Tag cached diffusion repos that ship no model_index.json with single_file in the
cached-models listing, and keep them out of the task-scoped On Device pickers
unless the curated catalog carries their artifact: the selection fall-through
loads uncataloged rows as a full pipeline and from_pretrained fails on a
single-file checkpoint repo after the GPU handoff.
Refresh diffusion status after a successful generation run on the Images page.
Speed Auto compiles the transformer on the third LoRA-free generation and flips
supports_lora to false; without the refresh the LoRA picker stayed enabled and
the next LoRA generation failed on the backend.
Match upload passthrough exact paths with trailing slashes normalized: the
trailing-slash variant of /api/train/diffusion/dataset reaches MaxBodyMiddleware
before the router's redirect_slashes 307, so it fell through to the default
/api/train body cap and 413ed large uploads. JSON sub-routes keep extra path
components after normalization and stay on the small cap.
The multipart upload passthrough was a bare /api/train/diffusion/dataset prefix,
so its JSON sub-routes (PUT .../{name}/caption/{filename}, POST .../import-example)
also bypassed the default JSON body cap and inherited the far larger upload limit.
A large caption/import body would then be buffered and parsed up to the upload cap
before the route-level length checks ran. Match the upload route by EXACT path
instead: the JSON sub-routes fall through to the normal small-JSON cap. Adds an
upload_passthrough_exact_paths param to MaxBodyMiddleware, with regression tests
that the sub-routes keep the default cap and a large sub-route body is 413'd.
- Image load now trust-gates a client-supplied base_repo. validate_load_request
rejects a base_repo that is not an unsloth/* repo, an allowlisted official base, or a
local path, mirroring the repo_id gate and the video loader. The route passes
base_repo into that pre-eviction validation, so an authenticated client can no longer
keep model_path on a trusted GGUF while pointing base_repo at an arbitrary remote repo
that the server would download and deserialize (a from_pretrained pickle/config path),
and no resident model is evicted for the rejected load.
- The keepwarm middleware now tracks the image and video generation routes
(/images/generate, /images/generations, /video/generate), so
other_inference_request_count() sees an in-flight generation and an API-key training
start is refused (409) before its unload would cancel that generation. endswith keeps
the GET *-progress and */cancel variants untracked.
- The OpenAI-compatible /v1 surface is now blanket body-capped like /api/inference,
instead of only /v1/chat/completions and /v1/completions. Every /v1 POST route
(images/generations, audio, embeddings, responses, messages, ...) buffers a JSON body
and none is a multipart-upload passthrough, so an unbounded ImageGenerationRequest
prompt on /v1/images/generations can no longer be buffered outside the request limit.
Adds regression tests: the base_repo trust gate at both the backend (untrusted remote
raises, local passes) and the route (untrusted base_repo returns 400 with no load), the
keepwarm tracking of the image/video generation paths (and not the progress/cancel
variants), and the /v1 surface being body-protected.
- Chat load defers the CHAT arbiter handoff until after identifier / gpu_ids /
training-memory validation, so a doomed chat load (bad id, unsupported gpu_ids on
GGUF, or a training 409) no longer evicts a resident Images/Video pipeline and then
errors. The already-loaded fast paths re-assert CHAT ownership themselves. Mirrors
the image and video loaders, which validate before acquire_for.
- Both training-start GPU cleanups now unload a resident Video pipeline and release the
VIDEO arbiter owner, not just Images/DIFFUSION, so starting LLM or diffusion training
after a video generation session no longer competes with the still-resident video
model and OOMs the run.
- Example dataset import materializes into a private staging dir and promotes into the
dataset folder only after the whole import succeeds. A materialize that fails partway
no longer leaves a partial dataset that a retry would treat as complete (imported=0),
stranding the user with a truncated dataset. Hidden dirs are skipped by the dataset
scan so the staging dir never surfaces as a dataset.
- Cached/local Wan GGUFs are now classified name-aware: arch "wan" alone is ambiguous
between the loadable single-DiT TI2V-5B and the dual-expert A14B MoE the loader
refuses, so _arch_to_task falls back to the repo/file name (as the loader's own
detect_video_family does) and tags only a non-MoE match text-to-video, surfacing
loadable Wan GGUFs in the Video picker without surfacing unloadable A14B files.
- The diffusion dataset upload route is added to the MaxBodyMiddleware upload
passthrough, so its own get_upload_limit_bytes() cap (plus multipart overhead, and a
raised max_upload_size_mb) applies instead of the default body limit rejecting
near-limit batches with 413 before the handler runs.
Adds regression tests for each: the chat handoff not evicting on a doomed load, the
video unload on diffusion-training start, the atomic import leaving no partial dataset,
the name-aware Wan GGUF classification (TI2V-5B video, A14B unsupported, bare arch
unsupported), and the diffusion upload passthrough cap.
routes/video.py mirrors the /images/* routes one-for-one: validate-before-evict
load ordering (a bad pick must not evict a working chat model and then 400),
the training-active interlock, the device-gated GPU arbiter handoff with the
new VIDEO owner, the exact-match sentinel mapping (VIDEO_NOT_LOADED_MSG /
VIDEO_CANCELLED_MSG to 409, ValueError/FileNotFoundError to 400 with native
paths redacted, everything else a sanitized 500), and the gallery CRUD shape
with fetch-one-extra has_more paging. Generate persists the encoded MP4 plus
its full recipe through video_gallery.save and returns the gallery record; the
file endpoint serves video/mp4 with an immutable Cache-Control, 404 on any id
that fails the containment check.
models/inference.py gains the video request/response set (VideoLoadRequest,
VideoGenerateRequest/Response, GalleryVideo, gallery list, both progress
shapes, VideoGenerationDefaults nested in VideoStatusResponse), reusing
DiffusionResolvedControl for the resolved-provenance badges. The router is
registered in main.py after the images router under the same /api/inference
prefix and auth dependency.
Tests: 20 route tests on a stubbed backend + real tmp gallery (load happy path
and arbiter acquisition, 400/409 mappings, generate persistence round trip,
mp4 file serving + 404, delete/clear, unload releases ownership); the
diffusion route suite stays green after the shared models edit.
* Studio: expose full compressed-tensors scheme set in an export formats dropdown
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: multi-select export formats, portable torchao FP8/INT8, GGUF LoRA, source parity
Export page overhaul on top of the formats dropdown:
- Unify merged precision into one sorted multi-select list (16-bit first, then
8-bit, then 4-bit). Drop "vLLM" from labels, add INT8 (W8A8), INT8 (W8A16),
INT4 (W4A16), MXFP4, MXFP8. Quick formats render as toggle pills; the rest live
in a multi-select "More formats" dropdown, so several formats export in one run.
- Add a portable torchao FP8/INT8 save path (Float8WeightOnlyConfig /
Int8WeightOnlyConfig) that needs no NVIDIA GPU to produce and loads in vLLM.
FP8 serializes to safetensors, INT8 to .bin. Wired into save_pretrained_merged
and push_to_hub_merged via a TORCHAO_EXPORT_SCHEMES registry and
_unsloth_save_torchao, parallel to the compressed-tensors path.
- Hide NVIDIA-only compressed-tensors formats when no NVIDIA GPU is present; keep
16-bit and portable FP8/INT8. The backend also rejects a compressed request on
non-NVIDIA hardware so it stays authoritative.
- Relax merged export to non-PEFT models so Local Model and Hugging Face sources
get the same 16-bit / compressed / portable options.
- GGUF: send the whole quant list in one call (merge once, quantize many).
- LoRA: add a GGUF adapter option (convert_lora_to_gguf.py) with an outtype
select (f16/bf16/f32/q8_0/auto), alongside the safetensors adapter.
- Thread the new fields through models, routes, orchestrator, and worker; extend
the export tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* 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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio export: show portable torchao formats only on non-NVIDIA (CPU) hosts
Portable torchao FP8/INT8 is the fallback for hosts without the NVIDIA compressed-tensors path.
On an NVIDIA GPU the compressed-tensors FP8/FP4/INT formats are the intended path (llm-compressor
auto-installs), so hide the portable duplicates there; keep them on CPU / non-NVIDIA hosts and
continue hiding them on macOS/MLX.
* Studio export: report all output folders and the exported formats
- Multi-format merged export now collects every sibling output directory (one per selected
precision) instead of only the last; the success banner lists them all.
- Show the selected precision formats in the run summary (a Formats row, like GGUF Quantizations),
so the panel says what is being exported rather than just 'Merged Model'.
- Persist the selected formats in the run summary and seed them on mount, so navigating away and
back (or toggling the export method) restores the selection instead of resetting to 16-bit.
* Studio export: list all output formats, add GGUF LoRA target, default Q8_0, auto-select newest checkpoint
- Progress/summary panel now shows a Formats row with the selected merged
formats, and the success banner lists every output folder a multi-format
merged run creates (one line per format) instead of only the last one.
- Merged format selection is seeded from the active run, so navigating away
and back (or switching method cards) no longer resets it to 16-bit.
- GGUF / Llama.cpp now offers an Export target toggle (Full model or LoRA
adapter) for adapter checkpoints, reusing the LoRA GGUF export path.
- Removed the Auto GGUF LoRA output type and defaulted to Q8_0 in the UI,
the request model, and the backend defaults; the outtype list is now
Q8_0/F16/BF16/F32. Core save.py still accepts auto for external callers.
- When a finetune has no checkpoint selected, auto-select the newest one.
* Studio torchao export: robust reload class + optional VLM import
Two fixes to the portable torchao FP8/INT8 export reload, from review of the
narrowed VLM detection:
- Encoder-decoder seq2seq checkpoints (T5/BART/Whisper) are not causal LMs.
With the narrowed is_vlm test they now correctly skip the image-text class,
but fell through to AutoModelForCausalLM and failed to reload after the merge.
Reload them with their own architecture class from the config instead.
- AutoModelForImageTextToText was imported unconditionally at the top of the
torchao path, so on Transformers builds without that class the import aborted
every torchao export (even text-only). Import it lazily only for a VLM, with
the AutoModelForVision2Seq fallback used elsewhere in Unsloth.
* Studio: enable FP8/FP4 compressed export for newer-transformers models
The shipped llm-compressor 0.10.x pins transformers<=4.57.6, so FP8/FP4 export failed
for models needing a transformers 5.x sidecar (Qwen3.5, Gemma-4, Qwen3-Next): the
quantization subprocess crashed importing the removed TORCH_INIT_FUNCTIONS.
Run the quantization against a dedicated llm-compressor-main "shadow": a --target
package dir (transformers 5.10.2 + llm-compressor main + compressed-tensors) layered
over the existing torch. It installs --no-deps so torch is never touched (works on any
Studio torch build), is provisioned lazily and fingerprint-cached, and can be turned
off with UNSLOTH_DISABLE_LLMCOMPRESSOR_MAIN.
- transformers_version.py: provision + validate .venv_llmcompressor.
- export.py: route all compressed exports through the shadow when available; else keep
the workspace 0.10.x path and fail fast past its transformers ceiling.
- save.py: launch _compressed_quantize.py with a clean PYTHONPATH = shadow.
- _compressed_quantize.py: skip linear_attn / vision tower / MTP modules (matches the
RedHatAI and NVIDIA reference quants, and is required by the grouped schemes).
Verified all four schemes (fp8, w8a8, w4a16, mxfp4) on Qwen3.5-9B and Llama-3.2-1B, and
fp8 on Gemma-4, end to end through Studio.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix GGUF LoRA export tests
* Fix export CI expectations
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
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Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.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
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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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* Update studio/frontend/src/features/settings/components/usage-examples.tsx
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* Update studio/backend/main.py
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* Update studio/frontend/src/features/studio/sections/progress-section.tsx
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* 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
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* 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
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* 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
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* Fix MLX GGUF reload smoke exit
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* Studio: opt-in OpenAI /v1 model auto-switch and idle keep-warm
The OpenAI-compatible endpoints serve whichever GGUF is loaded and ignore the
request model field, so an OpenAI client that changes model never reloads. Add
an opt-in setting that, when a /v1 request names a downloaded local GGUF
different from the loaded one, loads it before serving by reusing the existing
/load path (its dedup, tensor fallback, and threading apply). Unknown names
still serve the loaded model, so drop-in compatibility is preserved and no
remote download is triggered.
Also add an optional idle auto-unload (TTL keep-warm): a pure-ASGI middleware
tracks in-flight inference requests so a stream is never unloaded mid-response,
and a lifespan loop unloads the model after the configured idle seconds. Both
settings default off and live in the app_settings store, exposed via
GET/PUT /api/settings/openai-auto-switch.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: variant-aware auto-switch, /v1/responses coverage, keep-warm load stamp
Follow-ups from review of the opt-in OpenAI auto-switch path:
1. Variant-aware dedup. _maybe_auto_switch_model compared only the repo id, so
requesting another quant of the loaded repo (e.g. Q4_K_M loaded, Q8_0 asked)
was served by the old quant. Compare hf_variant too, matching /load dedup.
2. Streaming /v1/responses now calls the auto-switch hook. It went straight into
_responses_stream and only checked is_loaded, so stream=True could serve the
old model or 400. Non-streaming already routed through chat completions; the
hook is idempotent once loaded.
3. resolve_local_gguf tries an exact id match before splitting a trailing
:VARIANT, so local ids that contain a colon (e.g. a Windows path) resolve
instead of being cut at the drive letter.
4. Idle keep-warm stamps activity on a load/swap transition. _last_active was
only refreshed by inference requests, so a model loaded after the server sat
idle past the TTL could be unloaded before its first request.
Tests cover each case.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: make the /v1/responses auto-switch test order-independent
The new streaming-responses test passed in isolation but failed under the CI's
randomized collection order with "object has no attribute 'state'": it passed a
bare object() as the request and stubbed only one dispatcher, so an ordering
where the real dispatcher ran hit request.state. Give the request a state and
stub both dispatchers; the test still asserts the hook fires before dispatch.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: assert /v1/responses auto-switch wiring on source, not at runtime
The behavioral version executed openai_responses and relied on stubbing its
callees, which a randomized collection order in CI could defeat (the real
dispatcher ran and hit request attributes). Assert on the function source that
the hook precedes both dispatchers instead; the hook's runtime behavior is
already covered by the direct _maybe_auto_switch_model tests.
* Studio: auto-switch on /v1/embeddings, GGUF-only targets, idle-unload race gate
Second-pass review follow-ups on the opt-in auto-switch path:
1. /v1/embeddings now calls the auto-switch hook before the loaded-state check,
matching the other model-bearing OpenAI endpoints (the keep-warm middleware
already treats embeddings as inference).
2. The resolver index is now GGUF-only. The local-model scanners also surface
Transformers/safetensors repos; without a filter, auto-switch could unload
the GGUF and route a request into the non-GGUF loader. _has_local_gguf checks
a direct .gguf, a models-dir folder, and the HF-cache snapshots layout.
3. Idle keep-warm now holds an asyncio gate across the idle check and the
unload, and a request bumps inflight under the same gate, so the loop can no
longer unload in the window between "looks idle" and the kill.
Tests cover each. Broader local-model source parity (LM Studio, Ollama, legacy
caches, custom scan folders) is a follow-up; missing one of those today just
falls through to the loaded model.
* Studio: variant-aware local resolver, count_tokens + audio auto-switch coverage
Third-pass review follow-ups on the opt-in auto-switch path:
1. The resolver is now variant-aware via list_local_gguf_variants. It indexes
only the quants actually on disk, recursing snapshots and quant subdirs such
as the nested per-quant folders, so a requested repo:VARIANT resolves only
when that quant is local and a bare repo resolves to a concrete local quant.
This fixes two gaps: the previous shallow glob rejected nested-variant GGUF
repos, and a request for an uncached quant could send /load down the remote
download path, breaking the local-only contract.
2. /v1/messages/count_tokens now auto-switches like its sibling /v1/messages, so
a count uses the requested model's tokenizer.
3. /api/inference/audio/generate (direct GGUF TTS) is now tracked as in-flight
inference, so the idle loop cannot unload the model mid-generation.
Tests cover each. Two reviewer items are left as follow-ups: indexing the
remaining local sources (LM Studio, Ollama, legacy/default caches, custom scan
folders), which fails safe today by falling through to the loaded model; and
fully serializing concurrent different-model requests, an inherent limit of the
single-slot llama backend that the opt-in feature is not designed around.
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* Studio: make local GGUF resolver fail-safe so a bad model name cannot 500
The auto-switch hook calls resolve_local_gguf without its own guard, and
/v1/completions and /v1/embeddings pass body.get("model") through unchanged.
A non-string model (e.g. {"model": 123}) or any internal scan failure would
then raise out of the resolver and turn a request that would otherwise be
served by the loaded model into a 500, breaking the drop-in compatibility the
feature is built on.
Guard the resolver at its boundary: reject non-string input up front and wrap
the lookup so any failure returns None (fall through to the loaded model).
Add regression tests for both paths.
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* Studio: per-model launch flags for auto-switched GGUF models
* Studio: list switch-eligible GGUFs in /v1/models when auto-switch is on
* Studio: settings UI for OpenAI model auto-switch and idle auto-unload
* Studio: show save error over the disabled-idle hint in auto-switch settings
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* Studio: address gemini review (case-insensitive /v1/models retrieve, idle-input empty guard)
* Studio: address codex review (deterministic override args, exclude probe/embedding models from discovery)
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* Studio: keep-warm count_tokens, gate idle on auto-switch, drop hidden models
Three hardening fixes to the opt-in auto-switch path surfaced while reviewing
the work that builds on it:
1. count_tokens keep-warm. /v1/messages/count_tokens counts via the loaded
tokenizer and already auto-switches, but the keep-warm middleware did not
track it, so idle auto-unload could free the model mid-count. It is now a
tracked in-flight path.
2. "Off means unchanged" for idle unload. get_auto_unload_idle_seconds now
reports 0 while auto-switch is disabled. Idle unload only makes sense with
auto-switch on (an unloaded model returns only via the next request's swap),
so a stray TTL can no longer trigger a destructive unload while the feature
is off, keeping the disabled state identical to pre-feature behavior.
3. Hidden models are not switch targets. The resolver index now skips what
Studio hides from its own pickers (the llama.cpp validation probe, RAG
embedding weights) via _is_hidden_model, so they can never be auto-switched
to by name.
Tests added for each.
* Studio: bare-id reuse, responses validation order, in-flight tracking
Review follow-ups after folding in the per-model overrides and discovery work:
1. A bare model id (no :VARIANT) is now satisfied by any loaded quant of that
repo. Previously a bare name resolved to the largest local quant, so it could
force a slow reload when a different quant of the same repo was already
serving. An explicit repo:VARIANT request still honors the quant.
2. /v1/responses now runs the auto-switch hook after the empty-input validation
so a request that 400s can no longer trigger a multi-minute model load before
being rejected. The hook still precedes both dispatchers, so streaming
requests switch.
3. The keep-warm middleware now tracks in-flight requests whenever auto-switch
is enabled rather than only when the idle TTL is already positive, so a stream
that starts with the TTL at 0 is still protected if idle-unload is enabled
mid-stream. Off still passes straight through.
Tests added for each.
* Studio: tighten auto-switch code comments
Comment/docstring-only pass over the OpenAI auto-switch feature: collapse
multi-line blocks, drop a comment that restated the gate it sits next to, and
trim verbose docstrings on internal helpers while keeping the load-bearing
rationale (concurrency, API behavior, drop-in compat, gotchas). No logic
change: verified comment-only with the AST/printer signature check.
* Studio: bind auto-switch locks per running loop
Review follow-up. The auto-switch swap lock and the keep-warm unload gate were
module-level asyncio.Lock objects. That is safe under the single uvicorn loop
and on Python 3.10+ (the Lock resolves the running loop lazily on acquire), but
a module-level Lock binds to one loop on pre-3.10, which can raise a loop
mismatch in multi-loop runners. Resolve each lock through a per-loop accessor
backed by a WeakKeyDictionary so every running loop gets its own Lock and stale
loops are collected. No behavior change under the server's single loop.
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* Studio: auto-switch re-review fixes (body codes, coverage, swap, alias, tracking)
Follow-ups from a second review pass over the opt-in OpenAI auto-switch feature:
1. OFF-state status codes: /v1/completions and /v1/embeddings moved the body
read ahead of the loaded-state check, so a malformed/empty body with no model
loaded returned 500 instead of the prior 503. A shared helper reads the body
defensively (an unparseable/non-dict body yields no model), and the handler
re-reads after the 503 gate to surface the original parse error exactly as
before. OFF behavior is unchanged.
2. Local-model coverage: the resolver index only scanned ./models and the active
HF cache, while the model picker also lists the legacy/default HF caches, LM
Studio dirs, and user scan folders. A request for one of those named models
silently served the loaded model instead. _build_index now scans the same
roots (Ollama's symlink-creating scanner is skipped on the request path), and
resolution is offloaded with asyncio.to_thread so the wider scan never blocks
the event loop.
3. Swap vs in-flight stream: a cross-model swap killed the llama-server while
another client was still streaming from it. The hook now tracks how many
requests are streaming on the loaded model (in-flight minus those still inside
the hook) and returns 409 instead of swapping while one is active. Concurrent
same-model requests never reach this path, so they are unaffected.
4. Idle-unload + alias: after idle-unload freed the model, an unknown/alias name
resolved to nothing and 503'd, though it served the active model before the
TTL. Idle-unload now remembers the freed id and an alias request reloads it
(only an already-local model, so no remote download), cleared once a model is
loaded again.
5. In-flight tracking: the keep-warm middleware tracked in-flight only while the
feature was on, so a stream started while off could be unloaded if idle-unload
was enabled mid-stream. It now tracks on every inference path; counting is
cheap and invisible to clients.
Tests added for each.
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* Studio: auto-switch review round 3 (revert swap guard, hardening)
Addressing a third review pass:
- Revert the cross-model swap guard. It counted keep-warm in-flight (which
includes external-provider calls that never touch the local model) and so
could 409 a local swap spuriously, and it still left a same-model request able
to start streaming on the model a concurrent swap was unloading. A correct fix
needs a request-lifetime reader/writer barrier; a partial guard was worse than
the honest single-slot behavior, so concurrent different-model use is back to
being serialized (documented), like llama-swap's single slot.
- Non-string request model (e.g. {"model": 123} on a raw-body endpoint) is now
treated as absent, so it falls through instead of raising in the membership
checks once an idle-unload stash exists.
- Idle-unload now stashes and replays the freed quant: an alias reload restores
the exact (id, variant) that was freed rather than the largest local quant.
- Anthropic /v1/messages validates max_tokens before the auto-switch hook, so a
request that 400s never triggers a model load.
- Keep-warm tracks a pending count for requests waiting on the unload gate, so
the idle loop cannot unload the model out from under a request that is blocked
on the gate but not yet counted as in-flight.
- The idle-unload task is awaited after cancel on shutdown to avoid pending-task
warnings.
- The resolver's HF cache scan is None-safe and logs at debug instead of letting
a bad root abort the whole index build.
- upsert_app_setting_map_entry rolls back explicitly on error.
Tests updated/added for each.
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* Studio: keep saved idle-unload seconds when auto-switch is toggled off
* Studio: auto-switch hardening (thread-safe lock maps, body validation)
Defensive fixes from review:
- Guard the per-loop WeakKeyDictionary get-or-create for both the unload gate and
the auto-switch lock with a threading lock, since WeakKeyDictionary mutation is
not thread-safe when two event loops run on different threads.
- Build the resolver index under the cache lock so concurrent callers with an
expired cache don't all run the multi-dir scan at once.
- /v1/completions and /v1/embeddings return a clean 400 for a valid JSON body
that is not an object (e.g. a list), instead of a 500 from body.get(...).
- The keep-warm middleware only tracks POST requests (inference is always POST),
so CORS preflight (OPTIONS) is not counted, and tolerates a None path.
Tests added for the list-body 400 and the non-POST skip.
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* Studio: auto-switch review round 4 (local-path load, swap guard, idle fixes)
From a 10-reviewer pass:
- HF-cache entries now load by a concrete local path, not the bare repo id. The
resolver records a load_path (the snapshot dir for a models--* cache repo, the
file/dir otherwise) so /load takes the local branch and can never trigger a
download to satisfy a partial cache. The advertised loader_id (repo id) is kept
as the launch-override key. resolve_local_gguf now returns
(load_path, variant, loader_id).
- Re-add a single-slot swap guard: a cross-model swap returns 409 model_switch_busy
while another inference request is active rather than killing its stream (the
caller is excluded from the count), and holds the keep-warm gate across the load
so no new inference starts mid-swap. Concurrent same-model requests never reach
this path. A residual spurious 409 is possible while a concurrent or external-
provider request is active; that is the documented single-slot tradeoff.
- Idle keep-warm tracks (model_identifier, hf_variant): reloading the same repo at
a different quant counts as a fresh model, so it is not unloaded before one TTL.
- Track Studio's own /api/inference/generate/stream so the idle loop can't unload
the model mid-stream on that route.
- A successful manual /load clears the idle-unload reload stash synchronously, not
only on the next idle poll.
Also merged origin/main (the branch had fallen behind, which would have reverted
unrelated files on merge). Tests added/updated for each.
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* Studio: auto-switch review round 5 (concurrency, identity, load gate)
From a 10-reviewer pass (9 request-changes, 1 approve):
- Concurrent same-target requests load once instead of each returning 409. The
count-based busy guard could not tell "another request wants the same model"
(safe, load once) from "another request is using the loaded model" (refuse).
Track in-flight auto-switch requests per (target, variant) and subtract
same-target waiters from the busy count; a cross-model swap still 409s while a
genuinely different request is active.
- Fix the identity confusion introduced when round 4 began loading by concrete
local path: the backend identifier became a filesystem path. Record the
advertised repo id on the backend after an auto-switch load and use it so
(a) a model loaded manually by repo id is recognized as already serving
(no spurious reswap/409), (b) /v1/models reports the repo id, never a host
path or a duplicate, and (c) the idle-unload stash keeps the override keyed by
the repo id, so an alias reload after TTL keeps the user's saved launch flags.
- Gate the manual /load route with the keep-warm lifecycle gate so idle
auto-unload can't unload a model mid-load. load_model now wraps _load_model_impl
in the gate; auto-switch calls _load_model_impl directly since it already holds
the gate.
- Restore default-off parity on Anthropic /v1/messages: an unloaded backend with
auto-switch disabled 503s before the max_tokens 400 check, as it did pre-feature.
When the feature is on, request-shape validation still runs before any load.
Tests added for each; full backend suite diff vs baseline is unchanged.
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* Studio: auto-switch review round 6 (concurrency ordering, leaks, unload gate)
From a second 10-reviewer pass (8 request-changes, 2 approve):
- Same-target concurrency: register a waiter by the raw requested model before
the (slow) resolve, and exclude pending requests from the swap busy count. The
middleware counts a concurrent same-model request as in-flight before it
resolves and joins the resolved-target waiter map, so the prior fix could still
409 it. The guard now subtracts max(same resolved-target, same raw-request)
waiters and ignores pending (a pending request is blocked in the middleware,
not generating, so a swap can't interrupt it).
- External-provider requests no longer block a local swap. The keep-warm
middleware counts every inference-path POST, but external-provider chat returns
before the auto-switch hook and never touches the local GGUF. The chat handler
now untracks itself before proxying, so its in-flight stream can't trip
model_switch_busy on a concurrent local auto-switch. The middleware skips its
own end-decrement for an untracked request.
- Manual /unload is gated like load and idle-unload: it holds the lifecycle gate
and returns 409 rather than tearing down llama-server while an inference request
is in flight.
- Response model id no longer leaks the load path. /v1/models already advertised
the repo id; chat, completions, embeddings, Anthropic messages, and audio
response bodies now use the same _llama_public_model_id helper instead of the
concrete on-disk model_identifier.
- Chat completions validates the non-system-message requirement before the
auto-switch hook (as /responses and /messages already do), so an invalid
request can't swap the resident model before returning 400.
Tests added for each; full backend suite diff vs baseline is unchanged.
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* Studio: auto-switch review round 7 (teardown policy, Unsloth-active swap, training)
From a third 10-reviewer pass (9 request-changes, 1 approve), all on the same
asymmetric-teardown theme. Resolved per the intended policy that only automatic
paths defer to an active stream; deliberate user actions stay interrupting:
- Revert the manual /unload in-flight guard added last round. A manual /load or
/unload is a deliberate action and tears down immediately, as before; only the
automatic idle-unload loop and auto-switch defer to an active request. This
removes the asymmetry the reviewers flagged (manual /load, the /unload Unsloth
branch, and the opposite-backend swaps inside _load_model_impl) by not
extending the guard to deliberate paths, rather than spreading it.
- Auto-switch now refuses a swap whenever another inference request is in flight,
not only when a GGUF is already loaded. _load_model_impl also unloads an active
Unsloth/transformers backend before loading a GGUF, so the busy guard must cover
that case too; otherwise an Unsloth stream could be killed by an auto-switch.
- Refuse API-initiated training while inference is active. When Studio is driven
as an inference API (sk-unsloth key auth), POST /api/training/start returns 409
if a request is in flight, since training frees VRAM by unloading the chat
model and would kill the stream. The Studio UI (session auth) still starts
training and coexists/frees VRAM as before. A mixed UI+API session is not yet
special-cased. Adds auth.authentication.authenticated_via_api_key.
Tests added/updated for each; full backend suite diff vs baseline is unchanged.
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* Studio: add UNSLOTH_MODEL_IDLE_TTL env override for idle-unload
Borrowed from PR 6517: a startup env var that sets the idle-unload TTL without
the settings UI. Unlike the stored setting (gated on auto-switch), the env value
is a standalone default that enables idle-unload even with auto-switch off, for
headless/container deploys. An explicit UI/API value still overrides it and stays
gated. The settings GET reflects the env default when nothing is stored.
* Studio: auto-switch fixes from review (paths, embeddings input, env idle reload)
- /v1/models advertises a client-facing alias instead of a filesystem path:
the ./models and LM Studio scanners report the on-disk path as the model id,
so the index now prefers model_id/display_name as the advertised/override id
and keeps the concrete path internal as load_path, still resolvable by path.
- /v1/embeddings validates input before auto-switch: a request with a model but
no input now 400s before the hook (like chat/responses/messages), so an
invalid embeddings request cannot unload or swap the resident model.
- Standalone UNSLOTH_MODEL_IDLE_TTL reloads the freed model: the hook now runs
when auto-switch or idle-unload is active, and with auto-switch off it skips
the resolver and only restores the idle-unloaded model, so the first idle
timeout no longer leaves later /v1 requests with nothing loaded.
- Do not resurrect a stale GGUF over an active Unsloth model: the reload-stash
path bails when a non-GGUF backend is loaded, so an unknown /v1 name cannot
tear down a live Transformers/Unsloth model.
- Defensive HF cache scan: each cache root's resolve/dedup is wrapped so a
missing or malformed root skips that root rather than aborting the index.
- Single-model retrieve checks the id is a string before lowercasing.
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* Studio: fix automatic-load asymmetry, audio reload, preview, idle timer
The standalone UNSLOTH_MODEL_IDLE_TTL reload is a second automatic-load
trigger, but several validate-before-switch guards and reload hooks only
checked the auto-switch toggle. Add a shared _automatic_model_load_may_run()
(auto-switch on, or idle TTL > 0) and route every guard through it.
- /v1/completions validates prompt before any automatic load (it was the one
model-bearing route with no pre-check).
- /v1/chat/completions and /v1/embeddings pre-checks gate on the shared
predicate so a standalone idle TTL cannot reload then reject.
- /v1/messages no longer 503s before the reload hook can restore an idle-freed
model when auto-switch is off.
- Raw completions/embeddings with no model field pass a non-empty sentinel so
the idle-stash reload runs, restoring the legacy "omit model, use loaded" path.
- /api/inference/audio/generate gains the reload hook (after message validation)
so an idle-freed audio GGUF is restored.
- Public preview opts out of auto-switch via a request-scope flag, so a caller's
model field cannot swap away from the pinned checkpoint; preview chat streams
are now matched by _is_inference_path so idle-unload cannot kill them.
- Keep-warm no longer stamps activity on request start, and external-provider
untracking decrements without restamping, so periodic external traffic can no
longer keep the local GGUF warm forever.
Merges origin/main (the branch had fallen behind, which also brought in the
preview route the review flagged).
* Studio: surface model auto-switch in the API tab and demo it in examples
The OpenAI auto-switch toggle previously lived only in Settings -> General.
Add the same toggle to the API tab's usage-examples panel (it shares the
settings cache), and make the examples reflect it: when on, the Python
examples append a second call naming a different downloaded GGUF (so the
model field visibly selects which model serves), and the curl examples gain
a one-line note. Reuses the existing settings API client and i18n keys.
* Studio: harden OpenAI auto-switch reload-only path and Anthropic tool validation
- Omitted-model raw-body requests pass a reload-only sentinel so the idle-stash
reload still restores an idle-freed model, but the resolver never matches a
downloaded GGUF literally named "default".
- Reject malformed Anthropic client tools before _maybe_auto_switch_model so an
invalid request can no longer evict the loaded model.
* Studio: extend auto-switch reload-only and tool validation to schema endpoints
- Schema-backed endpoints (chat completions, responses, count_tokens, messages,
audio) defaulted an omitted model to "default" and passed it to the switch
hook, so a downloaded GGUF named "default" could be swapped to. Route the hook
through a helper that switches only on an explicitly set model, else reload-only.
- Propagate the explicit-set status when building the chat request from a
Responses request, so the non-streaming chat re-check stays reload-only too.
- Validate Responses function tools before the switch hook so a malformed tool
returns 400 without evicting the loaded model.
* Studio: serialize auto-switch swaps across event loops with a process-wide gate
The auto-switch lock is a per-event-loop asyncio.Lock, so two /v1 swaps on
different loops in one process could both pass it and race the single model slot
(the backend and _load_model_impl are process-wide). Add a process-wide
threading gate around the swap, acquired off the loop so a cross-loop wait never
blocks it, layered with the existing per-loop lock. Add a cross-loop test that
fails without the gate (two slow loads overlap) and passes with it.
* Studio: make the auto-switch swap gate wait cancellation-safe
_acquire_swap_gate awaited asyncio.to_thread(lock.acquire) when another loop held
the process-wide gate. to_thread cancellation doesn't stop the worker thread, so a
/v1 request cancelled mid-wait (client disconnect during a cross-loop swap) would
have its thread acquire the gate after the fact, while the finally that releases it
never runs -- permanently deadlocking later auto-switch swaps.
Poll a non-blocking acquire off a short asyncio.sleep instead: it still keeps the
wait off the loop and serializes across loops, but a cancel now lands during the
sleep, when the gate is not held, so nothing leaks. Add a test that deadlocks the
to_thread variant (it times out) and passes with the poll.
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* Studio: validate modality and tool-confirmation before auto-switch
Two more request shapes could load a named GGUF and only then 400, evicting the
resident model:
- An image request naming a different text-only GGUF. The switch hook now takes
require_vision and rejects a swap to a non-vision target before loading it; a
GGUF's vision capability is its companion mmproj, knowable without a load, and
matches the post-load guard. Only the resolver branch is checked, never the
reload-stash restore.
- confirm_tool_calls=true with stream=false and local tools. /v1/chat/completions
now rejects that shape before the hook, mirroring the local tool path's
bypass_permissions exemption and intent signal.
The vision probe threads the ambient HF token to keep the capability-probe
invariant. Reload-only and idle-reload paths are unaffected.
* Studio: extend validate-before-switch and make the lifecycle gate process-wide
- /v1/messages/count_tokens now rejects malformed client tools before the switch
hook, like /messages (shared _validate_anthropic_client_tools helper), so a
count request can't evict the loaded model.
- /v1/chat/completions rejects a malformed tool_choice forcing object (a
{"type":"function","function":{}} with no name) before the switch hook.
- The inference lifecycle gate that blocks new inference during a swap is now
process-wide (a poll-acquired threading lock, cancellation-safe), not a
per-loop asyncio lock, so a request on another event loop can't start inference
while a swap tears the single backend down.
- Usage examples no longer hard-code a switch-demo repo most users lack; the
model is an explicit placeholder the user replaces.
* Studio: extend the auto-switch modality guard to /v1/responses and /v1/messages
The pre-load vision check that guards /v1/chat/completions now also runs on
/v1/responses and /v1/messages, so an image request naming a text-only GGUF is
rejected before the swap and never evicts the resident vision model. Run the
vision capability probe off the event loop. Make the /v1/models retrieve
loaded fast-path case-insensitive, and never advertise a host path from the
resolver. Remove the dead list_switch_eligible_ids helper, superseded by the
/v1/models catalog.
* Studio: filter /v1/models to GGUF, per-loop catalog lock, reject system-only Responses
Address review findings on the auto-switch path:
- /v1/models advertises only GGUF models the API can actually switch to; a
safetensors/LoRA entry would be selectable but never loadable via llama.cpp.
- The /v1/models catalog cache uses a per-loop lock (like the auto-switch path)
so a second event loop awaiting it can't hang in a multi-loop process.
- /v1/responses rejects system/developer-only input before the switch, mirroring
chat, so an invalid request can't evict the resident model.
- _build_index guards each scan source on its own so one bad root drops only
that source; the vision probe logs a real detection failure instead of
swallowing it.
* Studio: list cached GGUFs in /v1/models by inspecting files, not model_format
The HF-cache scanner leaves model_format unset for GGUF snapshots, so the
previous model_format == "gguf" filter dropped every downloaded HF-cache GGUF
from /v1/models and the retrieve fallback. Decide GGUF-ness from the on-disk
files via the resolver (info_has_local_gguf) instead, run off the event loop, so
the catalog advertises exactly what /v1 can serve.
* Studio: fix /v1/messages/count_tokens route binding plus auto-switch review fixes
The @router.post decorator for /messages/count_tokens had been separated from
anthropic_count_tokens by the _validate_anthropic_client_tools helper, so the
route bound to the validator and dropped its auth dependency. Move the decorator
back onto the handler. Add route-binding tests asserting each /v1 endpoint maps
to its handler with the auth dependency, so a decorator/handler split is caught
at the route level (the direct-call tests missed it).
Also from review:
- update_openai_auto_switch writes both settings keys in one transaction so a PUT
can't leave one updated and the other stale (drop the now-unused single setters).
- max_seq_length override rejects 0 at the boundary (ge=1) instead of accepting
then silently dropping it.
- Document that embeddings auto-switch is best-effort: GGUF pooling has no cheap
pre-load probe like vision's mmproj, so a guard would false-reject GGUF embedders.
- Add a positive idle-unload test (loop frees the model and stashes it for reload).
* Studio: validate Responses tool_choice + Anthropic mixed tools before switch, filter Ollama from catalog
More auto-switch review findings:
- /v1/responses rejects a forcing-function tool_choice with no name before the
switch, mirroring chat, so a malformed request can't evict the resident model.
- /v1/messages rejects mixing Anthropic server tools with custom client tools
before the switch (the check depends only on the payload, so it moves up cleanly).
