page externalModels useMemo appends :<chosen> to the display
name for the openrouter/free option when the store has a value,
so ModelSelector renders e.g.
'openrouter/free:google/gemini-2.5-flash'
in the chip. Other models unaffected.
- Model-switch callback in chat-page clears the cached value when
the user moves to any model other than openrouter/free, so the
chip never shows a stale suffix from a previous session.
* studio/chat: shorten openrouter/free chip to openrouter:<short-chosen>
The full display name in use was:
openrouter/free:inclusionai/ring-2.6-1t-20260508:free
The `:free` suffix on the underlying id already conveys 'free model',
which made the leading `/free` on the router id redundant, and the
`inclusionai/` org prefix was just noise crowding the chip.
Trim both. Now the chip renders as:
openrouter:ring-2.6-1t-20260508:free
Strictly a display change in chat-page externalModels useMemo — the
backend wire id stays `openrouter/free`, the runtime store still
caches the full `inclusionai/...:free` value, and the model-switch
clearing logic is unchanged.
* studio/providers: switch OpenRouter to remote listing with org allowlist + cap
Same shape as Hugging Face Inference. The curated list had only four
entries; remote listing fetches OpenRouter's full ~300-model
catalog via /v1/models and the new allowlist + limit scope it back
down to a usable picker.
- model_list_mode: remote (was curated)
- model_id_allowlist matches the prefixes:
openrouter | openai | anthropic | google | meta-llama | qwen
| mistralai | deepseek | moonshotai | inclusionai | zai-org
| z-ai
Anything outside drops out.
- model_id_limit: 20 — first 20 post-filter matches from the live
fetch; default_models stays seeded so the most useful canonical
ids are always visible regardless of API response order.
- default_models seed extended from 4 to 6 (openrouter/free,
openai/gpt-4o, anthropic/claude-sonnet-4-5, google/gemini-2.5-flash,
mistralai/mistral-large-2411, deepseek/deepseek-r1).
openrouter/free remains the first entry, so the dialog's
loadModels() union-merge (registryDefaults first, then remote,
deduped via Set) keeps it at the top of the picker.
* feat: external mistral thinking toggle
* studio/chat: fix TS2540 by replacing readonly ContentPart instead of mutating
The ContentPart type from @assistant-ui/react marks `text` as readonly,
so the coalesce-adjacent-same-type-part optimization in
parseAssistantContent failed the tsc build with:
parse-assistant-content.ts(15,10): error TS2540: Cannot assign to
'text' because it is a read-only property.
parse-assistant-content.ts(25,10): error TS2540: ...
This broke npm run build, the Studio installer's `building frontend...`
step, and every downstream CI job that runs against an installed
Studio (Mac/Windows/Linux variants of Studio API CI, GGUF CI, UI CI,
Tauri CI, Wheel CI).
Replace the last element with a fresh merged object instead of
mutating its `text` field. Same allocation profile as the previous
path (one object swap per merge), type-safe under the readonly
declaration. Behaviour unchanged.
* studio/backend: restore summary='auto' on OpenAI Responses reasoning body
A recent refactor dropped the `summary: 'auto'` field from the
reasoning config we send to /v1/responses. Without it OpenAI does
not emit reasoning summary events on most reasoning models, which
means our SSE handler has no <think>…</think> to wrap and the chat
reasoning panel stays blank for any gpt-5.x / o3 response.
The expected wire shape is:
body['reasoning'] = {'effort': '<level>', 'summary': 'auto'}
Two backend tests pin this:
- test_responses_reasoning_effort_included_when_requested (high)
- test_responses_reasoning_effort_xhigh_passthrough (xhigh)
Both were failing with AssertionError because the produced body
omitted `summary: auto`.
Restore the field. Skip it only for the explicit "off" case
(effort: 'none'), where summaries serve no purpose. The
enable_thinking=True fallback (no explicit effort) also pairs
medium effort with summary='auto' so that branch produces
reasoning text too.
* chat: external reasoning, OpenRouter curation, Think toggle fixes
* fix: opus and sonnet 4.6 xhigh --> max
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Two gaps surfaced when running tests/test_import_fixes_drift.py on a
fresh main install (transformers 4.57.6, trl 0.25.1, peft 0.19.1,
triton 3.5.1, vllm 0.15.1):
* triton_compiled_kernel test predicate was strict: only accepted
a class-level num_ctas. fix_triton_compiled_kernel_missing_attrs
installs the attrs via a wrapped __init__ (the post-3.6 shape),
so the detector fired DRIFT DETECTED even with the fix correctly
applied. Relax to also accept the wrapped-__init__ signature
(closure freevars / co_names probe). Mirrors zoo's already-relaxed
predicate (unsloth-zoo PR #639).
