* Fix: skip fp16/bf16 validation for full finetuning in RL trainers
When doing full finetuning (FFT) of a bfloat16 model, the fp16/bf16
mismatch validation fires before the corrective logic runs, causing a
misleading error even though the code would properly handle it downstream.
Skip the validation when full_finetuning is active.
Fixes#6731
* Fix: auto-correct fp16/bf16 mismatches for full finetuning before validation
Instead of entirely skipping validation (which could let mismatches
through when mixed_precision_dtype is float32), auto-correct explicit
fp16/bf16 settings that conflict with the model's dtype for FFT. This
way the existing validation still catches real mismatches for non-FFT
cases, and the corrective logic below handles the normalized settings.
Fixes the issue raised in Codex review of PR #6813.
* Guard Windows ROCm torchao override skip
Detect installed ROCm torch directly before applying the torchao override so Windows ROCm environments never install the crashing torchao package even if the earlier ROCm-installed flag is missing.
* Update unsloth/models/rl.py
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* Update studio/install_python_stack.py
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* Harden ROCm probe and sync RL precision flags
Tolerate stray stdout noise when probing Windows ROCm torch installs by checking the last non-empty output line, matching the existing torch version probe behavior. Also keep args.fp16 and args.bf16 synchronized with the full-finetuning precision auto-corrections in the RL trainer patch so downstream eval settings see a consistent TrainingArguments state.
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* Add MLX trainer compatibility shims
Patch imported MLXTrainer and MLXTrainingConfig objects to preserve the expected dataclass field ordering and to provide a _train_dataset_for_batches fallback when older trainers or test doubles only expose train_dataset. Also add focused worker tests covering both compatibility paths.
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* Scope PR to Windows ROCm torchao guard
* Restore PR scope to Windows ROCm guard
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* test: cover Windows ROCm torchao skip behavior
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---------
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Co-authored-by: imagineer99 <samleejackson0@gmail.com>
* Fix TrainingArguments silently disabling unsloth gradient checkpointing
* Cover loaded adapters and preserve explicit None in GC restore
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* Studio: expose full compressed-tensors scheme set in an export formats dropdown
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: multi-select export formats, portable torchao FP8/INT8, GGUF LoRA, source parity
Export page overhaul on top of the formats dropdown:
- Unify merged precision into one sorted multi-select list (16-bit first, then
8-bit, then 4-bit). Drop "vLLM" from labels, add INT8 (W8A8), INT8 (W8A16),
INT4 (W4A16), MXFP4, MXFP8. Quick formats render as toggle pills; the rest live
in a multi-select "More formats" dropdown, so several formats export in one run.
- Add a portable torchao FP8/INT8 save path (Float8WeightOnlyConfig /
Int8WeightOnlyConfig) that needs no NVIDIA GPU to produce and loads in vLLM.
FP8 serializes to safetensors, INT8 to .bin. Wired into save_pretrained_merged
and push_to_hub_merged via a TORCHAO_EXPORT_SCHEMES registry and
_unsloth_save_torchao, parallel to the compressed-tensors path.
- Hide NVIDIA-only compressed-tensors formats when no NVIDIA GPU is present; keep
16-bit and portable FP8/INT8. The backend also rejects a compressed request on
non-NVIDIA hardware so it stays authoritative.
- Relax merged export to non-PEFT models so Local Model and Hugging Face sources
get the same 16-bit / compressed / portable options.
- GGUF: send the whole quant list in one call (merge once, quantize many).
- LoRA: add a GGUF adapter option (convert_lora_to_gguf.py) with an outtype
select (f16/bf16/f32/q8_0/auto), alongside the safetensors adapter.
- Thread the new fields through models, routes, orchestrator, and worker; extend
the export tests.
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* Studio: gate export by accelerator with a torch-aware reason; fix export save dir naming
Export runs through Unsloth, which requires a compute accelerator (NVIDIA/AMD/Intel
GPU or Apple MLX) and has no CPU code path, so a bare-CPU host cannot export even
with PyTorch installed. Add export_capability() in utils/hardware that reports
export_supported plus a precise reason so the UI stops showing a generic "no GPU":
- pytorch_not_installed: a --no-torch install (even a physical GPU is unusable)
- no_accelerator: PyTorch present but no supported accelerator (bare CPU)
- mlx_unavailable: Apple Silicon where the MLX stack is missing or too old
Expose the fields on /api/system/hardware and /api/system, and guard the mutating
export routes (load-checkpoint, export/merged|base|gguf|lora) with HTTP 400 and the
reason, leaving read-only endpoints usable so the Export page still renders.
Make core/export/export.py import without PyTorch and without a usable accelerator
(the Unsloth import is caught) so the export worker degrades to a clear message
instead of crashing at import.
Frontend: keep /export reachable on chat-only hosts and gray out the method and
format options with the backend reason (Alert plus disabled MethodPicker) instead
of silently redirecting to /chat, so users see why export is unavailable.
Also fix the export save directory producing "model/null" for Local Model and
Hugging Face sources that have no run/checkpoint, naming the folder from the model id.
* CI: validate Studio export capability gating on Linux, Windows and macOS
Add a small pytest matrix that runs studio/backend/tests/test_export_capability.py
on ubuntu-latest, windows-latest and macos-latest. It confirms, on each real OS,
that hardware.export_capability() reports the right decision and reason
(pytorch_not_installed, no_accelerator, or mlx_unavailable) and that the export
backend imports without PyTorch and degrades to a clear message instead of crashing.
Hosted runners have no GPU/MLX, so this covers the "export unavailable, here is why"
path a Mac/Windows user without an accelerator sees; a real accelerator export is
validated separately. The job installs only a CPU PyTorch plus the backend import
deps (no unsloth, triton, or llama.cpp), so it runs in seconds with no GPU.
* Studio export: address Codex review (source-aware gating, GGUF LoRA token/MLX/guard)
Frontend (export-page):
- Gate LoRA and quantized-model restrictions on the active source. isAdapter /
isQuantized come from the selected checkpoint; in Local Model / Hugging Face
("model") source mode they were stale, so LoRA stayed wrongly enabled for a
direct base model (backend then rejects "No adapter to export") and a stale
"quantized" flag disabled every method for an unrelated, exportable model. Add
effectiveIsAdapter / effectiveIsQuantized (false outside checkpoint mode) and use
them in the method-reset effect and the MethodPicker disabled state.
- Hide the GGUF LoRA option on a macOS/MLX host (the backend rejects GGUF LoRA on
MLX), so users no longer pick it, wait through the load, and always fail. Disable
the "GGUF adapter" button on a Mac host and never send loraGguf there.
Backend (core/export/export.py):
- Pass the HF token into the GGUF LoRA conversion (save_pretrained_gguf), so a
gated/private base model's config fetch in convert_lora_to_gguf.py is
authenticated; without it the load can succeed but the conversion fails.
- Guard the save_pretrained_gguf capability check with getattr so an older Unsloth
model that lacks the method returns the clean "not supported" message instead of
an AttributeError that surfaces as a generic 500.
* Studio export: address 2nd Codex review (CI index, empty merged, test import)
- studio-export-capability-ci.yml: add --extra-index-url https://pypi.org/simple to
the torch install so torch's transitive deps still resolve; --index-url alone
replaces PyPI with only the CPU wheel index, which does not serve all of them.
- export-page handleStart: reject an empty merged selection (mirrors canExport), so
clicking the panel's Start button with every precision pill deselected no longer
submits mergedSelections: [] and launches an unintended default 16-bit export.
- test_export_imatrix_compressed: the torchao-registry test now reads unsloth/save.py
as text (like the other ast/string checks) instead of `import unsloth.save`, which
raised ModuleNotFoundError in the CPU studio-backend suite that has no unsloth
installed.
* Studio export: make comments succinct across the export changes
* Studio export: use load token for local GGUF LoRA export of gated bases
* Studio export: harden portable torchao path and gate multi-format Hub push
torchao (_unsloth_save_torchao):
- merge to an isolated temp staging dir so a co-selected 16-bit output at save_directory is not deleted
- narrow VLM detection to vision_config / ForVisionText2Text so T5/BART/Whisper are not misrouted
- forward trust_remote_code (from auto_map) to the reload so custom-code models export
Export UI:
- hide portable torchao formats on macOS/MLX (backend rejects quantized export there)
- restrict a Hub merged export to a single format (each writes to the repo root)
* Studio export: torchao tokenizer remote-code + XPU offload, scale GGUF timeout
torchao (_unsloth_save_torchao):
- honor auto_map in the staged tokenizer/processor configs (not just model.config) when
deriving trust_remote_code, so custom-code tokenizers reload after the merge
- offload single-device XPU models to CPU (and empty the XPU cache) before the reload, matching
the CUDA path, so an Intel GPU that fits the model once does not OOM on the second copy
Export orchestrator:
- scale the GGUF wait timeout by the number of requested quants so a multi-quant list export of a
large model does not time out at a flat 3600s
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* Studio export: show portable torchao formats only on non-NVIDIA (CPU) hosts
Portable torchao FP8/INT8 is the fallback for hosts without the NVIDIA compressed-tensors path.
On an NVIDIA GPU the compressed-tensors FP8/FP4/INT formats are the intended path (llm-compressor
auto-installs), so hide the portable duplicates there; keep them on CPU / non-NVIDIA hosts and
continue hiding them on macOS/MLX.
* Studio export: report all output folders and the exported formats
- Multi-format merged export now collects every sibling output directory (one per selected
precision) instead of only the last; the success banner lists them all.
- Show the selected precision formats in the run summary (a Formats row, like GGUF Quantizations),
so the panel says what is being exported rather than just 'Merged Model'.
- Persist the selected formats in the run summary and seed them on mount, so navigating away and
back (or toggling the export method) restores the selection instead of resetting to 16-bit.
* Studio export: list all output formats, add GGUF LoRA target, default Q8_0, auto-select newest checkpoint
- Progress/summary panel now shows a Formats row with the selected merged
formats, and the success banner lists every output folder a multi-format
merged run creates (one line per format) instead of only the last one.
- Merged format selection is seeded from the active run, so navigating away
and back (or switching method cards) no longer resets it to 16-bit.
- GGUF / Llama.cpp now offers an Export target toggle (Full model or LoRA
adapter) for adapter checkpoints, reusing the LoRA GGUF export path.
- Removed the Auto GGUF LoRA output type and defaulted to Q8_0 in the UI,
the request model, and the backend defaults; the outtype list is now
Q8_0/F16/BF16/F32. Core save.py still accepts auto for external callers.
- When a finetune has no checkpoint selected, auto-select the newest one.
* Studio torchao export: robust reload class + optional VLM import
Two fixes to the portable torchao FP8/INT8 export reload, from review of the
narrowed VLM detection:
- Encoder-decoder seq2seq checkpoints (T5/BART/Whisper) are not causal LMs.
With the narrowed is_vlm test they now correctly skip the image-text class,
but fell through to AutoModelForCausalLM and failed to reload after the merge.
Reload them with their own architecture class from the config instead.
- AutoModelForImageTextToText was imported unconditionally at the top of the
torchao path, so on Transformers builds without that class the import aborted
every torchao export (even text-only). Import it lazily only for a VLM, with
the AutoModelForVision2Seq fallback used elsewhere in Unsloth.
* Studio: enable FP8/FP4 compressed export for newer-transformers models
The shipped llm-compressor 0.10.x pins transformers<=4.57.6, so FP8/FP4 export failed
for models needing a transformers 5.x sidecar (Qwen3.5, Gemma-4, Qwen3-Next): the
quantization subprocess crashed importing the removed TORCH_INIT_FUNCTIONS.
Run the quantization against a dedicated llm-compressor-main "shadow": a --target
package dir (transformers 5.10.2 + llm-compressor main + compressed-tensors) layered
over the existing torch. It installs --no-deps so torch is never touched (works on any
Studio torch build), is provisioned lazily and fingerprint-cached, and can be turned
off with UNSLOTH_DISABLE_LLMCOMPRESSOR_MAIN.
- transformers_version.py: provision + validate .venv_llmcompressor.
- export.py: route all compressed exports through the shadow when available; else keep
the workspace 0.10.x path and fail fast past its transformers ceiling.
- save.py: launch _compressed_quantize.py with a clean PYTHONPATH = shadow.
- _compressed_quantize.py: skip linear_attn / vision tower / MTP modules (matches the
RedHatAI and NVIDIA reference quants, and is required by the grouped schemes).
Verified all four schemes (fp8, w8a8, w4a16, mxfp4) on Qwen3.5-9B and Llama-3.2-1B, and
fp8 on Gemma-4, end to end through Studio.
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* Fix GGUF LoRA export tests
* Fix export CI expectations
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
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Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.com>
* fast_generate: clear error for vLLM-style inputs when fast_inference=False
When fast_inference=False, fast_generate falls back to HuggingFace
generate, and the wrapper already rejects vLLM-only usage (a
sampling_params or lora_request kwarg, or a string prompt). A vLLM prompt
dict ({'prompt':..., 'multi_modal_data':...}) or a SamplingParams passed
positionally slipped through and hit transformers.generate, raising a
cryptic 'SamplingParams object has no attribute update'. Detect both and
raise the same clear 'only supported with fast_inference=True' error.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fast_generate: also reject positional list of SamplingParams and list of vLLM prompt dicts
Address review feedback: the slow-mode guard missed SamplingParams passed inside a
positional list and a list of {"prompt": ...} dicts, both valid vLLM batched shapes
that leaked into transformers.generate. Fold the checks into small predicates and
extend the GPU-free test (now 7 reject + 3 pass).
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* test_fast_generate_slow_guard: expose assertions via a test_ function so pytest collects them
The assertions lived in run(), only called from __main__, so pytest reported no tests
collected and CI skipped the coverage. Rename to test_fast_generate_slow_guard; the
standalone script entrypoint still works.
* fast_generate: reject vLLM tokenized/embeds prompt dicts in the slow-mode guard
vLLM also accepts prompt dicts keyed by prompt_token_ids or prompt_embeds, not just
prompt/multi_modal_data. Those slipped past the slow-mode guard and fell through to
HuggingFace generate with a cryptic error. Recognize all vLLM prompt-dict keys and
add a TokensPrompt test case (now 8 reject + 3 pass).
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* fast_generate slow-mode guard: catch vLLM prompts= keyword form
vLLM's generate names its first argument `prompts`, so a slow-mode call
like fast_generate(prompts="hi") or prompts=[{"prompt": ...}] bypassed the
guard and leaked into HuggingFace generate as an unexpected kwarg. Check
kwargs["prompts"] with the same _is_vllm_prompt predicate and add two test
cases.
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* fast_generate slow-mode guard: reject vLLM tokenized prompt kwargs
vLLM's legacy call shape passes tokens as prompt_token_ids= (and prompt_embeds=),
which are not HuggingFace generate arguments. In slow mode these bypassed the
guard and leaked into HF generate as unexpected kwargs. Reject their presence
with the same tokenize-first message and add a test case.
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* fast_generate slow-mode guard: treat prompts= as vLLM-only
prompts is a vLLM keyword, not a HuggingFace generate argument, so any value
passed as prompts= (including a bare token-id list, which _is_vllm_prompt
deliberately ignores for positional HF token ids) is a vLLM-style call. Reject
prompts= / prompt_token_ids= / prompt_embeds= on presence, and keep the
conservative _is_vllm_prompt check only for the positional arg.
* fast_generate slow-mode guard: reject vLLM prompt kwargs on presence
prompts / prompt_token_ids / prompt_embeds are vLLM-only keyword names that
HuggingFace generate does not accept, so a defaulted call like prompts=None
should raise the actionable slow-mode error instead of leaking a None kwarg
into HF generate. Check membership in kwargs rather than a non-None value.
---------
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* feat: add mlx public trainer api
* test: cover mlx public trainer api
* fix: preserve mlx epoch trainer configs
* fix: pass mlx warmup ratio through config
* fix: align mlx trainer dataset order
* fix: keep mlx chat templates import-light
* fix: infer mlx trainer context length
* fix: mirror cuda mlx context defaults
* fix: align mlx notebook trainer defaults
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: keep mlx public helpers import-light
* refactor: reuse mlx optimizer normalization
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: address mlx review feedback
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: tighten mlx training argument parity
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: align mlx trainer eos default
* Fix MLX trainer to accept DataCollatorForSeq2Seq and handle TokenizerWrapper in get_chat_template
* Trim redundant docstrings on internal MLX helpers
* MLX review fixes: Studio optimizer import-safe on non-MLX hosts, preserve explicit max_length, skip MLX tests before import
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* MLX review round 2: defer max_length to model context, optimizer alias fallback for older zoo, skip non-MLX test on missing GPU deps
* MLX review round 3: keep chat_templates importable without torch on MLX
* fix: preserve MLX trainer notebook shims
* fix: ignore CUDA tokenizer moves on MLX
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: harden MLX trainer shims
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* fix: unwrap MLX scheduler enum args
* fix: coerce integral MLX epoch counts
* fix: spoof CUDA compatibility APIs on MLX
* fix: harden MLX notebook compatibility shims
* MLX: add torch.cuda.mem_get_info to the compatibility shim
Notebook memory cells call torch.cuda.mem_get_info()[0] directly (not gated by
is_available), so on MLX it raises without a shim. Return (free, total) bytes
from the MLX device stats, consistent with the other torch.cuda compat helpers,
and add a matching assertion to the compat-API test.
* MLX: use active memory for mem_get_info; fix BatchEncoding.to keyword device
Address review on the MLX compatibility shim:
- torch.cuda.mem_get_info() now derives free bytes from current active MLX
memory instead of the peak high-water mark, so a capacity check stays
accurate after a transient spike or a prior run.
- BatchEncoding.to(device=...) passed by keyword no longer forwards a positional
None alongside the keyword (which raised "multiple values for 'device'"), so
non-CUDA keyword moves like .to(device="cpu") delegate correctly.
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* MLX: accept preserve_dataset_order; stub RL trainers with a clear error
Two fixes so unmigrated notebooks behave predictably on MLX (torch present):
- preserve_dataset_order is a real MLXTrainingConfig field but was missing from
the extra-argument allowlist, so passing it (as a config or trainer kwarg)
could be rejected as unknown on a zoo without the field. Add it to
_MLX_IMPLEMENTED_EXTRA_ARGUMENTS so the documented no-shuffle path is reachable.
- GRPO/DPO/ORPO (and KTO/PPO/Reward) have no MLX trainer yet. Retarget the ones
the installed trl exposes to a stub that raises a clear 'not supported on MLX'
error instead of importing the real torch/CUDA trainer and crashing deep
inside it. Only existing trainers are retargeted (no invented attributes),
idempotent across re-imports.
* MLX: make RL-trainer stubbing import-safe; back current-memory APIs with active memory
Address review on the MLX shims:
- The RL-trainer stub loop probed trl with getattr(_trl, name), which triggers
trl's lazy trainer import and pulls torch -- that can crash import unsloth on a
torch-free MLX install just to check existence. Decide what to stub from
trl.__all__ + already-materialized attrs (vars) instead; never resolve the real
trainer. All trl trainer names are in __all__, so they are still stubbed (even
torch-free), and the probe no longer imports torch.
- torch.cuda.memory_reserved / memory_allocated (the current, non-max APIs) were
aliased to peak max_memory_reserved. Back them with current active MLX memory so
cleanup / capacity checks see live usage; max_* keep the peak high-water mark.
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* MLX: keep TRL's SFTConfig epoch default under the trl.SFTConfig alias
Unmigrated notebooks import SFTConfig from trl, which the MLX build aliases to
the public training-args class. TRL/HF SFTConfig defaults to num_train_epochs=3
(max_steps=-1); the native MLX config defaults to max_steps=60. So an SFTConfig
built without an explicit length silently ran 60 MLX steps instead of TRL's 3
epochs under the alias. Alias trl.SFTConfig to a thin subclass that seeds the
TRL epoch default only when neither max_steps nor num_train_epochs is given;
explicit lengths pass through untouched, and the native public args class keeps
its MLX default. Epoch mode is supported by the MLX trainer.
* MLX CI: keep the GGUF reload smoke under the job timeout
The RELOAD-GGUF-via-llama-cli step timed out at 300s. BF16 GGUF decode is
CPU-bound on the macOS runner (~10s+/token), so generating 24 tokens landed
right on the 300s cliff and killed the process. This step is a save/reload
integrity smoke (it only needs a few chars of output), so the token count is
incidental: generate 8 tokens with explicit threads and a small headroom on the
subprocess timeout, all env-tunable (UNSLOTH_GGUF_RELOAD_N / _THREADS /
_TIMEOUT). Cuts the reload well under the 25 minute job budget.
* MLX: broaden trainer stubs, real peak-memory reset, fix shim tests
Address review on the MLX public API:
- The SFTConfig identity tests asserted trl.SFTConfig is UnslothTrainingArguments,
but the alias now points at the _MLXSFTConfig subclass that preserves TRL's
epoch default, so the MLX suite failed before testing the shim. Assert
issubclass instead.
- torch.cuda.reset_peak_memory_stats was a no-op, so max_memory_reserved kept
earlier model-load peaks across a scoped run. Wire it to mx.reset_peak_memory
with the same core/metal fallback used for the reads.
- The unsupported-trainer stubs were a fixed list, so trainers outside it (a
newer RLOOTrainer) still routed to the real torch trainer. Derive the set from
trl.__all__ (every non-SFT *Trainer) so all non-SFT surfaces fail with a clear
MLX message; names come from __all__ so trl is never resolved.
- The non-MLX export smoke skipped only on missing bitsandbytes/triton; other
absent GPU deps (numpy/torch/unsloth-zoo, or _gpu_init re-raising ImportError)
made it fail on CPU hosts. Skip on any ImportError.