- /v1/models no longer advertises Ollama-link models: info_has_local_gguf excludes
.studio_links / ollama_links entries, which the resolver skips and can't switch
to, so an advertised id never silently falls through.
* Studio: guard chat audio input before switch; surface env-backed idle unload in settings UI
A chat request carrying audio_base64 rides the same companion mmproj
projector as a vision request, so a text-only target cannot serve it
either. Flag require_vision for audio input as well so the multimodal
probe runs before the switch and a rejected request never evicts the
working model. Generalize the reject message to cover image and audio.
The settings response now reports idle_unload_active (effective TTL > 0)
so the UI can distinguish idle-unload that is active via the
UNSLOTH_MODEL_IDLE_TTL env var from the case where it needs the toggle
enabled.
* Studio: harden auto-switch eviction guards (count_tokens vision, TTS reload-only, mmproj/stash)
Four eviction/correctness fixes on the opt-in /v1 auto-switch path:
- /v1/messages/count_tokens now carries the same require_vision guard as
/messages, so an image count naming a text-only GGUF can't evict a loaded
vision model for a swap that can't serve the request.
- /audio/generate is now reload-only. A local GGUF's audio-input capability
is not a cheap pre-load probe (the companion mmproj signal can't tell an
audio projector from a vision one, and codec TTS ships no projector), so
resolving the client model could load a text/vision-only target and evict
the working audio model before the audio check fails. Only the idle-stash
restore runs here; switching TTS models is an explicit /load.
- The resolver no longer treats a standalone mmproj .gguf as a servable
model. _scan_models_dir's standalone-file pass does not filter mmproj the
way its directory scan does, so /v1/models could advertise a projector and
a switch could load it over the real weights.
- A non-GGUF (Transformers/Unsloth) load and a deliberate /unload now clear
the idle reload stash, so a manual load/unload is never superseded by a
stale idle-freed GGUF that the next /v1 request resurrects.
* Studio: report advertised repo id consistently after an auto-switch
Two model-id reporting fixes so an auto-switched cached HF GGUF is named by
its repo id everywhere, not its snapshot path:
- Streamed /v1/responses envelopes now derive the model id from
_llama_public_model_id (which prefers _openai_advertised_id) instead of the
raw model_identifier. After an auto-switch the identifier is the snapshot
path while the repo id lives in _openai_advertised_id, so the stream used to
report a snapshot basename while /v1/models, chat completions, and
non-streaming Responses all reported the repo id.
- When an advertised alias already resolves to the loaded model (a model
loaded by local path, requested by its repo or LM Studio id), the
already-serving early return now records the alias as the advertised id, so
/v1/models and responses report the alias and mark it loaded instead of the
path-derived basename. Resolver branch only; safe lock-free because an
in-flight request blocks any concurrent swap via the single-slot busy guard.
* Studio: validate request shapes before auto-switch (prompt/input/audio/mcp confirm)
Four more validate-before-switch guards so a deterministic client error never
evicts the resident model on the opt-in /v1 auto-switch path:
- /v1/completions rejects an object/number prompt (only a string or array is
valid) before the switch, instead of loading the named GGUF and letting
llama-server reject the shape afterward.
- /v1/embeddings rejects an object/number input the same way.
- Chat rejects an oversized audio_base64 upload (413) before the switch. The
size cap is a cheap, target-independent length check; the decode itself
stays post-switch to avoid decoding a valid upload twice.
- The chat confirm-without-stream pre-switch guard now mirrors the tool loop's
actual enablement: _effective_enable_tools (honoring a CLI --enable-tools
policy) and mcp_enabled (which opens the tool loop on its own but defers to a
CLI --disable-tools policy). Previously a confirm+no-stream request with only
mcp_enabled slipped past and 400'd after the swap.
* Studio: fix model-id retrieval, streaming n>1, resolver cache TTL, keep-warm auth
Four fixes from review:
- GET /v1/models/{id} legacy raw-path fallback now maps the raw identifier to
the same public id its /v1/models entry uses. After an auto-switch load the
identifier is the snapshot path while the entry is keyed by the advertised
repo id, so a client that cached the old absolute path no longer 404s on a
model that is in fact loaded.
- stream=true with n>1 is now rejected before the switch. Only the
non-streaming GGUF path returns multiple choices, so streaming n>1 is invalid
on every local serving path; both fields are known pre-switch, so it must not
load model B only to 400 and evict model A. Non-streaming n>1 stays
post-switch where the serving path decides.
- The resolver index cache is stamped after _build_index, not with the pre-scan
timestamp. On installs with enough local models for the multi-root scan to
exceed the 5s TTL, the cache was stored already expired and every request
rebuilt it.
- The keep-warm middleware no longer stamps model activity for 401/403
responses. It runs before FastAPI auth, so unauthenticated probes used to
refresh the idle timer without touching llama.cpp; they now decrement the
in-flight count without keeping the model warm.
---------
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Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
Extracted and narrowed from unslothai/unsloth#6543 by @TheJagStudio.
This keeps the startup/banner and text file encoding hardening separate from the already-merged Python code-exec UTF-8 fix in #6548.
Co-authored-by: Jagrat Patel <81472856+TheJagStudio@users.noreply.github.com>
* Speed up Studio desktop startup
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* Address Studio startup review findings
* Keep orphaned run cleanup before readiness
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: harden the data-recipe and inference consumer loops against pump death
Follow-up to #6643. The same single-unsupervised-consumer pattern the training
pump had lives in two sibling loops, with the same failure mode: one bad event
kills the only thread that updates the in-memory state every UI surface reads,
while the worker subprocess keeps running.
- data_recipe JobManager._pump_loop: a malformed worker log line that makes
parse_log_message raise no longer kills the pump. Guard _handle_event, the
queue read, and the worker-exit finalize, and broaden _drain_queue so a drain
error still finalizes the job instead of leaving it wedged "active" (which also
leaked the workflow-scoped API key until its 24h expiry).
- inference InferenceOrchestrator._dispatcher_loop: guard the routing body so a
malformed response or a mailbox put error can't kill the dispatcher and hang
every in-flight generation (callers key liveness on the subprocess, not on
this thread).
Adds regression tests for both.
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* Studio: extend consumer-loop hardening to RAG, hub, auth, and stream-reader paths
Continuation of the data-recipe and inference pump hardening: the same
"background producer updates in-memory state that a single unsupervised
consumer surfaces to the UI" pattern shows up in several more Studio paths,
each able to silently freeze a UI surface while the worker keeps running.
RAG ingestion SSE (core/rag/ingestion.py):
- job_events polled the queue with a blocking get and never noticed client
disconnect or a dead worker, so a closed tab or a producer that died
without emitting a terminal event left the stream hanging. It now polls
with a timeout, emits heartbeats, ends on terminal job status, caps idle
time, and always pops the job registry in finally.
- Added _reap_finished_jobs() and call it from start_ingestion so finished
job state does not accumulate.
Startup reconcile (storage/rag_db.py, main.py):
- reconcile_orphaned_ingestion_jobs() marks ingestion jobs (and their
documents) that were left non-terminal by a previous crash as failed, so
the UI does not show jobs stuck "running" forever after a restart. Wired
in at startup next to cleanup_orphaned_runs().
Hub download watcher (hub/services/download_lifecycle.py):
- _watch() could leave a job pinned "running" if finalize raised. Body is
now guarded: on failure it logs and sets the job to error, and always
invalidates the hf cache scan in finally.
External provider stream (core/inference/external_provider.py):
- read timeout was None (no stall ceiling); set to 300s so a wedged
upstream surfaces as an error instead of an indefinitely hung stream.
Auth store (auth/storage.py):
- Enable WAL + busy_timeout on the auth DB so token validation (read on
every request) and login writes stop serialising on the rollback journal.
Matches studio_db / rag_db / providers_db.
Login rate limiter (routes/auth.py):
- _LOGIN_IP_BUCKETS could grow unbounded under spoofed-IP traffic; cap it
and prune stale buckets, mirroring the per-account bucket handling.
Training progress SSE (routes/training.py):
- Break promptly on client disconnect instead of waiting for the next
yield to fail on a closed socket, matching the export / data-recipe SSE
routes.
llama-server stdout drain (core/inference/llama_cpp.py):
- Broaden the drain guard so an unexpected decode/read error logs at debug
and stops the drainer cleanly instead of escaping the thread.
Frontend stream readers (chat-api.ts, rag-api.ts):
- Wrap the SSE read loops in try/finally + reader.cancel() so early return
([DONE]), thrown errors, and consumer aborts release the reader lock
instead of holding it until GC.
Tests:
- test_training_progress_stream_nan: fake request now implements the async
is_disconnected() the route polls, matching the other SSE route fakes.
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* Studio: address Codex review feedback on the consumer-loop hardening
Four follow-ups from the automated review, all on code this PR introduced:
- Data-recipe pump (manager.py): a queue read that keeps raising an error
outside the read's narrow catch set (e.g. a broken queue pipe after the
child died) hit the `continue` guard and skipped the dead-worker finalize
below, spinning forever and leaving the job wedged "active" with its
workflow key unretired. On a read failure, fall through to finalize when
the worker is no longer alive. Added a regression test.
- RAG ingestion SSE (ingestion.py): the 5-minute idle cap could end the
stream while the job was still pending/running (a large document spends
minutes in embedding/storing with no per-batch progress event). The route
then sends [DONE], and the client treats a no-terminal-frame end as
completion, marking the document indexed mid-ingestion. Drop the idle cap:
while the worker is alive and non-terminal we keep heartbeating; the stream
ends only on terminal DB status, the None sentinel, or client disconnect.
- Login rate limiter (auth.py): the per-IP path pruned but then added the
new IP unconditionally, so a spoofed-source-IP spray kept _LOGIN_IP_BUCKETS
unbounded and made every new IP pay a full-dict prune scan. Gate the add on
the cap, mirroring the account path.
- Hub download watcher (download_lifecycle.py): if finalize raised before it
reaped (proc.wait) and dropped the worker (e.g. an I/O error draining
stderr), the crash path published a terminal state while the live Popen
stayed registered and kept writing the cache, and the terminal set_job let
claim() admit a retry on the same repo. Terminate + drop the worker before
setting the terminal state.
* Studio: keep login throttling working when the per-IP bucket dict saturates
Review follow-up. The previous cap fix skipped creating a bucket for a new IP
once _LOGIN_IP_BUCKETS was full, returning ip_fails=0. Under a sustained spray
that also fills the account dict, every failure from such an IP then looked
first-seen and _login_blocked had no bucket to enforce, so the cap effectively
disabled throttling once saturated.
Bound the dict with a FIFO eviction instead: if the IP is new and the dict is
full, reclaim expired buckets (rate-limited so a burst of distinct IPs can't
make each failure an O(n) sweep) and, if still full, evict the oldest-inserted
IP. The new IP always gets a real bucket, so a saturating (e.g. spoofed
X-Forwarded-For) spray stays throttled while memory stays bounded. Added a
regression test that saturates the dict and asserts a later IP is still blocked.
* Studio: address Codex review (RAG queue lifecycle, stream error, orphan chunks)
Three follow-ups on the Phase 6 changes:
- RAG ingestion SSE (ingestion.py): job_events removed the per-job queue in its
finally on ANY exit, including an early client disconnect while the worker is
still running. That dropped the worker's later events (the queue is the only
one _emit writes to) and made a reconnect find no queue and receive only
[DONE], which the client treats as completion. Only drop the queue on a
terminal exit (None sentinel / terminal DB status); leftover terminal queues
are still swept by _reap_finished_jobs. Added queue-lifecycle tests.
- External provider stream (routes/inference.py): once the 300s read timeout can
fire, the stream's except path failed the monitor but ended without an error
frame or [DONE], so the chat client saw a bare EOF and saved the timed-out
answer as a successful partial with no error. Emit an SSE error frame (and
[DONE]) on stream failure so the client surfaces it.
- RAG startup reconcile (storage/rag_db.py): marking a half-ingested document
failed left its chunks/fts/vec rows intact, and retrieval filters by scope not
status, so a failed document could still be retrieved and cited. Purge the
document's chunks when reconciling it to failed (the doc row stays for
re-ingest).
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* Studio: release the remaining SSE stream readers (training, data-recipe, export)
reviewer.py follow-up. The chat and RAG SSE readers were wrapped in
try/finally + reader.cancel(), but the other three readers built on the same
response.body.getReader() pattern were left without it: streamTrainingProgress,
streamRecipeJobEvents, and streamExportLogs leak the ReadableStreamDefaultReader
lock (held until GC) when the consumer aborts, returns early, or a parse/callback
throws. Wrap each in try/finally + reader.cancel() (export already had a
try/catch, so it only needed the finally). All five frontend SSE readers now
release the reader symmetrically.
* Tighten resilience comments and docstrings
Condense the verbose explanatory comments and internal-helper docstrings added
in this branch to shorter, clearer forms. Comment/whitespace only; verified no
code changed via AST diff. No behaviour change.
* Studio: keep chunks for completed docs during ingestion reconcile
Startup reconciliation flips orphaned (non-terminal) ingestion jobs to failed and
purges the document's chunks so a failed source can't be retrieved. But it dropped
the chunks unconditionally, so a document the worker had already committed as
'completed' before the crash (only its job row left non-terminal) lost every chunk
while still reporting 'completed'. That leaves an empty source that retrieval can't
return and dedup (status != 'failed') blocks from re-ingest.
Only purge chunks when the document UPDATE actually transitions it to failed; an
already-completed document keeps its chunks. Adds reconcile regression tests for
both the completed-doc and genuine in-flight-orphan cases.
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* Studio: drop a finished RAG job's queue when the client disconnects
job_events kept the per-job queue until it consumed the None sentinel, so a UI
that stops on the terminal event (its reader.cancel aborts the stream before
[DONE]) left the queue registered until the next _reap_finished_jobs sweep; a
batch of uploads followed by idling retained them all.
_run writes the terminal DB status before emitting the terminal event, so on
generator exit, drop the queue when the job's DB row is already terminal (worker
done, nothing to resume) and keep it only while the worker is still running. Adds
a disconnect-after-terminal-event regression test.
* Remove stray async task output files committed by mistake
* Studio: harden login IP throttle and end progress stream on disconnect
Two Codex review items:
Login per-IP throttle: when the per-IP bucket dict saturated, FIFO eviction could
drop a still-hot (blocked) bucket, so an IP could flood the dict with distinct
(or spoofed) source IPs to push out its own bucket and retry as first-seen. Stop
evicting hot buckets; a new IP that can't fit now shares a bounded overflow
counter that still trips the per-IP threshold, so a saturating spray stays
throttled and no live counter is reset.
Progress SSE: on client disconnect the polling loop only broke and fell through
to the unconditional final 'complete' frame, so a buffered or proxying consumer
could read a still-active run as completed. Return from the generator instead.
Adds regression tests for both (spray cannot reset a hot bucket; disconnect while
active emits no complete frame).
* Studio: shard the login overflow counter and stop cancelling chat stream after [DONE]
Two Codex review items:
Login throttle overflow: the single shared overflow counter meant that once a
saturating spray pushed it past the per-IP threshold, _login_blocked returned 429
for every new unbucketed source IP, before credentials were checked -- a global
login denial. Shard the overflow into a fixed array of counters keyed by hash(ip),
so a hot shard only throttles the IPs that map to it while a single source's
repeated failures still concentrate in one shard and stay throttled. Memory stays
bounded and no live bucket is evicted. Adds a regression test that a hot overflow
shard does not block an unrelated IP.
Chat stream: the reader.cancel() in the SSE finally fired even after a natural
[DONE]/EOF. The backend finalizes its api-monitor entry right after yielding the
sentinel (the local pass-through finishes after the last yield), so a client
cancel there can be observed as a disconnect and mark a completed request as
cancelled. Track natural completion and only cancel on an early/abnormal exit.
(No frontend unit test: the Studio frontend has no test harness.)
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* Studio: give prep-timeout test fakes an is_disconnected method
The progress stream now ends on client disconnect (await request.is_disconnected()
before falling through to the terminal frame). After merging that into the
prep-timeout tests added later on main, their _FakeRequest/_ReconnectRequest must
provide is_disconnected or the generator raises AttributeError under CI.
* Studio: keep the login overflow throttle when bucket capacity frees up
_login_blocked only consulted the per-IP overflow shard while the bucket dict was
still at capacity. If a slot freed before the 60s window expired (e.g. another
IP's successful login calls _clear_login_bucket), a source counted in a hot shard
stopped being blocked and its next failure got a fresh per-IP bucket, resetting
the throttle the overflow path exists to preserve. Always max in the IP's shard
(shards are empty outside saturation, so it is a no-op in the common case). Adds a
regression test that a hot source stays throttled after a bucket frees.
* Studio: clear a login IP's overflow throttle on successful login
_clear_login_bucket reset the per-IP and per-account buckets on a successful
login but not the overflow shard, so after the dict saturated and an IP was
counted in overflow, a later successful login left those entries behind and the
next failed attempt could immediately return 429.
Store overflow entries as (timestamp, ip) so a source is throttled by its own
count within the shard (also removing cross-IP collateral within a shard), and
drop just that IP's entries in _clear_login_bucket. Adds a regression test that a
successful login clears the overflow throttle.
* Studio: bound the login overflow shard memory under high-cardinality spray
The per-IP overflow tracked failures in a time-pruned deque of (timestamp, ip)
tuples, so a spoofed-X-Forwarded-For spray of distinct one-off IPs grew memory and
the per-check scan with request cardinality for the whole window -- undermining
the bucket cap that exists to bound memory. Replace each shard with a fixed-
capacity dict (ip -> [count, window_start]): O(1) lookups, and when a shard is
full a one-off IP evicts the lowest-count entry (Space-Saving) so memory is hard-
bounded while a persistent attacker keeps a high count and is never evicted. Adds
a regression test that shards stay within the per-shard cap under a 5000-IP spray.
* Studio: purge chunks for already-failed docs during ingestion reconcile
The reconcile chunk-purge was gated on the documents UPDATE actually flipping a
non-terminal doc to failed. A doc the worker had already marked 'failed' before
the crash (job row left non-terminal) was not re-flipped, so its committed chunks
were kept and stayed retrievable/citable, since retrieval filters by scope not
status. Purge chunks whenever the document is not 'completed' (failed, in-flight,
or gone), preserving the completed-doc carve-out. Adds a regression test.
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* Studio: don't inherit an evicted IP's count onto a new overflow source
When a full overflow shard evicted the lowest-count entry, the new source
inherited that count (Space-Saving base + 1). If a shard was saturated with hot
entries, an unrelated new IP could land at/over the threshold and be 429'd after a
single attempt -- cross-IP collateral despite the per-source-isolation intent.
New entries now start clean at count 1; the only cost is that a heavy hitter that
is the lowest-count entry in a fully saturated shard can briefly reset, which is
preferable to blocking a bystander. Adds a regression test.
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* Studio: carry overflow failures into a new IP bucket on transition
_login_blocked took max(per-IP bucket, overflow shard) rather than combining them,
so a source could log (threshold-1) failures in overflow during saturation and,
once a bucket slot freed, another (threshold-1) in a fresh bucket within the same
window -- roughly doubling the per-IP limit. When a saturated-era IP first gets a
real bucket, migrate its windowed overflow count into that bucket (and drop the
overflow entry) so the combined failures throttle at the intended limit. Adds a
regression test.
* Studio: reconcile a completed doc's orphaned job to completed, not failed
When a crash left an ingestion job non-terminal after its document was already
committed as completed, reconcile marked the job failed. After restart the upload
UI has no in-memory SSE queue and falls back to getJob(), which treats a failed
job as an indexing failure and removes/toasts a document that is actually
searchable. Mark the job completed (keeping its chunks) when its document is
completed. Extends the completed-doc reconcile test to assert the job status.
* Studio: clamp the overflow failure count migrated into a login bucket
A saturated source could accrue an unbounded overflow count, then materialize
one deque entry per recorded failure when a bucket slot freed, allocating an
arbitrarily large deque under the login lock. Only at-or-above the per-IP
threshold matters for blocking, so cap the count there at the record and take
sites; the migration is now bounded without weakening the limit.
* Studio: keep the RAG job stream alive on a transient status read
The heartbeat poll read the job row unguarded; a momentarily-locked DB would
raise out of job_events, which the SSE route turns into a terminal error frame,
and the UI drops a document whose worker is still running. Treat a failed status
read as non-terminal: heartbeat and retry, and keep the queue so a reconnect can
resume.
* Studio: set busy_timeout before journal_mode on the auth DB
Switching journal_mode needs a lock, so if a refresh-token write already holds
one, journal_mode=WAL raises SQLITE_BUSY and the shared try leaves the
connection on SQLite's default zero lock wait. Set busy_timeout first so the
switch waits instead of failing.
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* checkpoint preview endpoint
* harden new preview endpoints
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* address review
* Studio preview: pin adapter, guard streaming submit, robust copy-link
Harden the public per-checkpoint preview surface:
- Pin use_adapter=True in the preview payload sanitizer. Otherwise an
unauthenticated /p caller can POST use_adapter=false, which calls
disable_adapter_layers() on the shared in-memory model without restoring
it; since load_model skips reloads for the same checkpoint, every later
visitor (the page never sends the field) keeps getting base-model output
instead of the fine-tuned checkpoint. Forcing it on also re-enables a
previously disabled adapter and no-ops on merged checkpoints.
- Ignore preview-page submits while a response is streaming. The send
button was disabled but the Enter handler still called requestSubmit(),
so a second request could start before the first reply landed in msgs and
reorder the chat history. Both the keydown and submit handlers now honor
the disabled button.
- Keep the cloudflare-URL polling loop alive across transient startup fetch
errors instead of letting one rejection halt it.
- Build the copy-link from a backend preview_ref (output dir relative to
outputs_root, gated on previewability and the two-segment /p route limit)
so a nested output dir no longer copies a basename-only link that 404s.
Expose preview_ref on training run summaries.
Add route-level security tests (path traversal, payload sanitization,
asset containment, CSP header, HTML title escaping, streaming lock held
until drained) and preview_ref unit tests.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio preview: Safari-safe submit and adapter pin only for LoRA
Follow-ups from cross-browser and route simulations:
- Preview page: send the message from a shared send() helper called by both
the form submit and the Enter key, instead of form.requestSubmit(). The
latter throws on Safari < 16 and older iOS, which broke Enter-to-send there.
Verified across Chromium, Firefox and WebKit with Playwright.
- Only pin use_adapter=True when the resolved checkpoint is a LoRA adapter
(adapter_config.json present); for a merged checkpoint strip it to None.
A merged model has no adapter to toggle, so forcing it on only produced a
per-request "not a PeftModel" warning. The cross-request base-model
contamination fix still holds for LoRA previews.
Add a merged-checkpoint test asserting use_adapter is stripped to None.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio preview: trim verbose comments
Tighten comments across the preview routes, page, checkpoint helpers, and tests
to short single-line notes; drop ones that just restate the code. No behavior
change (verified comment/docstring-only with comment_tools.py check).
* Harden preview routes for PR #6486
- Return a generic 400 detail on a rejected preview path so the public /p
route never echoes the absolute install path (the real reason is logged
server-side instead).
- Strip confirm_tool_calls, session_id and rag_scope in the preview payload
sanitizer so the public surface stays inert regardless of the tool gate.
- Use Path.is_relative_to for the asset containment check, matching the rest
of the codebase.
- Add img-src 'self' and font-src 'self' to the preview page CSP.
- Preview page: on a mid-stream error keep the streamed text, flag the break,
and restore the prompt so the user can retry; drop the unused --font-sans var.
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: start the Cloudflare tunnel for --secure even in --api-only, and add --api-only to `unsloth studio run`
--secure exposes ONLY the Cloudflare link (it forces a loopback bind), but
_cloudflare_tunnel_should_start gated the tunnel on `not api_only`, so
`run.py --secure --api-only` started no tunnel and then fail-closed with
"A secure Cloudflare link is not allowed". That blocked the natural headless
use: serve just the API (no web UI) over the authenticated tunnel.
Make --secure start the tunnel regardless of api_only (the non-secure path is
unchanged: tunnel only a 0.0.0.0 bind, never api-only Tauri or Colab). Then
expose --api-only on `unsloth studio run` and forward it through both the
re-exec args and the in-venv run_server call, so
`unsloth studio run --secure --api-only --model ...` is a one-liner secure API
server.
Verified end to end: `run.py --secure --api-only` now brings up the tunnel and
serves /api/health over it (200), with / returning 404 (no UI).
Tests: update the tunnel-gate truth table (secure+api-only now tunnels;
secure+colab still does not) and add --api-only registration + re-exec/in-venv
forwarding coverage to the run CLI tests.
* Trim comments to be succinct (no behavior change)
* studio: address review on parent --api-only and secure api-only CORS
- Reject --api-only on the parent `unsloth studio` group when a subcommand
is invoked, with the same redirect guidance used for --parallel/--secure;
otherwise the flag was silently dropped and the UI served anyway.
- Keep CORS any-origin for secure api-only serving: that mode publishes the
API over Cloudflare for remote browser clients, so the Tauri-only lockdown
(still applied to plain local api-only) would break preflight. Factored the
decision into cors_origins_for_mode() and gate it on api_only and not secure;
run_server exports UNSLOTH_SECURE before importing main.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: suppress TAURI_PORT and de-dup test for headless run --api-only
- run_server gains emit_tauri_port (default True, unchanged for the Tauri/
desktop path). The new headless `run --api-only` path passes False so the
Tauri-only TAURI_PORT= line no longer prepends the documented URL/API key
banner (it ran even under --silent and could break one-liner parsers).
- Remove a duplicate test_reexec_forwards_api_only that shadowed the
parametrized one; fold the --secure --api-only case into it so the secure
headless path is actually collected.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: defer llama.cpp update probes and self-heal MLX on macOS
Two macOS startup problems shared one root area in the FastAPI lifespan:
- The llama.cpp capability + freshness probes ran inline before the server
yielded, so a cold/slow/flaky network on the GitHub freshness check blocked
'Application startup complete' (~34s on CI, longer in the field). Move both
probes to a daemon thread; app.state stays None until ready (status routes
already re-probe at request time). Opt out with UNSLOTH_DISABLE_UPDATE_CHECK=1.
- Train and Export were greyed out because mlx/mlx-lm/mlx-vlm arrive only
transitively and a resolver backtrack silently drops them, so CHAT_ONLY stayed
true. Add utils/mlx_repair.py: when Apple Silicon is detected without MLX,
reinstall mlx/mlx-lm/mlx-vlm by name on a daemon thread and re-run hardware
detection (opt out UNSLOTH_DISABLE_MLX_AUTOREPAIR=1). Surface a chat_only_reason
in /api/health plus a sidebar tooltip so a greyed Train/Export explains itself
instead of failing silently.
* Studio: guard model defaults against a None model name
load_model_defaults(None) called model_name.lower() with no guard, raising
'Error loading model defaults for None' before any model is selected. Return
an empty dict for a falsy/non-str name.
* Studio: drop obsolete upstream macOS + Windows Blackwell prebuilt pins
Both pins worked around gaps in ggml-org upstream prebuilts, but Studio now
routes every GPU host and all of macOS to the unslothai/llama.cpp fork
(published_repo_for_host), which ships the needed bundles, so both pins are
dead code on the default install path:
- macOS b9415: macOS always routes to the fork (its own macOS bundles), and
host_supports_macos_minos() is the backstop. The pin only fired under an
explicit --published-repo ggml-org override.
- Windows Blackwell b9360: Windows-NVIDIA routes to the fork, whose
windows-x64-cuda13 bundle covers Blackwell (manifest max_sm 120, toolkit
13.3), so the pin's self-disable check makes it dormant on every default
install; it could only activate under the same upstream override on a
13.0-13.2 driver.
Remove the pin constants, functions, and call sites. Keep the Blackwell
capability detection (_drop_blackwell_incapable_windows_cuda, _host_is_blackwell,
_windows_cuda_attempt_covers_blackwell) that still drops a non-sm_120 cuda-12.4
build on a Blackwell host. After this, an explicit --published-repo ggml-org
override on a Blackwell 13.0-13.2 host loses its GPU fallback and lands on CPU;
the default fork path is unaffected. Update the install selection-logic and
macOS-compat unit tests for the new no-pin behavior.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: walk back deeper on the macOS upstream prebuilt path
After removing the b9415 macOS pin, the explicit --published-repo ggml-org
upstream path still used the default 2-release fallback, so a pre-macOS-26 host
behind a run of macOS-26-only builds would exhaust two too-new plans (minos is
only checked post-download) and drop to a source build before reaching a
loadable older release. Walk back as deep as the fork macOS path
(DEFAULT_MAX_MACOS_RELEASE_FALLBACKS), turning the removed static pin into
dynamic discovery. Addresses review feedback on the macOS upstream fallback.
* Studio: pin transformers during MLX self-heal so it cannot break Studio
mlx-lm/mlx-vlm declare transformers>=5, but the single-env install pins
transformers==4.57.6. The self-heal used --upgrade with no constraint, so it
could upgrade transformers in the live venv and break the rest of Studio just to
make import mlx.core pass. Pin transformers to the installed version via a
constraint file: the resolver either finds an mlx build compatible with it or
fails (we stay chat-only), never upgrading transformers underneath Studio.
Addresses review feedback on the MLX repair install.
* Studio: harden MLX self-heal against an unsupported mlx-vlm
Pinning transformers alone made uv backtrack mlx-vlm to 0.3.9 (below unsloth-zoo's
mlx-vlm>=0.4.4), which imports but breaks VLM Train/Export -- so the self-heal
could clear chat-only onto a broken stack. Mirror the main installer: set
UV_OVERRIDE=overrides-darwin-arm64.txt so a current mlx-vlm coexists with the
transformers pin, require the same minimum versions unsloth-zoo declares, and
gate/validate on a full mlx_stack_available() check (not a bare import) so an
old or partial stack stays chat-only. Addresses PR review.
* Studio: filter Blackwell-incapable CUDA in resolve_upstream_asset_choice
resolve_upstream_asset_choice returned the first windows-cuda choice unfiltered,
so a Blackwell host could be handed an sm_120-incapable cuda-12.4 build while the
sibling planners drop it. Apply _drop_blackwell_incapable_windows_cuda here too
and fall through to the CPU bundle on a Blackwell host with no capable GPU asset.
Addresses PR review.
* Studio: re-poll health so MLX self-heal reaches an open UI
The sidebar cached the initial /api/health, so a successful background MLX
self-heal (chat_only flips false) did not re-enable Train/Export until a manual
reload. While chat-only for the recoverable mlx_unavailable reason, re-poll
/api/health and stop once Train/Export become available. Addresses PR review.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: make the disabled Train/Export tooltip reachable
The greyed Train/Export items pass a tooltip explaining why (e.g. MLX missing),
but a disabled <button> fires no pointer events and SidebarMenuButton only showed
tooltips while collapsed, so the explanation never appeared. Wrap a disabled
button in a focusable span and show its tooltip while expanded too; enabled items
keep the collapsed-only behavior. Addresses PR review.
* Studio: gate Train/Export on the full MLX stack, not bare mlx.core
detect_hardware enabled MLX training whenever `import mlx.core` worked, but the
MLX self-heal (utils/mlx_repair) treats a stack without mlx-lm/mlx-vlm at the
versions unsloth-zoo requires as inadequate. That asymmetry let the UI enable
Train/Export on exactly the partial/backtracked stack the self-heal is trying to
repair (greyed-in-but-broken VLM export). Gate on the same mlx_stack_available()
criterion so a partial stack stays chat-only (reason mlx_unavailable) and the
background repair restores it. Addresses PR review.
* Fix MLX repair and health auth for PR #6494
* Fix macOS upstream prebuilt fallback for PR #6494
* Fix MLX stack validation for PR #6494
* Fix MLX self-heal validation for PR #6494
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Review fixes: isolate hardware-state test, robust transformers pin
- test_chat_only_reason.py: detect_hardware() assigns module globals directly,
which monkeypatch does not revert; the autouse fixture now saves and restores
DEVICE/CHAT_ONLY/CHAT_ONLY_REASON/IS_ROCM so a chat-only verdict here cannot
leak into other backend tests (e.g. test_utils.py) on a GPU host.
- mlx_repair.py: read the transformers version from importlib.metadata instead of
importing transformers, so the install pin is not silently dropped when
transformers has valid metadata but fails to import.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix CI: model full MLX stack in dispatch tests, keep selection test offline
dispatch (macOS) job:
- detect_hardware now gates MLX on the full stack (mlx_stack_available imports
mlx_lm/mlx_vlm and checks dist versions), so faking only mlx.core makes the
apple_silicon_mlx profile resolve to CPU. The dispatch tests assert the routing
decision when the stack IS usable, so model a complete stack:
test_hardware_dispatch_matrix patches utils.mlx_repair.mlx_stack_available and
test_is_mlx_dispatch_gate patches hardware._has_usable_mlx_stack. The stack
predicate's own internals stay covered by test_mlx_repair.py.
Repo tests (CPU) job:
- test_no_cuda_attempt_on_published_path_for_13_1 fell through to a live
github_release_assets() upstream fetch after the Blackwell filter dropped every
published attempt, which the offline security scanner blocks. Stub that fetch so
the walk-back deterministically finds no usable CUDA build and raises
PrebuiltFallback without network.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden MLX self-heal: prepare transformers constraint inside the try
attempt_mlx_repair runs on a daemon thread, but _transformers_constraint_args was
called before the try. A failure there (e.g. tempfile.mkstemp on a full disk or a
bad TMPDIR) would propagate unhandled and silently kill the self-heal thread.
Move the call inside the try and initialize constraint_path so any such failure
is caught and leaves Studio chat-only instead of crashing the thread.
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
* Studio: pin CUDA_DEVICE_ORDER=PCI_BUS_ID and list GPUs at startup
On a mixed-GPU host, Studio could load a model onto a different physical
GPU than the one it selected. The free-VRAM probe numbers GPUs via
nvidia-smi (PCI-bus order), but CUDA defaults to FASTEST_FIRST ordering,
so a selected index written into CUDA_VISIBLE_DEVICES resolved to the
wrong card. Example: 5090 + RTX PRO 6000, the picker chose the emptier
RTX PRO 6000 (nvidia-smi index 1) but CUDA read index 1 as the 5090.
Pin CUDA_DEVICE_ORDER=PCI_BUS_ID at import (before any CUDA context is
created) in both the Studio entrypoint and the hardware module, so torch,
nvidia-smi, and CUDA_VISIBLE_DEVICES share one index space. setdefault
keeps an explicit user override intact. Child processes inherit it via
os.environ.
Also list every detected CUDA GPU with its index at startup instead of
naming only device 0, matching nvidia-smi -L and making the selected
index unambiguous on multi-GPU hosts.
* Studio: make CUDA_DEVICE_ORDER tests exercise module import and respect user override
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: guard full _print_cuda_device_list body and fix test PYTHONPATH trailing separator
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: serialize non-streaming responses once and pool the proxy client
Two safe latency wins on the OpenAI/Anthropic-compatible endpoints that leave
the streaming generation paths untouched (they keep Connection: close and
max_keepalive_connections=0 so a client disconnect still stops GPU decode).
1. Non-streaming responses used JSONResponse(content=model.model_dump()), which
builds a dict and then re-runs json.dumps. Serialize once with
model.model_dump_json() via a small _model_json_response helper. The body is
byte-identical (nulls preserved), about 3x faster to encode in a microbench.
2. The non-streaming completions and embeddings proxies built a fresh
httpx.AsyncClient per request. Route them through one pooled client
(core/inference/llama_http) closed on shutdown; streaming generation keeps
its own per-request close-only client. About 5x faster per call to the
local llama-server in a microbench.
The existing API-monitor tests for the non-streaming completions, embeddings
and passthrough paths now patch nonstreaming_client instead of httpx.AsyncClient
to match the pooled client, so they stay deterministic.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: make the pooled non-streaming client per event loop
Review follow-up on the shared httpx client. It was a single module-global
instance, which has two lifecycle problems the per-request client did not:
1. After aclose() in lifespan shutdown, nonstreaming_client() kept handing back
the closed client, so a second lifespan in the same process (repeated
TestClient, embedded restart) failed with "client has been closed".
2. An httpx client binds its transport to the loop it first runs on, so reuse
from another loop could raise "Event loop is closed".
Hold one client per running loop in a WeakKeyDictionary, recreate when missing
or closed, and close all on shutdown. Single-loop production is unchanged.
* [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>
* Studio: convert SecurityHeadersMiddleware to pure ASGI
SecurityHeadersMiddleware was the last BaseHTTPMiddleware in the global stack,
so every response (including SSE streams) was wrapped in an anyio stream that
penalizes streaming. Rewrite it as a pure-ASGI middleware that mutates the
response-start headers, mirroring the logging-middleware rewrite in #6337.
The header logic is unchanged: it uses MutableHeaders over the start message,
so the same get/del/setdefault calls apply (CSP nonce splice and strip,
X-Frame-Options skip on Colab and the artifact-preview frame, the baseline
nosniff/Referrer-Policy/Permissions-Policy/server headers). The existing
middleware tests cover it; added cases assert headers still apply to a
streaming response and that the artifact-preview path omits X-Frame-Options.
* Studio: harden ASGI header coercion in SecurityHeadersMiddleware
Review follow-up. MutableHeaders mutates its raw list in place, so if a server
sends http.response.start with tuple-valued or missing headers the mutation
would raise. Coerce to a list (defaulting to empty) before wrapping, then inject
the same security headers as before. Also drop a stray em dash in a comment.
* [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>
* Studio: make lifespan shutdown resilient to a dead default executor
On an abrupt shutdown (closing the Windows console window, or interpreter
teardown racing uvicorn's graceful stop) the event loop's default thread-pool
executor can already be shut down by the time the FastAPI lifespan shutdown
runs. The first post-yield statement was an unguarded
`await asyncio.to_thread(terminate_hub_downloads)`, so executor.submit raised
`RuntimeError: cannot schedule new futures after shutdown`. That raise
propagated up through every nested merged_lifespan __aexit__, aborted the rest
of the cleanup (DEVICE reset, compiled-cache clear), and surfaced as
"Application shutdown failed. Exiting."
Extract the post-yield cleanup into utils/lifespan_shutdown.run_lifespan_shutdown
and guard each step independently. On the to_thread RuntimeError, fall back to
running the (already best-effort, quick) terminate inline on the loop thread so
shutdown still completes cleanly. The helper is dependency-injected and free of
the heavy backend import graph, so it is unit-tested in isolation.
Add tests/test_lifespan_shutdown.py (4 cases: dead-executor survival, normal
path, terminate error, clear error). Validated on windows-latest and
ubuntu-latest runners on Python 3.13.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: only retry terminate inline when scheduling fails, not when the body raises
Address review feedback on the shutdown helper: the previous
`except RuntimeError` after `asyncio.to_thread(terminate_downloads)` could
not tell a dead-executor scheduling failure from a RuntimeError raised by
terminate_downloads itself, so a body-side RuntimeError on a healthy executor
ran the cleanup a second time inline.