* tests/conftest.py applied ONLY the peft transformers_weight_conversion
stub fix via file-path loading. fix_vllm_guided_decoding_params /
fix_triton_compiled_kernel_missing_attrs / etc. never ran inside the
test process, so the corresponding drift detectors probed an
unpatched runtime state and pytest.fail'd. Replace the surgical
file-path loader with a guarded import unsloth (the GPU-free
harness above already pre-spoofs the device-type chain), so the
full import_fixes.py pass applies before pytest collects. Mirrors
unsloth-zoo's conftest pattern.
Local verification on transformers 4.57.6 + trl 0.25.1 + peft 0.19.1
+ triton 3.5.1 + vllm 0.15.1+cu130:
before: 16 passed, 2 failed (triton + vllm DRIFT DETECTED)
after: 18 passed, 0 failed
* tests: import_fixes drift detectors (HARD GATE on Core matrix)
Ports zoo PR #637's drift-detector pattern to unsloth as a new
test file + Core matrix step.
Background
unsloth/import_fixes.py is a 1932-line catalog of hand-rolled
patches for upstream regressions: protobuf MessageFactory drift,
datasets 4.4.x recursion, TRL tuple-vs-bool _*_available caching,
transformers PreTrainedModel.enable_input_require_grads source
pattern flip, triton CompiledKernel num_ctas missing, peft
weight-converter ctor compat, torch/torchvision pairing, vllm
guided_decoding params, etc. Today each fix runs unconditionally
at unsloth import; that's defensively correct but it means:
a fix becoming a no-op (upstream silently fixed itself) is
invisible.
a fix becoming needed-but-broken (upstream drifted in a new
way the workaround doesn't match) only surfaces as a
downstream crash.
tests/test_import_fixes_drift.py (18 tests)
One drift detector per fix_* / patch_* function in import_fixes.py.
Each test asserts the HEALTHY upstream shape absent the regression.
When the pathology is currently ACTIVE, fires
pytest.fail("DRIFT DETECTED: <fix function> needed because
<observation>") -- NEVER pytest.skip. CI must go RED so the
maintainer triages on the next PR.
First run on the current install surfaces 3 active drifts:
peft.utils.transformers_weight_conversion unimportable
(transformers.conversion_mapping missing) -- patch_peft_
weight_converter_compatibility will silently no-op.
triton 3.5.1 CompiledKernel lacks num_ctas + cluster_dims --
fix_triton_compiled_kernel_missing_attrs is live-needed.
vllm exposes only StructuredOutputsParams, not
GuidedDecodingParams -- fix_vllm_guided_decoding_params
is live-needed.
CI wiring (.github/workflows/consolidated-tests-ci.yml)
New step `import_fixes drift detectors (18 tests, HARD GATE)`
added to the Core matrix BEFORE the Bucket-A tests, so the matrix
cell fails fast on a real upstream regression. No
continue-on-error: a drift detection MUST go red.
This mirrors the same change just landed on
unslothai/unsloth-zoo#637 (commit ff5a3d8). Same fail-loud-on-drift
semantic; same set of fix functions covered; same 1:1 mapping
between test + import_fixes.py source-of-truth function.
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* chore: trim verbose docstrings in import_fixes drift detectors
Strictly comment / docstring trims. AST-verified comment-only.
* Module header: 36 lines -> 7 lines.
* Per-test docstring: collapse each 7-15 line prose block to a 1-3
line lead naming the import_fixes.py function + line range plus
the one-sentence why; pytest.fail messages stay verbatim so a
red CI cell still names the upstream regression.
* Helper docstrings (_safe_version, _is_custom_torch_build): drop.
* Inline narrative comments inside test bodies: drop.
* Section dividers and licence header: untouched.
Net: 700 -> 537 lines, zero behaviour changes.