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* fix: keep MLX notebook compatibility minimal
* MLX CI: force CPU + small context for the GGUF reload smoke
The RELOAD-GGUF-via-llama-cli step timed out even at 8 tokens (>420s), so it is a
fixed hang, not per-token cost: on the paravirtual macOS runner GPU llama.cpp's
Metal backend stalls, and the gemma3 GGUF advertises a 32768 context that llama-cli
would otherwise fully allocate. Run llama-cli CPU-only (-ngl 0) with a small context
(-c 256); keep generation short. All env-tunable (UNSLOTH_GGUF_RELOAD_NGL / _CTX /
_N / _THREADS / _TIMEOUT). Also print llama.cpp's partial stdout/stderr on timeout so
a future hang is diagnosable instead of an opaque TimeoutExpired.
* MLX CI: export the reload-smoke GGUF as q8_0, not bf16
The GGUF reload via llama-cli timed out on the runner even CPU-only with a tiny
context and 8 tokens. Root cause is the format, not the flags: the smoke exported
quantization_method='not_quantized', which maps to a bf16 GGUF, and llama.cpp's
bf16 CPU decode is unusably slow on the paravirtual macOS runner. Export q8_0
(fast_quantized, the exporter default and what users deploy) instead -- llama.cpp
has optimized q8_0 CPU kernels, so the fresh-process reload loads and generates in
seconds. The reload stays CPU-only (-ngl 0) with a small context.
* test: clear TRL shim before availability check
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: imagineer99 <samleejackson0@gmail.com>
* Add --with-llama-cpp-dir flag to install.ps1 and install.sh
Users can now pass --with-llama-cpp-dir /path/to/llama.cpp to the
installer to skip downloading or building llama.cpp and use a local
directory instead. A junction (Windows) or symlink (Linux/macOS) is
created at the canonical install location, bypassing both the prebuilt
download (Phase 3) and source build (Phase 4) steps in setup.ps1/setup.sh.
The path is passed via UNSLOTH_LOCAL_LLAMA_CPP_DIR env var which
setup.ps1 and setup.sh read directly.
Ported from the idea in unslothai/unsloth#4384, reimplemented against
current Studio architecture.
* test: add static wiring test for --with-llama-cpp-dir flag
Cross-checks install.sh, install.ps1, studio/setup.sh and studio/setup.ps1
so the flag's contract (parse -> UNSLOTH_LOCAL_LLAMA_CPP_DIR env var -> link
local dir, skip prebuilt download and source build) can't silently regress.
Wired into studio-backend-ci.yml alongside the other tests/sh installer tests.
* Address review feedback on --with-llama-cpp-dir flag
- setup.ps1: delete an existing junction/symlink via DirectoryInfo.Delete()
instead of a recursive remove, which can traverse the link and wipe the
user's real llama.cpp directory on PowerShell 5.1.
- setup.ps1: short-circuit the build chain when a local dir is linked so CMake
never runs inside the user's checkout when it lacks a Windows-layout binary.
- install.sh / setup.sh: resolve paths with CDPATH= cd -P so a set CDPATH
cannot corrupt the resolved path.
- install.sh: seed _WITH_LLAMA_CPP_DIR from UNSLOTH_LOCAL_LLAMA_CPP_DIR so an
exported env var (piped-install style) is honored instead of being clobbered.
- setup.sh: create the root llama-quantize shim when linking a local source
build so GGUF export's check_llama_cpp() still finds it.
- setup.sh / setup.ps1: drop a stale link before the custom-home ownership
assert so re-runs with the flag stay idempotent.
- test: pin the new linked-dir build short-circuit.
* Harden --with-llama-cpp-dir against Codex/Gemini review findings
- install.sh: error when --with-llama-cpp-dir is the final arg with no path,
matching the existing --package/--python post-loop guards (was a silent
fallback to the normal prebuilt/source install).
- studio/setup.sh: canonicalize LLAMA_CPP_DIR before the self-link no-op
compare. _RESOLVED_LOCAL is fully resolved while LLAMA_CPP_DIR was textual,
so a symlinked $HOME made the guard miss and the rm -rf could wipe the
user's real llama.cpp tree.
- studio/setup.sh: make the llama-quantize shim non-fatal; it writes through
the link into the user's tree, which may be read-only (shared/CI cache),
and under set -e a failed ln aborted an otherwise-good reuse.
- studio/setup.ps1: detect a broken junction via Get-Item -Force instead of
Test-Path so a dangling link from a prior run is removed and mklink can
relink to a new valid directory.
- studio/setup.ps1: use Copy-Item -LiteralPath so a source path containing
[ ] isn't treated as a wildcard in the junction copy fallback.
- tests: update the wiring assertions for the LiteralPath copy and the
canonicalized compare.
* Validate/reuse local llama.cpp tree and guard the in-use case
Addresses the second Codex pass on the --with-llama-cpp-dir flag:
- Validate the linked tree before disabling installs (setup.sh + setup.ps1):
reusing a local dir skips BOTH the prebuilt download and the source build,
so the dir must already contain a runnable llama-server (build/bin on
Linux/macOS, build\bin\Release\llama-server.exe on Windows). Bail out with a
clear message instead of linking an unbuilt/wrong-platform checkout and
leaving Studio with no usable binary.
- Treat a canonical-path target as already linked when it holds a build
(setup.sh + setup.ps1): point the flag at ~/.unsloth/llama.cpp itself and an
existing build is reused (skip prebuilt + source) rather than clobbered by
the staged prebuilt installer (which uses os.replace()/replace). An empty
canonical dir still falls through to the normal in-place install.
- Abort when an in-use llama.cpp can't be removed on Windows (setup.ps1):
Remove-Item -ErrorAction SilentlyContinue can silently leave a locked tree
in place; detect that and stop with the same active-process message + exit 3
the prebuilt path uses, instead of junctioning over a half-present dir.
Left as follow-up (already tracked by the PR author as a non-blocker): the
in-app "Update llama.cpp" updater does not yet recognize a local-link install
as externally managed; that fix belongs in studio/backend/utils/llama_cpp_update.py.
* Accept all backend llama-server layouts in --with-llama-cpp-dir validation
The linked-tree validation only accepted build/bin[/Release]/llama-server, but
LlamaCppBackend._layout_candidates() resolves a root-level llama-server first,
then build/bin, then build/bin/Release on Windows. A `make` build or a flat
release extract (binary at the dir root) was therefore rejected with a hard
installer failure even though Studio would have run it.
Validate the same candidate set the backend uses in both setup scripts, and add
wiring-test assertions so the check can't silently narrow again.
* Treat --with-llama-cpp-dir local links as externally managed
A --with-llama-cpp-dir install junctions/symlinks the canonical llama.cpp dir to
the user's own checkout, but two backend paths still treated it as a Studio-owned
tree:
- The in-app updater (llama_cpp_update) offered and could apply an official
prebuilt over the link, writing through it into the user's checkout (or
failing) and silently dropping the link the flag created.
- Orphan cleanup (LlamaCppBackend._kill_orphaned_servers) resolved the linked
root into its kill allowlist, so a llama-server the user launched from the same
checkout was classified as ours and killed on startup.
Detect the canonical dir being a symlink/junction (reparse point) and treat the
install as unmanaged: get_update_status reports unsupported, start_update refuses
with reason "local_link", and the linked root is left out of the orphan
allowlist. Adds behavioral tests (link vs plain dir, updater refusal, and the
spared-vs-killed orphan control).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add behavioral shell test for --with-llama-cpp-dir linking
The existing tests/sh/test_with_llama_cpp_dir_flag.sh is a static grep of the
scripts. This adds a behavioral test that extracts the real link block from
studio/setup.sh (by content anchors, with a self-validating extraction) and runs
it against hermetic fake dirs, asserting the outcomes that matter:
- an external CMake build links and arms neither the prebuilt download nor the
source build
- a flat / make tree (root-level llama-server, no build/bin) is accepted too
- an unbuilt tree is rejected with a non-zero exit and no link left behind
- relinking over a stale link preserves the target's contents (no data loss)
- pointing at the canonical path is a no-op reuse, not a self-referential link
Symlink-identity checks run only where real symlinks exist (skipped on Windows
git-bash copy-mode); the link/skip/no-data-loss checks run everywhere. Wired into
studio-backend-ci.yml next to the static test.
* Install psutil in backend CI so orphan-cleanup tests run
The new orphan-cleanup tests import psutil for the process scan, but the Backend
CI deps step installed studio.txt plus a fixed extras list that omits it, so the
two tests failed with ModuleNotFoundError. Add psutil to both backend pytest dep
steps (kept in shared shape), and guard the import with pytest.importorskip so a
minimal env without psutil skips these tests instead of erroring.
---------
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* feat: Implementation of the Portuguese (Brazil) language and VRAM/RAM monitor.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update studio/frontend/src/hooks/use-gpu-utilization.ts
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Update studio/backend/main.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Update studio/backend/main.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Update studio/backend/utils/hardware/hardware.py
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* Update studio/backend/utils/hardware/hardware.py
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Update studio/frontend/src/features/settings/components/usage-examples.tsx
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Update studio/backend/main.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Update studio/backend/utils/hardware/hardware.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Update studio/frontend/src/features/studio/sections/progress-section.tsx
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* fix: resolve automated review feedback on API shape
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix review issues for PR #6509: Cpu icon, VRAM percent, system polling
- model-inspector: use the exported CpuIcon (Cpu is not a Hugeicons export)
- app-sidebar: guard the VRAM percent on totalVram to avoid Infinity, and
reset the system poll cache only after each request settles so a slow probe
is reused instead of stacking overlapping requests
- use-gpu-info: populate CPU/RAM on hosts without a GPU
- progress-section: label GPUs by visible_ordinal instead of array index
- hub-page: base the RAM label on systemRamTotalGb
- usage-examples: emit JS sampling and tool options at the top level instead
of nesting them under extra_body (the JS SDK does not unwrap extra_body)
- main: read torch and transformers versions from package metadata instead of
importing the libraries on every system poll, and guard the VRAM math
against null values
- hardware: translate a leftover comment to English
* Harden /api/system: guard psutil.boot_time for PR #6509
Simulating restricted containers and some VMs (where psutil.boot_time can raise)
showed the /api/system endpoint would 500 on the unguarded boot_time call, the
same failure class already handled for cpu_freq, disk_usage, and Process. Wrap
boot_time and return uptime_seconds as null when it is unavailable so the sidebar
monitor degrades gracefully instead of breaking. Widen the uptime_seconds type to
number | null to match.
* Studio: make the sidebar hardware monitor a toggle (default on) for PR #6509
Adds a "Show hardware monitor" switch under Settings > Appearance > Layout,
backed by a localStorage preference (default on), mirroring the existing
useSidebarPin pattern. When turned off, the sidebar hides the VRAM/RAM meters
and useSystemInfo stops the 3s /api/system poll entirely, so no nvidia-smi /
SMI probes run while the monitor is disabled. Adds the en and pt-BR strings.
* Studio: default the sidebar hardware monitor to off (opt-in) for PR #6509
* Studio pt-BR: fix three small translation defects for PR #6509
- learningRateDescription: "5e-5 for CPT" -> "5e-5 para CPT" (leftover English)
- exportScopeRecents: "Recents" -> "Recentes" (untranslated)
- relativeMonthsAgo/relativeYearsAgo: add the missing space ("há {count} meses"/
"há {count} anos") so they no longer render as "há 3meses"
* Studio pt-BR: translate the last 10 fallback keys for PR #6509
Adds the settings.general.storage block (Armazenamento) and the
settings.chat.modelDisclaimer pair, so pt-BR now covers all en keys
(679/679) with no English fallbacks.
* Studio: hide sidebar VRAM row on CPU-only hosts for PR #6509
* Studio: tighten and trim code comments for PR #6509
* fix: UI issue in the stop button dialog box (fine-tuning)
* Studio pt-BR: translate 18 new keys from main merge (password dialog, GGUF export, dataset streaming) for PR #6509
* Rounding to GB
* Fix/adjust System resources tab for PR #6509
* Fix/adjust GPU monitor review items for PR #6509
* Fix/adjust remaining GPU monitor review items for PR #6509
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix/adjust MLX resource fallback for PR #6509
* floating window implementation
* resize for floating window
* Fix resource monitor review items
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Restore frontend optional dependency lock entries
* Make GPU selection tests hermetic
* Fix GPU monitor CI test failures
* Bound MLX GGUF reload smoke
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix MLX GGUF reload smoke exit
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
* Fix gpt-oss offload_embedding and generate() logits_to_keep on fused models
offload_embedding=True moved embed_tokens to CPU but left the input/output device-shuffling forward hooks commented out ('[TODO] Doesn't seem to work!'), so an eager forward/generate with CUDA input_ids hit the CPU embedding and raised a device-mismatch RuntimeError. Re-implement them in a testable helper _install_offload_embedding_hooks that saves the origin device on the module (the pre-hook returns a new tensor, so a device stashed on the original input is lost) and runs the lookup on the embedding weight's CURRENT device. Reading the weight device at call time (not a hard-coded cpu) also handles a non-quantized (bf16) embedding that a later model.to(...) pulls back onto the GPU, which the hard-coded version broke in the opposite direction.
unsloth_base_fast_generate injected logits_to_keep/num_logits_to_keep whenever an inner submodule forward accepted it, but transformers validates generate kwargs against the top-level prepare_inputs_for_generation (plus forward when it takes kwargs). On fused/PEFT-wrapped gpt-oss this raised 'model_kwargs are not used by the model: [logits_to_keep]'. Only inject when the top level would accept it, mirroring transformers _validate_model_kwargs. Behavior is unchanged for every model that works today.
Adds tests/test_offload_embedding_hooks.py and tests/test_generate_kwarg_gate.py.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* gpt-oss offload hooks: store origin device on the tensor, not the shared module
The pre-hook stashed the input device on embed_tokens itself, which races when
concurrent forwards share the module (serving). Ride it on the moved tensor and
read it from the post-hook args instead: stateless and thread-safe.
* Also strip mm_token_type_ids that generate() rejects (Qwen3-VL vision GRPO)
The vision processor (Transformers 5.x path) emits mm_token_type_ids, which
Qwen3-VL's generate() then rejects in _validate_model_kwargs on transformers
4.x, so vision GRPO fails at the first rollout:
ValueError: The following `model_kwargs` are not used by the model:
['mm_token_type_ids']
Unlike logits_to_keep this is an incoming kwarg rather than one we inject, so
drop it in unsloth_base_fast_generate when the top level generate does not
accept it, reusing the same _unsloth_generate_accepts_kwarg gate. Extends the
GPU-free gate test with the accept/reject mm_token_type_ids cases (7/7 pass).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Trim mm_token_type_ids comment
* Trim comments in gpt-oss offload/logits fix (comment-only)
* gpt-oss offload: return embedding output to the decoder device, not the input's
When offload_embedding moves the embedding to CPU, model.device can become CPU and
inputs then arrive on CPU, so returning the output to the input device left it on CPU
and the CUDA decoder hit a device mismatch. Capture the decoder device before offload
and always return there. This also drops the per-request tensor state (stateless, so
concurrent forwards stay correct).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* gpt-oss offload: refuse offload_embedding for tied word embeddings
Tied models share embed_tokens.weight with lm_head, so offloading the weight
to CPU strands the output projection there (device mismatch at generate) and
saves no VRAM since lm_head still needs it on GPU. Detect the shared weight via
get_output_embeddings and raise NotImplementedError instead of loading into a
crash. Untied models (gpt-oss, Llama-3.1-8B) offload unchanged.
Adds tests/test_offload_tied_guard.py.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* gpt-oss offload: skip embedding offload on fast_inference (vLLM)
vLLM manages its own weights, so offload_embedding cannot apply on the
fast_inference path (previously it was silently ignored). Disable it with a
notice, mirroring the WSL and Windows skips.
* Trim offload embedding comments (comment-only)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* gpt-oss offload: track decoder device live so it survives model.to()
The post-hook returned the embedding output to a device captured at load time.
If a model is loaded on CPU then moved with model.to(cuda), that device is
stale and the output lands on the wrong device. Read the decoder device live
from the (untied) output embeddings, keeping the captured device as a fallback.
Adds a stale-fallback regression test.
* Make generate-kwarg-gate cases pytest-collectable
Cases lived in run(), which pytest does not collect, so CI never exercised the
gate. Expose them as test_generate_kwarg_gate; still runnable via __main__.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* gpt-oss offload: skip a meta (disk-offloaded) lm_head as the return device
A device_map that disk-offloads an untied lm_head leaves its weight on the meta
device until that module's own hook runs, so reading it as the decoder device
would move real hidden states to meta. Skip meta (and a missing weight) and fall
back to the captured device. Adds a regression test.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Pin llm-compressor auto-install to a vetted version range
install_llm_compressor() auto-installs llm-compressor on first use of an FP8/FP4
compressed export when it is not already present. The install command used the bare
package name, so pip resolved to whatever the configured index served; a compromised,
dependency-confused, or inflated-version ("999.0.0") release could then run under the
Unsloth process at install and import time.
Bound the automatic install to a vetted range
(_LLM_COMPRESSOR_SPEC = "llmcompressor>=0.8.0,<0.13"), which the oneshot /
QuantizationModifier API this uses supports, so pip can no longer jump to an arbitrary
future or inflated version. An already-installed newer llm-compressor is still used
as-is (the import short-circuits), so this only constrains the auto-install, never a
user's own install.
Add UNSLOTH_DISABLE_LLM_COMPRESSOR_AUTOINSTALL=1 to forbid the automatic install
entirely and require a manual, vetted install, for locked-down or air-gapped
environments. Update the manual-install hints to the pinned spec.
Add tests/saving/test_llm_compressor_install_pin.py: static (ast) guards that the spec
stays a bounded pin, that the install command never passes an unpinned llmcompressor
literal, and that the opt-out env gate is evaluated before any install runs.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Loosen llm-compressor auto-install ceiling to <1.0 so new models still export
The earlier <0.13 ceiling was too tight: brand-new architectures (for example
Qwen3_5ForConditionalGeneration / qwen3_5, gemma-4 MoE) can require a newer
llm-compressor, and _unsloth_save_compressed_tensors already fails with
"requires a newer llm-compressor" when a scheme is unavailable. Capping the
auto-install at 0.12 would block getting that newer release and break compressed
export for new models.
Widen to llmcompressor>=0.8.0,<1.0. pip still auto-installs the latest 0.x
(where new-architecture support lands), while the <1.0 ceiling continues to block
a jump to an inflated-version ("999.0.0") or 1.0+ dependency-confusion release.
An already-installed newer llm-compressor is still used as-is (the import
short-circuits), and UNSLOTH_DISABLE_LLM_COMPRESSOR_AUTOINSTALL still forbids the
automatic install entirely for locked-down environments.
* Lower llm-compressor floor to 0.6.0 so supported old torch still resolves
The >=0.8.0 floor conflicts with the torch this install pins in its constraints
file. Unsloth supports torch>=2.4, but llm-compressor 0.7.0+ require torch>=2.7
(0.10+ need >=2.9, 0.12+ need >=2.10). On a supported torch 2.4-2.6 box pip then
has no candidate in [0.8.0, 1.0) and FP8/FP4 export fails before quantization.
Lower the floor to 0.6.0 (its metadata only needs torch>=1.7), which never
conflicts with any supported torch. pip still prefers the newest compatible
release, so modern torch continues to get the latest 0.x (0.12.0). The <1.0
ceiling that blocks an inflated-version supply-chain jump is unchanged.
Add a regression test asserting the floor stays <= 0.6.0.
* Cap llm-compressor auto-install ceiling to a vetted minor (<0.13)
A bare <1.0 ceiling still admits any 0.x, so an inflated "0.999.0" served by a
compromised or misconfigured index would win pip's highest-version selection --
the same dependency-confusion this pin is meant to block. Cap the ceiling to the
current vetted minor (<0.13) so that jump is blocked; bump it deliberately, after
vetting, when a newer llm-compressor is needed (e.g. for a brand-new architecture
scheme). Current new models are unaffected: 0.12.0 is < 0.13 and supports them.
The 0.6.0 floor (torch>=1.7 compatible) is unchanged, so resolution still works
across Unsloth's whole supported torch range (2.4 -> 0.6.0 ... 2.12 -> 0.12.0).
Add a regression test asserting the ceiling admits the current vetted release but
blocks an inflated 0.x and the next major.
* Trim comments in the llm-compressor pin (comment-only, no code change)
* Cap llm-compressor auto-install to the exact vetted patch (<=0.12.0)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
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* scan_packages: key baseline on matched-code hash
The baseline matched on (package, package-relative file, check), which
excluded the matched code, so a future finding of the same check in the
same file was suppressed regardless of what the code did. A malicious
future version of an already-baselined package could place a payload in
the same file under the same check and pass the enforcing gate.
Key the baseline on a hash of the matched code too. The hash is over the
deduped, sorted set of matched spans with L<NN>: line markers stripped, so
version bumps, line shifts and match reordering stay stable while new or
changed flagged code reopens the finding. Version is left out of the key so
routine dependency bumps do not reopen every entry. The hash is capped and
recomputable from the stored evidence.
Regenerate scan_packages_baseline.json against the current dependency set;
the hf-stack, studio and extras scan shards pass enforcing (no active
CRITICAL or HIGH).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scan_packages: refresh baseline for newer unsloth-zoo release
A newer unsloth-zoo published after the first regenerate added
tests/test_mlx_save_export_regressions.py, a benign test fixture
(temporary_location="/tmp/ignored") that trips the /tmp dropper check.
Regenerate the hf-stack shard against the current set so the entry is
allowlisted; studio and extras are unchanged.
* scan_packages: harden baseline loading against malformed JSON
Guard against a non-dict top-level baseline and non-dict entries so a
corrupt or hand-edited allowlist warns and fails closed instead of
crashing with AttributeError, and treat an explicit evidence: null as
empty.