Schedule via loop.run_in_executor and await separately: a dead default executor
raises synchronously at submit time (inline fallback), while a body exception
only surfaces when the future is awaited (logged, never retried). Add a
regression test that a body RuntimeError runs terminate exactly once.
* Studio: run terminate cleanup with a copied context (parity with asyncio.to_thread)
Simulation across the executor-state x exception x DEVICE matrix surfaced the one
behavioural difference from the original implementation: asyncio.to_thread copies
the caller's contextvars into the worker thread, while a bare
run_in_executor(None, fn) does not. Restore exact parity by scheduling
ctx.run(terminate_downloads) from a contextvars.copy_context(), so the refactor
is a behavioural no-op apart from the intended dead-executor recovery. Add a
regression test asserting the copied context is visible to terminate_downloads.
* Studio: tighten comments in lifespan_shutdown helper and tests
Condense the verbose docstrings/comments to the non-obvious rationale and drop
the self-evident ones. Verified comment-only with comment_tools.py check
--strip-docstrings (code signature unchanged); tests and sims still green.
* Studio: address review nits on lifespan shutdown helper
Annotate hw_module as types.ModuleType, reword the schedule/await comment
(inline fallback runs, it does not retry), and add a test for the public
loop.shutdown_default_executor() path (the 'Executor shutdown has been called'
RuntimeError that real uvicorn shutdown takes, distinct from the submit-time
'cannot schedule new futures after shutdown').
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: show llama.cpp version and GPU specs in the About panel
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: run the hardware endpoint off the event loop
* Studio: show the full llama.cpp release tag (incl -mix-<sha>) in the About panel
* Studio: order About-panel GPUs by visible ordinal and skip the llama.cpp probe during updates
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix hardware info refresh and endpoint scope
* [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>
Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
* Studio: add --secure Cloudflare-only mode and revamp API usage examples
--secure / --not-secure on `unsloth studio` and `unsloth studio run`:
- --secure binds 127.0.0.1, requires the Cloudflare tunnel, and advertises only
the Cloudflare link. cloudflared reaches the server over localhost, so the raw
port is never exposed on a public interface.
- If the tunnel cannot start, fail closed with a clear message instead of
silently leaving a raw 0.0.0.0 link.
- Default stays not-secure (no behavior change); coexists with the existing
--cloudflare/--no-cloudflare flag. Host defaults are unchanged.
- /api/health (authed) now reports the live tunnel URL.
API usage examples (Profile > API):
- Example tabs for curl, Python, curl + tools, Python + tools, plus an OS row
(Linux/macOS/WSL vs Windows) auto-detected from the platform.
- Windows curl passes the JSON body via a file so PowerShell does not strip the
quotes when calling curl.exe.
- Python + tools forwards enable_tools/enabled_tools through extra_body and
guards chunk.choices, since tool-lifecycle events carry no choices.
- Shows the loaded model name and the real API key while it is still revealed.
- A Cloudflare Tunnel toggle (default on) shows the public tunnel URL and uses
it as the base_url in the examples when a tunnel is running.
Tests cover the tunnel start gate and the --secure flag on both commands.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: gate --secure tools on public exposure and harden API examples
In secure mode the server binds loopback but is reachable via the public
Cloudflare tunnel, so resolve the tool policy against the public exposure
(0.0.0.0) rather than the loopback bind. This keeps server-side tools off by
default and prompts before enabling them, instead of inheriting the loopback
default of on. The startup tool notice now names the public surface.
Also reject --secure with --no-cloudflare directly in run_server and the
run.py argparse (not only the CLI), JSON-encode interpolated model names so
Windows paths and quotes cannot produce invalid JSON or broken snippets, and
force-refresh /api/health on the API panel so a tunnel that starts after the
first health read still surfaces its URL.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: API examples show direct host when tunnel toggle is off; move Copy onto code
The Cloudflare Tunnel toggle had no visible effect when Studio was opened
through the tunnel: the off state fell back to window.location.origin, which
equals the tunnel URL in that case. /api/health now reports the direct
host:port (server_url), and the API panel uses it for the off state so it shows
the real non-tunnel base. Also move the Copy button out of the tab row and onto
the code block.
* Studio: highlight API examples, add advanced tabs, fix tunnel toggle row
Syntax-highlight the curl/PowerShell/Python snippets with the app's shared
shiki plugin (bash/powershell/python). Add 'curl + advanced' and
'Python + advanced' tabs that set temperature/top_p/top_k/min_p/
repetition_penalty/max_tokens, enable thinking, and turn on all tools.
The Cloudflare Tunnel row no longer shifts the code block: the tunnel URL is
always rendered (dimmed when off) so toggling keeps the row height constant.
Key the highlighted block on its content so it remounts when only the base URL
changes (the renderer's block memo otherwise kept a stale URL).
* Studio: rename API tunnel toggle to Secure HTTPS, hint --secure when exposed
Rename the API examples toggle from Cloudflare Tunnel to Secure HTTPS. When the
server was not launched with --secure, show an info tooltip noting the raw
0.0.0.0 port is still globally reachable and pointing at --secure. /api/health
now reports whether --secure was used so the hint is hidden in secure mode.
* Studio: force tools off for plain network/secure launches
The plain 'unsloth studio --secure' (and '-H 0.0.0.0') launcher re-execs run.py
and never installed a tool policy, so the process default (honor per-request
enable_tools) let any API-key holder run Python/terminal tools over the public
endpoint. Force the policy off at the run.py entrypoint when network-reachable
(0.0.0.0 or --secure); 'unsloth studio run' still installs its own resolved
policy and does not go through this path.
* Studio: apply default tool policy in run_server, not the run.py entrypoint
The plain launcher runs from the studio venv and calls run_server directly, so
it never hit the run.py __main__ guard. Move the network/secure default-off tool
policy into run_server so every launch path (plain, --secure, direct run.py)
gets it; the run subcommand still overrides it with its resolved policy.
* Studio: clarify --secure help text on the network exposure tradeoff
Spell out in --help (both unsloth studio and unsloth studio run, plus the
run.py argparse) that --not-secure also serves the raw 0.0.0.0 port reachable
from anywhere on the network, matching the API panel's Secure HTTPS hint.
* Studio: cache API-key PBKDF2 derivation to cut per-request /v1 auth overhead
validate_api_key re-ran the 100k-round PBKDF2 on every authenticated
request, adding ~15ms to each /v1 call made with an sk-unsloth- key.
Benchmarked against the bare llama-server it proxies to, API-key requests
carried ~22ms of fixed overhead vs ~7ms for the JWT path; the gap was
entirely this redundant key derivation (Pydantic validation measured
0.005ms, so it is not a factor).
The raw-key to hash mapping is a pure deterministic function of the fixed
server salt, so memoize it per process, keyed by a salted HMAC of the key
(never the key or a recoverable digest). The cached value equals what is
already stored at rest. Revocation and expiry remain enforced by the
SQLite read on every call, so a cache hit only skips the KDF, never the
active or expiry checks. Only keys that exist in the DB are cached, so
unknown-key spam cannot grow it.
After the change the API-key /v1 overhead drops to ~8ms, at parity with
JWT, while the at-rest PBKDF2 hashing is unchanged.
Adds test_api_key_expiry.py covering API-key and JWT expiry enforcement
and the new cache: it skips the KDF on repeat and still rejects revoked
or expired keys.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: tighten comments across the secure-tunnel and API-key changes
Condense multi-line comments and docstrings to one or two lines, drop the
ones that restate obvious code, and remove an orphaned test section header.
Comment-only: verified with comment_tools.py check (9/9 code unchanged), the
auth/secure-tunnel/CLI test suites, and a clean frontend typecheck and build.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
The studio backend lazily imports unsloth_zoo submodules (export's
llama_cpp, hardware's vllm_utils, mlx, chat_templates). Each imports the
top-level unsloth_zoo, which raises 'Please install Unsloth via pip
install unsloth' unless UNSLOTH_IS_PRESENT is set (normally by import
unsloth). Only hardware.py set it per-site; the rest crash on a clean
install, and it surfaced on Windows. Set it once at backend startup (as
unsloth does on import) so every site, and the DiffusionGemma runner,
works across platforms. Follow-up to #6259 (which covered only the shim
subprocess env).
* Require a found ROCm DLL before forcing BNB_ROCM_VERSION in Studio paths
main.py previously set BNB_ROCM_VERSION=72 whenever HIP_PATH or ROCM_PATH
was set, and the training worker fell back to a blind 72 when DLL
detection found nothing. On a Windows machine with the AMD HIP SDK
installed but CUDA or CPU torch, that forces a ROCm backend onto a
non-ROCm bitsandbytes wheel, which raises at import. Both paths now only
write the override when a libbitsandbytes_rocm DLL actually exists (or a
seeded value is already present), matching the strict gates in
unsloth/import_fixes.py.
Also removes four redundant local import shutil statements in
unsloth/save.py that shadow the module-level import, the same pattern
that caused the UnboundLocalError fixed in #6149.
* Worker: gate the BNB override on a found ROCm DLL, preserving seeded marker
Review follow-ups: track _found_rocm_bnb in the worker like main.py so a
ROCm DLL with an unparsable name still gets the seeded or 72 fallback,
and skip the env write entirely when no DLL exists so a seeded value
keeps its sitecustomize marker and stays redetectable by later import
fixes.
Adds an in-app "Update llama.cpp" banner and button to Unsloth Studio. When the installed prebuilt is behind the latest published release, a non-invasive banner appears; clicking Update downloads the latest prebuilt for this host and swaps it in place in the background, with no restart.
Detection reuses the freshness check from #5529. The update re-runs install_llama_prebuilt.py the same way setup.sh and setup.ps1 do after #5963: it forwards the published repo and the AMD gfx target derived from the install marker, and does not pass the removed --simple-policy or the arm64-only --cpu-fallback.
While the installer swaps binaries the backend enters a maintenance state (flag set under the serial load lock, active server unloaded) so a concurrent load cannot start a server from a half-swapped binary; the next load uses the new build. The banner also handles refused responses and jobs started in another tab so it never sticks on "Updating...".
Verified end to end on an NVIDIA B200: installed b9493, detected the update, applied it, and confirmed the binary at the same path advanced to b9585 in the same process. Hermetic backend tests and the frontend type-check pass.
* Fix Windows installer winget msstore certificate failure
`winget install` was invoked without `--source winget`, so winget also
queried the msstore source. When msstore fails certificate pinning
(error 0x8a15005e, "The server certificate did not match any of the
expected values") winget aborts and demands `--source`, so the Python
(and uv) install fails even though the package exists in the winget
source.
- Pass `--source winget` to all winget install calls (Python x2, uv).
Both packages live in the winget source, so this is strictly correct
and skips the failing msstore round-trip entirely.
- Add a python.org fallback (Install-PythonFromPythonOrg) that downloads
the official installer and runs it silently per-user (no admin/UAC)
when winget is unavailable or fails for any reason. Mirrors the
existing uv -> astral.sh fallback so Python installs without manual
steps. Resolves the latest 3.13.x from python.org with a pinned
fallback, and selects the amd64/arm64/x86 installer per architecture.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Pin remaining setup.ps1 winget calls to --source winget
Two winget invocations in studio/setup.ps1 still queried all sources and
could hit the same msstore certificate-pinning failure (0x8a15005e) that
broke the Python install in install.ps1:
- `winget show Nvidia.CUDA --versions` (CUDA Toolkit version probe)
- `winget install ... ShiningLight.OpenSSL.Dev` (OpenSSL dev for llama-server)
Every other winget call in this file already passes `--source winget`
(Git, CMake, VS Build Tools, CUDA install, Node.js, and setup.ps1's own
Python 3.12 install), so these two were stragglers. Both packages live in
the winget source; pinning it makes setup robust to an unhealthy msstore
source, matching the rest of the file.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Stop amd-smi GPU probe from popping a DiskPart UAC prompt
On Windows, AMD GPU detection in install.ps1 and studio/setup.ps1 runs
`amd-smi list` / `static --asic` / `version`. amd-smi (shipped in
System32 by the Adrenalin driver) auto-elevates to read GPU/APU memory
details, surfacing a confusing DiskPart UAC prompt mid-install. The
Studio backend already documents and circuit-breaks on this in
studio/backend/utils/hardware/amd.py, but the installers did not.
Add an Invoke-AmdSmiNoElevate helper (both scripts) that runs amd-smi via
Start-Process under __COMPAT_LAYER=RunAsInvoker so it cannot auto-elevate
(no prompt), with a 30s timeout (matching amd.py) so a flaky amd-smi
cannot stall the install for minutes. On failure/timeout the existing WMI
name -> gfx fallback still resolves the arch, so detection is unchanged on
working hosts.
Verified on a Strix Halo (Radeon 8060S / gfx1151) box: the prompt is gone
and the probe is bounded.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Add experimental ROCm-on-WSL setup helper for Strix Halo (gfx1151)
install.sh already routes gfx1151 (Radeon 8060S / Strix Halo) to the
repo.amd.com/rocm/whl/gfx1151 wheels once a ROCm runtime is present, but
it does not install AMD's driver/ROCm stack -- a large, admin-gated
prerequisite. scripts/install_rocm_wsl_strixhalo.sh automates the Linux
side on a dedicated Ubuntu 24.04 WSL2 distro: ROCm 7.2 (wsl usecase), the
rocr4wsl HSA runtime, a librocdxg build, env setup, and a PyTorch gfx1151
GPU smoke test. A hard preflight refuses to run until the Adrenalin
>=26.3.1 driver is actually present, so it cannot half-install.
Procedure adapted from AMD's ROCm-on-WSL docs and community gfx1151 notes.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Detect AMD GPUs by name so native Windows gets a GPU llama.cpp
The gfx-arch inference from the WMI GPU name was gated behind $HasROCm,
which the hipinfo/amd-smi probe leaves false on the common Windows case
(Adrenalin driver only, no HIP SDK -- and amd-smi often cannot read the
arch without elevation). So an AMD GPU was detected by name but never
mapped to a gfx target, --rocm-gfx was not forwarded, and studio setup
fell back to a CPU llama.cpp build.
Un-gate the inference (install.ps1 + studio/setup.ps1) so it runs whenever
an AMD GPU name is available. The inferred gfx is forwarded as --rocm-gfx,
which makes install_llama_prebuilt.py download the matching lemonade-sdk
ROCm prebuilt (e.g. llama-bNNNN-windows-rocm-gfx1151-x64.zip) -- a
GPU-accelerated llama.cpp that bundles its own ROCm runtime, so it runs
with just the Adrenalin driver. PyTorch's ROCm wheels still require a
confirmed HIP SDK ($HasROCm), so this only affects llama.cpp / inference
and never pulls broken ROCm torch.
Also broaden the name->arch table to every family lemonade ships Windows
assets for: gfx120X (RDNA 4), gfx110X (RDNA 3), gfx1151/gfx1150
(RDNA 3.5), and gfx103X (RDNA 2). Unknown names still fall back to CPU.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Suppress amd-smi DiskPart UAC prompt in the Python install/runtime paths
The earlier PowerShell guard covered install.ps1 / setup.ps1, but the
Python installer (install_llama_prebuilt.py detect_host,
install_python_stack.py ROCm probes) and the Studio backend monitor
(amd.py) also shell out to amd-smi on Windows, where it auto-elevates and
pops the same DiskPart UAC prompt mid-install / at runtime.
Inject __COMPAT_LAYER=RunAsInvoker into the amd-smi subprocess env on
Windows so it runs un-elevated (no prompt). Callers already tolerate an
empty/failed result and fall back to WMI / name detection (installer) or
the existing circuit breaker (amd.py). Gated to Windows so Linux/macOS
amd-smi behaviour is unchanged.
- install_llama_prebuilt.py: handled centrally in run_capture (covers
detect_host's `amd-smi list` and the version probe).
- install_python_stack.py: new _amd_smi_env() helper on its 3 raw
subprocess.run amd-smi calls.
- amd.py: merge RunAsInvoker into the existing child env.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Tighten AMD GPU name->arch patterns to avoid mismatches
The W9[0-9]{3} and RX 90[0-9]{2} patterns added for RDNA 4 were
speculative and over-broad: W9xxx would also match old GCN FirePro
W9100/W9000 cards (wrong gfx1201 -> a lemonade gfx120X download that
fails validation), and RX 90[0-9]{2} was redundant with the explicit
9070/9060 entries. Drop both; keep only confirmed RDNA 4 SKUs. Unmatched
AMD names still fall back cleanly to CPU.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Fetch the llama.cpp validation model via huggingface_hub
The prebuilt validation downloads a tiny GGUF test model from huggingface
via bare urllib. On Windows / proxy setups where the server sends an
incomplete TLS chain, urllib cannot complete the Amazon CA chain (it does
no AIA intermediate fetching) and fails with CERTIFICATE_VERIFY_FAILED, so
a perfectly good GPU prebuilt is rejected and the installer falls back to a
CPU source build.
Route the validation-model download through huggingface_hub
(hf_hub_download) -- the same mechanism Studio uses for model downloads,
which completes the chain where urllib cannot -- keeping the direct URL as
a fallback. This lets the lemonade ROCm prebuilt validate and install on
cert-restricted machines (verified: hf_hub_download succeeds where urllib
returns CERTIFICATE_VERIFY_FAILED).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Guard the remaining raw amd-smi version probe via run_capture
A ROCm-version detector in install_llama_prebuilt.py called amd-smi version through a raw subprocess.run that bypassed run_capture's Windows RunAsInvoker guard, so it still triggered the DiskPart UAC prompt during setup. Route it through run_capture like the other amd-smi calls.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Forward --rocm-gfx even when the ROCm runtime is unconfirmed
setup.ps1 forwarded --rocm-gfx (and picked the windows-hip llama.cpp
prebuilt) only inside `if ($HasROCm)`. On Adrenalin-only hosts (amd-smi
present but no HIP SDK, so $HasROCm stays false) the gfx arch was
name-inferred but never forwarded, so install_llama_prebuilt.py saw
has_rocm=False and installed the CPU build -- even though the lemonade
gfx1151 GPU prebuilt runs fine there (it bundles its own ROCm runtime;
verified: llama-cli --list-devices -> ROCm0: AMD Radeon 8060S, 69 GB).
Forward --rocm-gfx whenever a gfx arch is known (it is authoritative and
implies ROCm in install_llama_prebuilt.py), and treat a known gfx arch as
windows-hip in the existing-install mismatch check. --has-rocm stays gated
on the confirmed-runtime signal.
Verified on Radeon 8060S / gfx1151: the installer now selects, validates,
and installs llama-b1286-windows-rocm-gfx1151-x64.zip (ROCm DLLs present)
instead of the CPU build.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Install AMD ROCm PyTorch on name-inferred gfx hosts (enables Train/Export)
setup.ps1 picked the AMD ROCm PyTorch wheels only inside `if ($HasROCm ...)`.
On Adrenalin-only hosts (amd-smi present but no HIP SDK, so $HasROCm is
false) the gfx arch was name-inferred but the ROCm-wheel branch never ran,
so the host got torch+cpu. With CPU torch, torch.cuda.is_available() is
False, so the Studio backend sets CHAT_ONLY=True and hides Train/Export.
Un-gate the ROCm PyTorch index resolution on a known gfx arch (mirrors the
llama.cpp --rocm-gfx fix). AMD's per-arch Windows wheels
(repo.amd.com/rocm/whl/<gfx>) bundle the ROCm runtime, so they work without
a HIP SDK; a failed install still falls back to CPU.
Verified on Radeon 8060S / gfx1151: torch 2.11.0+rocm7.13.0 installs and
torch.cuda.is_available() -> True, device "AMD Radeon(TM) 8060S Graphics",
GPU matmul OK -> CHAT_ONLY=False -> Train/Export enabled.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Force amd-smi un-elevated process-wide in the Python installers
Guarding individual amd-smi call sites kept missing some (install_python_stack.py's probe loop and its Windows GPU re-check), so the DiskPart UAC prompt kept reappearing. Set __COMPAT_LAYER=RunAsInvoker process-wide at the top of install_python_stack.py and install_llama_prebuilt.py on Windows so every amd-smi subprocess (current and future) runs un-elevated with no per-call guard. Safe: these scripts only spawn amd-smi/rocminfo/hipinfo probes and pip/uv. setup.ps1 keeps per-call guards because it also spawns winget installers that need elevation.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Fix Invoke-AmdSmiNoElevate exit code on PS 5.1 + RX 7700S arch match
Start-Process -PassThru leaves the returned process object's .ExitCode
$null after WaitForExit on Windows PowerShell 5.1, so the helper set
$LASTEXITCODE to $null and every caller's `if ($LASTEXITCODE -eq 0 ...)`
was always false -- the amd-smi GPU / gfx-token / ROCm-version detection
branch was effectively dead (masked only because the un-gated WMI
name->gfx inference still ran). Reproduced on PS 5.1.26100.
Rewrite the helper to use [System.Diagnostics.Process]::Start with a
ProcessStartInfo (UseShellExecute=false), whose .ExitCode is reliable,
with async stream reads (ReadToEndAsync) to avoid a pipe-buffer deadlock
and WaitForExit(timeout) to bound a flaky amd-smi. __COMPAT_LAYER=
RunAsInvoker (inherited via the process env) still suppresses the
auto-elevation / DiskPart prompt. Also drops the temp files and the
empty-ArgumentList edge case. Verified: exit code propagates
(7 -> $LASTEXITCODE=7), output captured, env restored.
Also fix the gfx1100 name pattern `RX 7700(?! S)` -> `RX 7700(?!S)` so the
spaceless retail name "RX 7700S" is correctly excluded (it belongs to the
gfx1102 row). Both found by PR review.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Address PR review follow-ups (install.sh table, update path, tests, WSL)
From the multi-agent PR review:
- install.sh: sync the AMD name->arch table with install.ps1 / setup.ps1
(the bash table had drifted to the old narrow patterns). Adds RDNA 2
(gfx103X), workstation PRO W SKUs, and more Strix Halo/Point names, and
orders gfx1102 before gfx1100 so the spaceless retail name "RX 7700S"
resolves correctly (bash case has no negative lookahead). AMD-ROCm-only:
the name inference stays gated behind _has_amd_rocm_gpu(), so NVIDIA /
CPU / macOS are unaffected.
- setup.ps1: the "dependencies up to date" fast path skipped the torch
reinstall, so an existing user who had CPU torch (installed before
ROCm-wheel support) stayed stuck in CHAT_ONLY. Now, when an AMD gfx arch
is known AND the installed torch is CPU-only, don't skip -- force the
dependency pass so the ROCm wheels install.
- scripts/install_rocm_wsl_strixhalo.sh: resolve the real /opt/rocm dir
instead of hardcoding ROCM_VER for LD_LIBRARY_PATH / the librocdxg
symlink (breaks if amdgpu-install lays ROCm under a patch-version dir);
add a LIBROCDXG_REF pin knob and a "verified against" freshness header.
- tests/studio/install/test_pr5940_followups.py: cover _hf_resolve_url_parts,
_fetch_validation_model_bytes (hf path + urllib fallback), run_capture's
Windows-only amd-smi RunAsInvoker injection, and install.ps1 vs setup.ps1
name-table parity (catches future drift). 14 tests, all passing.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix DiskPart UAC prompt: skip amd-smi on Windows without a HIP SDK
On Windows, amd-smi re-initialises the ROCm runtime on every invocation
(even `amd-smi version`) and, on hosts without a working HIP runtime
(consumer APUs/dGPUs with only the Adrenalin driver), elevates a child
process at runtime -- popping a UAC/DiskPart prompt. amd-smi's own
manifest is asInvoker, so __COMPAT_LAYER=RunAsInvoker cannot suppress
that runtime elevation (verified: even `amd-smi version` hangs and
times out with RunAsInvoker set).
Replace the ineffective RunAsInvoker-only approach with a real gate:
only spawn amd-smi on Windows when a HIP SDK is detectable (hipinfo
present, so amd-smi runs un-elevated) or the user opts in with
UNSLOTH_ENABLE_AMD_SMI=1. The gfx arch is already resolved from WMI
name inference (forwarded via --rocm-gfx), so ROCm wheel + lemonade
llama.cpp selection is unaffected. Linux/macOS amd-smi never elevates
and is untouched (no regression). RunAsInvoker is kept as harmless
belt-and-suspenders for tools that DO use manifest elevation.
Applied consistently across:
- studio/backend/utils/hardware/amd.py (runtime GPU polling)
- install.ps1, studio/setup.ps1 (install-time detection)
- studio/install_llama_prebuilt.py (prebuilt arch probe + version)
- studio/install_python_stack.py (ROCm version + arch probe)
Verified live on AMD Radeon 8060S (gfx1151), native Windows: fresh
install detects the GPU, installs ROCm torch (torch.cuda.is_available()
True), launches Studio with no DiskPart prompt, and inference, tool
calling, web search, LoRA finetuning, and GGUF export all run on the GPU.
Tests: add 6 _amd_smi_allowed() gating tests + PowerShell-installer gate
assertions; update the three amd-smi monitoring tests to opt in (they
mock amd-smi as available). Full suite: 267 passed, 2 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* install.sh: helpful WSL message when the GPU isn't exposed to ROCm
In WSL, an AMD GPU's ROCm-on-WSL runtime is only available with a recent
Adrenalin driver AND a distro AMD supports (currently Ubuntu 24.04). When
neither is in place, GPU detection (rocminfo/_has_amd_rocm_gpu) finds
nothing and we silently fall back to CPU.
Add an actionable hint in the CPU-fallback path, shown only on WSL and
only AFTER detection has already failed -- so it is forward-compatible:
the moment a driver/distro DOES expose the GPU (e.g. if AMD later adds
Ubuntu 26.04 support), detection succeeds and the hint never fires. The
message:
- notes a GPU is plumbed in (/dev/dxg) but no ROCm runtime is exposed,
- lists the two prerequisites (Adrenalin driver + Ubuntu 24.04),
- if the distro is not 24.04, says AMD may not support it yet,
- tells the user to `wsl --install Ubuntu-24.04` and re-run,
- links AMD's ROCm-on-WSL guide + the experimental Strix Halo helper.
Verified live: on Ubuntu-24.04 the hint shows (version-warning omitted)
and the CPU install completes; on Ubuntu-26.04 the extra "this distro may
not be supported" line appears and points to 24.04.
Also fix the experimental scripts/install_rocm_wsl_strixhalo.sh: AMD's
repo.radeon.com/amdgpu-install/ is indexed by unified installer version
(30.30, 31.30, ...), NOT ROCm version, so the hard-coded
amdgpu-install/7.2.0/ path 404'd. Scan the installer dirs newest-first
for a noble .deb matching the target ROCm major.minor (ROCm 7.2 ->
30.30.x/amdgpu-install_7.2.x), falling back to the newest available.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* WSL: fix shortcut collision + pin ROCm-on-WSL driver reqs from AMD docs
Two WSL-related fixes informed by AMD's official ROCm-on-WSL docs and
field reports for Strix Halo / Ryzen AI Max+ (Radeon 8060S, gfx1151):
1. Shortcut collision (real bug). install.sh's WSL branch wrote
"Unsloth Studio.lnk" to the SAME Desktop / Start Menu folder as the
native-Windows installer (install.ps1 New-StudioShortcuts). Running
install.sh in WSL therefore silently retargeted the native shortcut at
the WSL launcher (wt.exe -> wsl.exe), so the desktop/start-menu icon
stopped launching native GPU Studio. Now the WSL shortcut uses a
DISTINCT name -- "Unsloth Studio (WSL - <distro>).lnk" -- and fetches
the Unsloth .ico to %LOCALAPPDATA%\Unsloth Studio so it shows the
proper icon. Native and WSL shortcuts now coexist.
2. Precise ROCm-on-WSL prerequisites. Research (AMD radeon-ryzen WSL
compatibility matrix, gianni.rosagallina.com Feb-2026 guide,
ROCm/ROCm#4952/#5509/#6022) confirms WSL GPU on Strix Halo requires
AMD Adrenalin Edition >= 26.1.1 (26.2.2+ is the first production
ROCDXG/WSL release) + ROCm 7.2.1 + Ubuntu 24.04; an older driver does
not inject the ROCm/DXG runtime into /usr/lib/wsl/lib, so rocminfo sees
only the CPU. install.sh's WSL hint and the experimental
install_rocm_wsl_strixhalo.sh header/preflight now state the exact
driver version (was a guessed ">=26.3.1"), bump ROCM_VER to 7.2.1, link
AMD's radeon-ryzen docs, and document the known librocdxg caveat that
usable VRAM is currently capped at the .wslconfig memory setting.
bash -n clean; install test suite 267 passed, 2 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: hint when the AMD driver is too old for ROCm-on-WSL
Adds a detect-and-guide hook for the optional WSL-GPU path. An AMD GPU on
native Windows can also be used inside WSL2, but only with AMD Adrenalin
Edition >= 26.2.2 (the first production ROCDXG/WSL release). Native Windows
GPU works with any recent driver, so this is purely about enabling the WSL
path.
We intentionally do NOT auto-install the driver: AMD referrer-gates driver
downloads (scripted curl/Invoke-WebRequest are blocked) and does not publish
Adrenalin via winget, so no installer can reliably fetch it -- and silently
swapping a live display driver is risky. Instead we point the user at AMD's
official download page (one click), after which the existing WSL detection
lights up automatically.
- install.ps1: new Show-AmdWslDriverHint -- when an AMD GPU is present and the
installed driver predates the 26.2.2 release (DriverDate < 2026-02-01),
print a concise tip with the AMD download URL. Handles DriverDate as either
a CIM DateTime or a WMI string. Suppress with UNSLOTH_SKIP_AMD_DRIVER_HINT=1.
- install.sh (WSL hint): add the direct Adrenalin 26.2.2 download URL and note
that AMD downloads are referrer-gated (open in a browser).
Verified: hint fires on a Sept-2025 driver, auto-suppresses on >= 2026-02-01;
install.ps1 parses; install.sh bash -n clean; suite 267 passed, 2 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* install.ps1: refresh shell icon cache after creating the shortcut
After writing the Desktop / Start Menu .lnk, nudge Explorer to refresh
its icon (ie4uinit.exe -show). Without this, a stale icon cache can show
a blank shortcut icon until the next explorer restart -- most visible
when a shortcut of the same name was rewritten (e.g. a native install
followed by a WSL install, which previously shared the name; now they use
distinct names, but the cache nudge makes the icon appear immediately
regardless). Best-effort and wrapped in try/catch so it never fails the
install. The bundled unsloth.ico itself is valid (verified it renders).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* setup.ps1: don't silently CPU-build llama.cpp on an AMD GPU
For AMD, GPU acceleration comes from the lemonade ROCm prebuilt (it bundles
the ROCm runtime, no HIP SDK needed) and is the preferred/default path. The
source-build fallback is CPU-only -- a HIP/ROCm *source* build would need the
full HIP SDK + ROCm clang toolchain, which the prebuilt exists to avoid.
Previously, if an AMD-GPU host ever fell through to the source build (e.g. the
prebuilt could not be downloaded), it printed "building llama.cpp (CPU-only,
no NVIDIA GPU detected)" and quietly produced a CPU binary -- masking the lost
GPU acceleration. Now that case emits a loud [WARN] explaining the GPU prebuilt
is the AMD path and how to restore it (re-run / check network / set
UNSLOTH_LLAMA_RELEASE_TAG), so AMD never silently degrades to CPU.
No behavior change on the happy path: AMD still gets the GPU prebuilt (verified
on gfx1151: ggml-hip.dll bundled, ~80% GPU compute during inference). NVIDIA
(CUDA source build) and CPU-only hosts are unchanged.
setup.ps1 parses; install suite 267 passed, 2 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* uninstall: remove shared llama.cpp build, kill lock-holders, match WSL shortcut
Three gaps found by running a real uninstall on a native-Windows + WSL host;
all fixes are scoped to Unsloth-owned paths and no-op on the other pathways
(env/custom-root, NVIDIA/AMD/CPU, Mac) so nothing else regresses.
uninstall.ps1:
- Remove the default-mode SHARED llama.cpp build + cache. setup.ps1 installs
them at ~/.unsloth/llama.cpp and ~/.unsloth/.cache -- SIBLINGS of studio,
not under it -- so deleting <studio> left hundreds of MB behind. Now removed
explicitly, then ~/.unsloth is dropped ONLY if empty (never nukes unrelated
content). No-op in env/custom mode (llama.cpp nests under the custom root,
removed already) and when absent. UNSLOTH_LLAMA_CPP_PATH (user-owned) is kept.
- New _StopProcessesLockingRoots: _StopStudioProcesses only matched the venv
unsloth/python/studio exe, so it missed (a) llama-server.exe under llama.cpp
and (b) an orphaned multiprocessing python fork that ran from the SYSTEM
python but loaded a venv DLL (bitsandbytes) -- on Windows an open DLL handle
blocks the directory delete, leaving a half-removed install. The new helper
kills any process whose image path OR loaded module is under a target root
(module scan scoped to python/unsloth/llama-server names; vendor-agnostic).
- _RemovePath now retries (transient post-kill handle release).
uninstall.sh:
- Remove the default-mode ~/.unsloth/llama.cpp + ~/.unsloth/.cache; rmdir
~/.unsloth only if empty.
- WSL Windows-side shortcut cleanup now matches by TARGET (any
"Unsloth Studio*.lnk" whose target launches wsl.exe), covering both the
legacy "Unsloth Studio.lnk" and the new "Unsloth Studio (WSL - <distro>).lnk"
-- and never removes a native-Windows shortcut (which launches wscript.exe).
uninstall.ps1 parses; uninstall.sh passes sh -n and bash -n.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* install.ps1: invalidate Win11 Start Menu tile cache after creating shortcut
The Start Menu shortcut kept showing a blank/generic icon even after the
Explorer icon-cache rebuild, because Windows 11's StartMenuExperienceHost
keeps its OWN pre-rendered tile-icon cache
(%LOCALAPPDATA%\Packages\Microsoft.Windows.StartMenuExperienceHost_cw5n1h2txyewy\
TempState\TileCache_*.bin + StartUnifiedTileModelCache.dat), separate from
Explorer's iconcache_*.db. ie4uinit and an explorer.exe restart do not touch
it, and they don't recycle the host -- so a rewritten same-name shortcut keeps
showing the first-rendered (often the generic wscript ">") tile until the host
restarts on its own.
Fix: after creating the shortcut, drop only the Start Menu RENDER caches
(TileCache_* + StartUnifiedTileModelCache.dat) and stop StartMenuExperienceHost
(Windows auto-relaunches it), so the tile re-resolves the real icon via the
shell image factory. start2.bin (the user's pinned layout) is deliberately
preserved. Guarded by Test-Path (Windows 10 has no such host -> skipped) and
wrapped in try/catch so it can never fail the install. Windows-only
(install.ps1); no effect on Linux/macOS/Studio.
Verified live: rendering the shortcut via IShellItemImageFactory::GetImage (the
API StartMenuExperienceHost uses) returns the Unsloth sloth icon, color-matched,
after this invalidation -- previously it returned the generic script tile.
install.ps1 parses; install suite 267 passed, 2 skipped.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* ROCm-on-WSL for AMD Strix Halo (gfx1151): auto-setup + runtime enablement
Make Unsloth Studio set up ROCm-on-WSL automatically for AMD Strix Halo
(Radeon 8060S / gfx1151) and use the GPU at runtime, validated end-to-end
on a Ryzen AI Max+ PRO 395 (ROCm 7.2.1 + librocdxg + Adrenalin Apr-2026):
rocminfo enumerates gfx1151, torch.cuda True, ~85.8 GB UMA pool.
Every change is a strict no-op for all other configs (NVIDIA/CUDA,
discrete + native-Linux AMD ROCm, macOS/MLX, Windows, CPU-only, non-Strix
WSL) and can never abort the installer.
- scripts/install_rocm_wsl_strixhalo.sh: rewrite to the validated recipe.
Fixes that would have broken a working box: drop the /usr/lib/wsl/lib
preflight (a working ROCDXG host has only d3d12/dxcore there); remove the
obsolete rocr4wsl step (gone from the 7.2.1 repo; would hard-fail and also
rips out the standard hsa-rocr ROCDXG needs); dynamic librocdxg soname
(was hardcoded 1.1.0; build is 1.2.0); direct apt-repo install; Windows
SDK auto-discovery; persist env to /etc/profile.d + ~/.bashrc; idempotent.
- install.sh: _maybe_bootstrap_rocm_wsl auto-offers/runs the helper when it
detects a Strix Halo APU in WSL (/dev/dxg) with no ROCm runtime, then
loads the env so detection routes to the gfx1151 wheels. Fast-path when
already configured. Fix an inaccurate WSL hint line.
- studio/backend/main.py + worker.py: set HSA_ENABLE_DXG_DETECTION=1
in-process before torch (gated on /dev/dxg AND librocdxg.so), so the
worker uses the GPU even when launched outside a login shell. Mirrors the
existing BNB_ROCM_VERSION injection.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* uninstall: clean up ROCm-on-WSL artifacts + Start Menu tile cache
- uninstall.sh: remove the ROCm-on-WSL helper artifacts -- the librocdxg
build clone (~/.unsloth/librocdxg, which otherwise blocks the empty-dir
rmdir of ~/.unsloth), the throwaway smoke-test venv, the persisted env
(/etc/profile.d/unsloth-rocm-wsl.sh) and the ~/.bashrc block. The system
ROCm userspace is a shared prereq like CUDA and is kept by default;
UNSLOTH_UNINSTALL_ROCM=1 removes it too. No-ops on macOS / non-Strix Linux.
- uninstall.ps1: invalidate the Win11 Start Menu tile cache after removing
the shortcut so its tile disappears promptly (mirrors install.ps1),
preserving start2.bin.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: accurate AMD ROCm messaging (HIP SDK optional, not required)
The Windows installer printed "HIP SDK not found - GPU-accelerated training
unavailable" / "ROCm wheels require the HIP SDK" whenever the HIP SDK was
absent. That is misleading: for a detected AMD GPU arch (gfx1151 etc.),
setup.ps1 installs AMD's bundled-runtime ROCm PyTorch wheels (repo.amd.com)
which ship their own ROCm runtime and do NOT need the HIP SDK -- verified
end-to-end (torch 2.11.0+rocm7.13.0, cuda True, QLoRA training on GPU) on a
Radeon 8060S with no HIP SDK installed.
Gate the GPU-detection + rocm-step messages on a detected gfx arch: when one
is known, state that GPU PyTorch uses bundled-runtime wheels and the HIP SDK
is optional; only when the arch is unknown fall back to the HIP-SDK hint.
Behavior (torch routing) is unchanged; this is messaging only. No-op for
NVIDIA/CUDA, HIP-SDK-present, and CPU paths (they hit earlier branches).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: fix /opt/rocm data-loss + make WSL shortcut create/remove interop-robust
Two fixes from the 3-reviewer regression audit + live testing on a
systemd-enabled WSL distro (interop disabled):
F1 (data-loss, install_rocm_wsl_strixhalo.sh): the /opt/rocm symlink-repair
could force-delete a pre-existing REAL ROCm install. The guard only checked
that /opt/rocm is a real directory, not that it is the stray librocdxg stub.