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Strictly comment / docstring trims. AST-verified against 12295c1f via
scripts/verify_trim_comment_only.py:
* unsloth/import_fixes.py: collapse the 32-line peft+transformers-4.x
drift header to 10 lines; remove redundant per-stub docstrings and
per-step numbered comments inside fix_peft_transformers_weight_
conversion_import; keep one-line docstrings on helpers + on the
public entry-point.
* unsloth/_gpu_init.py: collapse the 8-line preamble above
fix_peft_transformers_weight_conversion_import() to 4 lines.
* tests/conftest.py: collapse the 13-line block comment above
_apply_unsloth_peft_import_fix_for_tests to 5 lines; tighten three
internal comments.
* import_fixes: stub transformers.conversion_mapping so peft 0.19.x imports on transformers 4.x
patch_peft_weight_converter_compatibility currently opens with
try:
from peft.utils import transformers_weight_conversion as twc
except (ImportError, AttributeError):
return
which silently no-ops on (peft 0.19.x, transformers 4.57.x): peft's
transformers_weight_conversion module unconditionally imports two
transformers-v5 submodules at module top
from transformers.conversion_mapping import ...
from transformers.core_model_loading import ...
and neither submodule exists on transformers < 5. peft itself only USES
those submodules inside an is_transformers_ge_v5 branch, but the top of
file import still explodes with
ModuleNotFoundError: No module named 'transformers.conversion_mapping'
The bare except above swallows that, so the weight converter compat
wrap never gets installed, and any downstream code that later does
from peft.utils import transformers_weight_conversion crashes with the
same ModuleNotFoundError.
Fix: synthesise minimal stub modules for transformers.conversion_mapping
and transformers.core_model_loading, install them into sys.modules, and
re-import peft.utils.transformers_weight_conversion so the kwargs compat
wrap can succeed on top. The stubs expose exactly the symbols peft 0.19.x
pulls in at module top (Concatenate / ConversionOps are real subclassable
classes since peft subclasses them as PeftConcatenate / FlattenDims /
PermuteDims), so peft's own class creation succeeds. None of the stubbed
callables actually fire on the 4.x branch because peft's runtime
is_transformers_ge_v5 gate keeps them unreachable.
Gating contract (strict no-op outside the (peft 0.19.x, transformers 4.x)
combination):
* No-op if peft is not installed.
* No-op if peft.utils.transformers_weight_conversion already imports
clean (transformers v5+, or any peft fork off the v5 path).
* Strictly additive: only stubs submodules that are currently missing
from sys.modules / find_spec. We never overwrite the real
transformers.conversion_mapping / transformers.core_model_loading
on transformers v5+.
* Idempotent: sentinel attribute (__unsloth_stub__) on the stub modules
makes a second call return False, a third call return False, etc.
* Surfaces drift unchanged: if peft fails for some reason OTHER than
these two specific missing submodules, the original ImportError is
left for the caller's own try/except to take over.
Forwards / backwards compatibility:
* transformers 4.57.6 -> install stubs.
* transformers 5.x (real submodules) -> first-import probe succeeds,
return False, never touch sys.modules.
* TRL 0.22 / 0.27 / 1.x -- none of these import either submodule
directly; they reach the peft conversion module (if at all) through
peft.tuners.tuners_utils, behind peft's own is_transformers_ge_v5
gate. Stubs are therefore unreachable from TRL on a 4.x install,
and on a 5.x install the real submodules win the import race.
* peft 0.18 / 0.19 / 0.20 -- the symbols stubbed cover the union of
what peft pulls at module top across the 0.19.x line; older peft
that doesn't import the v5 submodules at all hits the cheap
first-import-probe exit and we never touch sys.modules.
Wired into unsloth/_gpu_init.py to run BEFORE
patch_peft_weight_converter_compatibility (otherwise that function's
bare except would still silently no-op). Mirrors the equivalent fix
shipped in unsloth-zoo (the zoo-side stub installs itself via
apply_import_fixes() at zoo import time, but a user can run
unsloth without the zoo fix on an older unsloth_zoo, so the unsloth
side needs to own its own copy of the workaround).
tests/conftest.py is updated to pre-apply this specific fix via the
standalone import-fixes module so the GPU-free drift detector test
(tests/test_import_fixes_drift.py::test_peft_transformers_weight_conversion_importable_and_signature)
sees the same patched state that a real ``import unsloth`` would.
The pattern mirrors unsloth-zoo's tests/conftest.py
_apply_zoo_import_fixes_for_tests helper, scoped to just the peft fix.
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