* scan_packages: hash the full match set, keep indentation, strip only the marker
Address the evidence-hash review feedback:
- Capture every matching line, not the first three, so a payload appended
after existing matches in a baselined file and check reopens the finding
instead of riding the sample.
- Preserve leading indentation so a flagged line moved out of a guarded block
reads as changed.
- Strip only each span's prefix up to the first L<NN>: marker, so an L<NN>:
inside the matched code is kept and a change to it reopens the finding.
Evidence and its hash are stored in full and stay recomputable from the stored
field. Regenerate the baseline; hf-stack, studio and extras pass enforcing with
no active CRITICAL or HIGH.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scan_packages: bind baseline evidence to full matched code
Address review feedback on the evidence-hash baseline key:
- Split evidence only on real span delimiters (" | " before an L<NN>:
marker, or a newline), so a bitwise-or or union type in matched code
is no longer split apart into separate spans.
- Record matched lines in full (drop the 160-char per-line cap) and
record every distinct multiline match, so code appended past the cap
or a second cross-line match reopens the finding instead of riding the
first one.
- Give the large-JS-bundle and .pth base64-blob findings a content
digest instead of empty or prefix-only evidence, and record all .pth
import lines, so a changed bundle, blob or import no longer inherits a
baselined empty or truncated key.
- Warn when a loaded baseline has entries without evidence_hash so a
legacy baseline is regenerated rather than silently degraded.
Regenerate scripts/scan_packages_baseline.json against the current dep
set and add regression tests for each case.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scan_packages: harden multiline and duplicate evidence handling
Follow-up hardening so the evidence hash tracks the full matched code:
- For DOTALL patterns that match across lines, record every line the match
spans (not just the start line), so a change on a continuation line (the
URL inside a baselined C2 loop, a swapped credential path) reopens the
finding. A pathological greedy span is bounded to its head line plus a
digest of the rest.
- Keep duplicate spans in the canonical evidence so a second identical
matched line in a new code path changes the key instead of deduping away.
- Anchor the evidence prefix to strip only a genuine leading label or
line-number marker, leaving a marker-like "L<NN>:" inside raw .pth code
intact.
- Make the legacy-baseline warning explicit that entries without an
evidence_hash reopen rather than suppress under a coarse key.
Regenerate scripts/scan_packages_baseline.json (same finding set; entries
for same-file repeated checks are now tracked separately) and add tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scan_packages: bind every combo and large finding to its full content
Close the remaining asymmetric-evidence gaps so a changed payload cannot
ride a reviewed baseline entry:
- Digest a capped multiline span from the code without line markers, so a
pure line shift stays stable while a continuation-line change reopens.
- Give the "Unusually large executable .pth" finding a content digest
instead of keying on byte size and import-line count alone.
- Record both contributing signals for the JS credential+network stealer,
the shell credential+network and persistence-hook combos, and the hidden
network+exec docstring payload, so changing the network/exec side reopens.
- Allow punctuation in an evidence label prefix so a "network+exec:" label
is stripped and line shifts do not change the key.
Regenerate scripts/scan_packages_baseline.json and add tests for each case.
* scan_packages: bind remaining Python combos; key npm baseline on evidence
Python scanner: the openssl+key, anti-analysis, DNS-exfil and base64+exec+blob
combos recorded only one contributing signal, so a changed payload on the other
side could ride a reviewed baseline entry. Each now binds every co-occurring
signal (and the blob is digested, since it can sit on a separate line from the
decode call).
npm scanner: scan_npm_packages.py keyed its allowlist on (package, path,
pattern) only, the same coarse-key bypass the Python scanner just closed. Add an
evidence hash to the key (schema v3, fail-closed on older baselines) and store
full evidence. The committed baseline stays empty by design.
Regenerate scripts/scan_packages_baseline.json and add tests for each case.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scan_npm_packages: bind full blob evidence and harden baseline loader
Follow-up on the npm evidence-hash key:
- _evidence now records every match and, when a snippet is truncated for
display, appends a digest of the full match. The obfuscated-blob key was
hashing only the truncated first-match snippet, so a changed payload tail or
an appended blob in the same package/file/pattern could ride a reviewed entry.
- _load_baseline guards that the root is an object, entries is a list, and each
entry is a dict before reading it, so a malformed baseline warns and fails
closed instead of raising AttributeError.
Add tests for a changed blob tail reopening the key and for malformed entries.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scan_packages: symmetric baseline-loader guards; bind npm outbound host context
- Python _load_baseline now rejects a non-list "entries" with a warning instead
of raising TypeError, matching the npm loader.
- npm cred-surface-host (outbound) records the host with its URL path / fetch
call / host config, so a changed outbound path, headers or body reopens the
key rather than riding the bare host literal.
Add tests for both.
* scan_npm_packages: migrate v2 baselines and bind host-config outbound context
- _load_baseline now migrates schema v2 entries by recomputing the evidence
hash from stored evidence (with a legacy warning), matching the Python
loader, instead of discarding them; only pre-v2 basename schemas are rejected.
- The cred-surface-host (outbound) host-config branch now captures the whole
line (path, headers, body), so a changed outbound payload on the same
hostname line reopens the key instead of riding the bare host snippet.
Add tests for v2 migration and the host-config context binding.
* scan packages: bind PEM key bodies and npm windowed evidence to baseline keys
scan_packages: embedded-key findings now pin the full PEM block (BEGIN..END)
via a content digest, so a key body swapped under the same marker reopens the
finding instead of riding the unchanged BEGIN line. Single-line and DER keys
were already bound by their full matched line; marker-only references with no
END block (validation header lists) are unaffected, so the committed baseline
is unchanged.
scan_npm_packages: _evidence now digests the full containing line whenever the
shown snippet is only a window into it (short match on a long line, or a
truncated payload), so a changed payload tail outside the display window
reopens the key. The npm baseline is empty, so this changes no suppressions.
Adds regression tests for both cases.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scan packages: bind multi-line evidence and every blob to baseline keys
_extract_evidence now extends each single-line match over its bracket
continuations, so a multi-line call binds its argument lines and a changed
URL or body on a continuation line reopens the key. After the per-line pass it
also records cross-line matches the scan cannot otherwise see (a DOTALL regex,
or a multi-line construct appended under a check that already had a one-line
match), so an appended multiline payload reopens instead of riding the key.
_blob_digest hashes every large base64 blob (not just the first) for the
base64+exec finding and the .pth large-blob finding, so an appended or swapped
second encoded payload reopens; single-blob files keep the same digest.
scan_npm_packages _evidence digests the full logical line (the matched line
plus its bracket-continuation lines), so a multi-line fetch's option and header
lines bind and a changed payload on a following line reopens the outbound key.
Regenerated the Python baseline: same package/file/check set, 24 entries pick
up the wider multi-line evidence. Adds regression tests for each case.
* scan packages: stop giant greedy spans from binding a whole-file digest
When a greedy DOTALL pattern (reverse shell socket...subprocess, C2 loop) has
its anchor tokens far apart, the match span covers the whole file. Digesting
that span bound thousands of unrelated lines, so the evidence hash drifted on
any edit between the anchors (a dependency bump reshuffling the file), which
made a baselined finding reopen on an upstream release. The multiline pass now
skips an oversized span when the per-line pass already bound the signal lines,
so the evidence is the stable matched lines; a genuinely appended multi-line
construct stays under the cap and is still recorded.
Regenerated the Python baseline against Python 3.12 (the version the scan CI
shards run) so the resolved dependency set matches CI. Same package/file/check
set. Adds a regression test.
* scan packages: tighten evidence binding (order, string brackets, span size)
Address review follow-ups on the evidence extraction:
- _canon_evidence keeps discovery (line) order instead of sorting. Line-shift
stability already comes from stripping the L<NN>: markers, so order stays
significant and reordering matched lines (a multi-line call's arguments)
reopens the finding.
- _logical_line_end (Python) and _logical_line_text (npm) blank string literals
before counting brackets, so a ) inside a string argument does not close the
logical line early and drop later argument lines.
- The oversized-span skip now only drops a giant whole-file bridge (over 60
lines); a genuinely appended multi-line construct is recorded so its payload
reopens, rather than riding an existing one-line match.
- npm _logical_line_text binds the enclosing bracket group, so a host-config
object whose { is on a prior line binds its path/headers/body lines.
Regenerated the Python baseline (Python 3.12, matching the scan CI shards):
same package/file/check set. Adds regression tests for each.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scan npm packages: normalize and bound the logical-line digest
- _evidence whitespace-normalizes the logical line before digesting (matching
_evidence_hash), so a formatter-only reindent of the bound continuation lines
does not change the sha256 suffix and reopen an unchanged finding.
- _logical_line_text follows a bracket group to its close up to a hard 200-line
cap (digest input only), so a config object longer than the backward window
still binds its whole tail instead of silently truncating.
Adds regression tests. npm baseline is empty, so no regeneration is needed.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scan: cap single-line evidence and widen npm opener window
Cap each rendered evidence line at 200 chars in scan_packages.py: a long
or minified one-line file is shown as a bounded prefix plus a sha256 of the
full line, so a packed payload cannot dump unbounded content into the CI
logs or baseline while a change past the cutoff still changes the digest
and reopens the finding. Mirrors how the npm scanner bounds its snippets.
Widen the npm backward opener window (_MAX_CONT_LINES 12 to 200, symmetric
with the forward cap) so a host deep inside a large options object binds
the whole object, not just its own line; a changed path, header, or body on
any property reopens.
Regenerate the Python baseline with Python 3.12: only the protobuf
nspkg.pth and unsloth-zoo compiler.py evidence change, both from the new
line cap; the package/file/check key set is unchanged.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* scan: bind all host contexts, deep call continuations, far-back npm openers
Three fail-closed evidence gaps surfaced by review of the previous round.
scan_npm_packages.py: measure the forward bracket-group cap from the matched
line (idx + _MAX_GROUP_LINES) instead of the opener, so an opener found near
the widened backward limit no longer consumes the forward budget and drops
the path, headers, or body that follow the host.
scan_npm_packages.py: _outbound_host_evidence now records every outbound
context form for a host (URL, fetch-context, host-config), claiming each
non-overlapping match in form order, so a separate host-config request added
beside an already-baselined URL changes the evidence and reopens the key.
The common single-context case keeps its existing snippet.
scan_packages.py: follow a matched Python call over its continuations up to a
separate _MAX_CALL_LINES (40), decoupled from the 12-line display threshold,
so a multi-line requests.post( binds its whole argument list in the digest
and a changed body deep in the call reopens; bounded so a miscounted bracket
cannot swallow unrelated code. No baseline change: the current dependency set
has no matched call that closes between 13 and 40 lines, confirmed by a
Python 3.12 regenerate that produced a byte-identical baseline.
* scan: clamp npm depth, pin large bundles, follow backslash and bound .pth dump
Four fail-closed evidence gaps surfaced by review of the previous round.
scan_npm_packages.py: clamp the backward opener scan at depth 0 so a leading
unmatched closer (a preceding block whose opener is outside the backward
window) no longer drives depth negative and masks the real enclosing opener
that follows; a host-config object after such a block now binds and a changed
path reopens.
scan_packages.py: a large JS bundle now pins its whole content even when
another JS heuristic already fired. The bundle digest was only added when no
other finding existed; it is now appended to every finding's evidence on a
large bundle, so an unchanged obfuscation signature no longer lets changed
payload elsewhere ride the matched-line key.
scan_packages.py: _logical_line_end follows explicit backslash line
continuations, so a call split with a backslash before its parenthesis binds
the continuation line (URL/body) instead of returning at the zero-depth API
line.
scan_packages.py: the catch-all .pth import evidence is bounded through
_cap_line (prefix plus a digest of every line) so a large .pth of benign
imports cannot dump the whole member into the logs or baseline while an
appended or swapped import still reopens.
Baseline regenerated with Python 3.12: key set unchanged; one entry
(unsloth-zoo compiler.py) gains the backslash-continued banner lines now
bound by the continuation fix.
* scan: handle multi-line strings, lifecycle bodies, and de-quadratic evidence
Addresses a review round plus a performance audit of the evidence extractor.
Correctness (fail-closed):
- Bind the UNION of the single-line-blanked and multi-line-blanked bracket spans
in both scanners. The multi-line view blanks a triple-quoted Python string or a
backtick template literal that spans lines, so a `)` inside such a string no
longer closes the enclosing call early and drop later arguments. The single-line
view still counts a payload embedded INSIDE a string, so a dropper that hides a
call in a string keeps its argument lines bound. Taking the larger span never
shrinks the binding below either view, avoiding a fail-open regression.
- cred-env-in-lifecycle now pins the whole lifecycle script body via a digest, so
a changed non-token line (e.g. adding a curl exfil beside the token reference)
reopens, not just a change on the token line.
Performance / DoS (the scanner runs on attacker-controlled package files up to the
64 MiB / 16 MiB member caps, with no per-file time budget):
- _extract_evidence precomputes newline offsets once and maps match offsets with
bisect, removing the O(matches) whole-file content.count per match that made the
finditer fallback quadratic (a crafted minified file went from ~13 s/MiB and
hours at the cap to linear).
- npm _index_text splits and string-blanks the file once per evidence call instead
of per match (was O(matches x file) time and allocation).
- Bound evidence output: _MAX_EVIDENCE_SPANS (Python) and _MAX_EVIDENCE_MATCHES
(npm) fold the remainder into a digest so a file with thousands of matches cannot
build a multi-megabyte evidence/baseline blob while an added/removed match past
the cap still changes the key.
- _outbound_host_evidence caps matches per form and bounds the overlap claim so a
host repeated many times cannot make it quadratic.
No baseline change: a Python 3.12 regenerate is byte-identical (the union equals the
legacy single-line span for every current dependency file; the cap thresholds sit
above the largest real entry), so these are forward-looking hardening with no drift.
* scan: count all overflow matches, bind their context, blank JS regex literals
Follow-ups on the evidence output caps from the previous commit.
- _outbound_host_evidence no longer truncates each pattern's match iterator with
islice; it iterates every match and runs the overlap dedup only while the
display list is below the cap (so claimed stays bounded and the check is O(cap)
per match, not quadratic), folding every match past the cap into the overflow
digest. A host context beyond the 64th is counted again, so it reopens.
- The overflow digest (both scanners, via a shared _overflow_digest) binds each
overflow match's logical-line context, not just the regex match text, so a
changed payload on an over-cap line reopens even with the matched token
unchanged.
- The multi-line JS blanked view now blanks regex-literal bodies (tracking the
previous significant char for regex-vs-division and char classes for a literal
`/` inside `[...]`), so a `)` inside `/)/` no longer closes an outbound call
early. The bound span is the union of the single-line and multi-line views, so
an imperfect regex decision only ever grows the span, never shrinks it.
- The Python overflow digest canonicalizes spans (strips L<NN>: markers via
_canon_evidence) before hashing, restoring line-shift stability for the
over-cap region.
No baseline change: the overflow branches only trigger above the per-finding caps
(above the largest real entry), and the npm baseline is empty, so a Python 3.12
regenerate is byte-identical.
* scan: refresh baseline for ipython interactiveshell.py span drift
A newer ipython release changed the filesystem-enumeration span in
IPython/core/interactiveshell.py, so its content digest no longer matched the
baselined evidence and the studio scan shard flagged it as a non-baselined
CRITICAL. Regenerated with Python 3.12: only the ipython entry's evidence_hash
changes; the package/file/check key set is unchanged, and a studio enforcing
spot-check exits 0.
* Bound scanner evidence memory: stream overflow spans and cap lifecycle baseline size
scan_packages.py: _extract_evidence no longer materializes a rendered span
per match before slicing at the display cap. Once out holds _MAX_EVIDENCE_SPANS
spans, further spans fold straight into a running digest, so a minified or
padded file with hundreds of thousands of matching lines keeps memory bounded
to the display cap instead of the match count. The fold reproduces
_canon_evidence(" | ".join(overflow)) byte for byte, so the overflow digest and
every baseline key are unchanged.
scan_npm_packages.py: lifecycle-fetch-exec and cred-path-in-lifecycle stored the
entire install script body as evidence, so --write-baseline on a package with a
multi-MiB lifecycle script bloated the baseline JSON. Both now store a bounded
matched snippet plus a body-sha256 digest, matching cred-env-in-lifecycle. The
digest still binds the whole body, so a change to any line reopens the finding.
Adds tests for the streamed overflow bound and the bounded-but-reopens lifecycle
evidence. Baseline unchanged (byte-identical Python evidence; npm baseline empty).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Make npm bracket-group scan order-aware so a same-line close-then-open binds
_scan_group counted brackets with a per-line net (opens minus closes), which
collapses intra-line order: a line that closes a prior block and then opens the
host-config object on the same line, e.g. `}); const opts = {`, nets to <= 0, so
the trailing `{` was dropped and the group started at the hostname line. A
changed path/headers on the following lines then hashed to the same evidence and
could ride an existing baseline key.
Replace the net count with an order-aware (L, R) reduction per line (L closers
needing an opener to the left, R openers needing a closer to the right) and apply
it in order in both the backward and forward scans, clamping stray closers at 0.
The trailing opener now stays visible so the whole object binds and a changed
payload reopens. Per-line cost is unchanged (one C-level bracket findall), so the
existing outbound-host evidence is byte-identical on all prior shapes; only the
previously-dropped same-line case changes. Adds a regression test for it.
* Harden scanner evidence: bound memory and bind Python call tails fail-closed
Five fixes across both scanners, none of which change the committed baseline (a
full regen of all three pip shards produced a byte-identical 185-key set).
scan_npm_packages.py: _evidence and _outbound_host_evidence collected every regex
match into a list before applying the 64-match display cap, so a text file under
the size cap that repeats a cheap signal (such as NPM_TOKEN) millions of times
could allocate a huge list of re.Match objects and stall or OOM before the
overflow digest ran. They now stream from finditer and fold overflow as matches
arrive via a shared _fold_overflow_match helper, byte-identical to the prior
digest.
scan_packages.py:
- _extract_evidence kept inserting every unique over-cap span into the seen set
even after it stopped appending to the display list, so a generated file with
millions of one-line matches still grew that set unbounded. It now tracks spans
only while filling the display list (per-line spans are unique by line number,
so dropping them past the cap cannot miss a dedup).
- _scan_line_end counted brackets with a per-line net, so a continued statement
that closes on the same line it opens a flagged call (a leading "]" before
"requests.post(") had the call's open paren cancelled and bound only the opener
line. It now applies brackets in order via _bracket_lr (leading closers clamp at
0), matching the npm bracket fix.
- a single-quoted string continued by a trailing backslash was not tracked across
lines, so a close paren inside the continued string on the next line closed the
call early; _blank_code_strings now carries the continuation.
- a call with more argument lines than the soft cap was hashed only through the
cap, so a changed data=/headers tail past it stayed suppressed; a closing call
is now followed to its real close under a 200-line hard limit (a never-closing
opener still stops at the 40-line soft cap so it cannot swallow the file).
Adds regression tests for each. npm baseline is empty; the Python baseline is
unchanged (verified byte-identical by regenerating all three shards).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Bind giant DOTALL span anchors and add context to constant IOC evidence
Two fail-closed gaps where a changed payload could keep the same evidence hash
and stay suppressed by the baseline.
scan_packages.py: a giant greedy DOTALL span (a cross-line IOC match bridging
more than 60 lines, e.g. RE_TEMP_EXEC matching a /tmp line and a much-later
subprocess line) was dropped entirely once the per-line pass had any match, so an
appended cross-line payload -- a new /tmp line plus a later subprocess line that
share no single line, so the per-line pass never binds them -- produced the same
evidence and rode the key. The span is no longer dropped: it is bound by its head
and tail anchor lines plus a digest over just those (no line numbers, so a pure
line shift is stable). An added or moved anchor reopens the finding, while churn
in the bridged interior stays stable, so this does not reintroduce whole-file
drift. Two baseline entries (multiprocess test, unsloth-zoo scanner file) carry
such a span and are refreshed; a full three-shard regen confirmed only those two
keys change.
scan_npm_packages.py: known-ioc-string and cred-surface-host (always-bad) recorded
only the bare needle/host as evidence, so a reviewed tarball that kept the IOC
string while altering the adjacent fetch/exfil body produced an identical key.
They now bind matched-line context: known-ioc-string via the matched line and its
bracket-group continuation, cred-surface-host (always-bad) via the outbound call
context (path/headers/body, falling back to the bare host when not in an outbound
call). A changed payload on the same call now reopens.
Adds regression tests for each. npm baseline is empty; the Python baseline updates
only the two giant-span entries.
* Hash giant-span interiors, bind exec/eval trigger, JS content, intra-literal whitespace
Four fail-closed gaps where a changed payload could keep the same evidence hash.
scan_packages.py:
- A giant bridged DOTALL span was bound only by its head and tail anchors, so a
cross-line payload inserted into the bridged interior between unchanged outer
anchors kept the same key. The whole span content is now digested (via _render),
so any interior change reopens; a pure line shift stays stable because the digest
is over the markerless code. Two baseline entries (multiprocess test, unsloth-zoo
scanner file) carry such a span; with full-interior binding, multiprocess
resolved at two versions across shards now yields two distinct entries where the
anchor digest had collapsed them into one.
- The exec/eval-with-hidden-payload findings omitted the visible exec/eval line
that makes the hidden string executable, so flipping a harmless eval("1+1") to
exec(__doc__) kept the same key while arming the payload. The trigger line from
the real-code view is now bound into the evidence.
- check_js_file extracted evidence with the Python-string-aware extractor, which
does not blank JS backtick template literals, so a template containing a close
paren closed a call's bracket span early and omitted later option/body lines. The
full file content digest is now pinned to every JS finding (not just large
bundles), binding the whole call.
scan_npm_packages.py: the evidence canon collapsed all whitespace via split(),
erasing whitespace inside JS string literals along with harmless indentation, so a
changed request body 'a b' -> 'a b' kept the same key. A new _canon_preserve_strings
collapses whitespace only OUTSIDE string literals (reindent-stable) while preserving
it INSIDE single/double/backtick literals (intra-payload edits reopen). Used for the
evidence hash and the logical-line digests.