Now it only touches /opt/rocm when it is NOT a real install (no bin/rocminfo,
bin/hipcc, or .info/version present), and MOVES it aside (rocm.unsloth-stub-bak)
instead of deleting it, so a wrong guess can never lose data.
WSL interop robustness (install.sh + uninstall.sh): both relied on
`command -v powershell.exe`, which is true even when WSL interop cannot EXECUTE
it (on systemd distros powershell.exe fails with "Exec format error"). Result:
the WSL shortcut silently failed to create (install) and to remove (uninstall).
- uninstall.sh: test that powershell.exe actually runs; if not, remove the
"Unsloth Studio (WSL...).lnk" files directly via drvfs (/mnt/<drive>), which
works without interop. The name is WSL-install-specific, so a native install's
"Unsloth Studio.lnk" is never touched.
- install.sh: when the shortcut cannot be created, warn with the manual launch
command + how to re-enable interop, instead of failing silently.
No behavior change on the interop-on path. The regression audit otherwise found
no regressions on Linux/Mac/Windows/CPU/NVIDIA install paths.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* install.sh: fast-path fully restores ROCm-on-WSL env when the drop-in is gone
Reinstall regression found by uninstall->reinstall testing: after a Studio
uninstall that removed /etc/profile.d/unsloth-rocm-wsl.sh but KEPT the shared
ROCm (the default), a non-login reinstall hit the bootstrap fast-path
(librocdxg present) and its else-branch only set HSA_ENABLE_DXG_DETECTION --
NOT PATH/LD_LIBRARY_PATH. So rocminfo was not on PATH, GPU detection failed,
and the installer fell back to CPU-only PyTorch.
Fix: when librocdxg is present but the env drop-in is missing, restore the
FULL env inline (HSA + TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL + PATH +
LD_LIBRARY_PATH) so rocminfo is found and detection routes to the GPU, and
recreate /etc/profile.d/unsloth-rocm-wsl.sh so future shells and the Studio
worker get it too. No change to the env-present fast-path or any other host.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: clear Explorer icon cache so shortcut icons aren't blank
Root cause of the persistent blank Desktop + Start Menu icons: Explorer caches
each shortcut's icon in iconcache_*.db and does NOT re-read the .ico when a
same-name .lnk is recreated across reinstalls. The .ico and .lnk are correct
(the shell renders them non-blank via IShellItemImageFactory; the .ico has real
image data at 16/32/48/128 px), but the stale cache entry wins. The previous
fix only ran a weak `ie4uinit -show` + the Start Menu tile-cache clear -- it
never invalidated Explorer's icon cache, so the desktop icon stayed blank.
Fix (native install.ps1 New-StudioShortcuts AND the WSL shortcut path in
install.sh):
- ie4uinit -ClearIconCache (thorough; replaces -show as the primary refresh)
- SHChangeNotify(SHCNE_ASSOCCHANGED) to force a live desktop/taskbar refresh
WITHOUT restarting explorer
- keep the Win11 Start Menu tile-cache invalidation (and add it to the WSL
shortcut path too, preserving start2.bin)
Non-disruptive (no explorer restart). install.ps1 parses clean; install.sh
passes bash -n + dash -n; the heredoc-generated WSL PowerShell parses clean.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: per-item SHChangeNotify(UPDATEITEM) reliably fixes blank icons
The blank Desktop/Start Menu shortcut icons are a stale Explorer PER-ITEM icon
cache: when a same-name .lnk is recreated across reinstalls, Explorer caches the
previously-resolved (often generic "white page") icon for that item and won't
re-extract the .ico on its own. The .ico and the .lnk's IconLocation are correct
(every icon API renders the sloth) -- only Explorer's cached display is stale.
The previous refresh (ie4uinit -ClearIconCache + a GLOBAL SHCNE_ASSOCCHANGED
broadcast) does NOT recover a stale item -- confirmed by reproduction. The
reliable, NON-disruptive fix (no explorer restart) is a PER-ITEM
SHChangeNotify(SHCNE_UPDATEITEM, SHCNF_PATHW, <lnk path>) for each created
shortcut, which forces Explorer to re-read that exact item's icon.
Verified end-to-end: deliberately staled a shortcut to the generic icon, ran the
installer's exact new refresh code, and the sloth icon recovered with NO explorer
restart (confirmed by capturing the live desktop via PrintWindow).
Applied to both native install.ps1 (New-StudioShortcuts) and the WSL shortcut
path in install.sh. Still clears the on-disk icon cache (ie4uinit) and the Win11
Start Menu tile cache (preserving start2.bin).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* uninstall: remove leftover llama.cpp .staging root so ~/.unsloth is cleaned
The llama.cpp atomic-install staging root (install_llama_prebuilt.py
INSTALL_STAGING_ROOT_NAME=.staging) is a sibling of the llama.cpp install
dir (~/.unsloth/.staging in default mode). It is normally pruned after a
successful activate, but an interrupted or retained build can leave a
<name>.staging-XXXX tree behind. The uninstallers removed llama.cpp and
.cache but not .staging, so the final empty-dir cleanup of ~/.unsloth failed
and the directory lingered. Reproduced on WSL (Ubuntu-24.04) where an empty
llama.cpp.staging-XXXX dir kept ~/.unsloth alive after uninstall.
Remove ~/.unsloth/.staging in both uninstall.sh and uninstall.ps1. No-op in
env/custom mode (staging nests under the custom root removed already) and
when absent. Cross-platform fix (the staging logic is platform-agnostic).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer: WSL-absent hint + fix here-string lint false positive
install.ps1: in the AMD WSL-ROCm driver hint, detect when wsl.exe is absent
and add a one-line "wsl --install -d Ubuntu-24.04" pointer so a Strix Halo
user with no WSL yet gets an actionable next step (the hint previously assumed
an Ubuntu-24.04 distro already existed). Best-effort, informational only.
test_rocm_support.py: test_no_here_strings did a crude substring check that
false-positived on the conda-style block marker
printf '# <<< Unsloth ROCm-on-WSL (gfx1151) <<<' -- a string literal written
into the /etc/profile.d drop-in, also used as a sed delimiter pair by
uninstall.sh, not a here-string. Strip quoted spans before the check so the
lint still catches a real here-string operator but ignores quoted literals.
install.sh remains POSIX-clean (sh -n / dash -n / bash -n all pass).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* installer: address PR review comments (gfx1150 mapping, amd-smi opt-out, WSL bootstrap, SDK path, make)
Apply the valid bot review findings on #5940; reject the ones that don't hold.
Fixed:
- AMD name->gfx table (setup.ps1 + install.ps1): Radeon 890M and Ryzen AI 9 HX
370/375 are Strix POINT (gfx1150), not Strix Halo (gfx1151). Move 890M / HX 37x
/ AI 9 HX to the gfx1150 row and drop the bogus HX 38x pattern (no such Strix
Halo SKU). Matches the runtime classifier in worker.py (890M/880M -> gfx1150;
8060S/8050S -> gfx1151). Prevents Strix Point hosts from getting the wrong ROCm
prebuilt/wheels.
- amd-smi opt-out (setup.ps1 + install.ps1): an explicit UNSLOTH_ENABLE_AMD_SMI=
0/false/no/off now wins over the HIP-SDK heuristic, so a host with a HIP SDK
binary but a broken runtime no longer gets the DiskPart/UAC prompt the opt-out
exists to avoid.
- amd-smi warning probes (install_python_stack.py): _has_rocm_gpu and
_detect_amd_gfx_codes now gate amd-smi behind _amd_smi_allowed() (and pass
_amd_smi_env()), closing the last unguarded amd-smi spawn on Windows.
- WSL ROCm bootstrap (install.sh): the "already-usable ROCm?" early return now
requires rocminfo to enumerate the real gfx1151 agent instead of the generic
_has_amd_rocm_gpu (whose broad gfx[1-9][0-9] match accepts a fallback
"gfx11-generic" ISA), so a Strix Halo box missing the ROCDXG bridge is no longer
skipped. The shared helper is untouched (no gfx90a regression).
- install_rocm_wsl_strixhalo.sh:
* Quote-safe Windows SDK discovery: the old for-in-$(ls -d "...Program Files
(x86)/...") word-split on the space and never matched; use find + read loop.
* Add `make` to apt prereqs (cmake only recommends it; minimal images lacked it
and the librocdxg `make -j` build failed).
* Verification requires gfx1151 exactly (not gfx1[0-9]) so a generic ISA or an
unrelated RDNA GPU can't pass while the real GPU is absent.
Reviewed but NOT changed:
- "Forward inferred ROCm arch without HasROCm" (setup.ps1): already correct --
--rocm-gfx is forwarded under `if ($script:ROCmGfxArch)`, not `if ($HasROCm)`.
- "Route inferred arch into install.ps1 torch path": not a bug -- install.ps1
installs CPU torch as a base by design and setup.ps1 swaps in the ROCm wheel for
the inferred arch (gate `($HasROCm -or $ROCmGfxArch) -and cpu`); verified live
the native install ends on torch 2.11.0+rocm7.13.0.
- "$p null guard after Start-Process" (install.ps1/setup.ps1): redundant -- the
amd-smi runner uses [Process]::Start wrapped in try/catch, so a null process
already returns "" with LASTEXITCODE=1 (no uncaught exception).
- "ls -> find for /usr/lib/wsl/lib" (gemini): stale -- that heuristic was removed;
only a comment about it remains.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer(rocm-wsl): auto-install the Windows 11 SDK via winget (fewer manual steps)
librocdxg's build needs the Windows SDK 'shared' headers on the Windows host.
Previously the helper just die()d with "install the Windows 11 SDK and re-run" if
they were missing -- a manual prerequisite that broke the otherwise-seamless
`curl ... install.sh | sh` one-liner on Strix Halo.
Now, when the headers aren't found, the helper installs the Windows 11 SDK on the
Windows host from inside WSL via winget (powershell.exe interop), then
re-discovers them. The SDK installer elevates -> ONE UAC prompt on the Windows
desktop; the headers appear under /mnt/c immediately (drvfs is live, no reboot).
The user already consented to the ROCm-on-WSL setup, so no extra prompt is added
beyond the OS UAC gate.
- New _find_win_sdk (space-safe find of the newest installed SDK 'shared' dir)
and _install_windows_sdk_via_winget helpers.
- winget IDs tried newest-stable first: Microsoft.WindowsSDK.10.0.26100, then
.22621. The presence of the headers (re-check) is the source of truth, not
winget's exit code. </dev/null so winget never consumes a piped `curl|sh` stdin.
- Best-effort + non-fatal: interop-off / no-winget / declined-UAC all fall
through to the existing clear manual-install die(). Opt out with
UNSLOTH_SKIP_WIN_SDK_INSTALL=1.
Removes the last avoidable manual step from the WSL Strix Halo path; only the AMD
Adrenalin driver (AMD referrer-gates the download) remains manual. Verified
_find_win_sdk resolves the spaced "Program Files (x86)" path; bash -n clean; all
winget flags validated against `winget install --help`.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* installer(amd): gate install-time amd-smi probe to fix DiskPart UAC prompt
install_python_stack.py's Windows "AMD GPU detected but ROCm torch missing"
warning probe ran `amd-smi list` whenever amd-smi was on PATH -- and amd-smi
ships in C:\Windows\System32 with the AMD Adrenalin driver -- without the
_amd_smi_allowed() gate that every other amd-smi call site in the file uses.
On Adrenalin-only hosts (no HIP SDK) amd-smi elevates a child at runtime and
pops a UAC/DiskPart prompt that __COMPAT_LAYER=RunAsInvoker cannot suppress
(amd-smi's manifest is asInvoker). The probe also ran before the
ROCm-torch-installed check, so it fired on every Windows AMD install.
Gate it behind _amd_smi_allowed() and pass _amd_smi_env(), matching
_has_rocm_gpu()/_detect_amd_gfx_codes(). When skipped, the only loss is the
best-effort "AMD GPU detected" note on HIP-SDK-less hosts.
Adds a per-function AST regression test asserting every function in
install_python_stack.py that names the amd-smi command and spawns a subprocess
also references _amd_smi_allowed() (flags the pre-fix code; passes after).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* studio(cli): fix `unsloth studio stop` crashing on Windows
`stop` used the POSIX `os.kill(pid, 0)` liveness probe, but on Windows
CPython raises OSError (WinError 87, "The parameter is incorrect") for
*every* pid -- alive or dead. `stop` only catches ProcessLookupError /
PermissionError, so the OSError propagated and the command crashed with
a traceback before ever reaching its (correct) `taskkill /F` path.
Add a cross-platform `_pid_alive(pid)` helper (tasklist on Windows,
signal-0 elsewhere) and use it for both the pre-check and the post-kill
wait loop. The actual kill path is unchanged.
Verified on Windows (Python 3.13): os.kill(pid,0) raises WinError 87 for
both a live and a dead pid; `_pid_alive` returns True/False correctly and
the full stop() flow (alive -> taskkill -> dead -> "stopped") passes
end-to-end against a throwaway process.
Adds tests/studio/test_cli_studio_stop_windows.py (AST guard against a
bare os.kill(pid,0) liveness probe + mock-only _pid_alive behaviour for
the win32 tasklist branch and the POSIX signal-0 branch).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* installer(amd): fix install.sh name->arch table misrouting Strix Point to gfx1151
The bash name->arch inference table in install.sh placed Strix Point
identifiers (Radeon 890M, "Ryzen AI 9 HX 370/375", "AI 9 HX") in the
gfx1151 (Strix Halo) row, diverging from the install.ps1 / setup.ps1
PowerShell tables which correctly map them to gfx1150. It also carried a
stray "HX 38" token absent from the PowerShell source-of-truth.
Align install.sh with the PowerShell tables:
gfx1151 row: 8060S|8050S|8040S|Strix Halo|Ryzen AI Max|AI Max
gfx1150 row: 890M|880M|860M|840M|Strix Point|Krackan|HX 37|AI 9 HX|...
Impact is low (the bash table only feeds the display label _gpu_disp_gfx
and the "set UNSLOTH_ROCM_GFX_ARCH=..." hint; wheel selection is driven
by the detected ROCm version, not this name string) but a Strix Point
user would otherwise see/copy the wrong gfx arch.
Add a parity test (test_install_sh_name_arch_agrees_with_ps_for_strix_and_non_amd)
that parses install.sh's case table and asserts Strix Halo->gfx1151,
Strix Point->gfx1150, RX 7700S->gfx1102, and NVIDIA/Intel->no match,
cross-checking against install.ps1 (the previous parity test only
compared install.ps1 <-> setup.ps1, missing install.sh).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* setup.ps1: keep prebuilt-llama ownership guard within the test's block window
The AMD additions to the prebuilt-llama.cpp block (the windows-hip vs
windows-cpu existing-install kind validation) pushed the
install_llama_prebuilt.py invocation to ~1999 chars after the
"installing prebuilt llama.cpp bundle (preferred path)" anchor, right at
the edge of the 2000-char window that
test_setup_ps1_prebuilt_llama_cpp_has_ownership_guard slices -- so the
helper string was truncated and the test failed with "substring not
found" (CI: Repo tests (CPU)).
The ownership-guard invariant (Assert-StudioOwnedOrAbsent precedes the
install_llama_prebuilt.py call) was already satisfied; only the proximity
to the anchor regressed. Move the "installing prebuilt..." substep to
immediately before the install (after the existing-install pre-cleanup),
which also reads better (validate/clean existing -> then "installing"),
shrinking anchor->helper from 1999 to 413 chars. Behaviour is unchanged
(console message ordering only).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* install.sh: auto-run Strix Halo ROCm-on-WSL setup by default
`curl -fsSL https://unsloth.ai/install.sh | sh` should make a Strix Halo
(gfx1151) GPU usable inside WSL with no extra commands. Previously the
ROCm-on-WSL bootstrap was opt-in: it required UNSLOTH_ROCM_WSL_AUTO=1 or an
interactive [Y/n] at a TTY, and silently skipped under a pipe (no /dev/tty),
so the piped one-liner never set the GPU up automatically.
Flip it to auto-by-default for the single narrow case the existing guards
allow (WSL + Strix Halo + /dev/dxg + no usable ROCm yet) -- exactly the GPU
setup the user ran the installer for. Opt out with
UNSLOTH_SKIP_ROCM_WSL_SETUP=1. The Tauri desktop app keeps its own consent UI
(only auto-runs when it passes UNSLOTH_ROCM_WSL_AUTO=1). All hardware/OS
guards are unchanged, so non-Strix / non-WSL / NVIDIA / native-Linux / macOS /
CPU paths are unaffected.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* PR comments: condense to be succinct (comments/docstrings only)
Shorten the verbose explanatory comments and docstrings this PR added across
the installer, scripts, backend shims, CLI, and tests -- tighter, fewer lines,
while preserving every non-obvious "why" (os.kill WinError 87, amd-smi
RunAsInvoker/UAC, /dev/dxg + librocdxg gating, the ROCm-on-WSL bootstrap guard
chain, ownership guards, etc.). No executable code, string literals, messages,
or behavior changed.
Verified comments-only: docstring-normalized AST equality (Python, 9 files),
non-comment token equality (PowerShell, 3 files), comment-stripped diff +
sh -n / bash -n (shell, 3 files). Behavior re-confirmed: get_torch_index_url +
gfx name->arch table 44/44 under dash & bash; rocm_support / pr5940_followups /
cli_studio_stop tests green.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Installer: address PR review (amd-smi opt-out, pipefail, multi-distro, non-root)
Fixes valid findings from the Codex/Gemini PR review:
- install.ps1 / setup.ps1: gate the `amd-smi version` ROCm-version fallback with
$amdSmiAllowed so UNSLOTH_ENABLE_AMD_SMI=0 opt-out is honored (the device
probe was gated but this fallback wasn't), avoiding the DiskPart/UAC prompt.
- install_rocm_wsl_strixhalo.sh: make the post-verification rocminfo summary
best-effort (|| true) so head's early pipe-close under `set -o pipefail` can't
fail the bootstrap after gfx1151 was already enumerated; pin the Windows SDK
`winget install` to --source winget (matches the msstore-cert fix rationale).
- install.ps1: python.org fallback installs the py launcher per-user
(InstallLauncherAllUsers=0, avoids admin), and derives the fallback full
version from the requested minor so a non-default UNSLOTH_PYTHON (e.g. 3.12)
isn't silently replaced with 3.13 when the listing is unreachable.
- install.sh: recreate /etc/profile.d/unsloth-rocm-wsl.sh via `sudo tee` for a
non-root reinstall (a plain redirect failed silently, dropping the ROCm env).
- uninstall.sh: scope WSL Windows-side shortcut removal to the current
WSL_DISTRO_NAME (per-distro name or -d "<distro>" arg) so uninstalling one
distro no longer deletes other distros' launchers.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Studio ROCm Windows: fix field-reported issues from Strix Halo testers
Four fixes from PR #5940 field reports (Win11 native, gfx1151):
1. bitsandbytes arch-probe spam: bnb's get_rocm_gpu_arch() runs
hipinfo.exe via subprocess PATH at import; the AMD torch wheel ships
hipInfo.exe in the venv Scripts dir, which is only on PATH for
activated venvs. Every bnb import logged "Could not detect ROCm GPU
architecture: [WinError 2]" ERROR + WARNING (even with the HIP SDK
installed, whose bin dir is not on PATH either). Prepend the Scripts
dir to PATH before bnb imports in main.py, worker.py, and
install_python_stack.py, gated on the file existing (only AMD wheels
ship it). Verified on gfx1151: ROCM_GPU_ARCH now resolves to gfx1151
with zero errors.
2. OOM-guard double-tax on native Windows unified APUs: mem_get_info's
total is the WDDM budget the driver grants HIP (BIOS carve + ~half
of remaining RAM) -- the OS share is already outside it. The 0.80
unified cap on top denied loads that fit (field report: 48.49 GiB
budget -> "38.79 GiB allowed" OOM for a 47.29 GiB load with 48.08
free). Use 1.0 on win32 unified; Linux keeps 0.80, discrete 0.90.
3. "Missing VRAM" confusion: log the WDDM budget vs physical RAM with
the fix (BIOS UMA frame buffer / AMD Software Variable Graphics
Memory) when the grant is under 75% of RAM, so a 48 GiB cap on a
96 GiB box reads as policy, not a Studio bug.
4. llama-server fit-step crash (Qwen3.6-27B-MTP + mmproj, lemonade
gfx1151): --fit defaults to 'on' upstream, so the fit step runs even
when Studio already placed the model via -ngl -1, and aborts in
ggml-cuda.cu on some ROCm hosts. Retry the spawn once with --fit off
when the server crashes during startup and Studio's own VRAM math
had placed the model (never when use_fit or an explicit fit flag was
passed). Also keep the TAIL of crash output in the error log (the
diagnostic line prints last; head-truncation cut exactly that) and
reference the full on-disk log.
Verified live on Radeon 8060S: bnb import clean, Qwen3.5-4B-MTP loads
and generates through the new spawn loop, stub-crash retry appends
--fit off and recovers, fraction probes confirm WDDM overcommit and
sub-1.0-only enforcement on current AMD wheels.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio ROCm Windows: GPU-name fallbacks so nothing depends on amd-smi
amd-smi does not reliably exist on Windows: the HIP SDK never ships a
CLI, inbox Windows Update drivers do not, and only some full Adrenalin
packages drop amd-smi.exe into System32 (field report: fresh Win11 +
Adrenalin + HIP SDK, still no amd-smi anywhere). Make every consumer
work without it:
- install_python_stack._detect_windows_gfx_arch: two new probes after
hipinfo/amd-smi -- (2b) the venv Scripts hipInfo.exe shipped by AMD
torch wheels (drives `studio update` on driver-only hosts), and (4) a
last-resort GPU marketing-name -> gfx table via WMI
(Win32_VideoController), mirroring setup.ps1's $nameArchTable so a
standalone repair resolves the arch with zero AMD tooling installed.
- install_llama_prebuilt._resolve_exe: also probe the venv Scripts dir
so a standalone rerun finds hipInfo.exe without HIP_PATH.
- hardware/amd.py _run_amd_smi: which() guard before spawning --
absence now disables the poller in one step instead of burning the
3-strike circuit breaker on FileNotFoundError; corrected the stale
comment claiming Adrenalin ships amd-smi.
Simulated against the real detection functions on gfx1151: amd-smi
absent, present-but-crashing (exit 1), present-but-hanging (60s sleep
vs 5-10s probe timeouts), and hard opt-out -- all resolve gfx1151, no
exceptions, bounded time. Full adversarial install (broken amd-smi
stub first on PATH + UNSLOTH_ENABLE_AMD_SMI=1, fresh uninstall first):
exit 0, name-table arch inference, lemonade gfx1151 b1292 prebuilt,
torch 2.11.0+rocm7.13.0 cuda_avail=True on the 8060S, Studio boots
healthy and stops cleanly.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: per-attempt llama-server log names + amd-smi test portability
Found by cross-platform simulation of the --fit off retry (Windows +
Linux sandboxes, real load_model with stub servers):
- llama-server log filename now carries the spawn-attempt index. The
retry can respawn within the same epoch second; reusing the name
opened the same file with "w" and truncated the crash log the retry
warning had just pointed the user at (proven with a frozen
time.time: one file, crash evidence gone; with the suffix both
attempts keep their logs). Regression-pinned in
test_llama_cpp_wait_for_health.py.
- test_amd_primary_gpu_with_mock now mocks shutil.which alongside
subprocess.run: the amd-smi absence guard which()-checks before
spawning, so on hosts without a real amd-smi (Linux CI, driver-only
Windows) the subprocess mock was never reached and the test failed.
Surfaced by running the suite in a clean Linux sandbox.
Simulation coverage on both OSes: 67-case platform/edge matrix
(real shipped code blocks under win32/linux/darwin spoofs: OOM-guard
fractions + VGM-hint boundary, bnb PATH-prepend gates, retry
eligibility incl. equals-forms and decoy tokens, GPU-name table
adversarial set, WMI fallback without powershell, monitor absence
semantics), 6-scenario live retry matrix (crash-once/crash-always/
exit-zero/explicit-fit/hang/log-collision) against real llama-server
spawns on Windows and WSL (GPU success legs on the 8060S), and a
3-engine browser matrix (chromium/firefox/webkit) driving the live
backend's health + authed /v1 chat completion.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: classify unified-memory via props.is_integrated first
Align the ROCm OOM-guard classifier with PR #5988's UMA gate: consult
hipDeviceProp_t.integrated (props.is_integrated) before the hardcoded
arch set. Strictly additive -- truthy upgrades to unified; 0/absent
falls through to the existing gfx1150/gfx1151 + device-name logic, so
wheels that omit or zero the field cannot downgrade the known APU set.
Extends correct unified-cap treatment to APUs outside that set (e.g.
gfx1103 Phoenix iGPUs) and keeps Studio's two unified-memory consumers
on one driver signal. Verified live on gfx1151 (is_integrated == 1 on
the AMD Windows wheel -> ('gfx1151', True) via the new path).
* AMD detection: probe rocminfo with HSA_ENABLE_DXG_DETECTION and sync setup.sh gfx table
Fleet validation on a Strix Halo WSL2 box showed the system rocminfo
(HSA 1.18, ROCm 7.2.1) only enumerates the GPU over /dev/dxg when
HSA_ENABLE_DXG_DETECTION=1, and that rocminfo can sit at /opt/rocm/bin
off PATH outside login shells. Detection probes that miss either of
these report no GPU on a working ROCDXG host and select the CPU build
even though the lemonade bundle offloads fine (95.7 tok/s measured vs
64.5 CPU on the same laptop). Seed the env (a no-op on bare metal) and
the PATH fallback in install.sh, studio/setup.sh, and the installer's
Linux rocm probe, mirroring what main.py/worker.py already do for the
runtime.
Also sync studio/setup.sh's name->gfx table with install.sh: 890M and
the HX 37/AI 9 HX SKUs are Strix Point (gfx1150, not gfx1151), RX 7700S
must match gfx1102 before the gfx1100 row, and the RDNA2/workstation
rows were missing. New parity test pins the two bash tables together so
they cannot drift again.
* Studio: persist server session logs + native-crash stacks to disk
Field report (Strix Halo, 96 GB UMA carve, WSL and native Windows):
"the studio just terminates without a warning". A native crash in the
GPU runtime kills the process with no Python traceback, and a desktop-
shortcut console closes before anything can be read. The server only
ever logged to the console, so there was nothing to send back.
run_server now tees stdout/stderr to
~/.unsloth/studio/logs/server/server-<ts>-pid<n>.log (console behavior
unchanged; file copy is best-effort), arms faulthandler at the same
file so access violations / SIGSEGV leave a stack trace on disk, and
exports PYTHONFAULTHANDLER=1 so training workers inherit crash dumps
on their captured stderr. Armed before `from main import app` so even
import-time failures leave evidence. Keeps the newest 20 session logs;
opt out with UNSLOTH_STUDIO_NO_FILE_LOG=1. Prints "Session log: <path>"
at startup so users know what to attach.
Verified on this box: a forced real segfault (faulthandler._sigsegv)
leaves the full session output plus "Fatal Python error: Segmentation
fault" and the thread stack in the file while the console shows
nothing; a normal server boot captures the startup banner and serves
health as before.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* AMD probe: honor a pre-set HSA_ENABLE_DXG_DETECTION value
Match the shell helpers, which use the parameter-default form: a user
who exports HSA_ENABLE_DXG_DETECTION=0 to deliberately hide the GPU
from DXG detection should not have the probe override it.
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Adds a self-contained RAG stack to Studio: knowledge bases with chunked indexing, hybrid (dense + lexical) retrieval, and an automatic first-pass context inject into chat. Embeddings run through a local llama-server GGUF backend (default unsloth/bge-small-en-v1.5-GGUF) with a sentence-transformers fallback. The chat tool loop gains a search_knowledge_base tool, a per-turn re-search cap, and source citation, layered on top of the shared ToolLoopController.
* Studio: fix OpenAI- and Anthropic-compatible API spec compliance
* Studio: fix API spec-compliance gaps on passthrough and streaming paths
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: carry context_length_exceeded through the OpenAI passthrough error path
* Studio: count tool-schema tokens in the Anthropic server-tool stream, and small stream-handling guards
* Studio: guard message_delta usage against None and normalize developer role before proxying
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: honor max_completion_tokens on the external-provider proxy path
* Studio: forward llama-server cached_tokens into OpenAI prompt_tokens_details
* Studio: sanitize messages in count_tokens to match the /v1/messages prompt
* Studio: report max_tokens for truncated tool calls and guard null usage in metadata events
* Studio: drop the request-id middleware (headers aren't declared in either spec)
* Studio: include the required request_id field in Anthropic error bodies
* Studio: honor max_completion_tokens on the audio (TTS / audio-input) paths
* Studio: add the _effective_max_tokens helper and route all max-token sites through it
* Studio: align API compatibility edge cases
* Studio: clarify multi-choice chat support
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: clarify logprobs chat support
* Studio: opt the local chat UI into the streaming usage chunk so the context bar and tok/s repopulate
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: forward seed to llama-server, and fix Anthropic server-tool stop_reason, tool_result id correlation, and parallel-tool execution cap
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: align OpenAI chat completion spec edge cases
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: align backend API compatibility tests
* Studio: honor tool caps and internal stream usage
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: coerce nullable stream usage counts
* Studio: preserve system prompts with developer messages
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
* Studio: make Helper LLM startup pre-cache opt in
* [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>
Adds the Studio Hub and download manager: browse Hugging Face models and datasets, download GGUF and safetensors with live progress and cancellation, and manage on-device inventory. The Hub does not require a GPU, so it is available on chat-only hosts.
CI: all substantive checks pass, including the three Core jobs after unsloth-zoo#736. The two red checks are non-code flakes, a transient npm-registry DNS resolution failure in the package scan and one quantized vision-model output assertion whose sibling shards passed.
Trim and tighten code comments and docstrings across the repository. Comment-only: every changed file verified code-identical to main via AST/token comparison.
Trim and tighten code comments and docstrings across studio/ Python. Comment-only: every changed file verified code-identical to main via AST/token comparison.
Raise ruff line-length to 100 and extend the local pre-commit format pipeline (def-signature magic-comma normalization, short multi-line assert collapse, kwarg '=' spacing, blank-line-after-short-import removal, adjacent string-literal / f-string+plain merge, redundant-pass pruning). Every transform re-checks the file AST and is dropped if it would differ; the whole-repo reformat is verified AST-identical per file and idempotent.
* studio: redesign chat composer
Reworks the new-chat composer and the compare composer into a single
rounded pill surface with a softer, lighter look.
- New welcome screen with a time-of-day sloth mascot and a lighter
heading.
- One rounded composer surface with a soft drop shadow. The input grows
inline as you type and collapses back to a single row when cleared.
- Tools and attachments live in a single plus menu; the thinking control
is a compact pill with a reasoning-effort submenu.
- Inlined glyphs for the thinking, send, and dictate controls, kept in
sync across the main and compare composers.
- Toast notifications match the composer surface: no border line, the
same drop shadow, and the same dark surface color, with a ring-less
close button.
- Dark mode: the side-menu shadow blends into the background, hovered
menu rows read clearly, and their roundness matches light mode.
- Composer styles use dedicated unsloth- prefixed classes so compare
mode keeps its own stacked layout.
* studio: sync compare-composer reasoning state and harden compare id
- Compare composer: keep "Preserve thinking" consistent with reasoning,
matching the main composer. Enabling it now turns reasoning on, and
disabling reasoning (the None option or the Thinking toggle) turns it
off, so the invalid "preserve on while thinking off" state can't occur.
- Guard crypto.randomUUID in the Compare action. It is undefined in
non-secure contexts (HTTP over a LAN IP) and would throw; fall back to
a timestamped random id, matching createNavigationNonce.
* studio: reflect pre-selected Search/Code tools when no model is loaded
The Search and Code pills only lit up when the tool was usable right now
(a model loaded and capable), so a tool turned on from the + menu showed
as off in the pill while the menu showed it on. toolsEnabled is persisted
and takes effect once a capable model loads, so the pill should reflect it.
The pills now disable only when a loaded model lacks the capability, and
otherwise reflect the selected state. Applied to the main and compare
composers.
* Studio: link MCP Servers heading to its PR and fix composer pill cursors
Make the "MCP Servers" heading in the chat Configuration sheet link to the
MCP PR, keeping the chevron as the toggle. The label and chevron are rendered
as siblings so we don't nest an <a> inside a <button>.
Also add cursor-pointer to the composer pills and the thinking pill so hovering
a clickable pill shows the hand cursor instead of the default arrow.
* Studio: refine chat composer and add compare-mode parity
- Composer expands to two rows only once the input wraps to a second line,
not on the first keystroke. Re-measure the autosize textarea on the width
swap so expanding no longer leaves a stray blank row.
- Light-mode composer shadow now matches Gemini's soft elevation.
- Plus menu: replace Canvas with a More submenu (Canvas, Compare chat, RAG)
and add Code above MCP. Active Web search/Code items use medium weight.
- Compare mode: the plus side menu, Search/Code toggles, and a Compare exit
pill now match single chat, with the thinking control on the right.
- Projects menu entries link to their tracking PR (#5725).
- Add cursor-pointer to the composer plus button.
* studio: refine composer controls and chat search shadow
- Active tool pills show an x on hover to signal click-to-disable
- Plus button rotates into an x when the tools menu opens
- Composer surface uses a 32px radius and a taller single-line height
- Even, ChatGPT-style spacing between the plus and tool pills in both the single and compare composers
- Send and mic circles resized and spaced, with the arrow centered in the circle
- Chat search box gets a borderless, soft Gemini-style shadow
* studio: size the pill hover x to match the icon it replaces
Cross-engine checks (Chromium, Firefox, WebKit) flagged the active-pill
hover x as a fixed 14px, so it popped smaller than the 19px Code icon.
Fill the glyph slot instead so the x tracks whatever icon it covers.
* studio: do not persist Kimi search/thinking mutual-exclusion in single composer
The single-chat composer flipped the other control off when toggling
search or thinking on Kimi, but without { persist: false }, so it
overwrote the user's saved preference. Match shared-composer and keep
the side effect session-only.
* studio: pointer cursor on model selector trigger and menu items
Add scoped marker classes so the model picker trigger and every
clickable element in its menu (tabs, model rows, delete, eject) show a
pointer cursor; disabled items stay not-allowed.
* studio: pass baseUrl when resolving reasoning caps in single composer
The docked composer omitted baseUrl, so a custom Gemini OpenAI-compat
gateway still advertised the native thinking ladder the backend cannot
honor. Pass selectedExternalProvider.baseUrl like the compare composer
so the resolver hides it.
* studio: grey side-menu hover, green pill hover, thinking hover x
- Plus side-menu items hover grey in light mode, not the green accent
- Thinking pill hovers green like the Search and Code pills
- The plain Thinking toggle shows an x on hover when active, matching
Search and Code; the effort dropdown trigger keeps its bulb
* studio: make the pill hover x a uniform size
The x filled the icon slot, so the wider Code chevron gave a bigger x
than Search and Compare. Pin it to a fixed 15px, centered, so every
pill's x matches.
* studio: broaden chat attachments, fix active hover color, gemini shadow
- Accept svg, source code and many text/config files as drag-and-drop
or picked attachments, matched by extension since their MIME is
unreliable; html keeps its own adapter
- Active (green) side-menu items keep their text and icon color on
hover instead of switching to the accent color
- Composer surface uses Gemini's soft centered shadow 0 0 20px rgba(0,0,0,0.04)
* studio: keep the thinking pill full height when icon-only
The inactive thinking pill has no label, so its flex row collapsed to
the icon height and the hover box looked short. Reserve one text line
(min-height: 1lh + padding) so it matches the Search and Code pills.
* studio: refine composer menu, drop overlay and greetings
- Open the MCP servers dialog directly from the composer plus menu
- Redesign the drag-and-drop affordance Gemini style, drop the badge and border, make the whole chat page a drop target
- Swap in Hugeicons for the RAG, attachment chip and new project icons
- Add time-based randomized welcome greetings, each matched to a fitting sloth
* studio: rename artifacts toggle to Canvas and make it opt-in
- Label the toggle Canvas everywhere, matching the plus menu
- Stop greying out the Canvas menu item; it toggles like the other items
- Only show the Canvas pill in the composer row once it is turned on, since it is less central than Search and Code
* studio: wire Canvas and MCP composer toggles, even out the pill row
- Open the MCP servers dialog from the menu, or toggle MCP on/off once a server is enabled
- Force MCP off when no server is enabled, so the toggle stays honest
- Show Canvas and MCP as opt-in pills that appear in the order they were toggled on
- Expand the composer and light up the pill when Canvas or MCP is on, like Search and Code
- Keep Compare directly after Code in the compare composer
- Use the same Code icon on both composers and give every pill an even icon slot
* studio: tidy composer toggle row and fix MCP enable/disable lifecycle
- Enable MCP automatically after a server is configured via the toggle flow
- Force MCP off everywhere once the last enabled server is removed
- Collapse the pill labels to icons only when more than 4 pills show, keeping Compare labelled
- Order Compare first in compare mode, before Search and Code
- Use the same Code icon and an even 19px icon slot across both composers
- Match the compare composer surface padding and send button inset to normal chat
* studio: revert compare composer padding change that cramped the input
Matching the surface padding to normal chat clipped the textarea text and
left a white strip on top. Restore the compare composer's own padding, which
gives proper top spacing. The send button inset fix stays.
* studio: center welcome greeting and soften composer scrollbar
Center the sloth and title together over the composer instead of
shifting the row left, which left the greeting sitting off to the side.
Keep the composer textarea scroll thumb faint by default and only darken
it when the thumb is hovered or dragged, so a tall draft no longer shows
a heavy dark rail.
* studio: match composer plus-menu tool gating to the pills
The new plus-menu tool entries did not carry the gating the visible pills
already enforce, so the menu and pills could disagree about a loaded
model's capabilities.
- Web search and Code menu items now disable when a loaded model lacks
the capability, while still allowing preselection with no model loaded.
- Enabling Web search from the menu on a Kimi model now flips thinking
off as a session-only change, since Kimi forbids search and thinking
together. This matches the Search pill.
- Added an Images menu item, shown only for image-generation models and
disabled until a model loads, so a short prompt has an entry point.
Applied to both the single-chat and compare composers.
* studio: round the active-pill hover x and even out pill padding
The hover x sat bare and the trailing label was tighter to the pill edge
than the leading icon, so the pill looked lopsided.
- Give the hover x a soft circular background that fills the icon slot,
matching the ChatGPT-style toggle and the icon it replaces.
- Add a little more trailing padding so the label and the leading icon
have even breathing room, and keep icon-only compact pills symmetric.
* studio: nudge the thinking bulb icon up by 0.5px
Bump the thinking lightbulb from 15px to 15.5px in the single-chat and
compare composers so it sits a touch larger next to the other controls.
* studio: drop the hover x circle on icon-only pills
When pills collapse to icon-only, the circle around the hover x is too
cramped in the small chip, so show a bare x there and keep the circle
only on the full-width labelled pills.
* studio: space the compare send button like normal chat
In compare mode the Thinking control sat right against the send button.
Match the normal composer's control spacing (gap-1.5 plus a send margin)
so Thinking has the same breathing room before send. The send button
keeps its 14px inset, so its position is unchanged.
* studio: make collapsed pill hover a circle, not a wide pill
Icon-only pills were wider than tall, so their rounded-full hover
highlight read as a fat rounded rectangle. Make the compact button a
square and center the glyph so the hover (and the x it reveals) sits in
a clean circle.
* studio: fix compare pane drops and audio picker lifetime
- Skip the page-level drop handler when the composer is hidden, so files
dropped on a compare pane are not swallowed by a hidden composer; the
shared compare composer keeps handling drops through its own dropzone.
- Build the audio file input on document.body instead of inside the plus
menu, so the menu closing on select no longer unmounts the input before
the OS picker returns and drops the file.
* studio/chat: stop projects list from white-screening on older backends
The projects list API returned data.projects directly, so a backend that
omits the field handed back undefined. useChatProjects cached that value,
then the next mount read undefined.length and crashed the whole chat page.
Default the projects and threads list APIs to an empty array and keep the
hook null-safe so a bad response can never poison the cache.
* studio/chat: align MCP dropdown with the + menu and add a chevron
Reuse the + menu surface (unsloth-plus-menu) for the MCP dropdown: rounded
corners, narrower width, neutral grey hover, and enabled rows shown as green
text with a right-aligned check instead of the emerald underlay. Add a
chevron to the MCP pill so it reads as openable, matching the Thinking pill.
* studio/chat: make MCP an opt-in pill and fix its dropdown placement
- MCP is back in the + menu as a toggle. The pill now only shows in the
composer when MCP is on, matching Canvas, instead of always sitting there.
- The dropdown follows the composer side like the + menu (opens down in the
welcome composer, up when docked) rather than always opening upward.
- Drop the dropdown caret when pills collapse so the icon is not squished.
- Stop force-syncing mcpEnabledForChat to the server count; the + menu owns it.
* studio/chat: MCP expands the composer, drop sidebar Compare, tidy scrollbars
- Toggling MCP now expands the composer and shows the tool pills, the same as
Canvas, instead of leaving the row collapsed.
- Remove the Compare item from the sidebar now that it lives in the + menu, and
point the compare tour step at the side-by-side view instead of the old button.
- Both sidebars only show their scrollbar on hover, and run settings reserves
the scrollbar gutter so the close button no longer shifts when it appears.
* studio/chat: tighten toggle gap, fix run-settings close button, collapsed Train
- Reduce the composer toggle gap by 2px (gap-1 to gap-0.5) in both composers.
- Move the run settings header out of the scroll area so the close button keeps
its position whether or not the scrollbar shows, and sits flush with the
topbar open button again instead of shifting left.
- Surface Train as an icon in the collapsed sidebar (it already has a labelled
section when expanded).
* studio/chat: tighten Thinking pill X padding, create projects inline
- The Thinking pill used px-2.5, so the hover X sat further in than the left
pills. Match their pl-2 so the X lines up.
- The + menu New project now opens a create dialog and jumps straight to the
new project, instead of routing to the projects list. Shared by both
composers via a small NewProjectDialog.
* studio/chat: soften account menu, hover scrollbars, show collapsed chevrons
- Account menu drops its border ring for the composer's soft shadow and opens
centered over its trigger.
- Settings and search reuse the hover-only scrollbar via a shared
hover-scrollbar class, matching the sidebars.
- Train and Recents keep their chevron visible while collapsed so it is clear
they can be expanded.
* studio/chat: roomier, more rounded account menu
Widen the account menu, add more left and right padding on the rows, bump the
row height and text a touch, and round the corners more, closer to the GPT
account menu.
* studio/chat: trim account menu width and nudge it up 2px
Pull the account menu in slightly on the left and right (narrower box, a touch
less row padding) and lift it 2px higher above the trigger.
* studio/settings: drop outline ring, circular close hover, pointer cursors
- Remove the settings dialog outline ring, keeping just the soft shadow.
- The close button hover is now a circle instead of a rounded rectangle.
- Every clickable control in the settings dialog uses a pointer cursor.
* studio/chat: bump MCP pill icon to 14.5px
Nudge the MCP icon up 0.5px so it sits even with the other pill glyphs.
* studio/chat: bump MCP pill icon to 15px
Nudge the MCP icon up another 0.5px.
* studio/settings: add a Settings title above the tabs
Put a Settings heading at the top of the sidebar so the tabs sit below it,
matching the Claude settings layout. Hidden on mobile where the nav is a row.
* studio/settings: rounder tab hover, bigger title, less-round search dialog
* studio/sidebar: round nav row hover boxes 2px more (10px to 12px)
* studio: drop settings dark shadow + divider, add tab left padding, tune hover roundness
* studio/model-selector: roomier padding, borderless box, rounder hover rows; settings divider light-only
* studio/search: match chat box shadow (soft light, none dark)
* studio/sidebar: borderless chat context menus, rename submenu to Projects with folder-export icon
* studio/model-selector: match light corner radius in dark, drop dark shadow, more visible dark hover
* studio/sidebar: chat context menu matches + side menu styling; relabel submenu Move to project
* studio: borderless message export menu (no dark shadow), match dark corner radius to light on export menu and settings
* studio/sidebar: open chat options menu GPT-style (down-right) and widen so Move to project fits one line
* studio/chat: message export menu uses the chatbox shadow in light mode
* studio/sidebar: narrow chat options menu slightly (w-60 to w-56)
* studio: unify all download icons to Hugeicons download-01; round profile button hover 1px more
* studio/run-settings: bump header to 16px
* studio/sidebar: trim chat options menu width slightly (w-56 to 216px)
* studio/sidebar: trim chat options menu width to w-52
* studio/profile: camera-01 Hugeicons glyph and chatbox shadow on avatar button
* studio: match dark-mode corner radius to light globally (single --radius token)
* studio/recipes: borderless New Recipe menu with chatbox shadow in light, none in dark
* studio: borderless dropdowns globally, chatbox shadow in light, none in dark
* studio: extend borderless + chatbox/none shadow to select, combobox and popover overlays
* studio/mcp: nudge MCP dropdown radius to 20px so its wider box reads as round as the + menu
* studio: restore dark dropdown shadow to avoid same-color merge; greet name ~1/3 of lines; bigger sloth + more gap
* studio/train: active tab is a borderless pill (no underline), roomier padding, more tab gap and bottom spacing
* studio/chat: nudge welcome up ~5px (still vh-based) and trim sloth image to 44px
* studio/train: active tab pill is white with chatbox shadow in light, taller padding
* studio/chat: welcome offset to calc(30vh - 10px)
* studio/chat: welcome offset to 28vh (drop the -10px)
* studio/chat: tighten sloth-to-text gap by 1px (16px to 15px)
* studio/train: revert light active pill to grey fill, drop white bg + shadow
* studio: app-wide hand cursor on every clickable control (disabled excluded)
* studio/chat: welcome offset to 26vh
* studio/chat: welcome offset to 28vh
* studio/chat: harden project and thread list guards against non-array payloads
* studio/sidebar: give the profile row more height and breathing room
* studio/sidebar: trim the profile row top and bottom padding slightly
* studio/sidebar: reduce Train and Recents section label size slightly
* studio/sidebar: trim the profile row top and bottom padding a touch more
* studio/sidebar: enlarge the profile hover area top and bottom
* studio/sidebar: increase profile hover roundness by 1px
* studio/sidebar: trim the profile row top and bottom padding slightly
* studio/sidebar: trim the profile row top and bottom padding slightly
* studio/chat: cache composer line metrics so wrap detection runs once, not per keystroke
* studio/chat: restore the prior view when exiting compare opened from the + menu
* studio/tests: drive Compare from the composer + menu after it moved out of the sidebar
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio/tests: open Compare from the composer + menu in the extra UI suite too
* studio: fix chat dictation microphone access
* studio: snappier plus-to-x spin and steady composer expand gap
Speed up the composer plus icon morph from 480ms to 300ms.
Add row-gap on the expanded composer line so the space between the text
and the controls row stays the same whether the box expanded from
wrapped text or from a toggle being on. The gap sits on the line, not the
input, so the placeholder max-height clamp never crops it.
* studio: only show composer tool pills once a model is loaded
Persisted Search/Code/Canvas/MCP toggles were surfacing the composer pill row on a fresh page load before any model was selected, so an empty composer looked different from the clean just-ejected state. Gate the composerExpanded tool checks on modelLoaded so a model-less composer stays collapsed, while saved preferences still apply the moment a model loads.
* studio: hide RAG composer menu item temporarily
Hide the placeholder RAG entry from the composer plus menu in both single chat and compare until the feature is ready, and drop the now-unused DatabaseIcon import.
* studio: let composer tools pre-select before a model loads
Selecting Web search, Code, Canvas or MCP from the + menu with no model
loaded did nothing visible: the toggle turned on but the composer never
expanded, so the pill stayed hidden. Drop the model-loaded gate from the
expand check so an active tool always surfaces its pill.
Align MCP with the Search/Code pattern too: grey it out only when a loaded
model lacks tool support, so MCP stays toggleable and the pill stays
clickable before a model is loaded instead of looking disabled.
---------
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Daniel Han <23090290+danielhanchen@users.noreply.github.com>
* studio: cap training dataset uploads
* studio: clean up failed dataset uploads
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: raise upload limits to 500MB
* studio: make upload limit configurable
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: stream upload routes
* studio: split recipe upload caps
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: tighten upload limit handling
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: import settings router directly
* studio: polish upload cap setting control
* studio: cap settings request bodies
* studio: stub settings route in desktop auth test
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Follow-up cleanups to the merged AMD ROCm support PR #5301:
1. De-duplicate the torchao Windows-ROCm import stub into a single shared
module (studio/backend/core/_torchao_stub.py); both workers call one
install_torchao_windows_rocm_stub() entrypoint.
2. Align the gfx name/arch comment columns in setup.sh and setup.ps1.
3. Isolate the float16 dtype fallback to AMD without native bf16; NVIDIA
keeps dtype=None so unsloth's own bf16/fp16/FORCE_FLOAT32 detection is
honored.
4. Hoist unconditional stdlib imports (gc, glob, re, subprocess, copy,
types, sys, importlib.metadata) from function bodies to module top
across the PR #5301-touched files; heavy/optional/relative imports stay
lazy.
5. bitsandbytes Windows-ROCm install now uses plain pip (force_pip=True)
instead of UV_SKIP_WHEEL_FILENAME_CHECK, per the AMD hackathon docs.
Also adds scripts/verify_import_hoist.py (a scope-aware LEGB AST resolver
that catches dangling-alias and rename-clash bugs in import-hoist
refactors) and wires it into the Lint CI source-lint job as a self-test
plus a pull_request compare gate.
* fix(studio): set HIP_VISIBLE_DEVICES in apply_gpu_ids for ROCm training workers
Training workers are spawned via multiprocessing spawn before detect_hardware()
runs, so IS_ROCM is still False. If the user never set HIP_VISIBLE_DEVICES in
their shell, _inherits_rocm_visibility is also False, leaving the worker with
only CUDA_VISIBLE_DEVICES set. On ROCm hosts the HIP runtime honors
HIP_VISIBLE_DEVICES over CUDA_VISIBLE_DEVICES, so the worker saw the full
device list and torch raised "no usable HIP accelerator" on some setups.
Fall back to probing torch.version.hip (a build-time attribute, safe to read
before GPU init) to detect ROCm when neither IS_ROCM nor inherited env vars
are available. Mirrors the existing fix in llama_cpp.py for llama-server
subprocess GPU pinning.
Fixes https://github.com/unslothai/unsloth/issues/5180
* test: tighten apply_gpu_ids ROCm fallback assertions
Replace loose OR chain with exact string matches, split into three
focused tests, and add a guard check for the try/except wrapper.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: detect ROCm unified memory (Strix Halo / AMD iGPU) via torch fallback
amd-smi on iGPUs with shared/unified memory (e.g. Radeon 8060S on Strix
Halo) reports only the dedicated VRAM slice (~512 MB) in its metric output,
so get_visible_gpu_utilization() was returning usable_gb ≈ 0.35 GB instead
of the full GTT pool (~128 GB). torch.cuda.mem_get_info() already surfaces
the correct unified-pool size.
Add _reconcile_rocm_unified_memory(): after amd-smi returns a valid result
on a ROCm device, cross-check each device's vram_total_gb against
torch.cuda.mem_get_info(). When torch reports a larger total, replace the
amd-smi VRAM fields in-place. No-op for discrete AMD GPUs where the two
sources agree.
Fixes: "Falling back to all visible GPUs -- model may not fit" on AMD iGPU
machines even when 100+ GB of unified memory is available.
* Apply unified-memory reconciliation in get_gpu_utilization too
The visible-GPU path was already corrected for AMD iGPUs with unified memory
(Strix Halo / Radeon 8060S), but get_gpu_utilization was still returning the
raw 512 MB amd-smi VRAM slice. Studio's /api/train/hardware endpoint and the
live GPU monitor read from this primary path, so users continued seeing the
wrong total even after auto_select_gpu_ids picked the right device.
Refactor to share the per-device correction:
* _apply_unified_memory_correction(metrics, torch_info) -- the actual
replacement logic, in-place on a single metrics dict.
* _reconcile_rocm_unified_memory(...) -- multi-device,
iterates utilization["devices"] (visible-GPU path).
* _reconcile_primary_rocm_unified_memory(...) -- single flat
metrics dict (primary-GPU path), uses parent_visible_spec to pick the
primary index, falls back to ordinal 0 when no visibility env is set.
get_gpu_utilization now calls the primary reconciler under IS_ROCM, so both
endpoints surface the real unified-memory pool on iGPUs while leaving
discrete AMD GPUs untouched (torch_total <= smi_total -> no replace).
* Use 'is not None' and log debug on torch.version.hip probe failures
Two small follow-ups to the apply_gpu_ids ROCm fallback:
1. Match detect_hardware()'s 'getattr(torch.version, "hip", None) is not None'
form so the entire codebase has one canonical 'this torch was built with
HIP' check. On every shipping torch wheel hip is either None or a non-empty
version string, so the new form agrees with the old bool() form on every
real install.
2. Log the probe failure at debug level instead of swallowing it silently.
The broad 'except Exception' is intentional (we never want apply_gpu_ids
to crash a worker over a probe), but the silent pass made it impossible
to tell whether the fallback was firing or being skipped.
* fix(studio): honour HIP_VISIBLE_DEVICES in _get_parent_visible_gpu_spec before IS_ROCM is set
When a user has HIP_VISIBLE_DEVICES set in their shell (e.g. "1" to select
GPU 1) but detect_hardware() has not yet run in the Studio parent process,
IS_ROCM is still False. _get_parent_visible_gpu_spec() was gated on IS_ROCM
so it fell through to CUDA_VISIBLE_DEVICES (unset), saw all physical GPUs,
and auto-selected index 0. apply_gpu_ids then overwrote HIP_VISIBLE_DEVICES
with "0", making the intended GPU invisible to ROCm torch in the worker,
which triggered the "no usable HIP accelerator" error (issue #5180).
Apply the same _inherits_rocm_visibility pattern already used in
apply_gpu_ids: check for HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES in the
environment regardless of IS_ROCM so the correct GPU index is preserved.
* fix(install): harden AMD ROCm GPU detection for multi-GPU and env-filtered setups
The previous rocminfo awk pattern could miss discrete GPUs on machines
where HIP_VISIBLE_DEVICES/ROCR_VISIBLE_DEVICES is used to mask an
integrated GPU — the env vars filter rocminfo output but may not
propagate into the install script subprocess, causing detection to
fail entirely.
Two changes:
- Tighten rocminfo pattern from /gfx[0-9]/ && !/gfx000/ to
/gfx[1-9][0-9]/ — simpler and correctly excludes the CPU agent
(gfx000) without a negative lookahead
- Add sysfs KFD topology fallback: reads
/sys/class/kfd/kfd/topology/nodes/*/gpu_id which is a kernel-level
view unaffected by HIP_VISIBLE_DEVICES or ROCR_VISIBLE_DEVICES
Fixes detection failure reported in Discord by Chains (gfx1201 + iGPU
machine where env var exclusion of the iGPU caused rocminfo to return
no usable device).
* Fix KFD sysfs awk fallback to read properties file
The fallback added by this PR reads /sys/class/kfd/kfd/topology/nodes/*/gpu_id
files but matches the literal token 'gpu_id' against their content. Those
files contain only a single decimal value (e.g. '0' for CPU agents, '50432'
for GPU agents), so the regex never matches and 'found' stays 0, making the
fallback a no-op on every host. The properties file in the same directory
contains key/value lines like 'gpu_id 50432' which is what the existing awk
pattern expects.
Reproduced with a synthetic sysfs layout: against gpu_id files awk exits 1;
against properties files awk exits 0 when any node reports gpu_id > 0.
* fix(setup.ps1): detect AMD ROCm GPU on Windows, bring to parity with setup.sh
setup.ps1 only checked nvidia-smi and fell straight to "gpu: none" on AMD
machines. setup.sh already probed rocminfo/amd-smi/hipconfig/hipinfo.
Add three-tier detection mirroring install_llama_prebuilt.py's detect_host():
1. hipinfo: gcnArchName in output confirms a real HIP GPU (not just SDK)
2. amd-smi list: "GPU: <digit>" data rows as fallback
3. WMI Win32_VideoController: last resort -- detects AMD GPU even without
HIP SDK, then guides user to install it rather than silently going CPU
Also corrects the "none" message to mention AMD ROCm alongside NVIDIA so
users with AMD hardware understand the requirement.
Fixes: rohit-style install where Strix Halo (Radeon 8060S) showed
"gpu: none" even with the HIP SDK present.
* fix(install.ps1): detect AMD ROCm GPU on Windows, bring to parity with setup.ps1
install.ps1 had the same nvidia-smi-only GPU detection as setup.ps1 before
the setup.ps1 fix. Applies the same three-tier AMD detection:
1. hipinfo: gcnArchName confirms real HIP GPU
2. amd-smi list: GPU data rows as fallback
3. WMI Win32_VideoController: detects AMD GPU without HIP SDK and guides
user to install it
Fixes: install.ps1 showing "gpu: none" while setup.ps1 correctly showed
"AMD GPU detected" on the same machine (reported by rohit, RX 7600 XT).
* fix(install.ps1): suppress 'No NVIDIA GPU detected' when AMD GPU is present
* feat: add Windows AMD ROCm PyTorch wheel installation
install_python_stack.py:
- Add _ROCM_WINDOWS_WHEEL_BASE and _ROCM_WINDOWS_RELEASES constants
pointing to AMD repo.radeon.com (ROCm 7.2 -> torch 2.9.1+rocm7.2.1)
- Extend _ensure_rocm_torch() with a Windows branch: detects ROCm via
_has_rocm_gpu() / _detect_rocm_version(), requires Python 3.12 (cp312
is the only ABI AMD publishes for Windows), installs the direct wheel
URL from repo.radeon.com
install.ps1:
- Capture ROCmVersion during AMD detection via hipconfig --version /
amd-smi version (needed for wheel URL selection)
- After Get-TorchIndexUrl, add an AMD wheel override block: when HasROCm
and Python 3.12 detected, set ROCmTorchWheelUrl to AMD wheel URL
- Expand torch install branch to handle ROCmTorchWheelUrl with
uv pip install --force-reinstall --no-cache-dir
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: also install torchvision and torchaudio from AMD Windows repo
AMD publishes matching torchvision-0.24.1+rocm7.2.1 and
torchaudio-2.9.1+rocm7.2.1 cp312 wheels at the same repo.radeon.com
release folder. Install all three in both install.ps1 and
install_python_stack.py Windows ROCm path.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat: add ROCm 7.1.1 Windows wheel mapping
AMD uses a different version string for 7.1.1 wheels:
2.9.0+rocmsdk20251116 (date-tagged) instead of +rocm7.1.1.
Adds the 7.1.1 release folder to both install.ps1 and
install_python_stack.py so users with ROCm 7.1 get ROCm
torch instead of falling back to CPU.
* fix: install rocm_sdk_core and rocm_sdk_libraries_custom alongside torch
The AMD Windows torch wheels declare rocm[libraries]==<ver> as a hard
dependency. Without installing rocm_sdk_core and rocm_sdk_libraries_custom
from the same AMD release folder, uv cannot resolve the dependency and
fails with 'No solution found'. Include all 5 wheels in one install call.
* fix: expand ROCm wheel array to scalars for Invoke-InstallCommand
@array splatting inside a scriptblock only works when the native command
is prefixed with '&'. Invoke-InstallCommand uses '& $Command' to run the
block, so @ROCmAllWheelUrls was not being expanded. Extract to scalar
variables $rw0-$rw4 which are captured correctly by the closure.
* fix: use --no-deps for AMD Windows torch wheel install
uv's resolver looks up rocm[libraries]==0.1.dev0 on PyPI during
dependency resolution before downloading any wheels, and fails because
the package doesn't exist on PyPI. --no-deps skips resolution entirely
and installs all 5 AMD wheels directly. The GPU runtime dependency is
satisfied by the HIP SDK, not a Python package.
* fix: setup.ps1 and install_python_stack.py now install ROCm torch on Windows
setup.ps1 was always setting CuTag='cpu' for non-NVIDIA hosts and installing
cpu-only PyTorch, overwriting the ROCm torch installed by install.ps1.
Adds the same AMD wheel selection logic (ROCm version detection, Python 3.12
check, 5-wheel install with --no-deps) to setup.ps1's torch install block.
install_python_stack.py: remove IS_WINDOWS guard from _ensure_rocm_torch()
call site so the Windows path in _ensure_rocm_torch() is reachable during
'unsloth studio update' as well.
* fix: suppress manual-install warning when ROCm torch already present; fix progress counter
- Gate the 'must be installed manually' warning on torch.version.hip being empty
so it doesn't fire when our ROCm torch install succeeded
- Update _TOTAL counter to include the 3 ROCm steps on Windows now that
_ensure_rocm_torch() is called there (fixes 10/9 display)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat: add rocm step display in setup.ps1; fix warning and progress counter
- Add 'rocm' step after 'cuda' in setup.ps1 showing ROCm version or HIP SDK missing
- Move ROCm version detection up to GPU detection block so it's available early
- Suppress 'must be installed manually' warning when torch.version.hip is set
- Fix _TOTAL counter to include ROCm steps on Windows (fixes 10/9 display)
* fix: detect AMD SDK ROCm torch via __version__ when torch.version.hip is unset
AMD's repo.radeon.com wheels (e.g. 2.9.0+rocmsdk20251116) do not set
torch.version.hip, leaving it None. All three probes that relied solely on
torch.version.hip now also check for 'rocm' in torch.__version__.lower():
- hardware.py detect_hardware(): IS_ROCM was never set, causing the studio
to report 'Hardware detected: CPU' even after AMD wheels were installed
and HIP DLLs were on PATH.
- install_python_stack.py _ensure_rocm_torch(): skip-if-already-installed
probe would always reinstall on subsequent runs.
- install_python_stack.py Windows AMD warning: suppression check always
failed, so the 'must be installed manually' note kept appearing after
a successful AMD wheel install.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* perf: drop --no-cache-dir from AMD ROCm torch wheel installs
uv caches downloaded wheels by default; passing --no-cache-dir forced a
full redownload of the ~2 GB torch wheel on every install run. CUDA installs
never had this flag -- AMD was the only path affected.
* fix: use install-state flag instead of subprocess probe for AMD Windows warning
Replace the subprocess torch probe in the post-install warning block with a
module-level _rocm_windows_torch_installed flag set by _ensure_rocm_torch().
Subprocess re-import of torch is unnecessary and fragile -- the install
function already knows whether it succeeded.
* fix: hoist global declaration to top of _ensure_rocm_torch
Python requires the global statement to appear before any assignment
to the variable within a function. Moving it to the function top fixes
the SyntaxError on line 354.
* fix: pass AMD torch install status via env var to suppress false warning
setup.ps1 now sets UNSLOTH_ROCM_TORCH_INSTALLED=1 after a successful AMD
wheel install. install_python_stack.py reads this at the top of
_ensure_rocm_torch() to skip both the subprocess probe and the warning --
no re-import of torch needed, and the warning message now correctly says
'could not be auto-installed' rather than 'must be installed manually'.
* fix: register ROCm DLL directory before torch import on Windows
Python 3.8+ ignores PATH for extension DLL loading on Windows; amdhip64.dll
and other HIP runtime DLLs must be registered via os.add_dll_directory().
Without this, torch.cuda.is_available() always returns False on AMD ROCm
Windows even when HIP_PATH is correctly set in system environment variables.
Reads HIP_PATH / ROCM_PATH env vars first, then falls back to scanning
common ROCm install roots (C:\Program Files\AMD\ROCm, F:\ROCm, C:\ROCm).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: remove hardcoded non-standard ROCm paths from DLL directory scan
Only use HIP_PATH/ROCM_PATH (set by AMD installer) and the standard
C:\Program Files\AMD\ROCm\<version>\bin location. Custom drive paths
like F:\ROCm are user-specific and should not be hardcoded.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: prevent torchao overrides step from overwriting AMD ROCm torch
torchao==0.14.0 in overrides.txt declares torch as a dependency. Without
--no-deps, uv resolves torch from PyPI and installs 2.11.0+cpu on top of
the AMD ROCm wheels (2.9.0+rocmsdk20251116). This was the root cause of
'Hardware detected: CPU' -- the AMD wheels were installed but then
immediately overwritten by the overrides step.
When _rocm_windows_torch_installed is True, add --no-deps to the overrides
pip_install call so torchao is installed without pulling in CPU torch.
* fix: add rocm_sdk namespace tarball to Windows ROCm wheel installs
torch/_rocm_init.py calls `import rocm_sdk` at startup, which requires
the rocm namespace tarball (rocm-*.tar.gz) in addition to the SDK wheel
packages. This tarball was missing from both install.ps1 and setup.ps1,
causing ModuleNotFoundError on first torch import.
- Add rocm-0.1.dev0.tar.gz to ROCm 7.1.1 install (provides rocm_sdk namespace)
- Add rocm-7.2.1.tar.gz + rocm_sdk_devel to ROCm 7.2.1 install
- Install tarball in a dedicated step before main SDK/torch wheels
- Switch to @array splatting in install.ps1 scriptblock for dynamic wheel count
- Remove --no-cache-dir from Python-side ROCm wheel install (prevents ~2GB redownload)
* feat: enable ROCm 7.2 torch install + warn on gfx1151 with ROCm < 7.2
Chigoma333 (AMD Radeon 8060S / gfx1151, Strix Halo) confirmed that ROCm
7.1 segfaults when tensors are moved to GPU, but ROCm 7.2 + torch
2.11.0+rocm7.2 works fully including training.
Changes:
- Uncomment (7,2): "rocm7.2" in _ROCM_TORCH_INDEX (was blocked by <2.11.0)
- Add _ROCM_TORCH_PKG_SPECS dict with per-tag version bounds:
rocm7.2 → torch>=2.11.0,<2.12.0; all older tags → <2.11.0
- Add _detect_amd_gfx_codes() helper that parses rocminfo output
- Warn on gfx1151/gfx1150 (Strix Halo) when ROCm < 7.2 is installed,
pointing users at the known segfault and recommending upgrade
- install.sh get_torch_index_url(): enable rocm7.2 case (previously capped
to rocm7.1), cap unknown future tags to rocm7.2
- install.sh: override TORCH_CONSTRAINT to >=2.11.0,<2.12.0 when rocm7.2
index is selected, so pip can actually resolve torch 2.11.0
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: prefer Python 3.12 for AMD ROCm users when 3.13 is also installed
After GPU detection, if ROCm HIP SDK is found and the selected Python
is not 3.12, run a second pass to locate a 3.12 install via py.exe and
PATH (catches uv-managed installs). Switch $DetectedPython to 3.12 so
the venv is created with a compatible interpreter for the cp312-only AMD
Windows torch wheels.
NVIDIA and Intel GPU paths are unaffected -- the re-detection block only
runs when $HasROCm is true.
Fixes: #5301
* fix: also check uv-managed Python 3.12 for AMD ROCm #5301
* fix: hide amd-smi console popups on Windows, guard torch.distributed.is_initialized for ROCm #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: suppress remaining console popups on Windows, patch torch.distributed.is_initialized for ROCm #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: stub all missing torch.distributed attrs for ROCm Windows wheel #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: inject torch.distributed stub when C backend missing in ROCm Windows wheel #5301
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(rocm/windows): pre-stub torch._C._distributed_c10d + raise amd-smi timeout
Two fixes for Windows ROCm regressions reported by electroglyph on #5301:
1. worker.py — torch.distributed stub now fires unconditionally on Windows
The previous stub only injected sys.modules in the except branch, meaning
it was silently skipped when `import torch.distributed` happened to succeed
(the C backend is lazily resolved). The crash then hit later when
transformers/trl triggered the lazy load. Fix: on win32 we pre-populate
sys.modules['torch._C._distributed_c10d'] AND set the attribute on the
torch._C extension module *before* attempting the import, covering both
the early-ImportError and lazy-load failure modes.
2. amd.py — increase amd-smi timeout from 5 s to 30 s on Windows (10 s Linux)
amd-smi on Windows must cold-init the ROCm runtime on first invocation;
5 s was consistently too short, producing repeated 'Command timed out'
warnings in the server log. 30 s gives enough headroom without blocking
indefinitely on broken installs.
3. install.ps1 — widen Python 3.12 enforcement to ROCmGpuLabel (WMI-only path)
Users whose HIP SDK is not on PATH were detected via WMI but not switched
to Python 3.12 before the install started, causing a second pass. Guard
now fires on (HasROCm -or ROCmGpuLabel).
* fix(rocm): guard c10d stub, fix TorchIndexFamily for 7.1, clean dead code + comments
- worker.py: wrap c10d stub injection in `if _c10d_key not in sys.modules` so
Windows NVIDIA users with a real torch.distributed are never affected
- install.ps1: fix Get-TauriTorchIndexFamily receiving hardcoded "rocm7.2"
even when ROCm 7.1 wheels are installed; now branches on $ROCmVersion
- main.py: remove dead `import ctypes as _ctypes` (ctypes is never called)
- hardware.py, install_python_stack.py, worker.py, install.ps1: shorten
verbose multi-line comment blocks throughout
- tests: update 4 stale assertions that expected rocm7.2 to be absent/capped
* fix(tests): match windows AMD warning assertion to actual source string
* chore: trim verbose comment blocks across all ROCm-related files
* fix: guard reconcile call against None numeric_ids; add torchvision lower bounds
* fix(install.ps1): recreate venv with Python 3.12 after ROCm switch
Venv was created with 3.13 before GPU detection ran; switching
$DetectedPython to 3.12 had no effect since $VenvPython still
pointed to the 3.13 interpreter inside the already-created venv.
* ux: detect AMD GPU before Python selection to avoid double venv creation
- Early hipinfo + WMI probe runs before Find-CompatiblePython so Python
3.12 is selected upfront when AMD is detected; venv is now created
exactly once instead of 3.13 then immediately 3.12.
- Post-venv recreation block replaced with a simple warning for the rare
case where AMD was missed by the early probe.
- setup.ps1: show venv's actual Python version (e.g. 3.12) instead of
the system Python found by the pre-activation search (was showing 3.13).
* fix(rocm/win): auto-stub all _distributed_c10d symbols via PEP-562 __getattr__
The bare ModuleType stub caused ImportError when torch._dynamo was imported
(triggered by trainer.py accessing torch._dynamo.config at load time).
torch._dynamo pulls in torch.distributed.fsdp._flat_param which does:
from torch._C._distributed_c10d import FakeProcessGroup
and potentially other symbols. Adding module __getattr__ auto-creates a
stub class for any missing symbol so all such imports succeed without
enumerating every individual symbol. Applied to both the primary stub
and the fallback stub in the except branch.
* chore: trim c10d stub comment
* fix(rocm/win): auto-stub missing torch.distributed attrs (Store, ProcessGroup, …)
* fix(rocm/win): pre-stub fsdp submodules in sys.modules; fix __getattr__ subpackage clash
* feat(rocm/win): arch-aware wheel selector always picks newest ROCm release
Replace HIP-SDK-version-gated wheel selection with GPU arch-based logic.
Select-ROCmWheelRelease (PS) and _select_windows_rocm_release (Python) map
gcnArchName → minimum ROCm version, then pick the newest available release
that satisfies it (currently always rocm-rel-7.2.1 for any supported GPU).
Wheels bundle their own ROCm runtime so the installed HIP SDK 7.1 does not
prevent using 7.2.1 wheels on gfx1200 (RX 9060 XT) and similar RDNA 4 GPUs.
Also installs the bitsandbytes Windows ROCm continuous-release wheel and sets
BNB_ROCM_VERSION=72 in worker.py before ML imports so bnb loads the
libbitsandbytes_rocm72.dll that ships in that wheel.
* fix(rocm/win): stub class metaclass for ProcessGroup.BackendType; amd-smi circuit breaker
torchao.float8.inference accesses ProcessGroup.BackendType as a class-level
attribute. Plain type() stubs have no __getattr__ on the metaclass so this
raises AttributeError. Introduce _StubClassMeta whose __getattr__ returns
child stub classes, fixing the torchao import chain.
Add an amd-smi circuit breaker in amd.py: after 3 consecutive failures the
module stops spawning the process, eliminating the repeated Windows UAC /
DiskPart elevation prompts caused by polling a non-functional amd-smi.
Also guard BNB_ROCM_VERSION=72 behind a DLL existence check so bitsandbytes
fails with its own detection message rather than a harder "DLL not found" when
the Windows ROCm bnb wheel is not yet installed.
* fix: stub __members__ so torchao float8 enum check doesn't crash on ROCm Windows
torchao.float8.inference accesses ProcessGroup.BackendType.__members__
expecting a Python Enum registry dict. _StubClassMeta.__getattr__ was
blocking all dunder attributes, causing AttributeError. Return {} for
__members__ specifically so the isinstance/iteration checks pass cleanly.
* fix: stub distributed tensor/functional_collectives to prevent missing C++ op crash on ROCm Windows
torch._dynamo.trace_rules eagerly loads torch.distributed.tensor at import
time, which pulls in _functional_collectives.py. That file registers Meta
kernels for _c10d_functional C++ ops, but those ops are only registered
by torch._C._distributed_c10d — a C extension absent from ROCm Windows
wheels. Pre-stubbing the affected modules in sys.modules prevents the real
import chain from running and avoids the "operator does not exist" crash.
* fix: give mod stubs __path__ and pre-stub _tensor to fix 'not a package' import error
_make_mod_stub now sets __path__=[] so Python treats stub modules as
packages. Without it, any import of a submodule raises "is not a package".
Also pre-stub torch.distributed._tensor and its submodules so that
_tensor/__init__.py (which re-exports from torch.distributed.tensor) never
runs and torchao's `from torch.distributed._tensor import DTensor` gets a
harmless stub instead of crashing.
* fix: stub torch.ops._c10d_functional namespace with hashable op sentinels
torchao.dtypes.nf4tensor uses _c10d_functional ops as dict keys at import
time (all_gather_into_tensor.default, wait_tensor.default) and
torch.ops.c10d.scatter_.default. None of these ops are registered on ROCm
Windows because torch._C._distributed_c10d (the C extension) doesn't ship.
Replace the whole _c10d_functional namespace with a custom stub whose ops
return hashable .default objects, so dict-key construction doesn't crash.
Also inject a scatter_ stub into torch.ops.c10d if it's missing.
* fix: stub entire torchao package on ROCm Windows instead of individual ops
torchao is not supported on ROCm Windows and its import chain transitively
requires torch._C._distributed_c10d (absent from the ROCm Windows wheel).
Rather than stub each missing op one by one, stub the whole torchao package
upfront. Unsloth uses bitsandbytes for quantization, not torchao, so this
has no functional impact. transformers gracefully handles an importable-but-
empty torchao by disabling TorchAoHfQuantizer.
* fix: set __spec__ on mod stubs so importlib.util.find_spec doesn't raise
Manually-injected sys.modules entries have __spec__=None by default.
importlib.util.find_spec() raises ValueError when it finds a module in
sys.modules with __spec__=None (transformers.utils.import_utils hits this
when checking if torchao is available). Give every stub a minimal
ModuleSpec(name, loader=None, is_package=True) to satisfy find_spec.
* fix: add meta path finder to auto-stub subpackages of stub modules
`import torchao.prototype` goes through the import machinery, not
__getattr__, so an empty __path__ means ModuleNotFoundError. Rather than
list every submodule explicitly, register a MetaPathFinder that intercepts
any import whose parent is one of our stubs (detected by loader=None in the
parent's ModuleSpec). Real installed packages always have a SourceFileLoader
so they are never intercepted. Also register child stubs in sys.modules
from __getattr__ as a belt-and-suspenders measure.
* fix: use _unsloth_stub sentinel instead of loader=None for stub detection
The import machinery overwrites module.__spec__ with the spec returned by
find_spec (which has loader=_StubSubpackageLoader, not None), so the
loader=None check broke for second-level subpackages. Switch to a custom
_unsloth_stub object identity sentinel set directly on each stub module --
it survives __spec__ being replaced and correctly identifies stubs at any
depth (torchao.prototype.safetensors, etc.).
* refactor(rocm/win): switch to repo.amd.com arch-aware index, remove stubs
AMD recommends repo.amd.com/rocm/whl/{arch}/ as the Windows ROCm wheel
source. These wheels bundle their own ROCm runtime, support all Python
versions (not just cp312), and include the full torch._C extension set
(including _distributed_c10d) that the old repo.radeon.com wheel omitted.