Adds regression tests for each. npm baseline is empty; the Python baseline updates
the two giant-span entries and adds the second multiprocess version's entry.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* MLX CI: find llama-cli where save_pretrained_gguf actually installs it
The GGUF reload step hardcoded the CWD-relative paths llama.cpp/llama-cli and
llama.cpp/build/bin/llama-cli, but save_pretrained_gguf builds and installs llama.cpp
under unsloth_zoo's LLAMA_CPP_DEFAULT_DIR ($UNSLOTH_LLAMA_CPP_PATH, else
~/.unsloth/llama.cpp), so the reload could not find the binary and failed the Mac M1
job with "llama-cli not found". _find_llama_cli now searches that install directory
(and honors the env override) before falling back to the old CWD layout, with a
recursive glob as a last resort. The search is a strict superset of the previous
paths, so it cannot regress a layout that already worked.
* MLX CI: return an absolute llama-cli path from the locator
Resolve the located binary to an absolute path. If UNSLOTH_LLAMA_CPP_PATH is a
relative directory (e.g. "."), Path(".") / "llama-cli" normalizes to the bare name
"llama-cli", and subprocess.run treats a separator-less argument as a PATH lookup
rather than a file to execute, raising FileNotFoundError. resolve() makes the returned
path absolute so it always runs the intended binary.
* MLX CI: give llama-cli EOF on stdin so GGUF reload cannot hang
With the binary now found, the GGUF reload actually invokes llama-cli and it timed
out after 300s generating 24 tokens on a 270m model, which is a stdin block rather
than slow generation: subprocess.run captured stdout/stderr but left stdin inherited,
so -no-cnv still left llama-cli waiting for interactive input. Pass
stdin=subprocess.DEVNULL so it receives an immediate EOF and runs the single prompt to
completion.
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Condense the verbose comments and docstrings added by the recent
chat template, GPT-OSS detection, PEFT tensor-parallel, and Studio
inference proxy fixes. Comments and whitespace only; no code changes.
* fix: keep LoRA reloads working with PEFT 0.19
* test: exercise the PEFT tensor-parallel symbol extractor
* test: prove the full PEFT tensor-parallel seam
* fix: harden PEFT tensor-parallel shims
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: fall back when PEFT tensor-parallel source inspection fails
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: imagineer99 <samleejackson0@gmail.com>
* studio: announce Cloudflare tunnel state and warn about public exposure on startup
The startup banner only printed a line when a tunnel URL was up, so a plain
`unsloth studio -H 0.0.0.0` launch silently created a public trycloudflare.com
URL with no indication that Studio had become reachable from the internet. The
only hint at the tunnel was the CLI help, shown when an invalid command was typed.
Make the banner always state the tunnel state for wildcard binds:
- ON: the public URL plus a warning that anyone with it can reach Studio from
outside the network, and that --no-cloudflare keeps it local-only.
- FAILED: requested but did not start (local network only).
- OFF: --no-cloudflare was passed (local network only).
Secure mode keeps its existing wording (the authenticated tunnel is intended and
--no-cloudflare is not valid there). Clarify the --cloudflare help text in both
the argparse and typer definitions. Default behavior is unchanged.
Also surface the state on the `unsloth studio run` banner, which runs the server
with silent=True and prints its own banner: it now calls _print_cloudflare_line
too, so the ON/OFF/FAILED notice and public-exposure warning are no longer
skipped on that path (previously it only echoed the URL when a tunnel was up).
For the OFF and FAILED notices, do not claim "local network only" when the
reachability probe just confirmed the raw port is reachable from the public
internet: --no-cloudflare and a failed tunnel disable only the Cloudflare link,
not the wildcard bind, so the message is reworded to flag the public raw port.
* Fix/adjust Cloudflare banner warnings for PR #6515
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix/adjust Cloudflare banner comments for PR #6515
* Fix/adjust IPv6 Cloudflare tunnel gate for PR #6515
* Fix/adjust Cloudflare review comments for PR #6515
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix silent run Cloudflare notice
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Add FP8/FP4 compressed export to save_pretrained_merged
Adds compressed-tensors export (for vLLM) to save_pretrained_merged /
push_to_hub_merged via llm-compressor, alongside the existing lora /
merged_16bit / merged_4bit / gguf / torchao paths:
model.save_pretrained_merged("model", tokenizer, save_method="fp8")
Supported save_method values: fp8 (FP8_DYNAMIC), mxfp4, nvfp4 (W4A4) and
mxfp8. The LoRA is merged to 16bit at save_directory, then a quantized
checkpoint is written to save_directory + "-<fmt>". nvfp4 needs a small
calibration set (defaults to ultrachat, overridable via calibration_dataset).
Notes:
- llm-compressor is installed lazily on first use, pinning the current torch
and transformers via a constraints file so they are not upgraded (a plain
install pulls transformers>=5 and breaks Unsloth).
- Quantization runs in a separate process (unsloth/_compressed_quantize.py,
launched by file path) so Unsloth's transformers attention patches do not
interfere with the forward llm-compressor runs during calibration, mirroring
how GGUF export shells out to llama.cpp.
- mxfp8 needs a newer llm-compressor (transformers>=5); it is recognised and
raises a clear error until that stack is available.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: main-process guard, calibration subsampling, tokenizer + dtype handling
- Route the 16bit merge through unsloth_generic_save for both LoRA and full
finetuned models, so non-PEFT models are written in 16bit consistently
instead of saving the original (possibly quantized) weights directly.
- Honor is_main_process: only the main process quantizes and writes the
compressed output, so distributed ranks do not race on the same dirs.
- Subsample an in-memory calibration Dataset before save_to_disk so large
training sets are not fully copied to a temp dir.
- Tolerate a missing tokenizer in the converter (data-free exports); still
require one for calibration based schemes.
- Open config.json via a context manager in both files.
- Drop the redundant nvfp4 entry from the unsupported-name check (fp4 covers it).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add direct LoRA to GGUF export and harden FP8/FP4 compressed export
- Run llm-compressor install and scheme check before the 16bit merge so
unsupported schemes (e.g. mxfp8) fail fast without writing a checkpoint
- Only the main process installs, merges, quantizes and uploads; isolate
hub pushes to a temp dir and clean all temp dirs in a finally
- Forward standard save kwargs (state_dict, max_shard_size, ...) to the merge
- Fall back to the first dataset split for Hub calibration ids
- Export LoRA adapters to GGUF via convert_lora_to_gguf.py: modernize
save_pretrained_ggml/push_to_hub_ggml and add save_method="lora" to
save_pretrained_gguf/push_to_hub_gguf; resolve base from the adapter config
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix LoRA GGUF shell-injection test and compressed export trailing-slash path
- Update tests/saving/test_save_shell_injection.py for the new delegation: the
LoRA to GGUF conversion now lives in _unsloth_save_lora_gguf, so assert it
passes argv as a list with no shell=True and that the legacy ggml wrappers
delegate to it instead of calling subprocess.Popen directly
- Normalize the local save_directory before building the "<dir>-<fmt>" sibling
so a trailing slash no longer nests the compressed output inside the 16bit dir
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Polish FP8/FP4 and LoRA GGUF export after review
- Warn (not silently downgrade) when an explicit quantization_method is not a
valid LoRA GGUF outtype; default stays f16
- Correct the inference hardware note: MXFP8 is 8-bit (cc >= 8.9), only FP4
needs Blackwell for full activation quantization
- Document that a local fp8/fp4 save keeps the 16bit merge at save_directory
and writes the quantized checkpoint to save_directory + "-<fmt>"
* Use sequential calibration pipeline and validate Hub access early
- nvfp4 calibration no longer forces the memory-hungry "basic" pipeline. The
quantization runs in a clean subprocess, so llm-compressor's default
sequential pipeline (layer-by-layer onloading) works and lets large models
that do not fit at once still calibrate; fall back to "basic" only if tracing
fails
- For push_to_hub compressed exports, create/validate the repo up front so a bad
token or denied repo fails before the merge and quantization instead of after
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden compressed export: explicit sequential pipeline, base-tokenizer calibration, GPU memory
- nvfp4 calibration now passes pipeline="sequential" explicitly (layer-by-layer
onloading) instead of relying on the inferred default, with a "basic" fallback
- Calibration datasets with a messages column no longer require a chat template:
base / non-chat tokenizers fall back to concatenating message contents
- Free the in-memory model's CUDA memory before the quantize subprocess loads its
own copy from disk (best-effort, single-device non-quantized only; restored
afterward), so a single GPU need not hold two copies at once
- Create the calibration temp dir in the system temp location instead of next to
the save directory, avoiding stray dirs in the workspace
* Free the failed calibration model before the basic-pipeline retry
In the sequential -> basic NVFP4 fallback, release the partially-processed model
and clear the CUDA cache before loading a fresh copy, so the retry does not
transiently hold two model copies on the GPU.
* Harden calibration data handling and compressed-export edge cases
- Calibration messages without a chat template now handle multimodal (list)
content, None content, and null message rows instead of crashing on join
- Raise a clear error when the calibration dataset is empty after subsampling
- Reset llm-compressor's global session before freeing the model in the
sequential -> basic NVFP4 fallback, so the old model is actually released
- LoRA GGUF export accepts a single-element list quantization_method
- Attach datasets metadata to the pushed repo on compressed hub exports
- Warn (instead of silently) if the model cannot be restored to its device
- Raise a clear error if the LoRA base model id cannot be determined
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Handle DatasetDict calibration, MoE routers, and MTP models in compressed export
- Reduce an in-memory DatasetDict calibration set to a single split before row
subsampling, so save_to_disk does not copy every split to the temp dir
- For MoE models, keep the router/gate unquantized and pass
moe_calibrate_all_experts so every expert is calibrated
- Warn when a model carries MTP / speculative-decoding tensors that the
compressed export does not include
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Support many more compressed-tensors schemes and address review
- Expand save_method to cover the full set of compressed-tensors preset schemes:
FP8 (dynamic/static/block), INT8, W8A8, W8A16, W4A16(+asym), W4A8, W4AFP8,
MXFP4(+A16), NVFP4(+A16), plus the gated MXFP8; calibration is used only for
the static-activation schemes (FP8 static, NVFP4)
- Broaden the near-miss save_method error to cover int/w-prefixed names
- MoE: also keep the Qwen shared-expert gate unquantized
- Strip non-model-input columns from already-tokenized calibration data so the
collator does not choke on a leftover messages column
- Forward the Hub token to the LoRA converter and the quantize subprocess so
gated/private base models and calibration datasets work without a global login
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Collapse compressed-tensors export help line so ruff-format converges
The print line in print_quantization_methods needed two ruff-format passes to
reach a fixpoint (merge implicit string concat, then collapse the single-arg
print). pre-commit.ci applies one pass per run, so it kept reformatting. Land
the converged single-line form directly.
* Add CPU-only regression tests for the export API
Cover all export paths without a GPU, for slow CPU-only CI:
- pure-function checks of the compressed-tensors scheme registry and save_method
normalization (aliases, calibration flags, near-miss errors)
- AST checks that every merged saver dispatches compressed export, the GGUF savers
expose the lora branch, torchao routes PTQ/QAT, the public methods stay attached,
and the export subprocesses remain shell-safe (argv list, sys.executable, no shell)
- monkeypatched dispatch checks that fp8/nvfp4/merged_16bit, the LoRA-GGUF outtype
resolution, and torchao PTQ/QAT reach the right helper with the right arguments
* Run the CPU-only export tests in consolidated CI
tests/saving is --ignored by the Repo tests (CPU) job, so the new GPU-free export
tests are added by path to consolidated-tests-ci.yml (collection sanity + Bucket-A run),
alongside the existing CPU saving tests, so they actually execute on CPU CI.
* Add GPU GGUF export + llama-cli inference smoke test
tests/saving/test_gguf_export_and_inference.py: skipif no CUDA. Trains a tiny
phrase-imprinting LoRA, exports a full-model q8_0 GGUF (merge -> convert_hf_to_gguf
-> llama-quantize), asserts a valid GGUF (magic + size), and - when a llama-cli
binary is available - runs one bounded generation (byte cap + watchdog kill) and
asserts the trained phrase round-trips through HF -> GGUF -> quantize -> inference.
The llama-cli step skips gracefully since the export only builds llama-quantize.
* Fix variant mismatch in compressed (FP8/FP4) export
save_pretrained_merged(..., save_method=fp8/nvfp4, variant=...) forwarded
the variant into the intermediate 16bit merge, so Transformers wrote
variant-named shards (model.<variant>.safetensors). The converter
subprocess then reloaded that directory with the default weight filenames,
so the compressed export failed after doing the merge.
Pop the variant out of the intermediate merge (internal staging that the
subprocess reloads with default names) and forward it via --variant so it
is applied to the final compressed checkpoint instead. Add a CPU AST guard
for the contract.
* Harden export paths from review
- install_llm_compressor: fall back to uv pip when this interpreter has no
pip seeded (uv-created/relocatable venvs), instead of failing with
No module named pip.
- LoRA GGUF export: if convert_lora_to_gguf.py is missing (a prebuilt or
reused CWD llama.cpp install carries binaries but not the converter
script), force a dedicated source checkout that ships it.
- push_to_hub_gguf(save_method=lora): return on non-main ranks, matching the
local save_pretrained_gguf lora branch, so only rank 0 converts/uploads.
- compressed export VLM detection: require a vision_config or a
ForVisionText2Text architecture; a bare *ForConditionalGeneration also
matches text seq2seq models (T5/BART/Whisper) and is no longer treated as
a VLM on its own.
- GGUF GPU smoke test: drop SFTConfig(max_length=1024), which raises under
newer TRL padding-free training; length enforcement is not needed here.
* Add imatrix option to GGUF export, enabling IQ low-bit quants
save_pretrained_gguf / push_to_hub_gguf gain imatrix_file:
None -> no imatrix (unchanged)
'/path' -> pass to llama-quantize --imatrix (a *.gguf_file is renamed to *.gguf)
True -> download the upstream unsloth/<base>-GGUF imatrix (imatrix_unsloth.dat or
.gguf_file), raising a clear error if none exists
An importance matrix unlocks the IQ low-bit quants (iq2_xxs, iq4_xs, ...), which were hard
disabled before. They are gated: requesting one without an imatrix raises a clear error.
- _resolve_imatrix_file resolves path/True (PEFT base first, normalized via get_model_name,
derives unsloth/<base>-GGUF, copies out of the HF cache before renaming *.gguf_file).
- IMATRIX_QUANTS registry replaces the old commented-out IQ entries; save_to_gguf accepts a
resolved imatrix and threads it into the quantize calls.
- The --imatrix flag is emitted by unsloth_zoo's quantize_gguf (companion change). save.py
fails fast with an upgrade hint if the installed unsloth_zoo lacks the imatrix kwarg.
Tests: tests/saving/test_imatrix_export.py (CPU: resolution, repo derivation, IQ gate,
--imatrix wiring) wired into CI; tests/saving/test_gguf_export_and_inference.py extended with
GPU iq2_xxs/iq4_xs export + inference. Verified end to end on Llama-3.2-1B: imatrix
auto-downloaded, iq2_xxs/iq4_xs exported and run via llama.cpp.
Note: requires the companion unsloth_zoo quantize_gguf imatrix change.
* Address imatrix/compressed review feedback: unsloth org GGUF repo, fail-fast, calibration split
- imatrix auto-resolve (imatrix_file=True): derive the upstream repo as unsloth/<base>-GGUF
instead of <org>/<base>-GGUF, so official bases (e.g. meta-llama/Llama-3.1-8B-Instruct) find
the matching Unsloth GGUF imatrix repo rather than failing on a nonexistent meta-llama/...-GGUF.
- Resolve/validate the imatrix before the 16-bit merge in save_pretrained_gguf, so a bad path or
an unavailable upstream imatrix fails fast instead of after a long, multi-GB merge.
- Compressed calibration: when a Hub dataset has no "train" split, resolve the first split name
and slice it, instead of materializing the whole dataset just to take num_samples rows. Keeps
the original materialize-then-subselect path as a last resort.
Tests: add unsloth/<base>-GGUF mapping for an official base id, and create the imatrix file in the
quantize_gguf flag test (quantize_gguf now validates the imatrix exists).
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: stop handing CI/user secrets to downloaded llama.cpp binaries
The macOS prebuilt path installs llama.cpp from the unslothai/llama.cpp
fork's latest (unpinned, mutable) release and then executes the
downloaded llama-server / llama-quantize binaries during install-time
validation. binary_env() built that child environment from a full
os.environ.copy(), so a compromised or tampered prebuilt would inherit
every secret in the process: HF_TOKEN and the workflow GitHub tokens in
CI, and HF / cloud credentials for end users running install.sh /
setup.sh.
We publish prebuilts daily, so pinning a release tag is not workable.
Instead, neutralise the impact: these binaries have no reason to read any
token, so strip secret-bearing variables (exact names plus
TOKEN/SECRET/PASSWORD/CREDENTIAL/PRIVATE_KEY/API_KEY markers) before
handing the env to a downloaded binary. The installer's own GitHub and
Hugging Face API calls read os.environ directly, so authentication and
release-API rate limiting are unaffected; PATH, LD_LIBRARY_PATH,
DYLD_LIBRARY_PATH and CUDA/ROCm vars are preserved. One change covers the
install-time validation path for all six macOS workflows and end users.
Follow-up (separate, sequenced): publish build-provenance attestations
from the fork's prebuilt workflows and verify them in CI, so a forged
release is rejected rather than merely starved of secrets.
* Strip KUBECONFIG, SSH_AUTH_SOCK, and PASSPHRASE-marked vars from binary env
Extend the deny-list per PR review: KUBECONFIG and SSH_AUTH_SOCK are
credential pointers/capabilities a downloaded binary never needs, and a
PASSPHRASE marker catches SSH_PASSPHRASE / GPG_PASSPHRASE. Tests updated.
* Studio: also scrub proxy/index env vars and URL-embedded credentials before running prebuilt binaries
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Scope mlx-ci secrets to the install + download commands for PR #6696
Drop the ambient step-level env block and pass GH/GITHUB/HF tokens only
on the installer and GGUF-download commands, so the directly invoked
llama-quantize / llama-server smoke runs see no secrets. The installer
still reads tokens from os.environ for the releases API and probe fetch.
* Trim verbose comments around the secret-env scrubber for PR #6696
Comment-only: condense the block comments added across this PR. Logic
unchanged (comment_tools.py check confirms code-only signature equal).
* Redirect HOME / cache pointers to an empty dir for prebuilt binaries (PR #6696)
Address Codex P2: stripping token env vars still let a tampered binary
read on-disk token stores (~/.cache/huggingface/token, ~/.aws/credentials,
~/.config/gh) through $HOME and the cache/config pointers. Point HOME plus
the HF / XDG / Windows home pointers at a single empty throwaway dir for
the downloaded-binary env. Defense in depth: a binary resolving the real
home via getpwuid is out of scope and needs OS sandboxing.
* Close residual credential-probe gaps for PR #6696
Address the latest Codex review:
- Strip token-only URL userinfo too (scheme://ghp_token@host), not just
the user:pass form.
- Redirect HOMEDRIVE/HOMEPATH alongside USERPROFILE so a Windows binary
cannot reconstruct the real profile from %HOMEDRIVE%%HOMEPATH%.
- Drop explicit credential-file pointers (NETRC, PIP_CONFIG_FILE,
DOCKER_CONFIG, GIT_CONFIG_GLOBAL) that live outside HOME.
- Probe ldd with a secret-free env: linux_runtime_dirs ran ldd on the
untrusted prebuilt with the inherited os.environ, and ldd may execute
the binary, so it could observe HF_TOKEN/GITHUB_TOKEN during the probe.
Factored the shared scrub into secret_free_environ().
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Separate token-bearing install from binary smoke; drop CI command files (PR #6696)
Address the two P1s in the latest review:
- mlx-ci: GitHub bakes secrets into the run-script text, so inline token
assignments in a step that later runs the prebuilt let a tampered binary
read them from the script. Split into a token-bearing install + download
step that never launches a binary, and a secret-free smoke step that runs
llama-quantize / llama-server.
- secret_free_environ now drops the GitHub Actions command files
(GITHUB_ENV, GITHUB_PATH, GITHUB_OUTPUT, GITHUB_STEP_SUMMARY, BASH_ENV) and
the smoke step unsets them, so a tampered prebuilt cannot inject PATH/env
into the later token-bearing MLX steps.
* Run the prebuilt smoke last, after all token-bearing steps (PR #6696)
Address the P1 workspace-poisoning vector: even with no secrets in its env,
a tampered prebuilt could edit the checkout or installed modules, and the
later HF_TOKEN MLX steps would then execute that poisoned code on push
builds. Move the prebuilt install + smoke to the end of the job so the
untrusted binary runs after every token-bearing step, leaving nothing for it
to corrupt. The MLX GGUF reload uses a source-built llama-cli, not this
prebuilt, so nothing depends on the earlier position.
* Trim comments around the secret-env scrubber and prebuilt CI steps (PR #6696)
Comment-only: condense the security-rationale block comments and merge the
duplicated prebuilt-step description in mlx-ci. Logic unchanged
(comment_tools.py check confirms the code-only signature is equal; install
suite still passes).
* Authenticate the GGUF export release-API lookup with the read-only GITHUB_TOKEN (PR #6696)
* Rename env scrubber off the secret-named identifier CodeQL flags as a clear-text sink (PR #6696)
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix fast_inference crash on ABI-broken vLLM: force-load compiled extensions in the broken-vLLM probe
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Broaden broken-vLLM probe: catch non-libcudart .so failures and _moe_C_stable_libtorch
* Revert stray reformat of the PDL fix log line
* Trim verbose comments in the broken-vLLM probe
* Drop non-existent vllm._moe_C_stable_libtorch from the broken-vLLM probe
* Shorten comments in broken vLLM extension detection
Condense the docstrings and inline comments for the lazy-loaded vLLM probe
and the new regression test while keeping the rationale. Comments only, no
code changes (verified with an AST signature check and the existing tests).
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Two intermittent Studio CI failures, both runner-environment flakes unrelated
to test logic:
Windows 'Studio install + inference without Visual Studio': the 'Hide Visual
Studio + CMake' step renames C:\Program Files\Microsoft Visual Studio to
simulate a host with no build tools. A background handle on a Program Files
directory (Defender scan or an MSBuild node) makes Rename-Item intermittently
fail with 'Access is denied', and $ErrorActionPreference = Stop turns that into
a hard job failure. Wrap the VS and cmake renames in both Hide steps in a short
Rename-WithRetry (6 tries, 3s apart) to ride out the transient lock.
macOS 'Chat UI Tests': the re-login goto to /login can be interrupted by the
SPA auth guard redirecting to the same /login URL, which Playwright reports as
'Navigation to .../login is interrupted by another navigation to .../login'.
The goto already tolerated ERR_ABORTED; broaden it to also tolerate the same-URL
interrupt (the password-field wait right after confirms we landed on /login),
and add the same signature to the two Playwright flake-retry harnesses as a
safety net for any other navigation.
Validated: playwright_chat_ui.py parses + byte-compiles, both workflow YAMLs
parse, bash -n on the retry harnesses, PowerShell AST parse on all pwsh steps,
and a functional check of Rename-WithRetry (succeeds, and rethrows after
exhausting retries).
* fix: wrap unprotected evaluate() calls with robust_evaluate() to handle navigation context loss
Fixes PR #5911 - Playwright UI test error: 'Execution context was destroyed'
The test had several direct page.evaluate() and locator.evaluate() calls that
weren't wrapped with robust_evaluate(), which retries when navigation destroys
the execution context mid-operation.
Changes:
- Wrap picker_visible_text() evaluate in robust_evaluate()
- Wrap _bubble_count() evaluate in robust_evaluate()
- Wrap assistant text query in robust_evaluate()
- Wrap theme_item click evaluation in robust_evaluate()
- Wrap background color/theme query in robust_evaluate()
This ensures all execution context losses from concurrent navigation are
properly caught and retried with exponential backoff, preventing transient
failures in the UI test suite.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: revert robust_evaluate on theme_item.evaluate per Codex review
The theme_item.evaluate('el => el.click()') is side-effecting — retrying
after a context loss could double-toggle the theme. It's already inside
a 3-attempt try/except loop that handles click failures gracefully.
The other 4 changes (all read-only queries) remain wrapped in
robust_evaluate() since retrying them is safe.
* fix: wrap remaining chat UI evaluate
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: imagineer99 <samleejackson0@gmail.com>
* feat: improve Unsloth Studio chat title generation quality
* fix: address self-review (guard echoed role labels before punctuation stripping)
* Address title generation review feedback
Consolidate the echo guard into a single leading-label check (now also
covering base and lora) and drop the post-punctuation duplicate that
could never match a colon once punctuation is stripped. Swap the
slice-based first-assistant lookup for an indexed find to avoid copying
the messages array, and note the brace counter's assumptions in the
test helper.
---------
Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Co-authored-by: Daniel Han <unslothai@gmail.com>
* Fix offline checkpoint load/export failing with "tokenizer is weirdly not loaded"
Loading a fine-tuned checkpoint with no internet (e.g. a Studio export) crashed
with "Unsloth: The tokenizer is weirdly not loaded? Please check if there is one."
For a LoRA adapter the loader reassigns model_name to the base model repo id and
only keeps the local checkpoint dir as tokenizer_name when it contains
tokenizer_config.json, tokenizer.json AND special_tokens_map.json. Modern
tokenizers (e.g. Gemma) store special tokens inside tokenizer_config.json and
omit special_tokens_map.json, so tokenizer_name fell back to the base repo id.
The tokenizer/processor loads in vision.py then hit the Hub with no
local_files_only, so with no network they failed (AutoProcessor) or hung for
minutes (AutoTokenizer) even though every file was already cached.
loader.py: keep the local checkpoint dir as tokenizer_name when it has a
tokenizer config plus the actual tokenizer files (tokenizer.json / tokenizer.model
/ vocab files); special_tokens_map.json is no longer required.
vision.py: compute an effective local_files_only (explicit kwarg plus the
HF_HUB_OFFLINE / TRANSFORMERS_OFFLINE env vars, mirroring loader.py and
diffusion.py) and thread it through every AutoConfig, AutoProcessor,
AutoTokenizer and the manual VLM processor fallback, including the
hf_hub_download in that fallback (which now prefers a local file). When a load
fails and no offline env var is set, retry against the local cache. The retry
forces HF offline mode because local_files_only alone does not stop
AutoProcessor / AutoTokenizer from issuing a /api/models request during class
resolution. The final error now explains the offline/cache cause instead of the
misleading "weirdly not loaded" message.
studio export: probe Hub reachability once per checkpoint load and pass
local_files_only when offline so exports use the local checkpoint dir / cache
instead of hanging or crashing with no internet.
Online behavior is unchanged: the new flags default to off and the retry only
runs after a network related failure.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: safer offline forcing, cached fallback config, proxy-aware probe
Follow-up to the offline checkpoint load fix, addressing review feedback:
- vision.py: only flip the process-wide HF offline flag when offline is actually
requested (local_files_only / env) or after a real network failure, never
pre-emptively while we might be online. The flip is now guarded by a lock +
depth counter so nested or concurrent windows restore the flag correctly
(no stale value).
- vision.py: guard the get_auto_processor fallback so a network error there
returns None and the local-cache retry still runs instead of escaping.
- vision.py: in the manual VLM processor fallback, read tokenizer_config.json
via hf_hub_download(..., local_files_only=...) so a cached repo-id config is
still resolved offline and the model-specific image/video tokens are restored.
- studio export: make the reachability probe proxy aware (probe the configured
HTTP(S) proxy egress, honour NO_PROXY, use the endpoint port) so a proxy-only
setup is not wrongly marked offline; allow UNSLOTH_OFFLINE_PROBE=0 to disable.
- studio export: run the audio/vision type-detection probes inside the
forced-offline window when offline, so their config/tokenizer reads hit the
local cache instead of waiting out connection timeouts.
Online behavior remains unchanged.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: gate offline retry, safer tokenizer_name pop, skip audio net probe offline
- vision.py: only force the process-wide HF offline flag on the tokenizer
retry when offline was requested or the captured primary error is actually
network related, so a permanent tokenizer error no longer toggles global
offline mode for other concurrent loads.
- loader.py: always pop tokenizer_name out of kwargs and let a caller-supplied
value win, avoiding a "multiple values for keyword argument 'tokenizer_name'"
TypeError when it is also passed explicitly downstream.
- model_config.py / export.py: add local_files_only to detect_audio_type so the
raw requests.get tokenizer_config fetch is skipped offline (it ignores the HF
offline flag), and pass it from the export probe.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: classify LocalEntryNotFoundError as offline-related
huggingface_hub's LocalEntryNotFoundError subclasses FileNotFoundError, so the
"not isinstance(cur, FileNotFoundError)" guard in _is_offline_related_error was
swallowing it and it could never be recognised as offline, despite being listed
in the network error types. It means "not in cache and the Hub is unreachable",
which is genuinely offline. Capture the class into an isinstance-checkable tuple
(empty, hence a no-op, if the import is unavailable) and exclude it from the
FileNotFoundError guard, so a real offline failure now triggers the local-cache
retry while a plain missing-file error still propagates.
* Address review: require merges.txt for BPE, status-gate HTTP errors, isolate local-only audio cache
- loader.py: a local dir with vocab.json but no merges.txt (and no tokenizer.json)
is not a loadable BPE tokenizer, so do not treat it as self-sufficient; require
merges.txt alongside vocab.json in both gate blocks, otherwise fall back to the
base model tokenizer as before.
- vision.py: _is_offline_related_error no longer buckets every HfHubHTTPError /
requests HTTPError as offline. HTTP errors are judged by status code: only a
transient 5xx triggers the forced local-cache retry, while 401/403 (auth/gated)
and 404 (missing) propagate as the real error instead of being masked. Hard
signals (connection/timeout/OfflineModeIsEnabled/LocalEntryNotFoundError) still
classify as offline.
- model_config.py: include local_files_only in the audio-detection cache key so a
local-only (offline) negative result cannot be reused by a later online probe,
which would otherwise route an audio model through the text loader until restart.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address re-review: fix studio test stubs, force offline env in probe window, drop redundant retry
- studio/backend/tests/test_vision_cache.py: the three _detect_audio_from_tokenizer
stubs were called with the new local_files_only kwarg and raised TypeError, failing
Backend CI. Add local_files_only to the stub signatures and add a test that a
local-only negative does not poison a later online audio probe.
- export.py: the type-detection probe window now also sets HF_HUB_OFFLINE /
TRANSFORMERS_OFFLINE env vars (saved/restored), not just the in-process flag.
transformers_version._load_config_json / _check_tokenizer_config_needs_v5 gate
their urllib fetches on the env vars, and is_vision_model may spawn a subprocess
that inherits os.environ but not the in-process flag; without the env vars a
probe-detected offline export could still block on a network timeout.
- vision.py: only retry the processor load when the first attempt was online and
failed with a network error. When local_files_only was already requested the first
attempt was forced offline, so the previous retry just repeated identical failing
work before the last-resort path.
- model_config.py: correct the _audio_detection_cache type annotation to the 3-tuple
key (name, token_fingerprint, local_files_only).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address review: thread-safe probe-offline env window, clear error for local dir without config
- export.py: guard the HF_HUB_OFFLINE / TRANSFORMERS_OFFLINE mutation in
_force_offline_probe_window with a lock + depth counter (mirrors _force_hf_offline),
so concurrent / nested export probes only flip on first entry and restore on last
exit. This prevents overlapping export requests from permanently poisoning those
env vars or restoring a stale value.
- vision.py: in the VLM processor fallback, when tokenizer_name is a local directory,
read its tokenizer_config.json directly and raise a clear FileNotFoundError if it is
absent, instead of handing the local path to hf_hub_download (which would treat it as
a repo id and raise a confusing HFValidationError / RepositoryNotFoundError).
hf_hub_download is now only used for actual repo ids.
* Address review: classify raw socket.gaierror DNS failures as offline
Add the platform-specific getaddrinfo / DNS-resolution wording to the offline
detection list in _is_offline_related_error so a bare socket.gaierror (an OSError
subclass) is recovered from the local cache: "Name or service not known" and
"Temporary failure in name resolution" (Linux) and "nodename nor servname
provided" (macOS). Genuine non-network OSErrors (disk full, permission denied)
and plain FileNotFoundError still propagate.
* Address review: retry degraded VLM offline, force offline for text export + patch-tokenizer fallback
- vision.py: a degraded VLM processor (text-only, no image_processor) whose manual
fallback fails offline used to be kept, so image inputs broke even with cached
files. _construct_vlm_processor_fallback now returns its failure error;
_acquire_processor surfaces it, and the caller retries forced-offline when the
result is None OR a degraded VLM and the failure was network related, keeping the
original result if the retry is not strictly better (never regress). The retry is
still gated on an online first attempt + offline-related error so a permanent
error never flips the global offline flag.
- vision.py: wrap the patch_tokenizer except-branch AutoTokenizer.from_pretrained in
the same forced-offline-on-network-error pattern as the primary / last-resort
loads, so an offline export where patch_tokenizer raises does not hang or fail.
- export.py: force HF offline around the two FastLanguageModel loads (text and SNAC)
when the probe detected offline. Their text tokenizer path (load_correct_tokenizer
-> AutoTokenizer) does not forward local_files_only, so without this a text export
could still contact the Hub. Added a small _offline_window_if helper reused by the
probe and load windows.
* Consolidate offline loading into one entry-point decision
Decide offline once per entry point instead of at every HF call site. The
prior approach threaded local_files_only into ~15 scattered config / tokenizer
/ processor / weight loads, each wrapped in its own try-online, classify-error,
retry-forced-offline dance, which is what kept surfacing "another call site you
missed", "another error shape misclassified", and global-flag thread-safety in
review.
FastLanguageModel / FastModel / FastBaseModel.from_pretrained now share an
@_offline_aware_load decorator: when offline (explicit local_files_only kwarg or
HF_HUB_OFFLINE / TRANSFORMERS_OFFLINE env) it sets local_files_only and runs the
whole load inside one _force_hf_offline() window so every nested HF call inherits
it; when online it runs normally and, only if the load fails with a genuinely
network-related error, retries once forced-offline. The online path is unchanged
(no probe added) and 401 / 403 / 404 / permanent errors still propagate.
Centralise the offline helpers in loader_utils.py as the single source of truth
(shared by loader.py, re-exported from vision.py, and reused by the Studio
exporter):
- _force_hf_offline now sets the HF_HUB_OFFLINE / TRANSFORMERS_OFFLINE env vars
AND the in-process huggingface_hub / transformers flags, refcounted under one
lock so nested / concurrent windows restore correctly. Setting the env vars
covers env-gated urllib probes and spawned subprocesses too.
- _get_effective_local_files_only, _is_offline_related_error (unchanged
classifier, retains the 5xx-vs-4xx, LocalEntryNotFound and gaierror handling),
_offline_aware_load, and _resolve_checkpoint_tokenizer_name.
loader.py: wrap both entry points; drop the two duplicated env-var fallback
blocks and the two byte-identical local-tokenizer-gate blocks (now
_resolve_checkpoint_tokenizer_name).
vision.py: drop the per-site force_offline params and the three retry gates
(processor, patch_tokenizer fallback, last-resort). They now just surface the
underlying error so the single entry-point safety net retries forced-offline. A
network fallback error now takes precedence over a permanent primary error so the
offline retry still fires when the manual VLM fallback needs cached repo files.
studio/backend export.py: reuse the unified core _force_hf_offline (env + flags)
and drop the duplicate probe-window primitive; the snac / text branches no longer
need their own window. model_config.py: also gate the raw requests.get audio
fallback on the HF offline env vars so it is covered even without the kwarg.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address 10-reviewer P1 findings: vision cache split, PEFT offline, retry OOM
Split the Studio vision-detection cache by local_files_only, mirroring the audio
cache fix. is_vision_model / _is_vision_model_uncached / _raw_config_has_vision_config
/ load_model_config now thread local_files_only, the cache key includes it, and the
exporter passes it. Offline detection also skips the transformers-5 network
subprocess and stays on the local cache, so an offline negative can no longer be
keyed under the online entry and poison a later online probe. Adds a regression
test mirroring the audio poison test.
Forward local_files_only to both PeftModel.from_pretrained adapter-attach sites in
loader.py so a cached remote LoRA adapter resolves from the local cache under
explicit local-only / offline loads (defence-in-depth alongside the forced-offline
window).
_offline_aware_load: run the forced-offline retry OUTSIDE the except block and
collect + empty the device cache first. An except-scoped exception keeps its
__traceback__, which pins the failed attempt's frame locals (a partially loaded
model) until the block exits; loading the model again while that copy is still
alive could OOM a large VLM. Letting the except block close drops the traceback so
the partial load is freed before the retry reallocates.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address Codex review: env-offline cache key + rebuild HF sessions in offline window
Key the Studio audio and vision detection caches on the EFFECTIVE offline state
(local_files_only OR the HF offline env vars), not just the kwarg. detect_audio_type
and is_vision_model both skip the remote fetch / network subprocess when
HF_HUB_OFFLINE / TRANSFORMERS_OFFLINE is set even with the default
local_files_only=False, so the result reflects offline; storing it under the online
(False) key let an env-offline negative poison a later online lookup once the env var
was cleared. Both now compute effective_offline once and use it for the cache key and
the downstream call. Adds a regression test for the env-offline dimension.
_force_hf_offline now rebuilds huggingface_hub's cached sessions on enter and exit
(best-effort _reset_hf_sessions). On hub 0.x the offline adapter is baked into the
per-thread requests.Session at creation, so flipping the constant alone leaves an
already-cached online session able to hit the network inside the window (and an
offline one stuck offline after restore); resetting forces the next get_session() to
match the current flag. On hub 1.x offline is checked dynamically per request, so
reset_sessions does not exist and the helper is a safe no-op.
The third review point (release the failed load before retrying) was already fixed in
af0f58a: the forced-offline retry now runs outside the except block and frees the
device cache first, so the failed attempt's traceback-pinned partial model is
released before the retry reallocates.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Align Studio _env_offline parsing with the canonical offline helper
model_config._env_offline gates the raw requests.get tokenizer-config fallback in
detect_audio_type and the audio/vision detection cache keys, but it only accepted
unstripped "1"/"true"/"yes". unsloth's offline helpers (loader_utils._env_says_offline
and the from_pretrained env fallback) accept the canonical set {1,true,yes,on} after
strip + lowercase, so HF_HUB_OFFLINE=on or HF_HUB_OFFLINE=" 1 " was treated as offline
by the loaders but online here, leaving the raw network fetch reachable while
"offline". Use the same strip + lowercase {1,true,yes,on} set. Adds parsing tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix lint: drop dead offline-helper re-exports from vision.py
The import-hoist verifier (scripts/verify_import_hoist.py) flagged vision.py's
re-export block as HOISTED-IMPORT-UNUSED blockers: it imported eight offline
helpers from loader_utils but only used three internally
(_get_effective_local_files_only, _is_offline_related_error, _offline_aware_load).
The other five were imported purely to preserve `from unsloth.models.vision import
X`, but nothing imports four of them from vision, and loader.py already imports
_resolve_checkpoint_tokenizer_name straight from loader_utils.
Import only the three names vision.py actually uses, and point the Studio exporter
at the canonical source (from unsloth.models.loader_utils import _force_hf_offline)
instead of re-exporting it through vision. loader_utils stays the single source of
truth; no behaviour change.
* Address Opus review: chain probe errors, unify env-offline, status-less HTTP
Chain the original AutoConfig/PeftConfig probe exception into the combined
RuntimeError in both FastLanguageModel.from_pretrained and FastModel.from_pretrained
(`raise RuntimeError(combined_error) from (autoconfig_exc or peft_exc)`). The probes
caught every Exception and stringified it, so the re-raised RuntimeError had no
__cause__/__context__ and _is_offline_related_error could not classify it -- the
network-down-but-cached auto-retry never fired for these entry points. With the
cause chained, the decorator sees a ConnectionError/LocalEntryNotFoundError/5xx and
retries forced-offline from cache; a permanent cause (404 / bad config) is still not
offline-classified and propagates without a wasted retry.
Unify the third offline-env parser: studio/backend/utils/transformers_version._env_offline
now uses the canonical {1,true,yes,on} + strip + lowercase set (matching
loader_utils._env_says_offline and model_config._env_offline), so HF_HUB_OFFLINE=on
or " 1 " no longer leaks the direct urllib metadata fetches to the network.
_is_offline_related_error: a status-less HTTP error (no response / unparseable code)
now falls back to the network-wording check instead of being dropped, so a transient
HTTP failure with clear "couldn't connect" wording is treated as offline. HTTP errors
with a real status code still decide by code (4xx propagates, 5xx is offline).
* Condense offline-loading code comments, drop dead helper, dedupe import for PR #6554
* Add unit tests for offline-loading helpers for PR #6554
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Guard load cleanup with try/finally and add retry-contract tests for PR #6554
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add gc.collect retry-step test for PR #6554
* Tighten offline-loading comments and docstrings for PR #6554
* Raise the both-config-failed error before model-type lookup so offline retry fires for PR #6554
* Prefer offline cause for retry and bound export reachability probe for PR #6554
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Skip remote mapper while offline, harden text-load cleanup, and stop stacked offline retries for PR #6554
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Surface VLM fallback offline errors, probe offline before export version activation, and restore progress bars across retries for PR #6554
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Restore offline env after export version activation so the persistent worker re-decides per load for PR #6554
* Classify socket.gaierror and urllib URLError as offline by type for PR #6554
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Probe offline around export load preflights and never offline-retry TLS failures for PR #6554
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Force in-process offline for export preflights, verify proxy egress in probe, and skip caching offline version negatives for PR #6554
* Snapshot offline constants before forcing env and require local processor files for VLM checkpoints for PR #6554
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---------
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* Keep pad-named pad_tokens; defer pad repair to shared unsloth_zoo.pad_token
A pad-named token (e.g. <|vision_pad|>) is a valid pad. The narrow fallback that
stripped vision pad tokens on text-only models is now a no-op; the active path
delegates to the shared fix_pad_token in unsloth_zoo, which keeps pad-named tokens
and only heals missing / eos-collision / out-of-range pads.
This fixes the Qwen3-4B-Base load crash (its config ships pad_token=<|vision_pad|>):
the old swap could not find a safe text pad (eos is <|endoftext|>, no unk_token) and
left the tokenizer broken. Removes the unused _VISION_PAD_TOKENS / _SAFE_TEXT_PAD_TOKENS
sets. Tests updated.
Pairs with unslothai/unsloth-zoo#831.
* Remove _fix_vision_pad_token; inline the no-op fallback
A pad-named token (e.g. <|vision_pad|>) is a valid pad, so the old vision-pad swap
helper has no purpose. _fix_pad_token now returns the tokenizer unchanged when the
shared unsloth_zoo.pad_token module is unavailable, instead of routing through a
no-op helper. Test WANTED set updated.
* Installer: make UV_OVERRIDE space-safe on Apple Silicon (#6503)
On Apple Silicon, install.sh exports UV_OVERRIDE pointing at the bundled
overrides-darwin-arm64.txt. uv splits UV_OVERRIDE on whitespace, so a repo
cloned under a path containing a space (e.g. /Users/me/Open Source/unsloth)
truncates the value and every later uv call aborts with
'error: File not found: <truncated>' (the PyTorch install step in #6503).