Changes:
- install.ps1: remove Select-ROCmWheelRelease + hardcoded cp312 wheel
URLs; remove Python 3.12 forced-preference logic; install via
--index-url repo.amd.com/rocm/whl/{arch-family}/
- studio/setup.ps1: same -- remove Select-ROCmWheelRelease, switch to
repo.amd.com arch-aware index URL
- studio/install_python_stack.py: replace _ROCM_WINDOWS_RELEASES /
_select_windows_rocm_release with _windows_rocm_index_url() using the
_GFX_TO_AMD_INDEX_ARCH map; drop Python 3.12 restriction
- studio/backend/core/training/worker.py: remove all stub machinery
(_make_mod_stub, _StubSubpackageFinder, _StubSubpackageLoader,
_StubClassMeta, torchao/fsdp/dtensor stubs, _c10d_functional ops
stubs, BNB DLL detection) -- no longer needed with new wheel source
* fix(rocm/win): restore _distributed_c10d + torchao stubs; fix BNB install
repo.amd.com torch wheels also omit torch._C._distributed_c10d on Windows
(RCCL is not shipped on Windows). torch/distributed/__init__.py imports
from it unconditionally at module level, so the stub must land in
sys.modules before any torch.distributed import.
torchao (pulled in by transformers.quantizers) walks
torchao.float8.distributed_utils -> torch.distributed._functional_collectives
-> distributed_c10d at import time. Stubbing torchao up-front short-circuits
that chain.
worker.py:
- Restore _make_mod_stub / _StubSubpackageFinder / _StubSubpackageLoader
- Restore _StubClassMeta for ProcessGroup.BackendType attribute access
- Restore _distributed_c10d stub with __getattr__ (Windows only)
- Restore torchao stubs (5 modules, Windows only)
install_python_stack.py:
- BNB AMD wheel install was inside the early-return branch that fires when
torch is already a ROCm build (installed by install.ps1). Move BNB install
outside that branch so it always runs on Windows ROCm — the PyPI
bitsandbytes has only CUDA DLLs and fails to load on ROCm.
* worker: remove _distributed_c10d stub; stub only torchao
The installed torch/distributed/__init__.py from repo.amd.com
(torch==2.10.0+rocm7.12.0) is now properly guarded with
`if is_available():`, so `import torch.distributed` alone is safe.
The crash only comes via torchao's import chain:
torchao.float8.distributed_utils
→ torch.distributed._functional_collectives (unguarded import)
→ torch.distributed.distributed_c10d
→ torch._C._distributed_c10d ← absent on Windows ROCm
Stubbing torchao short-circuits the chain entirely. No need to stub
_distributed_c10d. Remove _StubClassMeta and the _c10d stub block;
keep only _make_mod_stub + _StubSubpackageFinder + torchao seeds.
* fix: BNB AMD wheel skipped + torch.compile segfault on Windows ROCm
install_python_stack.py: the UNSLOTH_ROCM_TORCH_INSTALLED=1 early-return
path (set by setup.ps1 when it installed torch itself) returned before
ever reaching the AMD BNB prerelease wheel install. The PyPI
bitsandbytes==0.49.x ships only CUDA DLLs, so loading it on ROCm fails
with "libbitsandbytes_rocm72.dll not found". Now installs the AMD
Windows BNB wheel before returning on that path too.
worker.py: torch._grouped_mm crashes on gfx1200 (null HIP kernel pointer,
0xC0000005) when torch.compile's JitDecomp system dispatches it during
the first forward pass. Detect Windows ROCm via torch.version.hip
(already in sys.modules from section 1e) and set TORCHDYNAMO_DISABLE=1
to bypass the broken kernel dispatch.
* fix: BNB AMD wheel install fails uv wheel filename check
The bitsandbytes continuous-release wheel is intentionally mismatched:
filename encodes 1.33.7.preview (= 1.33.7rc0 in PEP 440) but wheel
metadata reports 0.50.0.dev0. uv rejects this by default.
Introduce _install_bnb_windows_rocm() helper that sets
UV_SKIP_WHEEL_FILENAME_CHECK=1 only for this specific install, then
restores the previous env value. Both BNB install call sites (the
UNSLOTH_ROCM_TORCH_INSTALLED early-return path and the normal Windows
ROCm path) now use this helper.
* worker: patch _grouped_mm CUDA dispatch on Windows ROCm (gfx1200 null kernel)
TORCHDYNAMO_DISABLE=1 stopped the compiler frontend but not the autograd
JitDecomp system, which also dispatches _grouped_mm and hits the same
null HIP kernel crash (0xC0000005).
Verified that torch.library.Library("aten","IMPL").impl("_grouped_mm", fn,
"CUDA") successfully overrides the broken HIP kernel with a Python mm
fallback on torch==2.10.0+rocm7.12.0.
Schema: _grouped_mm(Tensor self, Tensor mat2, Tensor? offs=None,
Tensor? bias=None, ScalarType? out_dtype=None) -> Tensor
The fallback handles both the simple case (offs=None → torch.mm) and the
grouped case (offs provided → split self by offsets, multiply each group
against the corresponding slice of mat2, then cat results).
Keep _WINDOWS_ROCM_GROUPED_MM_LIB alive at function scope to prevent the
C++ dispatch registration from being freed by GC.
* worker: fix torchao stub — return stub classes not modules for isinstance()
peft/tuners/lora/torchao.py does:
from torchao.dtypes import AffineQuantizedTensor, LinearActivationQuantizedTensor
isinstance(weight, (AffineQuantizedTensor, LinearActivationQuantizedTensor))
The stub __getattr__ was returning stub modules, which isinstance() rejects
with "arg 2 must be a type, a tuple of types, or a union".
Add _StubTypeMeta metaclass whose __instancecheck__ always returns False,
and _make_stub_type() to create stub classes via it. Change _make_mod_stub
__getattr__ to return stub classes instead of stub modules for leaf
attribute access, so isinstance() gets a valid type and returns False.
_StubSubpackageFinder still handles import-style subpackage creation
(those still need module objects in sys.modules); __getattr__ only fires
for from-import or direct attribute access, which are the isinstance paths.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* tests: add coverage for Windows ROCm install paths and worker patches
Add conftest.py to fix pre-existing sys.path issue that prevented
test_rocm_support.py from running at all (install_python_stack.py
imports from backend.utils.wheel_utils which needs studio/ on sys.path).
New test classes cover everything added in this session:
- TestWindowsRocmIndexUrl: arch → AMD pip index URL mapping (gfx120X-all,
gfx1151, gfx1150, gfx110X-all, unknown → None, trailing slash)
- TestDetectWindowsGfxArch: hipinfo output parsing, missing/timeout/bad
returncode/no-gcnArchName paths
- TestInstallBnbWindowsRocm: UV_SKIP_WHEEL_FILENAME_CHECK set+restored,
env restored on exception, no-op when URL missing
- TestRocmTorchInstalledEnvVar: UNSLOTH_ROCM_TORCH_INSTALLED=1 skips
pip_install, calls _install_bnb_windows_rocm, sets flag
- TestWorkerWindowsRocmPatches: _grouped_mm CUDA dispatch override,
offs/grouped variant handling, GC-prevention sentinel,
_StubTypeMeta __instancecheck__, _StubSubpackageFinder registration,
torchao key submodule pre-stubbing, TORCHDYNAMO_DISABLE guard
- TestRocmTorchPkgSpecs: rocm7.2 torch 2.11.x spec, default <2.11 cap,
3-tuple shape, _GFX_TO_AMD_INDEX_ARCH RDNA4/3.5/3 coverage
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* tests: fix encoding, IS_WINDOWS patching, and wrong assertion
- Add encoding="utf-8" to all read_text() calls (54 occurrences) so
tests pass on Windows where the default codec is cp1252 and source
files contain UTF-8 emoji (e.g. ⚠️ in install_python_stack.py)
- Add @patch.object(stack_mod, "IS_WINDOWS", False) to Linux-path
TestEnsureRocmTorch tests so they reach the Linux code path when run
on a Windows machine instead of short-circuiting into the Windows branch
- Fix test_grouped_mm_patch_guarded_by_windows_and_hip_check: the source
uses getattr(_torch_for_rocm, "version", None) not torch.version, so
check for '"version"' and '"hip"' substrings instead
137 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: pin BNB_ROCM_VERSION=72 for torch==2.11.0+rocm7.13.0 compatibility
AMD's pip index now ships torch==2.11.0+rocm7.13.0 (ROCm 7.13).
bitsandbytes auto-detects HIP 7.13 from torch.version.hip and looks for
libbitsandbytes_rocm713.dll, which the AMD Windows prerelease wheel does
not ship (it only ships rocm72.dll), causing a load error at training start.
Fix:
- worker.py section 1f: set BNB_ROCM_VERSION=72 (via setdefault) before
section 2 ML imports, so bitsandbytes always loads rocm72.dll on Windows ROCm
- install_python_stack.py: set BNB_ROCM_VERSION=72 in _install_bnb_windows_rocm()
for any post-install imports; update comment to document root cause
- tests: 4 new assertions covering the fix (141 passed, 2 skipped)
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* fix: detect BNB ROCm DLL suffix dynamically instead of hardcoding '72'
BNB_ROCM_VERSION was pinned to '72' which works today (AMD wheel ships
rocm72.dll) but would break again if AMD ships a future wheel with a
different DLL suffix (e.g. rocm713.dll).
Add _detect_bnb_rocm_dll_ver() to install_python_stack.py: scans the
installed bitsandbytes package dir for libbitsandbytes_rocm{VER}.dll
using importlib.util.find_spec (no BNB import needed) and returns the
suffix. '72' remains the fallback when detection fails.
Apply the same detection inline in worker.py section 1f. Both paths
still respect a pre-set BNB_ROCM_VERSION (caller override wins).
Tests: +8 cases covering detection logic and fallback (147 passed, 2 skipped).
* fix: patch torch.distributed stubs in server process for Windows ROCm
On Windows ROCm, torch.distributed ships without process-group helpers
(is_initialized, is_available, get_rank, get_world_size). The worker
subprocess already patches these in section 1e, but the main server
process calls _determine_attention_impl_for_gpu_estimate() which calls
unsloth's resolve_attention_implementation() → is_initialized(), causing:
"Could not resolve attention implementation for '...':
module 'torch.distributed' has no attribute 'is_initialized'"
Fix: patch the missing attrs onto torch.distributed at the top of
_determine_attention_impl_for_gpu_estimate, matching the same stubs
already applied in worker.py section 1e. No-ops on Linux/CUDA where
torch.distributed is fully populated.
* fix: gate _grouped_mm dispatch patch on HIP < 7.13
AMD fixed the gfx1200 null HIP kernel in ROCm 7.13 (torch 2.11+).
Users on the new wheel now get the real GPU _grouped_mm kernel for
MoE workloads instead of the Python mm fallback.
Changes:
- worker.py: add _hip_ver_at_least() helper; wrap full _grouped_mm
patch in `if not _hip_ver_at_least(7, 13):` with else branch that
logs the skip reason; update section-1f comment to document the fix
- test_rocm_support.py: add 5 tests covering the helper definition,
the (7, 13) gate expression, the else branch, the skip log message,
and the AMD-format version string parsing (.split(".")[:2])
Verified: torch==2.11.0+rocm7.13.0 — 3D batch and grouped (offs)
variants both succeed; null crash only present on rocm7.12 and earlier.
* fix: stub is_torchelastic_launched on torch.distributed for Windows ROCm
resolve_attention_implementation calls is_torchelastic_launched() which
does not exist in the incomplete torch.distributed shipped with the
Windows ROCm wheel, causing a warning on every model config load in the
server process. Add it to the stub table alongside the four helpers
already patched in _determine_attention_impl_for_gpu_estimate.
Also adds two tests: one confirming the new stub and one confirming all
five core distributed helpers are covered.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: explicit warnings on AMD ROCm arch/version fallbacks + Fast-Install arg order
setup.ps1:
- Fix Fast-Install argument order: packages before flags, consistent with
all other Fast-Install calls in the file
(was: Fast-Install --force-reinstall --index-url $url torch ...)
(now: Fast-Install torch torchvision torchaudio --force-reinstall --index-url $url)
- Add explicit [WARN] substep when $HasROCm is true but arch mapping fails:
- GPU arch detected but not in supported wheel list → names the arch and
lists supported families so user knows exactly what to report
- HIP SDK present (amd-smi path) but gcnArchName unreadable → instructs
user to re-install the HIP SDK; previously fell back silently to CPU
install.sh:
- Add [WARN] to stderr before silent CPU fallback when AMD GPU is confirmed
(rocminfo/amd-smi) but ROCm version cannot be read from any source
(amd-smi, /opt/rocm/.info/version, hipconfig, dpkg, rpm)
- Add [WARN] to stderr when ROCm version is too old (< 6.0) with upgrade link
install.ps1 and setup.sh: no changes needed (already handle these paths correctly)
* fix: robust gfx arch detection for Strix Halo / HIP-runtime-only installs
Covers users who have the HIP runtime (amd-smi available) but not the
full HIP SDK (no hipinfo), which is common on Strix Halo iGPU systems.
Without this, $ROCmGfxArch stays null and the installer silently falls
back to CPU-only PyTorch despite a working GPU.
Detection waterfall (setup.ps1 + install.ps1):
1. hipinfo gcnArchName -- full HIP SDK (existing, unchanged)
2. amd-smi list gfx pattern -- newer amd-smi versions embed arch
3. amd-smi static --asic -- ROCm 6+ ASIC details with GFX target
4. UNSLOTH_ROCM_GFX_ARCH env -- manual override escape hatch
5. GPU name → arch table -- best-effort from marketing name:
890M / Strix Halo → gfx1151 (RDNA 3.5 iGPU, Strix Halo)
880M / Strix Point → gfx1150 (RDNA 3.5 iGPU, Strix Point)
780M / Phoenix → gfx1103 (RDNA 3 iGPU)
RX 7900/7800/7700 → gfx1100 (RDNA 3 desktop)
RX 9070 XT / 9080 → gfx1201 (RDNA 4)
RX 9070 / 9060 XT → gfx1200 (RDNA 4)
When arch is inferred from name, a Cyan substep tells the user to set
UNSLOTH_ROCM_GFX_ARCH to skip inference on future installs.
WMI block intentionally does not set $HasROCm (no runtime confirmation).
Tests: 11 new tests in TestStrixHaloGfxArchDetection covering all five
detection levels, WMI safety, and gfx regex in both ps1 files.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: resolve hipinfo/hipconfig via HIP_PATH/ROCM_PATH when not on PATH
AMD HIP SDK sets HIP_PATH on Windows but does not always add the bin
directory to PATH. Get-Command hipinfo therefore silently fails and
detection falls through to WMI, which cannot provide a gfx arch, leaving
the user with a CPU-only PyTorch install and no warning.
Changes:
- setup.ps1 / install.ps1: before falling through to amd-smi, attempt to
locate hipinfo.exe and hipconfig.exe under $env:HIP_PATH\bin (then
$env:ROCM_PATH\bin) when Get-Command returns nothing
- Emit a [WARN] with the resolved path and a one-liner to permanently fix
PATH via SetEnvironmentVariable
- Emit a [WARN] when HIP_PATH/ROCM_PATH is set but the exe is still not
found (incomplete SDK install)
- Emit a [WARN] with the first hipinfo output line when hipinfo runs but
returns a non-zero exit code (e.g. "no ROCm-capable device detected")
- 18 new tests in TestHipSdkEnvPathResolution; total 183 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat: print HIP SDK path and full hipconfig version in terminal on AMD detection
Both install.ps1 and setup.ps1 now emit substeps under the gpu step when
AMD ROCm is detected:
gpu AMD ROCm (gfx1200)
HIP SDK: C:\Program Files\AMD\ROCm\7.1
hipconfig: 7.1.51803-d3a86bd04
Previously only the gpu label (e.g. "AMD ROCm (gfx1200)") was shown with
no indication of where the SDK was found or which exact build was active.
The full hipconfig build string (e.g. 7.1.51803-d3a86bd04 instead of just
7.1) is now stored in ROCmVersionFull and also used in setup.ps1's
'rocm' step label.
9 new tests in TestHipSdkDetectedSubstep; total 192 passed, 2 skipped
* fix: Strix rocm7.1 segfault bypass + Ubuntu 24.04 HIP gcc-install-dir
Issue 1 (install.sh): gfx1151/gfx1150 + ROCm 7.1 causes a segfault in
torch._grouped_mm (moe_utils.py:167). The Radeon repo now ships cp313
wheels for rocm-rel-7.1, so _amd_gpu_radeon=true silently lands on the
broken combo. When Strix Halo/Point is detected and TORCH_INDEX_URL is
rocm7.1, override to rocm7.2 PyTorch index, update TORCH_CONSTRAINT, and
set _amd_gpu_radeon=false to bypass the Radeon repo entirely. Emits a
clear [WARN] explaining the segfault and linking to the ROCm upgrade docs.
Issue 2 (setup.sh): ROCm 7.x ships clang-20 which on Ubuntu 24.04+ picks
/usr/lib/gcc/x86_64-linux-gnu/14/ (runtime dir, no C++ headers), causing
'cstdlib file not found' and a failed llama.cpp HIP build. Iterate gcc
versions 14→11 to find the first install dir that has both runtime and
/usr/include/c++/<ver> headers, then pass --gcc-install-dir to clang via
CMAKE_HIP_FLAGS. Fix confirmed by h34v3nzc0dex (llama.cpp 417/417 clean).
11 new tests across TestStrixRocm71Override and TestSetupShGccInstallDir;
total 203 passed, 2 skipped
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* fix: BNB_ROCM_VERSION in server process + torch._C._distributed_c10d stubs
Two errors visible in training logs on Windows ROCm:
1. Server process bitsandbytes crash:
"Configured ROCm binary not found at libbitsandbytes_rocm713.dll"
The installed BNB wheel ships rocm72.dll (not rocm713.dll). The
training worker already sets BNB_ROCM_VERSION=72 via DLL detection
but the server process (main.py) imported bitsandbytes before that
ran. Fix: add the same DLL-scan + BNB_ROCM_VERSION assignment to
main.py inside the existing win32 guard, before any downstream
import can pull in bitsandbytes.
2. torch.distributed import failure:
"No module named 'torch._C._distributed_c10d'; torch._C is not a package"
torch._C is a C extension on Windows ROCm — Python cannot do
submodule imports from it, so torch.distributed fails to import
before our attribute stubs could ever run. Fix: inject empty
ModuleType stubs for _distributed_c10d, _distributed_autograd and
_distributed_rpc into sys.modules inside the win32 guard in
hardware.py BEFORE importing torch.distributed, so the import
succeeds and our attribute stubs take effect.
9 new tests in TestServerStartupRocmFixes; total 212 passed, 2 skipped
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix(win32): populate distributed c10d stub with dummy symbols
torch.distributed tries to `from torch._C._distributed_c10d import
FakeProcessGroup` (and ProcessGroup, Work, Store, etc.). The previous
empty ModuleType stub caused an AttributeError on those names.
Populate every stub with a _Dummy class for each known symbol so the
import chain completes silently on Windows ROCm where torch._C is a
compiled extension and its _distributed_c10d submodule doesn't exist.
Adds four new tests in TestServerStartupRocmFixes covering FakeProcessGroup,
ProcessGroup, setattr population, and all three _distributed_* siblings.
* fix(win32): distinguish HIP SDK installed vs GPU not ROCm-accessible
Previously, when hipinfo was found but exited non-zero (e.g. "no
ROCm-capable device detected"), both install.ps1 and setup.ps1 fell
through to the WMI-label-only branch and printed "AMD GPU detected --
HIP SDK not found" -- factually wrong since the SDK binary is present.
Add $HipSdkInstalled flag (set true when hipinfo binary is found,
regardless of exit code). When HipSdkInstalled && !HasROCm:
- Show "AMD GPU detected -- not ROCm-accessible (HIP <ver>)" instead
- Explain this is a driver issue, not an SDK issue, with a link
- Still run hipconfig version capture so version shows in output
- CPU-only hint now says "GPU not ROCm-accessible" not "require HIP SDK"
Also applies to setup.ps1 (same detection block, same branches).
Adds TestHipSdkInstalledButDeviceInaccessible (11 tests).
* fix(win32): scope ROCm workarounds to AMD hosts only
Three Codex-flagged issues where Windows ROCm workarounds incorrectly
applied to Windows CUDA (NVIDIA) machines:
main.py (P1): BNB_ROCM_VERSION was set unconditionally on all win32
hosts. On NVIDIA, bitsandbytes sees BNB_ROCM_VERSION and looks for a
ROCm DLL that doesn't exist, breaking bitsandbytes initialisation.
Fix: gate the block on HIP_PATH/ROCM_PATH being present (ROCm hosts only).
worker.py (P2): torchao stubs were seeded for all win32 runs, shadowing
real torchao on Windows CUDA and silently disabling torchao quantization
for NVIDIA users. Fix: gate on HIP_PATH/ROCM_PATH (win32 ROCm only).
install_python_stack.py (P1): _detect_windows_gfx_arch() only checked
shutil.which("hipinfo"), skipping the HIP_PATH/ROCM_PATH fallback that
the PowerShell installers use. On installs where the HIP SDK bin dir is
not on PATH, _ensure_rocm_torch() returned early without installing
ROCm wheels or bitsandbytes. Fix: mirror the env-var fallback.
* fix(linux): route Strix + ROCm 7.1 to AMD arch-specific index
Instead of falling back to pytorch.org/rocm7.2, the Strix override now
routes to repo.amd.com/rocm/whl/gfx1151/ (or gfx1150/) which serves
torch 2.11.0+rocm7.13.0 -- AMD's build containing the actual _grouped_mm
kernel fix, verified on real gfx1151 hardware by h34v3nzc0dex.
This exercises the real GPU kernel path rather than the rocm7.2 workaround.
UNSLOTH_AMD_ROCM_MIRROR can override the base URL for air-gapped installs.
Also teaches _tauri_torch_index_family to recognise AMD arch-specific URLs
(repo.amd.com/rocm/whl/gfx*) and return the rocm7.13 family label so
_tauri_gpu_branch correctly classifies these installs as rocm.
Suggested by h34v3nzc0dex based on hardware-verified probe results.
* fix(studio/rocm): gate ROCm-only side-effects on active torch runtime
Address five edge cases flagged during PR review:
1. studio/backend/main.py: BNB_ROCM_VERSION was set whenever HIP_PATH or
ROCM_PATH was present in the environment. A Windows CUDA user who once
installed the HIP SDK and reverted to a CUDA torch wheel still has those
env vars set, so bitsandbytes would try to load libbitsandbytes_rocm72.dll
against a CUDA torch and crash. Now probe torch.version.hip inside the
env-var guard (worker.py already does this).
2. studio/backend/main.py: os.add_dll_directory returned handles were
discarded. Per CPython docs, the directory leaves the DLL search list when
the handle is garbage collected. Retain handles in module-level
_ROCM_DLL_HANDLES list so they survive process lifetime.
3. studio/install_python_stack.py: _install_bnb_windows_rocm() returned None
regardless of pip_install_try outcome, and the caller flipped
_rocm_windows_torch_installed to True unconditionally. On a failed BNB
install the post-install "manual install may be required" warning was
suppressed and the user was misled. Helper now returns bool; caller gates
on it.
4. studio/install_python_stack.py: _detect_windows_gfx_arch returned the raw
capture group, so mixed-case hipinfo output ("Gfx1151") missed the
lowercase keys in _GFX_TO_AMD_INDEX_ARCH and silently fell back to CPU
torch. Lowercase the token.
5. studio/install_python_stack.py: UNSLOTH_ROCM_TORCH_INSTALLED=1 early-
return trusted the env var even when the venv was wiped between runs.
Subprocess-probe torch importability first; fall through to the full
install path if the probe fails.
Tests: 231 passed, 1 skipped in tests/studio/install/test_rocm_support.py
(adds one new test for case 5 fall-through).
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* fix(studio/rocm): worker.py parity + don't roll back ROCm torch on bnb failure
Addresses findings from a 10x reviewer pass on the prior fix commit:
1. studio/backend/core/training/worker.py (parity with main.py):
- Gate the torchao stub block on torch.version.hip / 'rocm' in
torch.__version__ instead of HIP_PATH / ROCM_PATH env-var presence.
Same root cause as main.py: HIP SDK env vars stick around on CUDA hosts.
- Add module-level Windows ROCm DLL registration block. Worker subprocesses
inherit env vars but not the parent's add_dll_directory handles, so the
first `import torch` in the worker could fail to find amdhip64.dll when
HIP_PATH\bin is not on PATH. Mirrors main.py setup. Handles retained at
module scope via _ROCM_DLL_HANDLES.
- Promote _WINDOWS_ROCM_GROUPED_MM_LIB to module scope with `global` in
run_training_process so the torch.library.Library registration survives
past function return / mid-run garbage collection.
- Harden _torch_has_hip() to also accept 'rocm' in torch.__version__
(AMD SDK / Radeon wheels may not set torch.version.hip).
2. studio/install_python_stack.py:
- Don't roll back ROCm torch when bitsandbytes install fails. The prior
commit gated _rocm_windows_torch_installed on _install_bnb_windows_rocm()
returning True; if torch installed successfully but bnb failed, the flag
stayed False and later install steps could overwrite ROCm torch with the
generic CPU torch wheel. Set the flag after torch install; surface bnb
failure as a separate warning instead.
- _detect_windows_gfx_arch now probes in three tiers: UNSLOTH_ROCM_GFX_ARCH
env-var override (matches the PowerShell installer), then hipinfo (PATH
or HIP_PATH\bin), then amd-smi (`static --asic`, `list`). Without the
amd-smi fallback, runtime-only Radeon installs without hipinfo on PATH
made `studio update` return early and leave the venv on CPU torch.
- Linux torch-already-rocm probe in _ensure_rocm_torch now matches the
Windows probe shape: accepts torch.version.hip OR 'rocm' in
torch.__version__ to cover AMD SDK / Radeon Linux wheels.
3. studio/backend/utils/hardware/hardware.py:
- apply_gpu_ids() final-fallback torch probe accepts 'rocm' in
torch.__version__ in addition to torch.version.hip, matching
detect_hardware(). AMD SDK wheels could otherwise leak through with
CUDA-only visibility masks on a spawned ROCm worker.
Tests: 231 passed, 1 skipped in tests/studio/install/test_rocm_support.py
(no test changes needed; the probe shape that prints the hip version (or
'rocm' sentinel) preserves the existing non-empty-string contract).
Not addressed in this commit (deferred or out of scope):
- Tag drift / lemonade checksum (PR 5303 surface, not this PR).
- install.sh rocm7.2.1 URL: small fix, separate.
- install.ps1 / setup.ps1 'Radeon 8060S' marketing-name fallback table.
- Strix Halo + ROCm 7.1 routing asymmetry in Python update path.
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* fix(studio/rocm): robustness pass - rocm tag normalisation, Strix routing parity, hardened detection
Robustness pass on top of 76137b2d. Four targeted fixes:
1. install.sh ROCm-tag routing normalisation.
`rocm7.2.1` would route to https://download.pytorch.org/whl/rocm7.2.1
which does not exist (PyTorch publishes major.minor URLs only). Same
for any future patch-level tag. Normalise every rocm{maj.min}* pattern
to the bare {maj.min} index URL.
2. install.ps1 + studio/setup.ps1 marketing-name fallback.
The gfx1151 row matched 890M / Strix Halo / HX 37x / HX 38x / AI 9 HX
but not the actual retail name 'AMD Radeon 8060S Graphics' shipped by
OEMs (Ryzen AI MAX+ 395). Add '8060S' to the regex.
3. install_python_stack.py Strix + ROCm 7.1 routing parity with install.sh.
The shell installer reroutes Strix Halo / Point + ROCm 7.1 to
repo.amd.com/rocm/whl/{gfx}/ (which serves torch 2.11.0+rocm7.13.0
with the upstream _grouped_mm fix). The Python `studio update` path
only warned and still installed the broken generic rocm7.1 wheel.
Mirror the override: detect gfx1151/gfx1150 on ROCm 7.1, route to
the AMD per-gfx index, honour UNSLOTH_AMD_ROCM_MIRROR override.
4. _detect_windows_gfx_arch amd-smi parsing tightened.
The amd-smi fallback added in the prior commit used a bare
`\bgfx[1-9][0-9a-z]{2,3}\b` match against the lowercased stdout,
which could pick up stray gfx references in warnings / device-name
strings. Anchor on labelled lines first (Target_Graphics_Version,
ASIC, Arch, gfx) and fall back to the bare match only when no
labelled line is present.
Tests: 231 passed, 1 skipped in tests/studio/install/test_rocm_support.py;
sim_5301 23 cases pass (6 new sims for the Strix override + amd-smi parsing).
* fix(studio/rocm): multi-GPU selection, Strix sibling handling, defensive cleanups
Round 4 robustness pass based on 5 parallel Opus reviewers of head 21773215.
Seven items from across regression / edge-case / error-paths / architecture
reviews:
1. studio/backend/main.py BNB gate: aligned with the broad ROCm check used
everywhere else in this PR (torch.version.hip OR 'rocm' in __version__).
AMD SDK / Radeon Linux wheels do not always populate torch.version.hip;
without this, main.py would silently skip BNB_ROCM_VERSION while worker.py
set it.
2. studio/install_python_stack.py _install_bnb_windows_rocm: init _ok = False
before the try block. Without this, if pip_install_try itself raises
(e.g. OSError on uv binary missing), the finally block restored env vars
correctly but the subsequent `if not _ok:` raised UnboundLocalError,
masking the original exception.
3. studio/install_python_stack.py _detect_windows_gfx_arch:
- Rewrote to use re.findall (not re.search) on both hipinfo and amd-smi
output, dedup tokens preserving order, and select via new
_pick_visible_index() helper.
- HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES (first comma entry, integer)
now picks the right GPU on multi-AMD-GPU hosts. Out-of-range or non-int
values fall back to the first GPU (matches detect_host behaviour in
install_llama_prebuilt.py).
4. studio/install_python_stack.py Strix override now consults the runtime
target before flipping:
- Previous behaviour intersected gfx_codes with {gfx1151, gfx1150} and
picked the first Strix arch, ignoring whether HIP_VISIBLE_DEVICES
selected a non-Strix sibling (e.g. discrete RX 7900 in a mixed APU+dGPU
box). Could install Strix-specific wheels onto a gfx1100 dGPU.
- Now resolves the runtime gfx via _pick_visible_index() and only
overrides when that runtime target is in the Strix set.
5. studio/backend/main.py + studio/backend/core/training/worker.py: ROCm
version dir scan no longer sorts lexically. Previous sort placed "10.0"
before "7.0" alphabetically, which would mis-prioritise ROCm 10.x bin
dirs once AMD ships them. New _ver_key() splits on "." and sorts
numerically with a string fallback.
6. install.sh Strix override URL: replaced ${var%/} (strips one trailing
slash) with a while-loop that strips all trailing slashes, matching
Python's .rstrip("/"). A user setting UNSLOTH_AMD_ROCM_MIRROR with
"http://corp/whl///" no longer ends up with "http://corp/whl///gfx1151/"
which strict pip proxies (artifactory, sonatype) 404 on.
7. studio/install_python_stack.py: bumped torch import probe timeout from
30s to 90s. PyTorch's lazy .so loading can take 60-90s on cold NFS or
USB-backed venvs. The shorter timeout was producing a false "torch
missing" classification and reinstalling a working ROCm torch.
Tests: 231 passed, 1 skipped. sim_5301 30 cases pass (added 7 new sims for
multi-GPU detection, Strix sibling handling, and _ok-init regression).
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* fix(studio/rocm): worker BNB/grouped_mm broad gate, install.sh Strix visibility, runtime-only ROCm detection
Round-5 robustness pass based on 20 parallel reviewers of head 96b9e465.
1. studio/backend/core/training/worker.py - BNB version pin / dynamo disable
/ _grouped_mm fallback block was still gated on torch.version.hip alone
despite the torchao stub block above already using the broad check. AMD
SDK / Radeon Windows wheels (torch.__version__ contains "rocm" but
torch.version.hip is None) silently skipped the Windows ROCm runtime
patches. Aligned to the same broad check (8/20 reviewers).
2. studio/backend/core/training/worker.py - _hip_ver_at_least() now also
parses the ROCm version out of torch.__version__ (e.g. "2.11.0+rocm7.13.0")
when torch.version.hip is missing, so the kernel-fix gate is correct for
SDK / Radeon wheels too.
3. studio/backend/core/training/worker.py - _grouped_mm_safe_impl with
offs=None now picks torch.bmm/matmul for 3-D inputs instead of always
calling torch.mm. The real _grouped_mm accepts 3-D batched matmul; the
prior fallback raised "self must be a matrix" on MoE workloads (2/20).
4. studio/backend/main.py - dropped the HIP_PATH / ROCM_PATH env-var gate
from the BNB block; probe torch directly. Runtime-only Radeon / AMD SDK
Windows installs do not set those SDK env vars but still ship ROCm torch
(5/20 reviewers).
5. install.sh - Strix override now collects every gfx token from
rocminfo / amd-smi (in enumeration order), then indexes by
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES so a mixed Strix iGPU + non-
Strix dGPU host where the user selected the dGPU does NOT get rerouted
to the Strix per-gfx index. Mirrors the Python update path (5/20 reviewers).
6. install.sh - Strix detection chain now also probes `amd-smi static --asic`,
matching the PowerShell installer (1/20). Closes the gap on runtime-only
Strix hosts where `amd-smi list` does not surface a gfx token.
7. studio/install_python_stack.py - _has_rocm_gpu() now has the sysfs KFD
topology fallback (/sys/class/kfd/kfd/topology/nodes/*/gpu_id), matching
install.sh. On minimal package-managed installs without rocminfo /
amd-smi GUI tools, `studio update` can now detect the GPU and repair the
venv instead of returning early (2/20).
8. studio/install_python_stack.py - _detect_amd_gfx_codes() now falls back
to `amd-smi list` and `amd-smi static --asic` when rocminfo is missing
(2/20). Strix routing on runtime-only Radeon hosts now matches what
install.sh has done for a while.
9. studio/install_python_stack.py - Strix override now applies even when
has_hip_torch is True. The whole point of the override is to repair an
existing broken torch.version.hip == "7.1" install; skipping the
reinstall left users on the known _grouped_mm segfaulting stack (3/20).
Tests: 231 passed, 1 skipped. sim_5301 30 cases pass. sim_cross 12 pass.
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* fix(studio/rocm): code review hardening pass
- main.py: numeric DLL sort (string sort picked rocm72 over rocm713);
add basename() to regex; log warning on detection failure; log info
when BNB_ROCM_VERSION is set (mirrors worker.py)
- worker.py: explicit len-guard in _hip_ver_at_least() with warning
logs instead of silent IndexError/ValueError swallow
- hardware.py: isinstance(result, dict) guard before result.get() in
_smi_query() to prevent AttributeError on non-dict backend returns
- amd.py: round() before int() on parsed GPU IDs; log warning when
truncation occurs (defensive against malformed amd-smi output)
- setup.sh: quote --gcc-install-dir value in CMAKE_HIP_FLAGS so paths
with spaces do not break the CMake argument
- install.ps1, setup.ps1: apply colon-split + ToLower() to hipinfo
gcnArchName match (consistent with each other and with setup.sh)
- install.sh: tighten ROCm tag case patterns to explicit
rocmX.Y|rocmX.Y.* to avoid unintended prefix matches
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* fix(studio/training): GPU OOM guard to prevent system freeze on VRAM exhaustion
On RDNA 4 (gfx1200/gfx1201) and other ROCm GPUs, exhausting VRAM can
cause a HIP driver hang that freezes the entire system rather than
raising a recoverable Python exception.
Two-part fix:
- set_per_process_memory_fraction(0.90) caps the HIP/CUDA allocator at
90% of VRAM so PyTorch raises OutOfMemoryError before hitting the
hardware limit, keeping the driver alive and the system responsive
- top-level exception handler detects OOM errors by type and message
and surfaces a clear actionable message to the UI (reduce
max_seq_length, enable gradient_checkpointing, lower batch size)
instead of the raw CUDA/HIP error string
* fix(studio/rocm): OOM guard ROCm-only + unified memory, multi-GPU arch selection
OOM guard (worker.py):
- Scope to _hw.IS_ROCM only -- NVIDIA CUDA has a graceful OOM path and
does not need the allocator cap
- Detect unified memory by comparing torch VRAM against psutil system RAM;
use 0.80 on unified-memory APUs (gfx1151 Strix Halo) where the GPU pool
is carved from host RAM, 0.90 on discrete cards
Multi-GPU arch selection:
- install.ps1 / setup.ps1: replace -match (first hit only) with
[regex]::Matches() to collect all gcnArchName entries, then index by
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES
- install_python_stack.py: index into full token list before dedup so
HIP_VISIBLE_DEVICES=2 on [gfx1100, gfx1100, gfx1151] resolves gfx1151
- install.sh: remove awk dedup from gfx token collection for same reason
GCC multiarch (setup.sh):
- Only append -linux-gnu when gcc -print-multiarch does not already return
the full triple, fixing double-suffix on Ubuntu 24.04
* fix(tests): update ROCm version cap expectations from rocm7.1 to rocm7.2
Daniel's normalisation commit updated the cap from rocm7.1 to rocm7.2
since PyTorch now publishes that index and rocm7.2 ships torch 2.11.0.
Test expectations were stale.
* fix(tests): correct MLX smoke test losses_per_step assertion
logging_steps=1 with max_steps=30 produces 30 loss entries, not 7.
The assertion was stale from a previous config.
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* fix(studio/worker): detect unified-memory APU by GPU name not VRAM/RAM ratio
The previous heuristic (VRAM > 50 % of system RAM) false-positived on discrete
cards in low-RAM systems — e.g. RX 9060 XT 16 GB on a 16 GB or 24 GB machine
would trip the unified-memory path and log "unified memory host" when it should
say "discrete".
AMD iGPUs (gfx1150/gfx1151 Strix Halo, Strix Point, etc.) expose names with a
digit+M suffix ("AMD Radeon 890M"), while discrete cards use "RX NNNN [XT|XTX]"
naming. Matching that suffix is reliable across all current ROCm-capable AMD
consumer GPUs and does not require psutil.
Also includes the device name in the log line to ease future debugging.
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* fix(install/setup.ps1): force array on hipinfo gcnArchName parse to fix single-GPU arch truncation
When [regex]::Matches() finds exactly one match, PowerShell's pipeline
unwraps the result to a scalar string. Indexing a scalar string with [0]
returns the first *character*, so a one-GPU system would parse
gcnArchName "gfx1200" as "g", which is not in the supported arch map
and triggers the CPU-only fallback.
Wrapping with @() forces the result to remain an array regardless of
match count. On a single-GPU machine the arch is now correctly read as
"gfx1200" (or whatever the full name is) so the ROCm wheel index is
selected.
Reproducer: hipinfo exits 0 and outputs exactly one gcnArchName line.
Without @(), $_hipAllArches = "gfx1200" (String); $_hipAllArches[0] = 'g'.
With @(), $_hipAllArches = @("gfx1200") (Object[]); $_hipAllArches[0] = "gfx1200".
* fix(studio/rocm): classify unified-memory APU via VRAM/RAM ratio, not arch list
Replace the gcnArchName allowlist {gfx1150, gfx1151} with a
psutil-based heuristic: unified APUs expose the entire system RAM
as the HIP pool (ratio ≥ 0.90), discrete cards are well below that.
No arch name required — future APUs classify correctly without code changes.
Also removes the stale import re / \d[Mm]\b device-name regex that
5d84704 left behind, and logs vram/sys GiB for easier on-hardware
verification.
Addresses h34v3nzc0dex review: Radeon 8060S (gfx1151, 128 GiB
unified) now correctly gets 0.80 cap instead of 0.90.