Copy the overrides file into a space-free temp dir and point uv at the copy
when the path contains a space, mirroring the macOS/Linux handling already
merged for the Python installer in #6534. The temp dir is removed in the
exit trap, and the code falls back to the original path when no space-free
temp dir is available, so the no-space and non-macOS paths are unchanged.
Adds tests/sh/test_install_uv_override_space.sh, which extracts and runs the
install.sh hardening block and checks the spaced, no-space, and
spaced-TMPDIR fallback cases.
* Installer: match all whitespace (not just spaces) in UV_OVERRIDE handling
uv splits UV_OVERRIDE on any whitespace, so use the POSIX class
*[[:space:]]* rather than a literal space in install.sh (catches tabs and
newlines in the path too) and the matching test assertions. Use the portable
awk bracket expression [$] instead of \$ in the extraction so the test runs
the same under BSD awk (macOS) and GNU awk (Linux). Adds a tab-in-path case.
* Installer: clear _UV_OVERRIDE_TMPDIR before the exit trap
The exit trap rm -rf's _UV_OVERRIDE_TMPDIR. Initialize it to empty before
registering the trap so an inherited environment value can never be removed;
only a temp dir this script creates (Apple Silicon, spaced path) is cleaned.
Adds a structural test asserting the init precedes the trap.
* Run the install.sh UV_OVERRIDE space test in CI via a pytest wrapper
The Shell installer tests job uses a fixed script list (not tests/run_all.sh),
so the new shell test would not run on PRs. Add a pytest wrapper under
tests/python/ that invokes it; the auto-discovered repo CPU test job collects
tests/python/ and so executes the Apple Silicon spaced-path regression.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
ensure_diffusion_visual_server() downloaded the visual-server release
asset with the unverified download_file() and marked it executable,
bypassing the approved-checksum manifest that gates every other prebuilt
llama.cpp artifact. The backend later auto-discovers that binary and
launches it through DG_VISUAL_BIN, so a compromised or substituted
release asset could place attacker-controlled native code in the install
tree and have it executed under the Studio user.
Require the matched asset to be present in the approved checksum manifest
and download it through download_file_verified() with the published
sha256. A name-matching asset that is absent from the manifest is refused
rather than executed.
Add regression tests covering the verified-download path and the refusal
of an unapproved asset.
* Pin isolated Node.js installer to committed sha256 digests
The isolated Node installer verified each downloaded archive only against
SHASUMS256.txt fetched from the same nodejs.org origin as the archive, so a
compromised CDN or TLS path could serve a malicious archive plus a matching
checksum and gain code execution when the extracted node is run during the
npm floor check and version probe.
Anchor trust in studio/node_prebuilt_pins.json, a committed manifest of
per-arch sha256 digests, and verify archives against it. The default channel
installs the pinned version and never fetches the remote SHASUMS. Unpinned
lts, latest, or explicit versions fail closed via UnpinnedNodeRefused unless
UNSLOTH_NODE_ALLOW_UNVERIFIED=1, and the refusal is not swallowed by the
keep-existing-on-transient-failure path. Ship the manifest in package-data.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review nits on the pinned Node installer
- Drop the unused npm_min_major field from node_prebuilt_pins.json; the floor is
the NPM_MIN_MAJOR module constant and the dead field could silently drift.
- Reword the unpinned-refusal message so it does not tell a user already on the
default to install it, and point the "add a pin" hint at the exact asset.
- Decode the opt-in SHASUMS body with errors="replace" so a non-UTF8 response
yields a clean PrebuiltFallback instead of an uncaught UnicodeDecodeError.
- Tests: assert the refusal message (guards the main() catch order, not just the
exit code), cover malformed-manifest parsing, and drive the opt-in remote-SHASUMS
path end to end through install_prebuilt.
* Tighten comments in the pinned Node installer
Collapse multi-line rationale comments to single lines, drop docstrings on the
obvious internal helpers (load_pins, pinned_sha256), and shorten the manifest
note. Comments/docstrings only; verified code-unchanged via AST comparison.
* Address Codex review: verify pins on existing installs; tomllib fallback
- existing_install_matches now takes an expected_sha and the short-circuit passes
the committed pin, so a version-matching but non-pinned or tampered install (e.g.
from the old remote-SHASUMS path) is re-verified instead of kept. An unpinned
target without opt-in no longer short-circuits on an existing install; it falls
through to the UnpinnedNodeRefused fail-closed path.
- The package-data test uses pytest.importorskip(tomllib/tomli) so it does not
ModuleNotFoundError on the supported 3.9/3.10 interpreters.
* Make the transient-failure keep-existing path pin-aware
The previous commit added the pinned-digest check to the existing-install
short-circuit but not to the post-download-failure fallback, which still kept any
runnable same-version install via existing_install_usable(). A same-version
install whose recorded sha256 is not the pin could therefore be kept on a
transient download failure, the exact artifact the short-circuit rejects. Refuse
to keep a same-version pin-mismatched install there too; a different usable
version is still kept for offline resilience.
* Bump pinned default Node to the current 24 LTS (24.18.0)
Node 24 LTS moved to 24.18.0; since the default channel now resolves straight to
the manifest, a frozen 24.17.0 would downgrade fresh installs and make
UNSLOTH_NODE_VERSION=lts refuse the current LTS as unpinned. Update default_version
and all six per-arch digests (verified against the official SHASUMS256.txt), and
point the test INDEX/short-circuit fixtures at the new LTS.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Copy uv `-c`/`-r` paths to a space-free temp dir on macOS/Linux
uv 0.11.x truncates a constraints/requirements path passed via `-c`/`-r`
at the first space, so `unsloth studio` setup from a repo cloned under a
path containing a space (e.g. `/Users/me/Open Source/unsloth`) fails with:
error: File not found: `/Users/me/Open`
_uv_safe_path() already worked around this on Windows via the 8.3 short
path but returned the space-containing path unchanged on macOS/Linux,
which have no 8.3 equivalent. Extend it to copy the (small, flat)
constraints/requirements file into a space-free temp dir and hand uv the
copy; the temp dirs are removed at process exit. Falls back to the
original path on any error, so it is never worse than before.
Refs unslothai/unsloth#6503
* Route UV_OVERRIDE through _uv_safe_path and fix temp-dir leak (#6503)
The -c/-r fix did not cover UV_OVERRIDE, which uv also truncates at the first
space. On Apple Silicon the overrides file is handed to uv via UV_OVERRIDE at
install time (install_python_stack.py) and during the MLX self-heal
(utils.mlx_repair), so a repo under a path containing a space still broke every
uv call there. Move _uv_safe_path into backend.utils.uv_path_safety so both
sites share it, and route UV_OVERRIDE through it.
Also stop leaking the temp dir when shutil.copyfile fails after mkdtemp, and add
tests for the UV_OVERRIDE channel, the TMPDIR-with-space fallback, the atexit
cleanup, and the no-leak path.
---------
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Fix _SameTaskStreamingResponse disconnect test bypassing __init__
test_same_task_response_closes_body_iterator_on_send_disconnect builds the
response via __new__ to skip Starlette's __init__, then wires body_iterator,
background, and stream_response by hand. It never set _unstarted_cleanup, so the
disconnect-before-first-chunk branch of __call__ raised AttributeError instead of
ClientDisconnect, failing the Backend CI "Repo tests (CPU)" job on main.
Set response._unstarted_cleanup = None in the manual construction, matching the
default __init__ assigns.
* Shorten the _unstarted_cleanup comment to one line
* Fix construct_chat_template leaking {INPUT}/{OUTPUT} sentinel into the template
In construct_chat_template's inner process() helper, the branch handling a
section that starts with the {INPUT}/{OUTPUT} sentinel sliced the part from
part.find(which) (which is 0 in that branch), so the literal sentinel was
re-included in the generated Jinja chat template. The endswith branch already
slices correctly with part[:part.find(which)]; this slices past the sentinel
with part[len(which):], so a template whose input or output section begins
with the sentinel (for example a user turn that starts with {INPUT}) renders
correctly instead of emitting a literal {INPUT}/{OUTPUT}.
Added a regression test covering {INPUT}-leading and {OUTPUT}-leading sections.
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* Fix SyntheticDataKit.chunk_data dropping single-chunk documents
chunk_data turns the n boundary points from np.linspace into n-1 ranges
via the boundaries[:-1] / [1:] pairing. When a document fits in a single
chunk (n_chunks == 1) that produces zero ranges, so the loop writes no
files and the whole document is silently dropped. Emit the full
[0, length] range when n_chunks <= 1; the multi-chunk path is unchanged.
Added a regression test covering the single-chunk and multi-chunk cases.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* chunk_data: emit nothing for an empty document (no empty chunk file)
Addresses review feedback: when the input document is empty (length == 0),
return no chunks instead of writing a single empty chunk file. Added a
regression test for the empty-document case.
* chunk_data: reject overlap >= chunk size (non-positive stride)
Per review feedback: when overlap >= max_tokens the chunk stride is
non-positive, which would divide by zero or silently emit one oversized
chunk. Raise a clear RuntimeError for that unusable configuration. Added
a regression test.
* Broaden single-chunk guard to length <= max_tokens (also fixes sub-overlap docs); expand tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
* Studio: drop OpenEnv and unused ExecuTorch/open_spiel install deps
* Studio: drop 8 more unused install deps from extras
* Studio: restore tomli<3.11 for kernels; tidy dep-cleanup comments and tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: refresh scan-packages baseline for scipy _external + unsloth-zoo tests
scipy moved its vendored array_api_compat from scipy/_lib to
scipy/_external, so the four allowlisted array_api_compat __init__.py
entries stopped matching and resurfaced as unsuppressed CRITICAL
"Downloads and executes remote code" findings on all three pip
scan-packages shards (extras, hf-stack, studio). Add the _external
paths next to the existing _lib ones so both scipy layouts stay covered.
Allowlist two unsloth-zoo test-file false positives now present in the
hf-stack shard: tests/test_mlx_save_export_regressions.py (writes to
/tmp dropper) and tests/test_mlx_trainer_internals.py (obfuscation plus
exec/eval).
Drop nine stale entries for packages removed from the Studio
requirements and no longer in any shard closure (evaluate, pytest,
hypothesis, kgb, langid), confirmed absent via with-deps resolution of
all three shards.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Fix Qwen3 NaN: self-heal vision pad_token in load_correct_tokenizer
Text-only Qwen3 (and Qwen2.5) models share Qwen3-VL's vocab, so their
Hub tokenizer configs ship <|vision_pad|> as pad_token. Padding text-only
training with a vision token corrupts attention/loss and produces NaN
losses and gradients on affected stacks.
patch_tokenizer already heals this, but only when a model with config is
passed. The standalone load_correct_tokenizer path (and custom training
loops) still returned <|vision_pad|>. This adds a model-independent guard
in load_correct_tokenizer that replaces a vision pad_token on text-only
tokenizers with the first safe text token (<|endoftext|>, <pad>, [PAD],
<unk>), falling back to eos_token only if it differs from pad_token.
The result now matches upstream Qwen configs (pad_token <|endoftext|>,
id 151643) with no new token added. Vision processors (image_processor
present) and non-vision pad tokens (Llama, Qwen2) are left untouched.
Fixes#3155
* Format pad_token helper for ruff kwarg-spacing hook (pre-commit)
* Harden vision pad_token fix: drop unk candidate, guard get_vocab, skip vision eos
* Tighten code comments (no logic change)
* Delegate pad_token fix to shared unsloth_zoo.pad_token
Generalize the narrow _fix_vision_pad_token by delegating to unsloth_zoo's
shared fix_pad_token (single source of truth, AGPL-3.0), which scans the
reserved-token families instead of only the vision-pad case. A guarded import
keeps this working against an older unsloth_zoo that has not shipped the module
yet: on ImportError it falls back to _fix_vision_pad_token.
allow_add=False is passed so the early load_correct_tokenizer call stays
side-effect free (no model here to resize embeddings); the later model-aware
patch_tokenizer call finishes the job and is idempotent.
Adds tests/python/test_pad_token_fix.py covering both dispatch paths offline.
* Fix CPOTrainer crash with multimodal processors
CPOTrainer shares build_tokenized_answer/tokenize_row and __init__ with
ORPOTrainer, but the ORPO replacement functions that route tokenization
through the underlying text tokenizer and resolve pad_token_id were only
registered for orpo_trainer. With a multimodal processing class (e.g.
Gemma4Processor) the positional self.processing_class(prompt, ...) call binds
prompt to images=, leaving text=None and raising
TypeError: 'NoneType' object is not subscriptable.
Register the existing orpo_trainer_text_tokenizer and
orpo_trainer_processor_pad_token under cpo_trainer as well so CPO/SimPO
fine-tuning of multimodal models works. No change for plain tokenizers.
* Add CPO processor tokenizer regression test
Static, CPU-only checks that cpo_trainer registers the same
orpo_trainer_text_tokenizer and orpo_trainer_processor_pad_token rewriters as
orpo_trainer, and that the rewriter drops the broken positional
self.processing_class(prompt, ...) call. Guards against issue #4952 regressing.
* Format CPO test assert for ruff line length (pre-commit)
* Bind CPO __init__ pad/eos token reads to underlying tokenizer
TRL 0.28+ CPOTrainer.__init__ reads bare processing_class.pad_token and
processing_class.eos_token before pad_token_id, which raises AttributeError
for multimodal processors (e.g. Gemma) where those live on .tokenizer.
Extend orpo_trainer_processor_pad_token to route that block through the
underlying tokenizer, and add a regression test.
* Tighten code comments (no logic change)
* Make CPO/ORPO rewriters reach the trainer on TRL 1.x
TRL 1.x moved CPOTrainer and ORPOTrainer out of trl.trainer into
trl.experimental.<algo> and dropped the trl.trainer.<algo>_trainer shim that
older TRL (0.26 - 0.28) kept. patch_trl_rl_trainers() discovers trainers via
dir(trl.trainer), so on TRL 1.x cpo_trainer and orpo_trainer are never found and
the multimodal-processor tokenization fix (#4952) silently stops applying, even
though the rewriters themselves still match the source.
Re-expose experimental-only trainers that Unsloth has rewriters for (RL_FUNCTIONS
keys) under trl.trainer before discovery, so the existing patch machinery and its
thin-wrapper resolution work unchanged. The alias is a no-op on older TRL where
trl.trainer.<algo>_trainer already exists.
Also rebind the patched Trainer/Config into every already-imported trl.* module
that holds the original class so the fix is visible at the experimental import
site (from trl.experimental.cpo import CPOTrainer), not only via trl.trainer.
Verified on transformers 4.57.6 + trl 0.22.2, transformers 4.57.6 + trl 0.27.1,
and transformers 5.12.1 + trl 1.6.0: CPOTrainer with a multimodal processor
tokenizes through the underlying text tokenizer with no crash on all three, and
the SFT/GRPO/DPO patch paths are unchanged.
* Format for ruff (pre-commit)
* Simplify CPO fix to mirror ORPO registrations (#4952)
Register the existing ORPO row-tokenizer/pad-token rewriters for cpo_trainer.
Under the trl<=0.24.0 pin CPOTrainer lives in trl.trainer.cpo_trainer (found by
dir(trl.trainer)), shares ORPO's build_tokenized_answer and uses
processing_class.pad_token_id, so the two registrations are sufficient.
Drop the trl 1.x experimental aliasing/rebind machinery in rl.py and the
bare-pad_token rewriter: trl 1.x (CPO in trl.experimental) and the bare
pad_token pattern (trl>=0.28) are not installable under the pin.
* CPO: route bare pad_token/eos_token default through inner tokenizer
TRL 1.x CPO/ORPO __init__ (the trl.experimental source unsloth resolves on TRL
0.26+) defaults processing_class.pad_token from processing_class.eos_token
before tokenizing. Multimodal processors (Gemma3/Gemma4 Processor) expose those
attributes on .tokenizer, not on the processor, so that bare access raises
AttributeError during __init__ even with the pad_token_id fallback registered.
Extend orpo_trainer_processor_pad_token to rewrite that defaulting block to run
on the inner tokenizer. The pinned TRL range (<=0.24.0) has no such block, so
the regex is a no-op there and only the existing pad_token_id fallback applies.
Verified the rewrite against the real trl 1.6.0 experimental CPOTrainer.__init__
(bare access removed, result compiles, a processor without pad_token no longer
raises) and added offline regression tests for both the rewrite and its no-op.
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
* Fix Gemma 4 GGUF OpenAI API streams
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Avoid duplicate Responses stream disconnect watcher
* Keep reasoning-only Responses output hidden
* Address Gemma stream review comments
* Avoid Responses stream task-group cleanup
* Harden OpenAI chat completion streams
* Address OpenAI stream review issues
* Clean up Studio OpenAI stream helpers
* Fix Studio passthrough cold stream timeout
* Fix tool parser compatibility exports lint
* Preserve audio stream disconnect cancellation
* Avoid synthetic finish after passthrough errors
* Address stream cleanup and Gemma parser reviews
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Gemma 4: parse bare-string tool args and keep safetensors tools for native <|tool_call>
- Quote bare unquoted string values in Gemma native tool-call args (e.g.
{location:Tokyo,unit:celsius}) so they parse; JSON scalars stay typed.
- Stop _detect_safetensors_features from suppressing supports_tools for
templates that emit Gemma native <|tool_call>, which the shared parser
now reads.
- Add tests for both.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Harden Gemma tool-call parsing and stream-error detection
Address three issues in the Gemma-native tool-call path:
- _quote_gemma_object_keys stopped a bare (unquoted) string value at the
first comma, so an argument like `location:New York, NY` was split
mid-value and the synthesized JSON failed to parse, dropping the whole
tool call. A bare value now ends only at `}` or a comma that begins the
next `key:` pair.
- parse_tool_calls_from_text scanned the entire response for Gemma markers
even inside a tool call already parsed from a `<tool_call>{...}` JSON
block, so a marker-like string inside an argument (data) was promoted to
a second, unintended tool call. Matches inside an already-consumed call
span are now skipped.
- _openai_passthrough_stream relied on _monitor_openai_sse_line to flag a
stream error, which returns early when monitor_id is None
(skip_api_monitor), so an upstream error chunk left saw_stream_error
unset and the synthetic-finish guard emitted a successful finish_reason
after a failed stream. Error chunks are now detected independently of API
monitoring.
Adds tests/test_gemma_tool_parse_edge_cases.py covering the comma and
marker-injection cases.
* Emit the terminal finish_reason chunk in GGUF streams
The OpenAI chat-completions GGUF tool stream and plain stream both built a
final ChatCompletionChunk carrying finish_reason but never yielded it, so
clients received the optional usage chunk and [DONE] with no chunk carrying
finish_reason. OpenAI-compatible consumers rely on that terminal choice to
distinguish stop/length/tool_calls. Yield it before the usage chunk and
[DONE], matching the other streaming paths.
* Parse tool calls in document order and skip nested markers both ways
Unify the JSON- and Gemma-format tool-call passes into a single
position-ordered scan:
- Calls are now emitted in byte order across both formats, so a mixed
output like `<|tool_call>call:create{...}<tool_call|> ... <tool_call>
{"name":"read",...}</tool_call>` executes create before read, matching
the order they appear in (tools run in returned order).
- A candidate that starts inside an already-accepted call's span is
skipped, in both directions: a JSON marker inside a Gemma argument and a
Gemma marker inside a JSON argument are treated as data, not promoted to
a second executable tool call.
Extends tests/test_gemma_tool_parse_edge_cases.py with the ordering and
JSON-in-Gemma nesting cases.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Quote bare Gemma array elements; order finish before trailing usage
- _quote_gemma_object_keys skipped array values, so a Gemma call with a
bare-string array argument like labels:[bug,ui] produced invalid JSON and
the whole tool call was dropped. Array values are now scanned and bare
string elements quoted, while numbers, quoted strings, and JSON literals
are preserved.
- In the OpenAI passthrough stream, a trailing usage-only chunk
(stream_options.include_usage) that arrived before any finish chunk was
relayed before the synthetic finish, producing usage -> finish -> [DONE].
Emit the synthetic finish before that usage chunk so the order matches the
other streams (finish -> usage -> [DONE]).
Extends tests/test_gemma_tool_parse_edge_cases.py with the bare-array cases.
* Harden Gemma array parsing, XML-parameter guard, and stream teardown
Address five review findings on the Gemma tool-call and OpenAI passthrough
streaming paths:
- parse_tool_calls_from_text collected JSON and Gemma markers without the
_inside_open_parameter guard, so a marker embedded in an existing
<function=...><parameter=...> value was promoted to a separate tool call.
Candidates that start inside an open XML parameter are now skipped, matching
the guard the XML-style parser already applies.
- _quote_gemma_array_elements preserved array elements starting with { or [
verbatim, so an array of objects (items:[{path:a}]) or a nested array failed
json.loads and the whole call was dropped. Object and nested-array elements
are now normalised recursively.
- _openai_passthrough_stream synthesized a finish chunk before a trailing
usage-only chunk and set saw_finish_reason, which made the EOF guard skip the
[DONE] sentinel. The EOF path now emits [DONE] whenever the upstream omitted
it, even after a finish chunk was already synthesized.
- /generate/stream drove generation through asyncio.to_thread with no
disconnect watcher, so a client disconnect during a long generation went
unnoticed until the next send. It now runs _await_disconnect_then_cancel
against the request, matching the other local streaming endpoints.
- _SameTaskStreamingResponse closed the body iterator with aclose() on a
send-side disconnect, raising GeneratorExit so the generators' cancellation
handlers (which finish the api_monitor entry) never ran. It now throws
CancelledError, falling back to aclose() when athrow is unavailable.
Extends tests/test_gemma_tool_parse_edge_cases.py with array-of-objects,
nested-array, and marker-inside-XML-parameter cases.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Watch disconnects on Anthropic streams; keep timestamps in Gemma values
Two follow-ups on the streaming and tool-parse paths:
- _anthropic_tool_stream and _anthropic_plain_stream drove generation through
asyncio.to_thread(next, gen, ...) and only polled is_disconnected() between
events, so a client disconnect during prefill or a long generation/tool step
held the decode slot until the next event or a failed send. Both now run the
_await_disconnect_then_cancel watcher used by the other local streams, stop it
in finally, and break promptly when cancel_event is set.
- _GEMMA_NEXT_KEY_RE treated any comma followed by word-chars-then-colon as the
next key, so a bare value such as "meet at 10:00, 11:00 tomorrow" was split
into bogus keys. The next-key token must now be identifier-shaped (start with
a letter or underscore), so a comma before a timestamp, ratio, or other
numeric-then-colon text stays part of the value.
Adds a timestamp-in-bare-value regression test.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Guard nested markers, reset on disconnect, clean unstarted streams
Three follow-ups on the tool-parse and streaming paths:
- parse_tool_calls_from_text only skipped markers that fell inside a span it
had already parsed successfully, so when an unquoted Gemma argument contained
a literal marker (code:<|tool_call>call:terminal{...}<tool_call|>) the outer
object failed to normalize, its span was never recorded, and the inner marker
was promoted to a standalone terminal call. Candidates nested inside any other
candidate's brace span are now skipped regardless of whether the enclosing
candidate parsed, so a marker in malformed outer data is never executed.
- /generate/stream skipped backend.reset_generation_state() when the disconnect
watcher set cancel_event between chunks: the loop broke and the finally's reset
is guarded on cancel_event being unset. A subprocess backend kept decoding
after the client left. The cancel-break path now resets the backend.
- _SameTaskStreamingResponse threw CancelledError / called aclose() on the body
iterator on a send-side disconnect, but neither runs the try/finally of a
generator that never started (early disconnect on http.response.start), so the
passthrough's eagerly-opened upstream httpx stream and cancel-registry entry
leaked. It now tracks whether the body started and, when it did not, runs an
optional unstarted_cleanup hook; the OpenAI passthrough wires it to close the
upstream resp/client and exit the cancel tracker.
Adds a nested-unquoted-marker regression test.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
torchao has no working Windows ROCm build. transformers.quantizers imports it,
and it loads torch's c10d distributed backend at module level, which the AMD
Windows wheels omit (no RCCL). The import aborts, transformers can no longer
expose PreTrainedModel, and the sentence-transformers embedder silently falls
back to the llama-server GGUF embedder. Linux ROCm and NVIDIA are unaffected
(the c10d ops are present / torchao is real there).
The training and export workers already install the shared torchao stub before
importing transformers, but the RAG embedder runs in the main backend process,
which never did. Two fixes, both no-ops off Windows ROCm:
- embeddings.py: install_torchao_windows_rocm_stub() before the first
sentence-transformers import, so an already-installed torchao is neutralized
(fixes existing venvs).
- install_python_stack.py: stop installing torchao on Windows ROCm; it can only
crash on import there, so new venvs never ship it.
Add tests covering the embedder stub call and the install skip.
* Fix FlashAttention fp32 crash with DoRA (use_dora=True)
DoRA upcasts lora_magnitude_vector to fp32 for the optimizer, which promotes
the q/k/v_proj output to fp32. FlashAttention only accepts fp16/bf16, so the
fp32 q/k/v raised 'FlashAttention only support fp16 and bf16 data type'.
Downcast q/k/v to the compute dtype before the flash kernels.
Fixes#1013
* Apply kwarg-spacing format hook to DoRA dtype test (pre-commit)
* DoRA+FA2: downcast any fp32 among Q/K/V and clamp to a flash-supported dtype
* Tighten code comments (no logic change)
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* Fix misleading 'only for image models' error for Qwen3-VL when torchvision is missing
transformers >= 5.4 hard-requires torchvision for VLM image/video processors and
no longer falls back to a slow processor. Without torchvision the processor load
raises ImportError, unsloth degrades to a text-only tokenizer, and the vision data
collator later fails with 'UnslothVisionDataCollator is only for image models!'.
Detect this case at load time and raise a clear, actionable error pointing at the
missing torchvision dependency instead.
Fixesunslothai/unsloth#4202
* Apply kwarg-spacing format hook to vision torchvision guard (pre-commit)
* Make torchvision-missing detection precise: check availability first, match specific error text
* Tighten code comments (no logic change)
* Make missing-torchvision VLM error version-agnostic
The raise also fires on transformers 4.57.x for VLMs with a video processor
(Qwen2.5-VL, Qwen3-VL), where AutoVideoProcessor requires torchvision. The old
message claimed 'transformers >= 5.4 requires torchvision', which is inaccurate
on 4.57.x. Reword to state torchvision is required for this model's vision
processors without a version-specific claim.
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* fix: use EMPTY_LOGITS on the fused-CE not-return_dict path (#2068)
CausalLM_fast_forward's fused cross-entropy path (small batch, labels set,
UNSLOTH_RETURN_LOGITS off) computes the loss straight from hidden_states
via unsloth_fused_ce_loss and never materializes `logits`. The
return_dict=True branch returns EMPTY_LOGITS, but the `not return_dict`
branch returned `(logits,) + outputs[1:]`, raising
"UnboundLocalError: cannot access local variable 'logits'" whenever it ran
(e.g. training with return_dict=False). Same bug in the llama and mistral
fast-forward paths.
Return EMPTY_LOGITS on that branch too, matching the adjacent return_dict
output. Verified on GPU: a forward(return_dict=False, labels=...) that
raised UnboundLocalError now returns (loss, EMPTY_LOGITS, ...) and
backward() succeeds.
Adds tests/test_fused_ce_not_return_dict_logits.py, a CPU source-drift guard
(the fused path itself is GPU/triton only) asserting both fast-forward paths
keep using EMPTY_LOGITS there.
* Address review: parse the fused-CE drift line with whitespace-tolerant regexes
The drift detector sliced the source with exact string matching
(source.index("output = (") + the next newline), so a formatter respacing or
rewrapping the assignment would break the parse. Switch to anchored regexes that
tolerate whitespace and line wrapping, keeping the match anchored after the
fused guard so it targets the fused-CE branch and not the normal
output = (logits,) path. Behavior and the two drift assertions are unchanged.
* Tighten code comments (no logic change)
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
* studio/setup.sh: guard empty CUDA arch detection in the source build
PR #5826 hardened setup.sh for fresh CUDA toolkits, but the source build
still set -DCMAKE_CUDA_ARCHITECTURES only when nvidia-smi reported a
compute capability. When that query returns nothing the build proceeded
with no explicit arch list, so llama.cpp built PTX only. On a driver older
than the toolkit that binary fails at runtime with "the provided PTX was
compiled with an unsupported toolchain" - the build succeeds, so neither
the build-time check nor the CPU fallback caught it (issue #5854).
Resolve the arch list before committing to a CUDA build. A new pure helper
_resolve_cuda_archs parses and de-duplicates the nvidia-smi compute_cap
output and honors an explicit UNSLOTH_LLAMA_CUDA_ARCHS override. When the
result is empty, build CPU llama.cpp instead of a PTX-only binary, with a
clear message pointing at the override - so the user still ends up with a
working llama-server. The override also lets advanced users force a native
build on hosts where nvidia-smi cannot report compute_cap.
No behavior change when an arch is detected: -DGGML_CUDA=ON plus the arch,
CUDA flags and NVCC_PREPEND_FLAGS are assembled exactly as before.
Adds tests/sh/test_resolve_cuda_archs.sh (single/multi/dedup/empty/garbage/
whitespace/override cases), wired into tests/run_all.sh and the
studio-backend-ci.yml shell-test loop.
* studio/setup.sh: resolve nvidia-smi via /usr/bin fallback for arch detection
Addresses review feedback on the empty-CUDA-arch guard: _setup_has_usable_nvidia_gpu
classifies a host as NVIDIA-usable using nvidia-smi on PATH OR /usr/bin/nvidia-smi,
but the new arch detection probed only `command -v nvidia-smi`. On a GPU host where
nvidia-smi is off PATH (reachable only at /usr/bin), arch detection returned empty
and the new empty-arch branch dropped the build to CPU, losing CUDA. Mirror the same
PATH-then-/usr/bin resolution so those hosts still get a native CUDA build.
Also scope _resolve_cuda_archs locals with `local` (no behavior change; it already
runs under command substitution).
* tests: update compute_cap-probe assertion for $_smi_bin resolution
The nvidia-smi /usr/bin fallback parameterized the binary in the compute_cap
probe (_setup_run_smi "$_smi_bin" ...), so the literal-string assertion in
test_compute_cap_probe_timeout_wrapped no longer matched. Assert the probe is
preceded by _setup_run_smi (timeout-wrapped) instead, scanning all occurrences
so the comment mention is ignored. Same intent, binary-agnostic.
* tests: ruff-format the compute_cap probe assertion (pre-commit)
Collapse the backslash-continued assert onto one line and normalize slice
spacing so the ruff-format pre-commit hook (0.6.9) is satisfied. Formatting
only; no behavior change.
* Tighten code comments (no logic change)
* studio(windows): build CPU when CUDA arch is undetectable (#5854)
The Windows source build added -DGGML_CUDA=ON unconditionally but only set
-DCMAKE_CUDA_ARCHITECTURES when $CudaArch was detected. With no detectable
compute capability that produced a PTX-only binary, the same hole the Linux
fix closed. Build CPU llama.cpp in that case, and honor UNSLOTH_LLAMA_CUDA_ARCHS
to force a CUDA build, matching setup.sh. Detected-arch builds are unchanged.
* test: anchor NVCC_PREPEND_FLAGS scope check on the final CPU branch
The undetectable-arch CPU fallback adds an earlier -DGGML_CUDA=OFF, so the
ordering check now anchors on -DGGML_CUDA=ON and the last -DGGML_CUDA=OFF
instead of the first.
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
* Studio Playwright: snooze update banner before sending
The llama.cpp update banner is a fixed bottom-right toast (z-9998). When an
update is available it overlaps the composer's Send button and its subtree
intercepts the click, so send_and_wait times out (flaky; surfaces on the
Windows studio UI smoke, passes otherwise). Snooze the banner if it is
showing before each send, then wait for it to detach.
* Also snooze the web update banner before sending
The web update banner (web-update-banner, z-9999) is a fixed bottom-right
toast like the llama.cpp one and can overlap the Send button too. Loop over
both banners and snooze whichever is showing.
* Add HF dataset streaming mode to Studio
* Added default value for datasetStreaming in training-config-store.ts
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Handle None max_steps for streaming validation
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: fast-fail streaming validation and guard incompatible modes
Reject dataset_streaming at the API boundary when hf_dataset is empty,
the dataset is vision/audio, or max_steps is not set. Probe eval split
with get_dataset_split_names before the streaming load so typos fail
immediately instead of mid-training. Guard column_names=None after map
on iterables. Hide the UI toggle for non-text configurations and clear
the stale flag when config becomes incompatible.
* studio: add streaming dataset tests, iterable helper, and streaming template/format support (WIP)
Work-in-progress on top of feat/studio-dataset-streaming-mode (PR #4946):
- new test_training_streaming.py and iterable.py dataset helper
- streaming support in chat_templates.py and format_conversion.py
- additional streaming guards in trainer.py / models / routes
- frontend streaming wiring in params-section and training-config-store
Committed to preserve uncommitted work before merging latest main.
* studio: fix review-team findings for streaming + main merge
BLOCKER: streaming + raw-text/CPT crashed on len(IterableDataset). Guard it in the
start route (reject format_type=="raw" or training_type=="Continued Pretraining")
and in isStreamingSupported (datasetFormat !== "raw").
Also:
- models/training.py: validate hf_dataset/subset/split (charset+length, block ..//);
cap dataset slice indices (le=1e9); note validator ordering
- chat_templates.py: guard _apply_custom_mapping .map() for streaming
- trainer.py: warn when packing+streaming
- training-config-store.ts: persist-migration bump to v11 (standalone datasetStreaming
backfill); add isVisionModel to NON_PERSISTED; toast on silent streamingCompatiblePatch
mutations in the 4 indirect setters
- tests: route rejections (max_steps, raw/cpt), slice cap, unsafe hf_dataset
* studio: enable raw-text/CPT dataset streaming + streaming UX polish
- raw_text: keep the lazy filter but skip len()-based row counting for
IterableDatasets so raw-text / CPT can stream; guard the eval-size log
- routes/trainer: drop the raw/CPT streaming block; add a defensive
not-streaming guard on the eval auto-split (train_test_split)
- dataset-section: streaming toggle is visible-but-disabled and lists the
exact unmet requirement(s) in its tooltip; block embedding models
- training-start-overlay: show "streaming (no full download)" instead of a
stuck download bar for streaming runs
- trim the streaming test suite to the high-value cases
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: address streaming review (MLX/embedding guards, sliced eval split, rehydrate timing)
- routes: reject dataset_streaming for embedding training and on Apple Silicon
(MLX); both loaders materialize the full dataset instead of streaming
- trainer: validate the base eval split name so streaming eval accepts HF slice
syntax such as "validation[:1000]"
- training-config-store: defer the onRehydrateStorage setState to a microtask so
it doesn't hit the store's TDZ during synchronous hydration
- test: streaming start rejects embedding models
* studio: harden HF dataset streaming (column_names, split slicing, empty/eval bounds, gating)
Address a deeper streaming review:
- raw_text: resolve_column_names() guards IterableDataset.column_names=None
(from_generator / unresolved features) so raw-text and CPT streaming no longer
raise TypeError before training
- models/routes: reject HF slice syntax in train_split/eval_split when streaming
(load_dataset(streaming=True) raises "Bad split"); reject mixed sources
(local/S3) and embedding/MLX streaming at the API, not just in the UI
- trainer: an empty post-slice/filter stream fails preflight with a clear message;
streaming eval is capped (STREAMING_EVAL_MAX_SAMPLES) so each eval terminates;
the manual-slice shortcut falls back to a regular load when train_split is sliced
- format_conversion: streaming conversions preflight the first mapped row so
format errors surface before training, not mid-iteration
- frontend: block streaming on Apple Silicon; clear datasetStreaming when a
dataset is detected as image/audio at start
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fix CI for streaming PR (lint blocker + no-torch sandbox + preflight test)
- trainer.py: drop unused `IterableDataset` import (hoist safety-net blocker).
- test_training_streaming.py: only select real classes (isinstance type) when
locating the trainer class, so a MagicMock-stubbed global is never passed to
object.__new__ (fixes TypeError on the Python 3.10-3.13 jobs).
- no-torch import sandboxes (test_e2e_no_torch_sandbox.py,
test_studio_import_no_torch.py): teach the chat_templates/format_conversion
exec stubs and the full-import-chain copy list about the new `.iterable`
module so the AFTER/runtime cases import without torch again.
---------
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>
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
* Installer: respect a declined Studio auto-start and keep Ctrl+C shutdown logs ordered
The `curl | sh` Studio auto-start prompt had two issues on Linux/macOS/WSL
(install.sh). install.ps1 already gates on input redirection, so Windows is
unaffected.
1. Typing n, or any closed/EOF /dev/tty, still launched Studio. The read
fallbacks defaulted to "y" (read failure, and the no-tty branch), so any
answer other than a cleanly delivered y/n line auto-started a blocking
foreground server. Default those to "n"; a real Enter still counts as yes
via ${_reply:-y}.
2. On Ctrl+C the shell prompt printed in the middle of Studio's shutdown logs.
The non-interactive installer shell took the default SIGINT action and died
before the child finished its graceful shutdown, so the prompt raced ahead
of "All subprocesses cleaned up". trap '' INT in the installer shell so it
waits for Studio's own graceful shutdown.
* Studio: wait for the uvicorn thread before the terminal returns on Ctrl+C
Builds on #6565 by @Imagineer99. The studio server runs uvicorn in a daemon
thread, so on Ctrl+C the process could return to the shell while that thread
was still writing its shutdown logs, interleaving them with the prompt.
Retain the uvicorn thread and join it (flushing stdout/stderr) before terminal
entrypoints return, from run.py's main shutdown path and the CLI shutdown paths.
Refinements over #6565:
- Bound the join at 5s (_SERVER_SHUTDOWN_JOIN_TIMEOUT, matching the existing
_graceful_shutdown subprocess timeouts) so a stalled uvicorn shutdown cannot
hang the terminal; the timeout warning branch is now reachable.
- Restore SIG_DFL for SIGINT/SIGTERM at the start of the signal handler so a
second Ctrl+C force-quits, and drop the redundant in-handler wait (the
post-loop wait already covers the signal path).
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: keep child Ctrl+C working and restore SIGBREAK
- install.sh: run studio in a subshell that resets INT to default
(trap - INT; exec ...) so the foreground child does not inherit the
installer shell's ignored SIGINT, which would otherwise swallow the
studio process's own Ctrl+C and graceful shutdown.
- run.py: also restore SIGBREAK to SIG_DFL in the signal handler so a
second Ctrl+Break force-quits on Windows, matching SIGINT/SIGTERM.
* install.sh: capture studio exit with || under set -e so the migration hint still prints
* Trim shutdown-fix comments to be terser (comments only, no code change)
* Dedup CLI shutdown-wait into finally blocks (review follow-up)
---------
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Add regression tests for the stray-forward compile-cache reset
Follow-up to #6511, which fixed the bug but whose squash merge did not
include the tests. These cover the two issues that fix addressed, under
the GPU-free tests/conftest.py harness:
- _unsloth_reset_stray_compile_cache is an exported module-level symbol in
unsloth.models._utils (it previously lived only inside the RL trainer
template string, so every non-RL import silently no-op'd)
- _unsloth_install_pretrain_detector keeps a recorded "seen" forward on an
idempotent reinstall with a live hook, and only resets it after teardown
- only a grad-enabled pre-train forward marks the cache poisoned
- the reset warns and clears seen when a stray forward was seen, tears the
hook down even on the clean path, and walks the .model/.base_model/.module
wrapper chain to reach a nested marker
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Pin UNSLOTH_COMPILE_DISABLE in the warn-path reset tests
The reset only warns and resets Dynamo when UNSLOTH_COMPILE_DISABLE != "1".
A GPU-free CI env that sets it to "1" would make the warn assertion in
test_reset_clears_seen_and_warns_when_a_stray_forward_was_seen flaky.
monkeypatch it to "0" in both warn-path tests so the warn / no-warn
assertions are deterministic and test the seen flag, not the env.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Windows installer: fix DiskPart UAC, drive-root cache, spurious rename warning, CPU-base messaging
amd-smi gate (DiskPart UAC mid-install): the AMD torch wheel ships hipInfo.exe
inside the venv, and the bitsandbytes fix prepends that venv Scripts dir to PATH.
shutil.which("hipinfo") then found it and flipped _amd_smi_allowed() to True, so
the post-install AMD probe fell through to `amd-smi list` (the venv hipInfo failed
to report gcnArchName, which is why the arch came from the GPU-name table) and
amd-smi elevated, popping the DiskPart UAC. Fix: a hipinfo resolved inside the
active venv (sys.prefix) is the torch-wheel binary, not a HIP SDK, and must not
open the gate. Mirrored in install_python_stack.py, install_llama_prebuilt.py, and
backend utils/hardware/amd.py (the runtime VRAM poller had the same latent prompt).
TORCHINDUCTOR_CACHE_DIR: move from C:\tc to <StudioHome>\TORCHINDUCTOR_CACHE_DIR so
the inductor/Triton cache lives under the user's Studio home, not the system drive
root. Long paths are already enabled above so deep inductor paths still fit.
unsloth.exe rename: skip the rename (and its "pip may fail with WinError 32"
warning) when SKIP_STUDIO_BASE=1. In the install.ps1 flow base packages are not
reinstalled, so unsloth.exe is never rewritten; the self-rename only failed because
setup runs via unsloth.exe (the running launcher holds its own file). The
'studio update' flow still attempts it.
CPU PyTorch messaging: clarify that the CPU base is temporary and setup replaces it
with GPU ROCm wheels, and print an explicit "GPU ROCm PyTorch installed" line after
the AMD wheels land, so the log makes clear the final install is GPU-accelerated.
Adds two regression tests covering the venv-internal vs external hipInfo gate.
Verified end-to-end on a Strix Halo box (Radeon 8060S / gfx1151): install.ps1
--local from this branch completed exit 0 with no DiskPart prompt, no rename
warning, the cache under the Studio home, and "GPU ROCm PyTorch installed
(gfx1151)"; Studio then booted and detected "ROCm (HIP 7.13.99004) -- AMD Radeon
8060S Graphics".
* Windows installer: drop the unreliable unsloth.exe rename and its WinError 32 warning
setup.ps1 used to rename the running unsloth.exe out of the way before the
base-package upgrade so pip could replace it. That rename never actually
worked: setup runs *via* unsloth.exe, so renaming our own running
uv-trampoline launcher failed with a sharing violation (WinError 32) and only
printed a scary 'could not rename unsloth.exe; pip may fail with WinError 32'
warning on every Windows install and update.
It also was not needed. pip tolerates a running/locked console-script .exe: it
moves the old one aside and writes the new one. The base upgrade routes through
pip on Windows, so the upgrade succeeds (or, in the install.ps1 flow with
SKIP_STUDIO_BASE=1, the base is not touched at all) and unsloth.exe is left
intact either way.
Removing the rename block and its failed-install restore block removes the
false warning for all Windows devices in both the install and update flows.
* Windows installer: gate venv-internal hipInfo.exe in PowerShell amd-smi probe; harden venv path checks
Follow-up to PR #6296.