* fix(studio/rocm): revert to gcnArchName for unified-memory APU classification
VRAM/RAM ratio >= 0.90 false-positives on machines where discrete VRAM
equals system RAM (e.g. RX 9060 XT 16 GB + 16 GB system RAM → ratio 1.0,
incorrectly classified as unified → wrong 0.80 cap applied).
gcnArchName is the correct signal: naming-independent, stable within a
product family, and already parsed throughout this PR. Unified set is
{gfx1150, gfx1151} (Strix Point + Strix Halo).
* fix(studio/llama-prebuilt): resolve hipinfo via HIP_PATH/ROCM_PATH on Windows
shutil.which("hipinfo") returns None when the HIP SDK bin dir is not on
PATH -- the HIP SDK installer sets HIP_PATH/ROCM_PATH but does not always
add the bin dir to PATH. This caused has_rocm=False in the prebuilt asset
selector, so AMD ROCm machines got the CPU llama.cpp zip instead of the
HIP one, silently running all chat inference on CPU.
Add _resolve_exe() that falls back to %HIP_PATH%\bin and %ROCM_PATH%\bin
when shutil.which() finds nothing, mirroring the same fallback already
present in setup.ps1.
* fix(studio/llama-prebuilt): pass --has-rocm from setup.ps1 to skip re-detection
The Python prebuilt installer re-detects ROCm independently via
shutil.which("hipinfo"), which fails when hipinfo is not on PATH
(HIP SDK sets HIP_PATH but doesn't always add the bin dir to PATH).
This caused has_rocm=False and downloaded the CPU llama.cpp zip even
on confirmed AMD ROCm machines.
setup.ps1 already performs reliable ROCm detection with its own
HIP_PATH/ROCM_PATH fallback. Add --has-rocm flag to
install_llama_prebuilt.py so setup.ps1 can forward its result directly,
and pass it whenever $HasROCm is true. The Python script then overrides
has_rocm=True in the HostInfo without re-probing.
* fix(studio/llama-prebuilt): add HIP asset to simple-policy Windows path
direct_upstream_release_plan (used by --simple-policy, which setup.ps1
always passes) only checked has_usable_nvidia on Windows and fell
straight to CPU for AMD ROCm machines, ignoring has_rocm entirely.
The --has-rocm override had no effect because the simple-policy code
path never reached resolve_asset_choice where has_rocm was checked.
Add an elif branch for has_rocm that tries the upstream HIP asset
(llama-TAG-bin-win-hip-radeon-x64.zip) before falling through to the
CPU fallback, consistent with the non-simple-policy path.
* fix(studio/setup.ps1): auto-remove mismatched llama.cpp install kind
When an existing llama.cpp install is the wrong kind for the current
GPU (e.g. windows-cpu on an AMD ROCm machine that should have
windows-hip), the prebuilt installer skips on tag match and never
upgrades. Read install_kind from UNSLOTH_PREBUILT_INFO.json before
invoking the installer and remove the directory if the kind doesn't
match, forcing a fresh download of the correct variant.
* fix(studio/setup.ps1): show live PyTorch install output in verbose mode for ROCm
The ROCm torch reinstall (setup.ps1 phase) always silently captured
output, so in --verbose mode the torch downgrade mid-install
(2.11.0+rocm → 2.10.0 → 2.11.0+rocm) looked like the final state was
2.10.0. Match the CPU/CUDA blocks which show live uv output when
$script:UnslothVerbose is set.
* fix(rocm/windows): set ROCBLAS_TENSILE_LIBPATH for bundled rocblas.dll
The llama.cpp ROCm prebuilt bundles rocblas.dll next to the binary but
not the Tensile kernel library files it depends on at runtime
(rocblas/library/TensileLibrary*.dat + *.hsaco). The bundled DLL
searches for these files relative to its own location by default, i.e.
<binary_dir>/rocblas/library/, which does not exist in the prebuilt
install tree. This causes a silent crash on the very first GEMM
(prefill) with no output from llama-server, seen by the caller as
WinError 10054 / 10061. Model load and the single-token warmup pass
because they use simpler code paths that do not trigger rocBLAS GEMM.
Fix: set ROCBLAS_TENSILE_LIBPATH in the subprocess env to
<HIP_PATH>/bin/rocblas/library so the bundled DLL finds the kernel
files from the system ROCm installation. Uses setdefault so a user-
supplied env var is never overwritten. No-ops on CUDA and CPU (no
HIP_PATH) and on Linux (win32 branch only).
Reproducer log:
rocBLAS error: Cannot read .../Release/rocblas/library/TensileLibrary.dat
rocBLAS error: Could not initialize Tensile host:
directory_iterator: The system cannot find the path specified.
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* fix(install.sh): restore gfx token dedup in Strix multi-GPU awk indexer
536a54df removed the per-source `| awk '!seen[$0]++'` dedup from the
_gfx_all collection step but left the indexer awk as bare NF, so on a
mixed-arch host (e.g. dGPU gfx1100 + Strix iGPU gfx1151) where
rocminfo emits each gfx token twice (Name: field + ISA triple),
HIP_VISIBLE_DEVICES=1 indexed vals[1] = the second gfx1100 occurrence
instead of gfx1151, triggering the Strix routing on the wrong GPU.
Add !seen[$0]++ to the indexer awk so duplicate tokens from the same
GPU collapse to one entry before the HIP_VISIBLE_DEVICES index is
applied -- matching exactly what the Python side does with dict.fromkeys()
in _detect_amd_gfx_codes(). The comment above the block ("skip
duplicates") already documented this as the intended behaviour.
* fix(studio/install): correct _TOTAL progress count on Windows
base_total += 3 fired for all non-macOS platforms including Windows,
but flash-attn (line 1620) and ROCm torch final (line 1705) are both
guarded by 'not IS_WINDOWS and not IS_MACOS', so on Windows with torch
enabled _TOTAL was 13 while only 11 _progress() calls actually execute.
Split into +1 for the ROCm torch check (all non-macOS) and +2 for the
two Linux-only steps, so Windows gets _TOTAL=11 and Linux gets 14.
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* fix(install.ps1): enforce torch>=2.11.0 for gfx120X and Strix on Windows
The AMD arch-specific index (repo.amd.com/rocm/whl/gfx120X-all/ and
gfx1151/) publishes torch wheels from 2.7.1 through 2.11.0. Without a
version floor pip can resolve to torch 2.10.0+rocm7.12 on RDNA 4
(gfx120X) or torch 2.10.0+rocm7.1 on Strix (gfx1151/gfx1150), both of
which have a null-pointer crash in torch._C._grouped_mm (TheRock
issues #5284 / #3284). torch 2.11.0+rocm7.13 contains the fix.
Add $ROCmTorchFloor alongside $ROCmIndexUrl: set to torch>=2.11.0 for
the two affected arch families, null for all others. Wire it into the
uv pip install call so the broken wheels are never selected.
* fix(rocm/windows): address Codex nits - deterministic DLL suffix, CUDA llama.cpp kind, HIP_VISIBLE_DEVICES arch indexing
- install_python_stack.py / worker.py: _detect_bnb_rocm_dll_ver() and the
inline worker probe now collect ALL libbitsandbytes_rocm*.dll suffixes and
return max() by numeric value instead of stopping at the first glob hit.
Filesystem glob order is not guaranteed; this ensures '713' always wins
over '72' when both variants are present in the wheel.
- setup.ps1 (expectedKind): add 'windows-cuda' branch so NVIDIA hosts are
not treated as 'windows-cpu'. Previously an existing windows-cuda prebuilt
was always considered a mismatch on non-ROCm machines, forcing an
unnecessary re-download on every update.
- setup.ps1 (amd-smi gfx arch): collect ALL gfx tokens from amd-smi list
output in GPU order and honour HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES
when selecting which arch to use. On mixed-arch AMD systems where the
visible GPU is not the first enumerated one, this prevents installing an
incompatible wheel index. Falls back to index 0 (same as before) when the
visibility var is unset or is a comma-separated list.
- test_rocm_support.py: add test_picks_highest_suffix_when_multiple_dlls to
cover the multi-DLL case that was previously untested.
* fix(rocm): misleading amd-smi log, BNB spec consistency, torch ceiling for AMD index
amd.py: split 'returncode != 0 or not stdout' into two separate branches.
Previously, exit-0 with empty output logged 'amd-smi returned code 0' (which
reads as success, not a warning) and incorrectly incremented the circuit-breaker
counter. Now: non-zero exit logs the code and counts toward the limit as before;
empty stdout on exit 0 logs at DEBUG level and does not penalise the counter
(amd-smi --json always emits at least [] on exit 0, so this branch is rare and
is not a tool failure).
main.py: replace spec.origin / os.path.dirname() with
spec.submodule_search_locations to match install_python_stack.py and worker.py.
For normal wheel installs both approaches reach the same directory, but using
submodule_search_locations is the canonical way and handles editable bitsandbytes
installs correctly. Also use max() by numeric suffix (same as the other two sites)
instead of a sort-then-break loop.
install.ps1: add <2.12.0 ceiling to the torch constraint for gfx120X (RDNA 4)
and gfx1151/gfx1150 (Strix). AMD actively publishes new versions on their
per-arch index; without a ceiling, a future 2.12.0+rocmX.Y wheel would be
pulled in automatically before being validated on these architectures. The
ceiling matches the existing Linux install_python_stack.py constraint for the
same arches. Bump both when 2.12.x is confirmed working.
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* fix(rocm): torch floor in setup.ps1, torchvision pin for Strix, rocmsdk in _hip_ver_at_least
setup.ps1: add \ (mirrors install.ps1) and derive \
from it. Previously the AMD index install called 'Fast-Install torch torchvision
torchaudio --force-reinstall --index-url \' with no version
constraint, so pip could resolve torch 2.10.0+rocm7.12 for gfx1151/gfx1200 --
the exact broken wheel the PR is meant to avoid. Now gfx120X and Strix enforce
'torch>=2.11.0,<2.12.0', matching install.ps1 and the Linux constraint.
install_python_stack.py: pin torchvision and torchaudio in _strix_override_pkgs.
The Strix Linux override uses --index-url (exclusive, no PyPI fallback); bare
unversioned 'torchvision' and 'torchaudio' could resolve a build from AMD's
index targeting a different torch major, causing ABI/version mismatches at
runtime. Now pinned to '>=0.26.0,<0.27.0' and '>=2.11.0,<2.12.0' respectively,
matching _ROCM_TORCH_CONSTRAINT['rocm7.2'].
worker.py: extend _hip_ver_at_least to handle AMD SDK wheel version strings.
The fallback regex r'rocm(\d+)\.(\d+)' cannot match '2.9.0+rocmsdk20251116'
(no rocmX.Y component), so the function always returned False on SDK/Radeon
wheels -- installing the Python _grouped_mm workaround on wheels that already
have the working HIP kernel. Added a second check: if the version string
contains '+rocmsdk', assume >= 7.13 (the rocmsdk format post-dates the
gfx120X null-kernel fix) and skip the fallback.
* fix(rocm): warn on OOB HIP_VISIBLE_DEVICES, bail on empty numeric_ids mask
- setup.ps1: when HIP/ROCR_VISIBLE_DEVICES names an index beyond the
detected GPU count, emit a yellow warning and fall back to GPU 0
instead of silently reading allGfxArches[-1] (wrong arch)
- hardware.py _reconcile_primary_rocm_unified_memory: distinguish
numeric_ids=None (no env var, use torch ordinal 0) from numeric_ids=[]
(empty mask / HIP_VISIBLE_DEVICES=-1, no GPU visible); bail out early
in the empty case to avoid querying torch.device(0) incorrectly
* fix(rocm): gate StubSubpackageFinder on win32 ROCm, add gcnArchName fallbacks
- worker.py _StubSubpackageFinder: the meta_path append was running on
every platform on every call to run_training_process; moved it inside
the if _is_win32_rocm: block since stubs are only seeded there and the
finder is a pure accumulation on Linux/Windows CUDA
- worker.py OOM guard: AMD SDK / Radeon wheels may not populate
gcnArchName, causing Strix Halo to be misclassified as discrete and
get the 0.90 cap (12.8 GB OS headroom) instead of 0.80 (25.6 GB);
now tries gcn_arch_name / arch_name / gfx_arch_name variants first,
then falls back to device-name matching (890M -> Strix Halo,
880M -> Strix Point) with a debug log when the fallback fires
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* fix(rocm): pin torchvision/torchaudio in setup.ps1, remove -Unique from arch array
- setup.ps1 ROCm torch install: torchvision and torchaudio were passed
bare alongside pinned torch>=2.11.0,<2.12.0 for gfx1151/gfx1200 arches.
AMD publishes packages independently so a future torchvision 0.27 (for
torch 2.12) on the same arch index would cause pip ResolutionImpossible
or an ABI-incompatible install. Added torchvisionFloorMap and
torchaudioFloorMap mirroring install_python_stack.py's strix override
(torchvision>=0.26.0,<0.27.0, torchaudio>=2.11.0,<2.12.0) and derived
ROCmVisionSpec/ROCmAudioSpec used in all three Fast-Install call sites.
- setup.ps1 amd-smi arch detection: Select-Object -Unique was collapsing
same-arch multi-GPU arrays (e.g. two gfx1151 APUs -> 1-element array)
causing HIP_VISIBLE_DEVICES=1 to trigger a false out-of-range warning
and fall back to GPU 0 even though the correct GPU would have been at
index 1. Removed -Unique; added comment noting the positional-index
assumption and its non-contiguous-GPU limitation.
* fix(rocm): add 8060s/8050s to OOM guard device-name fallback, extract classifier helper
Path 3 of the OOM guard device-name fallback only checked for 890m/880m
(gfx1150 Strix Point SKU names). Strix Halo (gfx1151) ships as Radeon 8060S
(Ryzen AI MAX+ 395) and Radeon 8050S (cut-down SKU) -- neither matches, so
the fallback returned is_unified=False and applied the 0.90 fraction instead
of 0.80, leaving ~12.8 GiB OS headroom on a 128 GiB pool instead of ~25.6 GiB.
Fix: add 8060s and 8050s to the name-match set. Also correct the comment that
mislabelled 890M as a Strix Halo name (it is Strix Point).
Refactor: extract the three-path classifier into _rocm_classify_unified_memory()
so it can be unit-tested directly. Add 31 test cases in test_rocm_oom_guard.py
covering all three paths and the regression case (Radeon 8060S Graphics).
Reported-by: h34v3nzc0dex
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* fix(rocm): pass explicit dtype on bf16-unsupported hardware (RDNA2)
dtype=None lets unsloth auto-detect the model dtype. On RDNA2 (gfx103x,
e.g. RX 6600) is_bfloat16_supported() incorrectly returns True, so unsloth
picks bf16 and the first bf16 kernel dispatch triggers:
LLVM ERROR: Cannot select: intrinsic %llvm.amdgcn.fdot2.bf16.bf16
Replace every dtype=None in load_model() with _auto_dtype which resolves
to None when bf16 is supported (all modern NVIDIA + RDNA3+) and
torch.float16 otherwise. This gives RDNA2 users a working float16
training path without touching NVIDIA behaviour at all.
Fixes: https://github.com/unslothai/unsloth/issues/5337
* fix: reduce log noise for expected non-issues on Windows ROCm
Three log lines fired at warning/error level for conditions that are
completely expected on a Windows HIP SDK-only setup:
amd.py
- amd-smi WinError 2 (FileNotFoundError): downgrade warning -> debug.
amd-smi ships with Adrenalin, not the HIP SDK; absence is normal.
- 'disabling' message: downgrade warning -> info with clearer text
'not available (not installed; expected on HIP SDK-only systems);
GPU VRAM polling disabled'
hardware.py
- torch.distributed.Store missing: downgrade warning -> debug.
The distributed stub added in this PR intentionally omits Store; the
attention-impl fallback to eager is expected and non-actionable.
worker.py
- causal-conv1d: add early Windows exit (info) in both
_ensure_causal_conv1d_fast_path and _causal_conv1d_install hook;
no cp313/win_amd64 wheel exists, so the install always fails.
- FLA: add early Windows exit (info) in
_ensure_flash_linear_attention_unconditional; triton dependency has
no cp313/win_amd64 wheel.
- Defense-in-depth: _install_package_wheel_first non-HIP PyPI failure
logs info+debug on Windows instead of error; FLA failure logs
info+debug on Windows instead of warning.
* [AMD] FIx installation of bitsandbytes when it's from .dev and skip rebuilding llama.cpp if we build it manually.
* fix: use force_pip for Windows ROCm bitsandbytes prebuilt wheel install
uv rejects the bnb continuous-release wheel due to filename/metadata
version mismatch (1.33.7.preview vs 0.50.0.dev0). Switch to force_pip=True
(pip bypass) instead of the UV_SKIP_WHEEL_FILENAME_CHECK env var workaround
-- cleaner and consistent with how the Linux path handles it.
BNB_ROCM_VERSION is still set post-install to the detected DLL suffix so
the worker subprocess loads the correct libbitsandbytes_rocm{VER}.dll even
when torch.version.hip reports a newer HIP version than the wheel ships.
* fix: three small correctness fixes found in PR review
- _install_bnb_windows_rocm: use UV_SKIP_WHEEL_FILENAME_CHECK=1 with
try/finally instead of force_pip=True so the env var is always
restored and the failing CI test passes
- _determine_attention_impl_for_gpu_estimate: gate torch._C distributed
stubs on IS_ROCM so Windows CUDA users keep the real extension
- install.ps1 amd-smi fallback: collect all gfx tokens and index by
HIP_VISIBLE_DEVICES, matching the hipinfo path on multi-GPU hosts
* fix: stub torchao in export subprocess on Windows ROCm
On Windows, the ROCm build of PyTorch ships without the distributed
C extension (torch._C._distributed_c10d). torchao, which is pulled in
transitively by transformers.quantizers at import time, walks into
torch.distributed._functional_collectives -> distributed_c10d and
crashes with:
No module named 'torch._C._distributed_c10d'; 'torch._C' is not a package
This only affected the export subprocess because the training subprocess
already applied an identical torchao stub (introduced separately to fix
the same root cause). The export subprocess had no such guard and died
during 'Importing Unsloth...' before any model loading could happen.
Fix: apply the same _StubSubpackageFinder / torchao stub pattern to the
export subprocess entry point, gated on Windows ROCm detection, before
any import of transformers or unsloth_zoo.
Root cause tracked in ROCm/TheRock#3284 (libuv / torch.distributed
missing on Windows ROCm builds).
Ref: https://github.com/ROCm/TheRock/issues/3284
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* install.sh, setup.sh: add GPU arch step logging to match PS1 scripts
Both shell scripts were missing the step "gpu" terminal log block that
install.ps1 and setup.ps1 emit. This adds equivalent output: GPU label
with gfx arch (e.g. "AMD ROCm (gfx1151)"), ROCm root path, hipconfig
version, and marketing name substep. Includes the same gfx arch detection
chain (rocminfo → amd-smi list → amd-smi static --asic), UNSLOTH_ROCM_GFX_ARCH
env override, and name-based arch inference table (Strix Halo/Point, RDNA 3/4)
as the PS1 versions. install.sh also replaces bare echo blocks for the AMD
ROCm and CPU-only cases with formatted substep output.
* Fix BNB_ROCM_VERSION gate, ROCm GPU mask preference, APU unified memory and Release build for PR #5301
- main.py: gate BNB_ROCM_VERSION on the rocm bnb DLL or HIP_PATH/ROCM_PATH instead of importing torch on every Windows host
- hardware.py: prefer HIP/ROCR visible-device masks only on ROCm hosts so a stale mask cannot override CUDA_VISIBLE_DEVICES on NVIDIA
- llama_cpp.py: set GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 only for unified-memory APUs (gfx1150/gfx1151)
- setup.sh: pass -DCMAKE_BUILD_TYPE=Release for the HIP source build
- add test_amd_apu_unified_memory.py
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* fix: guard recompile_limit + fix AMD VRAM monitor fallback
trainer.py: torch._dynamo.config.recompile_limit does not exist in
some ROCm torch builds (e.g. pytorch.org/whl/rocm6.2 wheels). Guard
the assignment so training doesn't crash on RDNA2/RDNA3.
hardware.py: when amd-smi/nvidia-smi is unavailable or returns no
usable data (HIP SDK-only Windows, Docker, unexpected JSON format),
the existing fallback used torch.cuda.memory_allocated() which is
process-specific and reads near-zero even with a fully loaded model.
Switch to torch.cuda.mem_get_info() via _torch_get_per_device_info()
which reports system-wide VRAM occupancy so the GPU monitor shows
real usage on all AMD systems without requiring amd-smi.
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* fix: Windows VRAM monitor via Performance Counter API
When amd-smi/nvidia-smi is unavailable on Windows, query dedicated GPU
VRAM via Windows Performance Counters (same source as Task Manager).
This gives system-wide cross-process usage, fixing the near-zero reading
caused by torch.cuda.mem_get_info only seeing the Studio server process.
Linux fallback path unchanged (mem_get_info is system-wide on ROCm).
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* fix: rename to _rocm_windows_perf_counter_vram_gb, scope to IS_ROCM
Function is AMD ROCm specific — amd-smi absent on Windows when only the
HIP SDK is installed. Scoped to IS_ROCM so NVIDIA Windows path is
untouched (nvidia-smi handles that case).
* fix: AMD VRAM monitor — Linux DRM sysfs + Windows perf counter
Linux: read /sys/class/drm/card*/device/mem_info_vram_used|total for
system-wide GPU memory across all processes. No tools required, always
present on Linux AMD systems.
Windows: Windows Performance Counter API (already added).
Both paths are gated on IS_ROCM and only fire when amd-smi is absent.
torch mem_get_info remains as last resort (process-local).
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* fix: AMD GPU monitor — utilization, temperature, and power for Windows and Linux fallback paths
- Windows: GPU utilization via \GPU Engine(*engtype_3D*)\Utilization Percentage perf counter
- Windows: temperature and power via ADL (atiadlxx.dll, ships with Adrenalin)
- Linux: GPU utilization via DRM sysfs gpu_busy_percent
- Linux: temperature via hwmon temp1_input (millidegrees C)
- Linux: power via hwmon power1_average / power1_input (microwatts)
All paths are no-op fallbacks (None) when the source is unavailable.
Mirrors what nvidia-smi provides on the CUDA path.
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* fix: remove ADL ctypes — does not support AMD iGPU (Strix Halo)
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Erland366 <erland.pg366@gmail.com>
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* fix(studio/colab): merge iframe+keepalive into start(), add proxy_headers to uvicorn
- Move serve_kernel_port_as_iframe and keepalive loop into colab.start()
so both run in the same cell execution context, eliminating the race
where the proxy URL was shown before the iframe cell had a chance to run
- Add a 2s sleep after run_server() before show_link() to give Colab's
proxy infrastructure time to register the bound port
- Add proxy_headers=True and forwarded_allow_ips="*" to uvicorn Config
so X-Forwarded-Proto/Host from Colab's reverse proxy are trusted
- Simplify notebook start cell (no more separate iframe cell needed)
* fix(studio/colab): fix iframe blocking and server thread crash in Colab
Two root causes for the long-standing proxy/iframe breakage:
1. SecurityHeadersMiddleware set X-Frame-Options: DENY and
frame-ancestors 'none' unconditionally, blocking
serve_kernel_port_as_iframe regardless of server health.
Fix: detect Colab via COLAB_BACKEND_URL/COLAB_GPU env vars,
relax frame-ancestors to *.prod.colab.dev and omit X-Frame-Options.
2. asyncio.run() in the daemon thread conflicted with nest_asyncio's
global patches applied on the main thread, causing the server to
crash silently after ready_event fired.
Fix: use explicit new_event_loop() + run_until_complete() in the
daemon thread to bypass nest_asyncio's asyncio.run patch.
Also replace blind time.sleep(2) with a health endpoint poll so the
link and iframe are only shown once the server is truly reachable.
* fix(studio/colab): use reliable /content + google.colab path for Colab detection
COLAB_BACKEND_URL and COLAB_GPU env vars aren't consistently set across
all Colab runtime versions. Use /content dir + google.colab package path
as a more reliable signal, computed once at module load.
* fix(studio/colab): fix port mismatch, health-check silence, and CSP framing
Four bugs causing the iframe and URL button to always fail:
1. Port not propagated back: run_server auto-increments when 8888 is taken,
but start() kept using the original port for show_link() and
serve_kernel_port_as_iframe() — now reads app.state.server_port.
2. Silent health-check failure: the poll loop never checked whether any
attempt succeeded; on all-fail it continued and showed a dead link —
now exits early with a clear error message.
3. CSP frame-ancestors too narrow: '*.prod.colab.dev' only matches one
subdomain level; actual Colab proxy URLs are two levels deep
(e.g. foo.region.prod.colab.dev), and the parent frame may also be
colab.research.google.com or a sandboxed null-origin output iframe —
changed to '*' in Colab mode (single-user sandbox, no security loss).
4. _IS_COLAB detection hardcoded python3.10/3.11 paths: Python 3.12+
Colab runtimes wouldn't match when env vars aren't set — replaced with
a glob over python3.*/dist-packages/google/colab.
* fix(studio/colab): harden Colab startup against every known failure mode
colab.py:
- get_colab_url: retry eval_js up to 3x (10s timeout each), validate that
result is a real https:// URL containing the port before accepting it;
log a clear warning when falling back to localhost
- show_link: safe short_url truncation (try/except around str.index so an
unexpected URL shape never blocks the link card from rendering); also
emit the URL via logger so it's visible in cell text output even if
HTML display is suppressed
- start: detect "already running" at entry — on cell re-run Studio is
still healthy on port 8888; skip re-launch and go straight to
show+iframe so the user never ends up with mismatched port state
- start: wrap run_server in try/except (SystemExit + Exception) so
startup errors surface as readable messages rather than cell crashes
- start: check frontend_path/index.html exists, not just the directory
- start: remove unused `import sys`
- start / keepalive: catch KeyboardInterrupt so interrupting the cell
prints a clean "stopped" message instead of a raw traceback
- extract _is_studio_healthy() and _show_and_embed() helpers to
deduplicate the fast-path and normal-path logic
main.py:
- _build_csp: in Colab mode, extend script-src to include
*.prod.colab.dev and *.googleusercontent.com (Colab injects scripts
from these origins into the output iframe scaffolding)
- _build_csp: in Colab mode, extend connect-src with blob:, data:,
wss://*.prod.colab.dev, and wss://*.googleusercontent.com so
WebSocket streams and Colab kernel traffic are not blocked by CSP
* fix(studio/colab): fix iframe width responsiveness and height sizing
Replace serve_kernel_port_as_iframe with a raw CSS iframe for two
reasons:
1. Width responsiveness: serve_kernel_port_as_iframe sets the width as
an HTML attribute (width="100%") which Colab's output machinery can
bake into a fixed pixel value on first render, causing the Studio to
stop following the notebook panel width when it opens/closes or the
window resizes. A CSS style property (style="width:100%") participates
in normal reflow and always tracks the parent container width.
2. Height sizing: the hardcoded height=1200 was too tall on short monitors
(forced outer-page scroll) and wasted space on tall ones. A small JS
snippet reads screen.availHeight and sets height to ~82% of the screen,
clamped to [600, 1100]px, with a resize listener that re-fits on zoom
changes and panel open/close events.
Also eliminate the double eval_js call: _show_and_embed now fetches the
Colab proxy URL once and passes it to show_link via the new _url kwarg,
so google.colab.kernel.proxyPort is only called once per invocation.
Falls back to serve_kernel_port_as_iframe if IPython.display.HTML is
unavailable for any reason.
* fix(studio/colab): fix link button + add fullscreen hover button to iframe
Link button: target="_blank" is blocked by Colab's output sandbox.
Switch to onclick="window.open(url,'_blank')" which the sandbox allows.
Fullscreen: add a small button that appears on hover in the top-right
corner of the iframe. Clicking it calls requestFullscreen() on the
wrapper div and stretches the iframe to 100vh/100vw. Exits back to
normal on fullscreen change.
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* revert(studio/colab): remove fullscreen button
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* fix(studio/colab): address review feedback
- Wrap both urlopen calls in with statements to prevent socket/fd leaks
- Replace JS resize listener with CSS height:82vh — simpler, responsive,
and no risk of leaked window listeners on cell re-runs
- Use importlib.util.find_spec("google.colab") instead of a glob path
to detect Colab; more robust across Python versions and venv layouts
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* fix(studio/colab): fall back to href navigation when window.open is blocked
window.open from a cross-origin sandboxed Colab output iframe can be
silently blocked by the browser (returns null, no exception). The old
code returned false unconditionally, so a blocked popup left the button
doing nothing. Now: if window.open succeeds the new tab opens and the
href is suppressed; if it returns null the browser follows the href,
navigating the output cell to Studio — always does something useful.
* fix(studio/colab): remove button, give iframe a branded header bar
The "Open Unsloth Studio" button was unreliable in Colab's sandboxed
output context regardless of how window.open was called. Since the
iframe already loads Studio inline, the button added no value and
confused users with a URL that 404s outside the output cell.
Replace the separate link card + bare iframe with a single block:
a slim black header bar (Unsloth logo + truncated URL) flush on top
of the full-height responsive iframe. Cleaner and removes the broken
button entirely.
* studio: gate uvicorn proxy_headers/forwarded_allow_ips behind _IS_COLAB
forwarded_allow_ips="*" was applied unconditionally, so every Studio
deployment trusted X-Forwarded-* headers from any client. Only Colab needs
that, because its reverse proxy fronts the kernel. For a normal
local/standalone Studio this is an unwanted relaxation, especially when bound
to 0.0.0.0.
Now proxy_headers/forwarded_allow_ips are only set when _IS_COLAB. Standalone
runs fall back to uvicorn's defaults (proxy_headers honored from loopback
only), restoring the prior security posture, while Colab keeps the wide trust
its proxy requires.
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* added remote MCP server support
* trim
* added tests
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* increased timeout
* disabling MCP chat toggle
* Fix MCP OpenAI function-name validation + cancel propagation for PR #5750
OpenAI requires function.name to match ^[a-zA-Z0-9_-]{1,64}$ before
streaming starts. The existing 64-char length check is necessary but
not sufficient: MCP servers can return tool names containing '.', '/',
spaces, etc. that would 400 the whole chat request. Validate the
composed mcp__<server_id>__<tool> name against the regex, skip + warn
on miss, and drop duplicate tool names from the same server (which
would also 400 the request as "duplicates").
Also propagate the agentic-loop cancel_event into MCP tool execution
so a /cancel POST during a long-running MCP call (e.g. GitHub MCP
search across a large repo) actually interrupts the in-flight HTTP
call instead of waiting out the 300 s timeout. The watcher polls the
threading.Event at 50 ms cadence inside the asyncio loop (matches
routes/inference.py's existing cancel-watcher cadence) and races
against the call task with asyncio.wait FIRST_COMPLETED.
Tests added:
- test_mcp_specs_skip_invalid_openai_function_names: drops bad chars
- test_mcp_specs_skip_empty_tool_name
- test_mcp_specs_drops_duplicate_names
- test_call_tool_sync_respects_pre_set_cancel_event
Also fix test_desktop_auth.py's router stub that listed every existing
router but missed mcp_servers_router, so importing main.py fails after
this PR adds it to routes/__init__.py.
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* PR #5750 round 2: OAuth cleanup on delete/url-change + mcp_enabled standalone
Round 2 of cross-platform validation surfaced two more P1 findings:
1. OAuth tokens never get cleared. fastmcp keys tokens by MCP URL, not by
server row, and delete / URL change / use_oauth toggle only updated
the SQLite row. Re-registering the same URL would silently reuse the
old account's credentials. Adds clear_oauth_tokens_async() in
mcp_client.py and calls it from the delete + put route handlers when
the row had use_oauth=True and either the URL changes or OAuth is
turned off.
2. mcp_enabled=true was ignored unless the caller also sent
enable_tools=true. The frontend always sends both together so the UI
path was fine, but a direct API caller sending only mcp_enabled would
silently get no MCP tools, which contradicts the field's documented
"append tools from every enabled MCP server" behavior. Loosens the
use_tools gate in both the GGUF and safetensors paths so mcp_enabled
opens the tool loop on its own; when the caller did not also opt
into built-ins, the built-in list starts empty.
Tests added:
- test_clear_oauth_tokens_async_no_op_safe
- test_delete_server_calls_oauth_cleanup_when_oauth_was_on
- test_delete_server_skips_oauth_cleanup_when_oauth_off
- test_update_server_clears_oauth_on_url_change
- test_update_server_clears_oauth_when_oauth_disabled
26 backend MCP tests pass; full studio/backend suite 1710 passed locally.
Cross-platform CI (Linux, macOS, Windows) green on staging fork.
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* PR #5750 round 3: reject null bool updates + /test surfaces 400
Round 3 of cross-platform validation:
1. PUT /api/mcp/servers/<id> would 500 with TypeError when the body
explicitly set is_enabled or use_oauth to null. Pydantic accepts
None for an Optional[bool] and _changes_from_payload then passed
None into mcp_servers_db.update_server, which int(None)d. Reject
explicit null at the validation layer with 400 instead.
2. POST /api/mcp/servers/test caught HTTPException under
"except Exception", so an invalid URL came back as HTTP 200 with
{"ok": false, "error": "400: ..."} instead of a real 400. The
create + update paths return 400 for the same input. Move
validation outside the transport try/except so it surfaces 400.
Tests added:
- test_changes_from_payload_rejects_null_is_enabled
- test_changes_from_payload_rejects_null_use_oauth
- test_test_endpoint_surfaces_url_validation_as_400
* PR #5750 round 4: hyphenated MCP tool names + empty-tool-list gate
Round 4 surfaces two more interaction bugs between the new MCP path
and existing safetensors tool plumbing:
1. OpenAI accepts ^[a-zA-Z0-9_-]{1,64}$ for function.name, and round 1
widened the MCP regex to that set, so MCP tools can now be advertised
as `mcp__srv__list-issues`. But the XML tool-call parser in
tool_call_parser.py used `\w+` (no hyphen), so the model could call
the tool but Studio could not parse the call. Same in
routes/inference.py's `_TOOL_XML_RE` stripper, which would leave
hyphenated tool-call XML in the visible content. Both regexes now
use `[\w-]+`.
2. safetensors_agentic treats `tools=[]` as "allow all" (documented
contract, exercised by test_empty_tools_list_does_not_enforce_allowlist).
When a caller sends `enable_tools=true` + `enabled_tools=[]` +
`mcp_enabled=true` and MCP discovery returns 0, the resolved tool
list is genuinely empty and built-in tools (web_search / python /
terminal) could execute via the model's emitted call. Fix at the
route gate instead of breaking the documented contract: set
`use_tools=False` when the resolved list is empty, in both GGUF and
safetensors paths. Existing callers who omit `enabled_tools` still
get ALL_TOOLS and are unaffected.
Tests added (32 total):
- test_tool_xml_parser_handles_hyphenated_function_names
- test_tool_xml_strip_handles_hyphenated_function_names
- test_safetensors_agentic_empty_allowlist_still_means_allow_all
(documents the contract round 4 preserved)
1716 passed locally; cross-platform CI on staging fork still green.
* PR #5750 round 5: GGUF allow-list + CLI policy + hyphenated params + cancel race
Round 5 of parallel-reviewer aggregation surfaced six additional
findings; five are real and fixed here:
1. Hyphenated MCP parameter names (`<parameter=issue-number>`) were
dropped by the XML parser's `\w+` regex. Extended to `[\w-]+` in
both core/inference/tool_call_parser.py and core/tool_healing.py.
The latter is GGUF's own copy of the parser/strip patterns and was
missed by round 4.
2. core/tool_healing.py's `strip_tool_call_markup` still used
`<function=\w+>` so hyphenated MCP tool-call XML leaked into the
GGUF visible content even after round 4 fixed the shared parser.
3+4. `mcp_enabled` re-opened the tool loop even when the operator
passed `unsloth run --disable-tools` (CLI policy False). Round 2's
`(_tools_on or payload.mcp_enabled)` gate ignored the raw process
policy. Now reads `state.tool_policy.get_tool_policy()` and gates
mcp_enabled on `_cli_policy is not False`. Applied to both GGUF
and safetensors paths.
5. GGUF's agentic loop called `execute_tool(tool_name, ...)` without
checking the model-emitted name against the per-request tool list,
while the safetensors loop already enforces this. Added the same
allow-list check so a model that hallucinates a filtered MCP name
or a built-in the caller opted out of returns "not enabled" instead
of executing.
Bonus P2 fixes:
- `call_tool_sync` now checks `cancel_event.is_set()` BEFORE
creating the call task, so a pre-set cancellation does not open
the HTTP transport.
- `clear_oauth_tokens_async` moved the OAuth import + construction
inside the protected try block; a fastmcp.client.auth load error
used to escape and 500 the delete / update route.
NOT fixed (verified false or out of scope):
- finding #10 "structured_content vs structuredContent": fastmcp's
CallToolResult dataclass uses snake_case (verified live against
structured-only tool result; fields are
`dict_keys(['content', 'structured_content', 'meta', 'data', 'is_error'])`).
- finding #11 "asyncio.run from running loop": call_tool_sync is
invoked from `asyncio.to_thread` worker threads which have no
event loop; asyncio.run() is safe there.
Tests added (37 total): hyphenated param names, tool_healing strip,
GGUF allow-list gate, cancel pre-set short-circuit, OAuth cleanup
constructor-error swallowing. 1721 passed locally, no regressions.
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* Studio: stop leaking seeded admin pw to cross-origin callers
The "/" SPA fallback serves index.html with an inline
``window.__UNSLOTH_BOOTSTRAP__`` script containing the seeded admin
password while a password change is pending. Default web mode runs
``CORSMiddleware`` with ``allow_origins=["*"]`` + ``allow_credentials=
True``, which reflects an attacker-controlled ``Origin`` back on every
request and sets Access-Control-Allow-Credentials true. The combination
let any cross-origin page ``fetch('/')`` with credentials and read the
bootstrap admin password out of the HTML body. The API smoke
``CORS: GET / leaks bootstrap pw to cross-origin caller`` audit already
tracked this (tests/studio/studio_api_smoke.py:224) but did not gate CI.
Gate ``_inject_bootstrap`` on a same-origin check: legitimate top-level
navigations omit ``Origin`` on most engines, so the absence of the
header is treated as same-origin; when the header IS present and does
not match ``request.url.scheme://request.url.netloc`` exactly, we now
skip injecting the bootstrap tag. ``Vary: Origin`` is added so an
intermediary cache cannot serve a same-origin response (with bootstrap)
to a later cross-origin caller (and vice versa).
Coverage: ``test_index_bootstrap_origin.py`` exercises the helper with
missing / matching / evil / scheme-mismatch / port-mismatch origins.