- install.ps1 and setup.ps1: ignore the AMD torch wheel hipInfo.exe that lives
inside the Studio venv when probing for a HIP SDK, so amd-smi no longer reopens
the DiskPart UAC during install/update. Mirrors _path_inside_venv in the Python
installers, which already do this.
- amd.py, install_llama_prebuilt.py, install_python_stack.py: normcase the venv
containment check (Windows paths are case-insensitive) and run the
HIP_PATH/ROCM_PATH candidate through it too.
- setup.ps1: fall back to a short TORCHINDUCTOR cache dir when long paths are
unavailable, and create the dir wildcard-safely.
- tests: isolate sys.prefix in the gate helper, add HIP_PATH/ROCM_PATH cases, and
assert the PowerShell venv exclusion.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Windows installer: install ROCm PyTorch directly for a known AMD arch
When the GPU arch is known (name-inferred from the GPU-name table) but ROCm
could not be probe-verified (no HIP SDK, no amd-smi), the bootstrap installed
a CPU PyTorch base that setup.ps1 then force-reinstalled as ROCm. The
repo.amd.com wheels bundle their own runtime (no HIP SDK required), which
setup.ps1 already relies on, so the CPU base was a pure wasted download/install.
- Gate the ROCm index on a known arch, not only on probe-verified ROCm, so a
mapped arch installs ROCm torch directly. Unmapped arches and no-GPU hosts
still get CPU (unchanged).
- Fall back to a CPU base if the ROCm-index install fails, so a transient
repo.amd.com outage does not abort the install (setup.ps1 retries ROCm).
- Correct the stale comment that claimed ROCm wheels need a confirmed HIP SDK.
- Add a regression test for the arch-based gate.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Windows installer: correct the unsloth.exe rename-removal comment
The comment claimed the base upgrade 'routes through pip on Windows' and that
pip 'moves the old unsloth.exe aside, then writes the new one'. That is not what
the code does. install_python_stack tries uv first; on a locked launcher uv
aborts and falls back to pip, but the pip fallback strips --upgrade-package and
base.txt lists only bare unsloth/unsloth-zoo, so pip finds them already
satisfied and no-ops. The running unsloth.exe is left intact at its current
version either way. Reword the comment to describe the real uv-first /
pip-fallback-no-op behavior. No functional change.
* Windows installer: close two gaps in the venv-internal hipinfo exclusion
Review follow-up. The amd-smi/DiskPart gate could still reopen in two cases:
- setup.ps1 ran the HIP probe long before $VenvDir is assigned, so without
VIRTUAL_ENV (the `unsloth studio update` path) $venvRoots was empty and the
venv-internal hipInfo.exe was not recognized. Seed the venv root from
UNSLOTH_SETUP_PYTHON and the default Studio home too (both installers).
- The HIP_PATH/ROCM_PATH candidate was accepted without the venv filter, so an
env var pointing into the venv (AMD wheel) still set $HipSdkInstalled. Run
Test-HipinfoIsVenvInternal on the candidate as well (both installers).
Extend the PS gate test to assert both. Both .ps1 parse clean; install tests
pass (the venv-internal / HIP probe coverage at 359 passed).
* Windows installer: correct the CPU-base message for arches with no ROCm wheels
After gating the ROCm index on a known arch, a mapped arch sets $ROCmIndexUrl
and installs ROCm directly, so it no longer reaches the "temporary CPU base"
branch. That branch is now reached only by a name-inferred arch with no ROCm
wheels (e.g. RDNA2 gfx103X), where setup.ps1 does NOT install ROCm. The old
text ("setup replaces it with GPU ROCm wheels ... the final install IS
GPU-accelerated") was therefore always wrong there. Say plainly that PyTorch
stays on CPU for this GPU.
* Windows installer: seed the venv-internal hipInfo check from a custom Studio home
Test-HipinfoIsVenvInternal seeded the venv root from VIRTUAL_ENV, VenvDir, the
setup python, and the default %USERPROFILE% path only. A standalone
`unsloth studio update` with a custom UNSLOTH_STUDIO_HOME (or STUDIO_HOME alias)
and none of those set would not recognize the venv hipInfo on PATH, reopening the
amd-smi/DiskPart gate. Seed the custom home too, in both installers, and assert
it in the gate test.
* Studio installer: resolve venv aliases and expand ~ in the hipInfo venv filter
Two review points on the amd-smi/DiskPart UAC gate:
1. _path_inside_venv compared os.path.abspath of sys.prefix and the hipInfo
path, which does not resolve symlinks, junctions, or 8.3 short names. A venv
reached through an aliased path then fails the check, so its bundled
hipInfo.exe is mistaken for an external HIP SDK and amd-smi runs (the
DiskPart prompt this fix exists to suppress). Switch to os.path.realpath in
all three copies (amd.py, install_llama_prebuilt.py, install_python_stack.py).
2. setup.ps1's early venv-internal hipInfo probe seeded the venv root from a
custom Studio home (UNSLOTH_STUDIO_HOME / STUDIO_HOME) without expanding a
leading ~, while the canonical resolver does. With a tilde form,
[IO.Path]::GetFullPath kept the literal ~ relative to cwd, so the custom-home
hipInfo escaped the filter and reopened the gate. Expand ~ in the probe the
same way as the resolver.
tests/studio/install/test_pr5940_followups.py: 30 passed (adds a symlink
realpath case and a setup.ps1 tilde-expansion guard).
* Studio installer: mirror the hipInfo venv filter and ROCm wheel pins into install.ps1
Follow-up review on the same install.ps1 paths:
1. install.ps1's venv-internal hipInfo probe (Test-HipinfoIsVenvInternal)
seeded the venv root from a custom Studio home without expanding a leading
~, unlike the canonical resolver and setup.ps1. A tilde form left
[IO.Path]::GetFullPath with the literal ~ (relative to cwd), so the
custom-home hipInfo escaped the filter and reopened the amd-smi/DiskPart
gate. Expand ~ in the probe, matching the setup.ps1 fix.
2. The AMD ROCm path installed torchvision/torchaudio bare while pinning torch
to below 2.12. AMD's per-arch index publishes the companions independently
and may ship torchvision 0.27 (for torch 2.12) before removing 0.26, so a
bare resolve can pick an ABI-incompatible set and fall back to CPU. Add
torchvision/torchaudio floor maps and pass the pinned specs, mirroring
setup.ps1 and install_python_stack.py.
3. The ROCm-to-CPU fallback torch install used Invoke-InstallCommand (no
retry), the only torch step in the file without it. Switch to
Invoke-InstallCommandRetry so the recovery path survives a transient index
failure.
tests/studio/install/test_pr5940_followups.py: 33 passed (parametrized tilde
check over both installers, a torch/companion floor-map parity test, and a
CPU-fallback retry guard).
* Studio installer: scan all PATH hipinfo so the venv copy can't shadow a real HIP SDK
The amd-smi HIP-SDK probe used shutil.which("hipinfo") / Get-Command hipinfo,
which return only the first hit on PATH. The AMD torch wheel ships hipInfo.exe
inside the venv and the bnb fix (plus the Studio backend) prepend the venv
Scripts dir to PATH, so that venv-internal copy lands first. When a real HIP SDK
hipinfo sits later on PATH with HIP_PATH/ROCM_PATH unset, the first-hit probe
stopped at the venv copy, treated it as "not a HIP SDK", and closed the amd-smi
gate -- AMD users in that PATH-only SDK setup lost amd-smi telemetry and could
fall back to CPU. Scan every PATH entry and keep the first hipinfo that is not
venv-internal; only the venv copy is ignored, so the UAC/DiskPart suppression is
unchanged.
Applied to all three Python copies (install_llama_prebuilt.py,
install_python_stack.py, backend/utils/hardware/amd.py) via a new
_external_hipinfo_on_path helper, and both PowerShell callers (install.ps1,
setup.ps1) now use Get-Command hipinfo -All filtered by Test-HipinfoIsVenvInternal.
tests/studio/install/test_pr5940_followups.py: 36 passed (real-PATH scan tests, a
shadow-regression test for the exact venv-first ordering, and a parity check that
every Python copy uses the scanning helper).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio uninstallers: fix leftovers (false "removed", shared icon, llama lock)
Auditing a dual native+WSL uninstall on a real device surfaced three leftovers:
1. uninstall.ps1 removed the data dir (which holds unsloth.ico) before the
shortcuts that reference that icon, so Explorer's icon cache briefly held it
open. Remove-Item -Recurse reported success yet left the locked file, and the
dir was never re-attempted, so it orphaned with a false "removed" log.
_RemovePath now verifies the path is actually gone (retrying transient locks)
and reports honestly, and the data dir is re-swept after the shortcuts go.
2. install.sh writes a shared unsloth.ico to %LOCALAPPDATA%\Unsloth Studio for
the WSL shortcut, but uninstall.sh never removed it, orphaning the icon (and
dir) after a WSL uninstall. uninstall.sh now drops that icon and the dir when
empty, in both the powershell.exe and drvfs-fallback paths.
3. ~/.unsloth/.llama.cpp.install.lock was never removed, so the rmdir of
~/.unsloth failed and the dir lingered. Both uninstallers now remove the lock.
Verified by running both uninstallers on a real dual install: device fully clean
(no install dirs, shortcuts, PATH/registry entries, shared icon, or lock left).
* install.sh: auto-route Strix Halo WSL to an existing Ubuntu 24.04
ROCm-on-WSL is the GPU runtime for Strix Halo and only targets Ubuntu
24.04. When the installer runs in a newer default distro (e.g. 26.04) it
cannot enable the GPU and silently falls back to CPU. If a 24.04 distro
already exists, re-run the install there and stop in the current one so the
GPU path is taken without the user having to know about the distro
requirement.
Runs before venv creation so the wrong distro is left untouched, guards
against re-route loops via UNSLOTH_WSL_REROUTED, leaves a working ROCm
distro alone (librocdxg present), and skips the GGUF-only / opt-out /
non-Strix cases. When no 24.04 distro exists we keep today's behaviour:
continue to CPU and print the `wsl --install Ubuntu-24.04` guidance, never
auto-downloading a distro.
Adds tests/sh/test_strixhalo_wsl_reroute.sh (hermetic: extracts the
function, rewrites its paths to fixtures, mocks wsl.exe) covering the full
decision matrix, wired into tests/run_all.sh.
* uninstall.ps1: keep shared unsloth.ico for a surviving WSL shortcut
A dual native+WSL install shares %LOCALAPPDATA%\Unsloth Studio\unsloth.ico:
install.sh points the WSL shortcut's icon there while the native install owns the
dir. The native uninstaller removed the whole dir unconditionally, so uninstalling
native while keeping WSL left the WSL shortcut with a blank icon. The old code only
avoided this when Explorer happened to hold the icon open, which is unreliable; on a
real dual install the dir was deleted and the WSL shortcut went blank.
_RemoveDataDirKeepingWslIcon now scans the Start Menu + Desktop for a surviving
"Unsloth Studio (WSL ...).lnk" and, if found, removes everything in the data dir
except unsloth.ico (keeping the dir) instead of deleting it; with no WSL shortcut it
removes the dir as before. uninstall.sh still drops the icon and the empty dir when
WSL itself is uninstalled, so every uninstall order ends clean.
Adds tests/studio/test_uninstall_dual_install_icon.ps1 (AST-extracts the helper and
runs it against a temp dir with controlled shortcut dirs) covering the dual,
native-only, empty, and missing-dir cases, wired into the windows-inference smoke
workflow. Verified on a real dual install: native uninstall now keeps unsloth.ico
and the WSL shortcut's icon stays intact.
* installer: condense AMD/ROCm code comments (no behavior change)
Tighten the comments added for the Strix Halo native+WSL installer work so
they are shorter and clearer without losing intent: the venv-internal hipInfo
amd-smi gate, the ROCm torch/companion floor maps, the WSL 24.04 reroute, and
the dual-install uninstall icon handling. Comment-only; code paths unchanged.
107 insertions, 166 deletions across 11 files.
* install.sh: run the Strix Halo WSL reroute before any STUDIO_HOME write
The reroute fired after mkdir -p "$STUDIO_HOME" and the legacy-venv migration,
so rerouting 26.04 -> 24.04 left an empty ~/.unsloth/studio stub in the origin
distro (and ran venv migration in the distro about to be abandoned). Move the
reroute ahead of the venv section so the origin distro is left untouched, matching
the function's own comment. Behavior is identical on every non-reroute path.
* installer: fix ROCm CPU-fallback, hipinfo gate edge cases, uninstall icon, WSL 22.04
- install.ps1: clear $ROCmIndexUrl/$ROCmTorchFloor after the CPU fallback so the
flavor-repair block does not retry the failed ROCm index and abort the install;
pin the ROCm companion specs ($visionSpec/$audioSpec) in the repair path too.
- install.ps1 + setup.ps1: skip a bare drive root in Test-HipinfoIsVenvInternal so a
non-venv UNSLOTH_SETUP_PYTHON does not match the whole drive; iterate
HIP_PATH/HIP_PATH_57/ROCM_PATH and take the first non-venv hipinfo.
- amd.py, install_llama_prebuilt.py, install_python_stack.py: strip surrounding
quotes from PATH entries before probing for hipinfo.
- install.sh: pipefail the WSL reroute curl|sh; do not reroute supported Ubuntu 22.04.
- uninstall.sh: keep the shared unsloth.ico while any Unsloth shortcut (native or
another WSL distro) still references it, in both the powershell and drvfs paths.
- tests: regression coverage for all of the above.
* installer: forward reroute options, guard ROCm bootstrap, harden hipinfo gate
- install.sh: forward the caller's --package/--python/--verbose/--tauri and a custom
UNSLOTH_STUDIO_HOME into the WSL reroute (was a bare default install); bail on
--local; run the reroute BEFORE dependency/uv install so the origin distro is left
untouched; set UNSLOTH_SKIP_ROCM_WSL_SETUP after a failed reroute so the later
ROCm-on-WSL bootstrap does not install into the unsupported origin distro.
- install.ps1 + setup.ps1: Get-Command hipinfo -CommandType Application so only real
executables match (not an alias/function named hipinfo).
- uninstall.ps1: guard $env:APPDATA when building the default shortcut search dirs.
- tests: cover option forwarding, --local bail, the bootstrap guard, and the gate change.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* installer: guard origin ROCm bootstrap on every CPU-only fallback; harden ~ expansion
WSL reroute: the no-wsl.exe, no-24.04-target and --local fallbacks all tell the
user the install continues CPU-only, but only the failed-reroute branch set
UNSLOTH_SKIP_ROCM_WSL_SETUP=1. The later _maybe_bootstrap_rocm_wsl gate keys off
that flag, so the other three branches could still install ROCm into the
unsupported origin distro (e.g. 26.04). Set the skip guard on all of them.
Forward UNSLOTH_ROCM_WSL_AUTO into the reroute so a Tauri/consented GPU bootstrap
carries through to the rerouted 24.04 child instead of dropping to the prompt path.
install.ps1/setup.ps1: guard the venv-probe ~ expansion on a non-empty
$env:USERPROFILE so Join-Path does not throw on a profile-less service account.
Tests: add no-wsl.exe and UNSLOTH_ROCM_WSL_AUTO reroute cases, the USERPROFILE
guard assertion, and route shell-test fixtures through a single trap-cleaned root.
* installer: pin + soften Windows ROCm Python repair, reroute to 22.04, harden gates
install_python_stack.py: the Windows AMD ROCm repair in _ensure_rocm_torch()
installed bare torch/torchvision/torchaudio via the fatal pip_install -- the same
asymmetry already fixed on the PowerShell side. A transient repo.amd.com failure
could abort the whole install even after install.ps1/setup.ps1 fell back to CPU.
Pin companions per-arch (gfx120X/Strix -> the rocm7.2 trio, mirroring the PS floor
maps) and make the retry nonfatal: keep the existing build and let the user re-run
update to retry ROCm, so the chain install.ps1 -> setup.ps1 -> stack stays CPU-safe.
install.sh: reroute now targets an installed Ubuntu 24.04 OR 22.04 (24.04 preferred);
both are AMD-supported for ROCm-on-WSL, matching the leave-alone set, so a box with
only 22.04 reaches the GPU instead of staying CPU-only.
install.ps1/setup.ps1: a bare ~ for UNSLOTH_STUDIO_HOME left an empty Join-Path child
(PS 5.1 throws); fall back to USERPROFILE directly and only join a real remainder.
_path_inside_venv (amd.py + both installers): guard a root-dir sys.prefix so commonpath
can't classify every path on the drive as venv-internal (defensive; venv never at root).
uninstall.sh: guard an empty LOCALAPPDATA in the PS-interop icon cleanup (mirror APPDATA).
Tests: add 22.04-target reroute cases, Windows ROCm pin+nonfatal coverage (text +
behavioral), root-dir guard coverage, and bare-~/LOCALAPPDATA guard assertions.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* install.sh: match WSL reroute target by exact distro name, not substring
The 24.04/22.04 reroute target was chosen with grep -F (substring), so a custom
distro such as 'Ubuntu-24.04-test' (with no exact Ubuntu-24.04) was picked as the
target; the later 'wsl -d Ubuntu-24.04' then fails and the Strix Halo install stays
CPU-only. Match whole lines (grep -ixF) and reuse the matched name so only a real
Ubuntu-24.04/22.04 is targeted. Adds substring-rejection + exact-vs-custom tests.
* install.sh: keep the WSL reroute target to Ubuntu 24.04 (helper-supported only)
The ROCm-on-WSL bootstrap (scripts/install_rocm_wsl_strixhalo.sh) dies on any
VERSION_ID other than 24.04 and pins the noble repo, so treating 22.04 as
GPU-supported let the parent report a successful reroute while the child fell
back to CPU. Drop 22.04 from the supported set and the reroute target list;
24.04 stays the sole target (keeping the exact whole-line distro match). An
already-working ROCm on any other version is still left alone by the librocdxg
check above.
tests: reroute 22.04 cases updated to the 24.04-only behavior; make the
"no wsl.exe" case hermetic so a real host wsl.exe can't leak in on dev boxes;
stop the tauri exit-order check from mis-flagging the reroute helper's
[ "$TAURI_MODE" = true ] && ... --tauri one-liner.
* installer: tighten comment wording across the Strix Halo install/uninstall paths
Condense the verbose multi-line comment blocks (amd-smi hipinfo gate, ROCm
torch install + CPU fallback, WSL reroute, uninstall icon-keep) into fewer,
clearer lines. Comments and a few docstrings only; no code, logic, or
behavior change. Verified with bash -n, the PowerShell parser, and ast.parse,
and the installer test suite still passes.
* add AGPL-3.0 SPDX headers to the .sh/.ps1 scripts missing them
Every shell and PowerShell script under the Studio/installer surface now
carries the standard SPDX-License-Identifier: AGPL-3.0-only + copyright
header (after the shebang where present): the installer (install.sh,
install.ps1), build.sh, the .github and src-tauri scripts, the installer
test suite, and the moe kernel test. Header-only, line endings preserved;
bash -n, the PowerShell parser, and the installer tests all pass.
* installer: drop the duplicate AGPL header from install.sh and install.ps1
Both already carry an SPDX-License-Identifier: AGPL-3.0-only header below
their usage comment block; the prior header pass added a second one at the
top because it only scanned the first few lines. Remove the duplicate so each
file keeps a single original header.
* installer: force-reinstall CPU fallback torch; propagate Tauri NEED_SUDO from reroute
install.ps1/setup.ps1: when the AMD ROCm wheel install fails and we fall back to a
CPU base, force-reinstall the torch/vision/audio triplet. A failed ROCm install can
leave an unpinned ROCm torch (e.g. 2.10.0+rocm on gfx110X/gfx90a) that still
satisfies the CPU torch>=2.4,<2.11.0 range, so without --force-reinstall uv keeps the
ROCm build and only swaps the companions -- a mismatched venv the flavor-repair block
won't fix. setup.ps1 scopes the forced reinstall to the ROCm-fallback path
() so the genuine CPU-only install stays fast.
install.sh: the Strix Halo WSL reroute treated every nonzero child exit as a reroute
failure and fell back to CPU. In --tauri mode the child uses exit 2 ([TAURI:NEED_SUDO])
to ask the desktop app to elevate for the target distro; capture the child's exit code
and propagate exit 2 in Tauri mode (the child already printed the NEED_SUDO line)
instead of masking it. CLI mode still falls back to CPU on a generic failure.
Tests: reroute Tauri exit-2 propagation (and non-Tauri CPU-fallback) cases;
run_func now preserves the child exit code; force-reinstall assertions for both
PowerShell installers.
Note: codex's _rr_q apostrophe finding is a false positive -- the helper already
emits POSIX-correct 'O'\''Brien' and round-trips under both sh and bash.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* setup.ps1: fix $cpuForce array collapse in the ROCm->CPU torch fallback
An if-expression assignment ($cpuForce = if ($ROCmCpuFallback) { @("--force-reinstall") })
collapses the single-element array to a scalar string, so @cpuForce splatting enumerated
it character-by-character into broken single-letter args (- - f o r c e ...), which made
uv/pip reject the install and aborted the whole Studio setup on the AMD ROCm->CPU fallback
path. Build $cpuForce as a real array assigned outside the if-expression so the splat passes
a single --force-reinstall arg. Genuine CPU-only installs stay fast (empty array, no flag).
Test now asserts the array-build form and rejects the if-expression form.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* uninstall: remove the isolated Node.js runtime (~/.unsloth/node)
The isolated Node.js runtime (install_node_prebuilt.py, added with the managed-Node
change) installs to ~/.unsloth/node in default mode -- a sibling of studio, so deleting
<studio> leaves it behind (~200MB orphaned after uninstall). Both uninstallers already
remove the other default-mode siblings (llama.cpp/.cache/.staging); add node alongside
them. uninstall.ps1 also adds it to the handle-lock sweep so a held node.exe can't block
the delete. Env/custom mode nests node under the custom root, removed with that root.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Ayushman