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* Studio: tighten comments on bootstrap cross-origin helper
* Studio: canonicalise Origin before same-origin gate
A plain string-compare between the Origin header and request.url.netloc
misclassifies legitimate same-origin requests as cross-origin in three
scenarios:
- Browser strips the default port from Origin (https://example.com)
but Starlette's netloc keeps it (example.com:443). Per RFC 6454 the
default port is dropped on the wire, so the strings will not match
even though the requests share an origin.
- Host case differs (Origin: http://Example.com vs netloc:
example.com). Per RFC 3986 host comparison is case-insensitive.
- Scheme case differs (HTTP:// vs http://). Per RFC 3986 the scheme
is also case-insensitive.
These are usability degradations rather than security gaps (legitimate
user denied the bootstrap injection, no attacker gain), but worth
shipping so non-default Studio deployments keep the change-password
auto-fill.
Adds _canonical_origin(scheme, netloc) -> (scheme, host, port) and
compares the canonical tuples. Default-port lookup covers
http/https/ws/wss; userinfo (user:pass@) is stripped per RFC 3986
since Origin never carries credentials. Origin: "null" (sandboxed
iframes, file:// pages) and unparseable values collapse to cross-
origin so the bootstrap pw is never leaked through those paths either.
Tests: 14 cases (was 5). Covers the original same/missing/evil/
scheme/port matrix plus default-port stripping in both directions,
host + scheme case folding, Origin: null, garbage values, and
userinfo-in-netloc.
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* Fix IPv6 netloc parsing for PR #5739
The canonical-origin helper used ``netloc.partition(":")`` which
mis-parses bracketed IPv6 hosts (``[::1]:8902`` -> host=``[``,
port-str=``:1]:8902``). The int() then raises and the canonicaliser
returns None, so every IPv6 same-origin request is misclassified as
cross-origin and Studio refuses to inject the bootstrap pw on a
legitimate top-level nav when launched with ``unsloth studio -H ::1``.
Bracket-aware split per RFC 3986 §3.2.2, plus extra regression tests
for IPv6, opaque (data:/blob:/file:), comma-joined multi-Origin and
localhost-vs-127 cases.
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* Guard urlparse ValueError in same-origin gate
urlparse raises ValueError on malformed bracketed Origin values
(unclosed [, invalid IPv6 hex, text after ]) and on a few NFKC
edge cases since Py 3.8. Without a guard, a request carrying
Origin: http://[malformed surfaced as HTTP 500 from the SPA
handler rather than being treated as cross-origin per the
docstring's safer-default rule. Wrap both urlparse calls in
try/except ValueError and return False on parse failure.
Also distinguish a missing Origin header (top-level same-document
GET, treat as same-origin) from an explicit empty string (not a
valid serialised origin per RFC 6454 §6.1, treat as cross-origin).
Four new regression tests pinned down by the PR audit: malformed
IPv6 bracket, invalid IPv6 hex, bracket with trailing garbage,
and the empty Origin header.
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* Shorten origin-gate comments for PR #5739
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* feat: Persist chat history in backend storage
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* Address chat tombstone batching review
* fix: update desktop auth routes stub
* chat db settings storage
* chat db settings routes
* chat db settings client
* chat db settings store
* chat db settings wiring
* chat db history storage
* chat db settings migration
* chat db settings fallback
* chat db container metadata
* chat db legacy migration fixes
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* chat ci auth background reads
* chat auth storage fixes
* chat migration final fixes
* chat export batch message lookup
* chat history review fixes
* chat prune sync fix
* chat settings hydration retry
* gate settings persistence
* Scope chat-history rows by subject; fix hijack, clear-confirm, hydrate race
Backend storage and routes:
- chat_threads / chat_messages / chat_settings carry a NOT NULL subject
column with composite PRIMARY KEY (id, subject). Two authenticated
identities can no longer see or wipe each other's data.
- Pre-existing rows on an existing studio.db migrate under sentinel
subject __legacy_unscoped__ via rename + rebuild + copy; single-user
installs see no behavior change.
- ON CONFLICT(id, subject) DO UPDATE ... WHERE chat_messages.thread_id =
excluded.thread_id refuses cross-thread re-parenting via upsert.
upsert_chat_message + sync_chat_messages now raise
ChatMessageThreadMismatch which the routes map to HTTP 409.
- replace_thread_messages rejects body messages whose threadId does not
match the URL thread (HTTP 400) instead of silently rewriting them.
- DELETE /api/chat requires ?confirm=true, returns row count, logs the
subject and count.
- upsert_chat_settings_merge does read + deep-merge + write inside a
single BEGIN IMMEDIATE so concurrent writers no longer drop each
other's updates. The route delegates to this helper.
- New POST /api/chat/messages:batch returns {thread_id -> messages[]}
for many threads in one HTTP call. Subject-scoped. Unknown ids return
empty lists instead of 404 so the sidebar/search caller can rebuild
atomically.
Frontend:
- chat-runtime-store: hydrate-failure catch sets settingsHydrated:true
so a transient backend blip no longer permanently disables
persistence. setParams bumps inferenceParamMutationVersions
unconditionally so a slow hydration response cannot clobber a
pre-hydrate user edit. saveSettingsPatch replaces the serial chain
with a debounced pendingPatch + deep merge; flush on beforeunload.
- chat-history-storage: clearStoredChats returns ClearStoredChatsResult
distinguishing backend / legacy / both outcomes.
listStoredChatThreadsWithMessages uses the batched fetch (one HTTP
call) instead of Promise.all per-thread; legacy Dexie fallback only
fires when the batch result is empty.
- chat-api: batchListChatMessages with graceful 404 / 405 fallback to
per-thread listChatMessages for older servers.
- chat-thread-tombstones: store {id, deletedAt} tuples with 90-day GC
and a 5000-entry cap so localStorage stays bounded. Back-compat reads
pre-fix plain strings. Adds removeChatThreadTombstones (rollback) and
clearAllChatThreadTombstones (post-legacy-purge clean-up).
- use-chat-sidebar-items: deleteChatItem tombstones synchronously
BEFORE the backend round-trip and rolls back on failure (restores
pre-PR optimistic UX). 300 ms trailing debounce on
CHAT_HISTORY_UPDATED_EVENT plus requestSeq guard so stream-time event
bursts produce at most one fetch per quiet window.
Tests:
- studio/backend/tests/pr5272_sim/ adds 64 regression tests covering
schema migration from pre-fix shape, subject scoping, cross-thread
hijack, bulk-replace mismatch, clear-confirm, concurrent settings,
unicode + 2MB content + SQL-injection-safe binding, chunking
boundary at 900 and 901 ids, batched endpoint (multi-subject + 1200
ids + per-thread order), and grep contracts for the frontend patches.
test_chat_history_storage.py updated to pass subject.
Verified locally on Linux + macOS + Windows GitHub Actions runners
(staging fork): 64 pass + 2 from the PR's own backend test on all
three OSes.
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* Drop subject scoping and clear-confirm gate (Studio is single-user)
Per maintainer feedback: subject scoping, cross-thread message hijack
guard, and DELETE /api/chat ?confirm=true gate are unnecessary because
Studio is intentionally single-user (the client already shows a confirm
dialog before clear-all).
This commit reverts those backend changes and keeps only the
non-multi-user pieces from the earlier fix commit:
- studio_db.py: restored to pre-fix shape; adds upsert_chat_settings_merge
which does atomic read + deep-merge + write under BEGIN IMMEDIATE so
two concurrent slider drags cannot drop one another's updates.
- routes/chat_history.py: restored; put_settings now calls the atomic
merge instead of doing the read-merge-write across three separate
connections. Adds POST /api/chat/messages:batch to collapse the
sidebar/search rebuild from N round-trips to 1.
- frontend/api/chat-api.ts: align batchListChatMessages request and
response keys with the backend (threadIds / messagesByThreadId).
- tests/test_chat_history_storage.py: add atomic-merge concurrency test,
deep-merge nested-key test, and 901-id chunking-boundary test.
- Drop the pr5272_sim test directory (those tests covered the reverted
subject-scoping/hijack/confirm behavior).
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* Fix sidebar delete crash, keepalive on settings beforeunload flush, search rebuild race
Two correctness bugs and one perf race surfaced by a fresh code review of
the prior fix commit:
- chat-api.ts: notifyChatHistoryUpdated was declared as a non-exported
function, but use-chat-sidebar-items.ts imports it. The import would
fail tsc with TS2305 and at runtime the optimistic-delete and
delete-failure rollback paths would both throw.
- chat-runtime-store.ts + chat-settings-api.ts + chat-settings-storage.ts:
the beforeunload settings flush is now actually keepalive. Without it
the browser cancels the in-flight PUT on tab close, so the last slider
drag is silently dropped (which is exactly the case the
debounce+beforeunload combination was meant to protect against).
- use-chat-search-index.ts: rebuilds now coalesce with a 300ms trailing
debounce and discard out-of-order responses via a requestSeq guard.
Matches the sibling pattern in use-chat-sidebar-items.ts so two rapid
CHAT_HISTORY_UPDATED_EVENTs (run-start + run-end save during a turn)
cannot land with stale data winning.
- chat-thread-tombstones.ts: drop dead clearAllChatThreadTombstones with
no call sites; Dexie is never wiped so the function has no use.
* fix(studio): protect chat persistence writes
* fix(studio): align chat history clear semantics
* fix(studio): show partial chat clear feedback
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* fix(studio): preserve chat persistence fallbacks
* fix(studio): harden chat thread persistence checks
* Preserve chat message timestamps
* Gate chat stream on history save
* Make chat thread backfill best effort
* Avoid chat message 404 probe
* Tighten chat legacy fallbacks
* chat: server-side ledger so legacy Dexie import is recoverable
The boolean localStorage sentinel
(unsloth_chat_legacy_imported_to_studio_db) made importLegacyChatsIfNeeded
non-recoverable: deleting studio.db while the browser keeps the flag
silently hides every legacy Dexie thread from the sidebar (verified by
the 3-GPU validation probe; matches the third review comment on PR
#5272). Same trap fires for browser-profile sync to a fresh machine
and any other path that wipes studio.db while keeping IndexedDB.
Source of truth moves into studio.db itself via a new
chat_legacy_import_log table keyed by legacy thread id. The ledger
disappears together with studio.db, so the next launch re-runs the
import from whatever Dexie still holds. localStorage stays as a
per-session perf hint only.
Performance, all bounded by the three new fast-paths before any
backend work:
A) localStorage hint says "imported earlier in this session" -- 0
network, ~0 ms. Covers the warm sidebar mount.
B) indexedDB.databases() reports no "unsloth-chat" DB -- 0 network,
~1 ms. Covers every new user who never had the old browser-only
Studio (the common case after launch).
C) db.threads.count() + db.messages.count() are both 0 -- 0 network,
~5 ms. Covers returning users who migrated long ago and Dexie was
never repopulated.
Only when all three miss does the code talk to the backend
(GET /api/chat/import-ledger -> diff vs Dexie -> existing import path
-> POST /api/chat/import-ledger to record what was just imported).
Per-thread tracking is enough because Dexie is read-only after this
PR; a thread's message set does not grow.
Backend deployments that predate the import-ledger routes are
handled transparently: the client treats 404/405 as an empty ledger
and re-runs the (idempotent via UPSERT) import on next launch.
Changes:
- storage/studio_db.py: new chat_legacy_import_log table (WITHOUT
ROWID, PK on legacy_thread_id) + list_chat_legacy_import_log() +
record_chat_legacy_import_log() (idempotent batch UPSERT).
- routes/chat_history.py: GET + POST /api/chat/import-ledger with the
obvious request/response models.
- frontend api/chat-api.ts: listChatImportLedger() (returns a Set for
O(1) diff) + recordChatImportLedger(), both with 404/405 fallback.
- frontend utils/chat-history-storage.ts: importLegacyChatsIfNeeded
gains three fast-paths, ledger fetch on the slow path, and writes
the ledger after a successful import. The localStorage helper is
unchanged on the surface; it just stops being authoritative.
- tests: 5 new test_legacy_import_log_* cases (empty default, record
+ list round-trip, idempotency, input dedup, empty/null ignore).
All 9 pre-existing tests still pass.
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* Make the legacy-import recovery actually recoverable
The previous commit added a server-side ledger to make Dexie -> studio.db
import recoverable after a studio.db wipe, but the localStorage perf hint
still short-circuited the import gate before the ledger was ever consulted.
After a wipe, the hint stayed "true" and the bulk re-import never ran -- the
ledger sat empty and only the per-thread lazy materialize-on-continue path
restored data.
Changes:
- Remove the localStorage short-circuit from importLegacyChatsIfNeeded so
the ledger is checked on every fresh tab. legacyChatImportPromise keeps
the per-session cache; the hint now only matters for the listing paths.
- Batch the slow path: one db.messages.where().anyOf().toArray() and one
batchListChatMessages() instead of 2N round-trips. At 1k threads this
drops a multi-second blocking import to a single request pair.
- recordChatImportLedger returns {accepted, inserted, supported}. The
localStorage hint is only flipped when supported is true, so old
backends (404 / 405 / 501) no longer permanently poison recovery.
- Ledger backfill: threads already present in chat_threads but missing
from the ledger now get added too, so old-FE-then-new-FE deployments
don't redo the diff every launch.
- Backend response field renamed recorded -> {accepted, inserted}.
accepted is the deduped non-empty input count; inserted is the rows
actually new (via INSERT ... RETURNING). Bounded by Field(max_length=
10_000) on the request payload.
- Storage helpers renamed: chat_legacy_import_log -> chat_legacy_imports,
record_* -> upsert_* to match the existing noun/verb conventions.
- DEXIE_DB_NAME exported from db.ts; duplicate constant in
chat-history-storage.ts removed.
- 3 new route-level tests for /api/chat/import-ledger covering the
round-trip, the (accepted, inserted) split, and the 10k payload cap.
All 18 chat-history tests pass.
---------
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Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: warn when llama.cpp prebuilt is at least 3 days behind
Layered on #5528. Generalises the MTP-specific staleness warning to
every llama.cpp prebuilt update, not just the ones that add MTP. If
the installed prebuilt is at least 3 days old AND its tag differs
from the latest published tag on the helper release repo (default
unslothai/llama.cpp), Studio nudges the user to run
"unsloth studio update".
How it works
Reads the install marker UNSLOTH_PREBUILT_INFO.json that
install_llama_prebuilt.py already writes to install_dir. The marker
carries the installed tag, the helper repo, and an installed_at_utc
timestamp. Studio compares those against the latest published tag
from the GitHub releases API for the helper repo.
GitHub fetch is cached at two levels:
- Process-level memo for /status hot path.
- Disk-level cache (24h TTL) at ~/.unsloth/studio/cache/llama_cpp_freshness/
so cold-start Studio launches do not always hit the API.
On a transient fetch failure (offline, rate-limited) we keep the
last-good disk value alive rather than poisoning the cache with None.
The check fails open: if anything is missing (marker, timestamp,
GitHub response), stale stays False so users never see a misleading
banner.
Surfaced in two places
1. Startup banner (logs + stderr) in main.py:lifespan(), alongside the
MTP capability probe added in #5528. Single line, e.g.:
WARNING: llama.cpp prebuilt is 5 days behind: installed b9190,
latest b9300. Run "unsloth studio update" to refresh.
2. /api/inference/status now returns:
llama_cpp_prebuilt_stale: bool
llama_cpp_installed_tag: str | None
llama_cpp_latest_tag: str | None
so the frontend can render a banner / popup with the actual tag
delta the user is missing.
3-day threshold
Mirrors the typical Unsloth llama.cpp release cadence. Anything
shorter would nag users who restart Studio at the wrong moment;
longer leaves real bugs sitting on the user's machine. Configurable
via the threshold_days kwarg if a future call site wants a different
window.
Tests
17 new cases in tests/test_llama_cpp_freshness.py cover marker
discovery in both cmake and root install layouts, missing / invalid
marker, GitHub fetch caching across process restarts (disk cache hit
after the in-memory cache is reset), the stale / not-stale decision
matrix (tag mismatch + age threshold), fail-open behaviour when
GitHub is unreachable, custom threshold, singular/plural day in the
warning string, and unparseable installed_at_utc. The broader
205-test inference regression suite still passes.
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* Studio: warn when llama.cpp prebuilt is too old for MTP
Layered on #5527. Adds a one-shot llama-server --help capability probe
so users get a clear signal when their prebuilt is missing MTP support,
plus a graceful fallback if they load an MTP GGUF against an outdated
binary.
What's surfaced:
1. Startup log + stderr line in main.py:lifespan() if MTP isn't
advertised:
WARNING: llama.cpp prebuilt is missing MTP support
(--spec-type mtp / draft-mtp). Run `unsloth studio update` to
refresh it. MTP GGUFs will load without speculative decoding.
2. Load-time graceful fallback in load_model's spec block: skip the
auto-emit and log a clear warning instead of letting llama-server
fail with an unknown-flag error.
3. /api/inference/status now returns llama_cpp_supports_mtp: bool so
the frontend can show a banner / popup.
Probe internals:
- Class-level cache keyed on (binary_path, mtime). One subprocess call
the first time, instant thereafter. Touching the binary (e.g. via
`unsloth studio update`) invalidates the cache automatically because
the mtime changes, so the new build is picked up without restarting
the server.
- Recognises both upstream naming forms: the original draft-mtp from
llama.cpp PR #22673 and the renamed mtp variant in later commits.
- Spec block uses whichever token the binary accepts so we emit the
right value regardless of which release the user has.
Tests:
- 6 new cases in test_llama_cpp_mtp_detection.py covering each probe
variant (draft-mtp, renamed mtp, pre-MTP build, missing binary,
mtime-based cache invalidation).
- Existing 38 MTP detection cases still pass; broader 188-test
regression suite (server args, reload inheritance, gguf metadata,
load progress, context fit, model validation) still green.
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---------
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* studio: proxy-aware login rate-limit; allow google favicons in CSP
Two follow-ups to #5375's auth + headers hardening.
Login rate-limit:
The per-IP bucket keyed on request.client.host alone. Behind any
reverse proxy or shared NAT it lumps everyone together (one user's
typos lock everyone out for 60 seconds; the 429 detail leaked the
proxy/internal IP back to clients). The bucket key is now
(client-ip, username.lower) so:
- one wrong-password run does not block another user from the same IP
- one IP does not block the same user from a different IP
The 429 detail body no longer interpolates the IP. Behind a proxy
clients can set UNSLOTH_STUDIO_TRUST_FORWARDED=1 so the limiter
honours X-Forwarded-For / Forwarded; off by default so a direct
caller cannot spoof the header.
CSP img-src:
components/assistant-ui/sources.tsx renders citation favicons from
https://www.google.com/s2/favicons. The current img-src allows
t0..t3.gstatic.com (used for other Google-hosted icons) but not the
main host the favicon URL points to, so every citation icon
CSP-blocks and falls back to gray initials. Adding www.google.com to
img-src is the same shape as #5409's connect-src HF allowlist fix.
Tests:
- test_login_rate_limit.py (new): _client_ip respects
UNSLOTH_STUDIO_TRUST_FORWARDED for X-Forwarded-For and Forwarded;
bucket key is composed of (ip, lower(username)) and isolates
cross-user and cross-IP buckets; 429 detail does not contain the
client IP; Retry-After header preserved.
- test_middleware.py: new test_img_src_allows_google_favicons pins
that www.google.com is in the img-src directive and the existing
gstatic CDNs stay allowed.
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* studio: normalise forwarded IPs, IP-wide aggregate cap, unknown-user sentinel
Reviewer follow-ups to the proxy-aware login rate-limit PR.
Forwarded address normalisation: with
UNSLOTH_STUDIO_TRUST_FORWARDED=1, raw `X-Forwarded-For` and
`Forwarded: for=` values such as `198.51.100.7:50001` or
`"[2001:db8::1]:50001"` were carried verbatim into the bucket key,
so one client emitting a fresh source port per attempt split into
many buckets and bypassed _LOGIN_MAX_FAILS. _normalize_forwarded_addr
now strips quotes, optional `[..]:port` for IPv6 and `host:port` for
IPv4, and validates as an IP literal; garbage values fall through to
the direct request.client.host. Forwarded parsing also isolates the
first forwarded-element so a multi-element header cannot create
attacker-controlled bucket strings.
Spray protection: the (ip, username) key removed the aggregate
per-IP throttle the pre-PR limiter provided. A client rotating
nonexistent usernames produced [401, 401, 401, 401, 401, 401] where
pre-PR produced [401, 401, 401, 401, 401, 429]. Restored the
aggregate via a parallel _LOGIN_IP_BUCKETS table (max 30 fails / 60s
per IP) checked alongside the per-(ip, username) bucket; both
buckets must be cleared on a successful login.
Bucket cardinality: every distinct unauthenticated username
allocated a new (ip, username) bucket entry without bound. 1,000
random usernames from one IP produced 1,000 buckets. Failures whose
username does not exist now record into a single sentinel key
(ip, "\x00unknown-user") so cardinality stays at one per IP for the
unknown path. The known-user path additionally enforces a global
hard cap (_LOGIN_MAX_BUCKETS = 4096) that prunes stale empty buckets
on overflow and otherwise folds the failure into the per-IP bucket
only.
Test:
- python -m pytest studio/backend/tests/test_login_rate_limit.py -q
-> 19 passed (was 12 before this commit; +5 forwarded-address
normalisation, +1 sentinel bucket, +1 bucket cap)
CSP comment refreshed to mention `www.google.com` alongside
*.gstatic.com so future readers see why the host is allowlisted.
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* studio: tokenise img-src assertion to silence CodeQL substring rule
The new CSP google-favicon test used 'host string in directive string'
which CodeQL flagged as py/incomplete-url-substring-sanitization
(the substring could appear at an arbitrary position in a URL).
The assertion is checking a CSP directive, not URL sanitisation, but
splitting the directive on whitespace and asserting against the
tokenised source list expresses the same intent and matches the
exact CSP source expression. CodeQL no longer treats it as a URL
substring check.
Test: python -m pytest studio/backend/tests/test_middleware.py -q
-> 14 passed
* studio: use any(src == host) for CSP source asserts
CodeQL's py/incomplete-url-substring-sanitization still flagged the
tokenised "host in img_sources" check. Switching to
`any(src == host for src in img_sources)` makes the comparison an
exact-equality (not substring) match, which the rule does not flag.
Test: python -m pytest studio/backend/tests/test_middleware.py -q
-> 14 passed
* studio: trim verbose rate-limit + CSP comments
Compress the 6-line constants header on _LOGIN_BUCKETS to 3 lines and
the per-helper docstrings on _trust_forwarded_for / _normalize_forwarded_addr
to one line each. Same code, fewer in-flow tutorials.
Note in the CSP comment that www.google.com is the active favicon host
(used by sources.tsx for s2/favicons citations); *.gstatic.com stays as
legacy faviconV2 coverage but the SPA no longer fetches it.
33 tests in test_login_rate_limit.py + test_middleware.py still pass.
---------
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* studio: expose launcher capability bits on unauth /api/health
PR #5375 reduced the unauthenticated /api/health response to {status,
timestamp} only, on the theory that the rest of the payload was useful
fingerprinting. That was too aggressive: the Tauri watchdog reads
`service == "Unsloth UI Backend"` and `studio_root_id` to re-adopt its
own backend across restarts (src-tauri/src/desktop_backend_owner.rs
and commands.rs), and the SPA bootstrap fetches the same payload
unauth to detect chat-only mode and native path lease support before
any token is available (frontend src/config/env.ts and
features/native-intents/use-native-readiness.ts). With the post-#5375
shape, the watchdog kills its own healthy backend, the SPA never
flips out of "full Studio" mode on chat-only Linux/Windows, and the
About tab shows "dev" in place of the real version.
The actual fingerprint-ish fields are `version` / `studio_version` /
`device_type` (and to a lesser extent the hostname inside
`device_type`). `service`, `studio_root_id` (already a hex digest of
the install path, not the raw path), `chat_only`, the desktop_*
capability flags, and `native_path_leases_supported` do not leak the
install path or version.
This patch keeps the auth gate but rebalances which fields sit on
each side of it:
unauth service, studio_root_id, chat_only, desktop_protocol_version,
desktop_manageability_version, supports_desktop_auth,
supports_desktop_backend_ownership, native_path_leases_supported,
desktop_owner (when present)
authed + version, studio_version, device_type
Existing must-change-password sessions still fall through to the base
payload because get_current_subject (strict) rejects them; that
matches prior behaviour.
test_middleware.py is updated to pin the new contract: launcher bits
present unauth, fingerprint fields present only with a valid bearer.
* studio: complete launcher-bits health unauth contract on Tauri + About tab
Reviewer follow-ups to the unauth /api/health launcher bits split.
Tauri preflight:
backend_capability_stale_reason() fell through to
backend_version_stale_reason(health.version.as_deref()) when capability
bits were present but version was absent. With the unauth payload now
exposing service + studio_root_id + desktop_* bits but gating version
behind a bearer, the desktop watchdog was reading the new payload,
parsing all capability bits, then classifying the same-root backend
as desktop_backend_version_missing and refusing to adopt it.
A backend that exposes desktop_protocol_version=1,
desktop_manageability_version>=1, supports_desktop_auth=true and
supports_desktop_backend_ownership=true was introduced together with
MIN_DESKTOP_BACKEND_VERSION=2026.5.3 in #5341, so a present capability
bitset is itself a version-compatibility signal. Skip the version
sub-check when version is None/empty; keep it for non-empty values
so genuinely-too-old backends that do echo a version still get
desktop_backend_version_too_old.
About tab:
fetchStudioVersions() did a bare fetch(apiUrl("/api/health")), which
the unauth payload no longer carries version/studio_version for, so
Settings -> About kept rendering "dev"/"dev" for any logged-in user.
Attach Authorization: Bearer <token> when getAuthToken() returns one;
fall back to bare fetch (still 200, just truncated payload) for the
not-logged-in case. No new endpoint.
Comment:
studio_root_id is no longer a hex digest of the install path; it is
an opaque per-install id written by the launcher. Updated the inline
comment to match.
Test:
- python -m pytest studio/backend/tests/test_middleware.py::TestHealthAuthGate studio/backend/tests/test_desktop_auth.py -q
-> 29 passed
- npm run typecheck clean, npm run build produces fresh dist
* Trigger CI rerun for flaky Mac Chat UI step
* studio: add external provider support for chat inference
Adds the ability to connect to OpenAI, Mistral, Google, Cohere, Together,
Fireworks, and Perplexity from the Studio chat interface.
- Provider configs stored in SQLite (no API keys persisted)
- RSA-2048 key pair generated at startup for client-side key encryption
- httpx proxy client streams SSE responses in OpenAI-compatible format
- New /api/providers routes: registry, CRUD, test, models
- /v1/chat/completions routes to external provider when provider fields present
- Integration test suite covering CRUD, connection, model listing, and inference
- Frontend spec doc with full API contract
* remove frontend spec doc from branch
* fix auth fixture: handle forced password change on fresh install
* fix tests: default port 8000, allow 400 for no-model-loaded
* fix: update Cohere models to current (command-r retired Sept 2025)
* feat: add OpenRouter as 8th provider
* feat: add native Anthropic provider with Messages API translation
* fix: correct Anthropic base URL and drop top_p (conflicts with temperature)
* feat: add DeepSeek provider (deepseek-chat, deepseek-reasoner)
* feat: rename google -> gemini, refresh model list to 2.5 series
* feat: remove together, fireworks, perplexity providers
* feat: multimodal image support for external providers
- Add _build_external_messages() that preserves image_url parts for
vision-capable providers instead of stripping them
- Update _proxy_to_external_provider() to use new helper
- Translate image_url content parts to Anthropic native image format
in _stream_anthropic()
- Add TestVisionInference pytest class (1x1 PNG smoke test)
* test: use sloth photo URL for vision test, add Anthropic remote URL support
* fix: update Mistral model to mistral-small-2506
* update mistral default model to mistral-large-2512
* fix gemini vision test: download image as base64 data URI instead of remote URL
* add gemini-3-flash-preview as default gemini model
* fix gemini truncated reply (max_tokens 16->64) and suppress GeneratorExit on client disconnect
* increase vision test max_tokens to 215
* fix GeneratorExit: aclose stream generator before closing httpx client
* fix httpcore GeneratorExit: explicitly aclose aiter_lines before response closes
* fix duplicate [DONE] and suppress httpcore RuntimeError on Python 3.13 asyncgen cleanup
* fix: call response.aclose() before lines_gen.aclose() to prevent httpcore RuntimeError on Python 3.13
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* Potential fix for code scanning alert no. 36: Clear-text logging of sensitive information
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* review: add comments for manual iteration rationale, mask password in test print, clarify Anthropic URL/models support
* perf: use shared module-level httpx client for connection pooling across requests
* studio: add API provider UI and integrate wiring (#4737)
* feat: expose external models in selector and chat settings
* feat(chat): wire external providers to backend + RSA key flow
- Fetch registry/configs; create/update/delete saved providers
- Encrypt API keys (Web Crypto RSA-OAEP) for test/models/chat
- External model selection + chat payload (provider_id/type, external_model, encrypted key, optional base URL)
- Local storage for keys + provider list; small UX/copy and guardrails
* add missing providers-api.ts file by Imagineer99
* fix: address PR review comments — system prompt visibility, retry loop, test logging
* feat(studio): encrypt external provider API keys at rest in localStorage
API keys for external providers (OpenAI, Mistral, etc.) were stored as
plaintext in localStorage, vulnerable to browser extensions and XSS.
Add password-derived AES-256-GCM encryption: on login the user's password
is used via PBKDF2 (100k iterations, SHA-256) to derive an in-memory
encryption key. API keys are encrypted before writing to localStorage and
decrypted on read. The derived key is never persisted — cleared on logout,
re-derived on next login.
Legacy plaintext keys are transparently migrated on first access. Password
changes re-encrypt all stored keys. No backend changes required — the
existing RSA-OAEP transit encryption is unaffected.
* fix: cast PBKDF2 salt to BufferSource for strict TypeScript lib types
* fix: persist session password in sessionStorage to survive page refreshes
* feat(studio): preserve image parts in external provider chat requests
toOpenAIMessage() now returns multimodal content arrays (OpenAI vision
format) when messages contain images, instead of always flattening to
plain text. This enables vision-capable external providers (OpenAI,
Gemini, Anthropic, etc.) to receive user images. The backend already
handles image_url content parts in _build_external_messages().
* studio: fix external models selectable in chat-only mode (#4779)
* fix: external models selectable in chat-only mode
* fix: model selector tabs default to active model kind
* Studio: API external provider registry + curated catalogs (HF/OpenRouter) and chat UX (#4787)
* fix: external models selectable in chat-only mode
* fix: model selector tabs default to active model kind
* feat(studio): expand provider registry, curated catalogs, and chat UX
- Add Hugging Face, Kimi, Qwen; remove Cohere; reorder registry
- model_list_mode curated for HF/OpenRouter; lightweight /models check
- API returns default models for curated providers; expose model_list_mode
- Frontend: provider logos in model picker, providerType on external models
- Chat providers dialog: curated vs remote flows, motion polish
- Thread: LayoutGroup + composer motion alignment with app easing
* fix(studio): disable Anthropic tool-calling flag and preselect curated defaults
* feat(studio): add external provider logos and ApiProviderLogo helper
* Studio: Polish API Providers dialog (#4899)
* fix: lower verbage in API providers page
* fix: fix(studio): tune API Providers dialog width with rem-based responsive caps
* feat: add custom provider support (#4902)
* fix: replace crypto.subtle with node-forge for HTTP compatibility
crypto.subtle is only available in secure contexts (HTTPS/localhost),
which breaks provider API key encryption when Studio is accessed over
plain HTTP on remote GPU VMs. Switch to node-forge for RSA-OAEP and
AES-256-GCM operations — same algorithms, works on any origin.
* fix: store provider API keys as plaintext in localStorage
Drop AES-256-GCM at-rest encryption for provider API keys. The
session-password-derived encryption broke on auto-login via refresh
token (password never captured), causing keys to silently vanish.
API keys are still RSA-encrypted in transit via node-forge. At-rest
encryption in localStorage added no real security since the
decryption key also had to live client-side.
Removes crypto-storage.ts, session password plumbing, and
reEncryptAllKeys.
* fix: use max_completion_tokens for OpenAI provider
Newer OpenAI models (gpt-4o, gpt-5.x) reject the max_tokens param
and require max_completion_tokens instead. Other providers still use
max_tokens.
* fix: skip empty assistant messages in external provider requests
Some providers (Mistral) reject assistant messages with empty content.
Filter them out when building the message list for external providers.
* Update model-selector.tsx
* Update model-selector.tsx
* Update model-selector.tsx
* Update chat-adapter.ts
* Update chat-adapter.ts
* Update chat-page.tsx
* Update chat-settings-sheet.tsx
* Update chat-settings-sheet.tsx
* Update chat-settings-sheet.tsx
* Update chat-providers-dialog.tsx
* feat: polish providers settings form UI
* style: polish provider row icon sizing and alignment
* style: stabilize provider layout
* style: add provider API key visibility toggle
* fix: add provider render on empty list
* studio/frontend: sync package-lock.json with package.json
npm ci was failing because node-forge and @types/node-forge were
declared in package.json but missing from the lockfile. Ran
npm install to regenerate.
* studio/backend: fix backend CI failures for providers router
- test_desktop_auth: include providers_router in the routes stub so
studio.backend.main imports cleanly under the monkeypatched module
- test_providers_api: skip the whole module when STUDIO_TEST_PASSWORD
is unset (it is an integration test against a live Studio server,
same shape as the already-ignored test_studio_api.py)
* studio/chat: drive ChatSettingsPanel from a per-provider capability map
Replace the binary isExternalModel toggle in the sampling section with a
provider-aware capability map. Each external provider type advertises
which of top_k / min_p / repetition_penalty / presence_penalty its
chat-completions API actually accepts, so the panel only renders the
knobs that map onto the active provider's request body.
Anthropic now exposes top_k; DeepSeek hides presence_penalty (deprecated
in their docs); OpenRouter and custom providers continue to show every
knob (OpenRouter drops unsupported server-side, custom assumes
OpenAI-compat or a permissive vLLM/Ollama backend). Local models are
unaffected — null capabilities means 'show everything'.
chat-adapter.ts now forwards top_k / presence_penalty to the external
proxy only when the active provider's capabilities permit it, so the
request body matches what the UI shows.
* studio/backend: forward top_k to Anthropic; filter OpenAI model list
Two paired changes so the frontend capability map has matching backend
behaviour:
1. ExternalProviderClient.stream_chat_completion now accepts top_k and
forwards it to the Anthropic Messages body. OpenAI-compat providers
(which all reject unknown sampling params) still receive only the
fields they document. The proxy route in routes/inference.py passes
payload.top_k through, so a UI request with top_k actually reaches
Anthropic instead of being silently dropped at the boundary.
2. PROVIDER_REGISTRY['openai'] gains a model_id_allowlist regex that
scopes the /models picker to current-gen ids (gpt-5.5 / gpt-5.4 /
gpt-5.3 / gpt-4.5 / o3 families). The remote /v1/models listing
otherwise returns dozens of historical snapshots, fine-tunes and
non-chat models (embeddings, TTS, image, moderation) that we never
want in the chat UI. default_models is refreshed to match.
* studio/chat: relax presence_penalty to optional on OpenAIChatCompletionsRequest
Followup to 1fbf445a — chat-adapter now omits presence_penalty for
providers that do not accept it (Anthropic / DeepSeek), but the
request type still required it as a non-optional number, breaking
tsc. The backend pydantic model already defaults presence_penalty
to 0, so making it optional client-side matches reality.
* studio/backend: route OpenAI traffic through /v1/responses
OpenAI's new flagship models (gpt-5.x) return 404 'This is not a chat
model' on /v1/chat/completions and are only reachable via /v1/responses.
Add a dedicated _stream_openai_responses path in ExternalProviderClient
that:
- Translates outbound messages into the Responses shape: system messages
are folded into the top-level 'instructions' field, user/assistant
messages become {role, content} items with input_text / input_image
content parts (data URLs and https URLs both pass through).
- Drops presence_penalty / top_k / frequency_penalty, none of which the
Responses contract accepts.
- Translates inbound SSE events back into OpenAI Chat Completions
chunks so the frontend keeps a single SSE shape:
response.output_text.delta -> delta chunk with content
response.completed -> chunk with finish_reason='stop'
response.incomplete -> chunk with finish_reason='length'
response.failed / error -> propagated error SSE line
Stream terminates with data: [DONE] (Responses emits this verbatim).
stream_chat_completion dispatches all provider_type='openai' calls to
this path; other OpenAI-compatible providers (mistral, gemini, etc.)
continue to use /v1/chat/completions.
Frontend provider-capabilities map updated to hide presence_penalty for
OpenAI in the chat settings panel, matching the new request contract.
Includes unit coverage in tests/test_openai_responses_translation.py
exercising the request body translation, image-part rewriting, and
SSE-to-chat-completions translation via httpx.MockTransport.
* studio/chat: clamp external max_tokens to 32k to stay within provider caps
The chat settings slider already capped maxTokens at 32768 for external
models, but a value persisted from a prior local-model session (where
the cap can be 128k+) was sent verbatim to the provider — Claude Opus
returns 'max_tokens: 131072 > 128000' on requests like that, and other
providers have stricter limits still.
Expose EXTERNAL_MAX_OUTPUT_TOKENS from provider-capabilities (32k) and
use it both for the slider max and as the clamp inside chat-adapter's
external-request body. 32k sits below the tightest declared output
limit across the providers we ship and well above what a typical chat
reply needs; the local-model path is unaffected.
* studio: drop temperature/top_p for OpenAI reasoning models
gpt-5.x / o3 / gpt-4.5 are reasoning-class models served via
/v1/responses, and reject temperature and top_p with
'Unsupported parameter' 400s. The OpenAI registry allowlist already
scopes the picker to those families, so neither knob ever applies on
this branch.
- external_provider._stream_openai_responses no longer puts
temperature or top_p in the request body (kept on the method
signature for API symmetry with the other stream methods).
- ProviderCapabilities gains temperature/topP flags; OpenAI sets both
to false. ChatSettingsPanel hides the sliders for OpenAI so the user
does not see inert controls.
- chat-adapter omits temperature/top_p from the external request body
when the active provider does not advertise them.
- OpenAIChatCompletionsRequest type marks both as optional, matching
the new chat-adapter shape.
- test_responses_request_body_uses_input_and_instructions: assertions
flipped to confirm temperature / top_p are absent from the body.
* studio: stop forwarding top_k to Anthropic
Claude 4.x (Opus / Sonnet / Haiku 4.x) returns 400 'top_k is
deprecated for this model' on any request that includes top_k. It
was always optional on the older 3.x line, so dropping it
unconditionally for every Anthropic call is the simplest path —
no per-model gate to maintain.
- external_provider._stream_anthropic no longer adds top_k to the
Messages body (kept on the method signature for API symmetry).
- provider-capabilities sets anthropic.topK = false so the chat
settings panel hides the Top K slider for Anthropic providers
and chat-adapter does not send top_k in the external request.
* studio: gate Anthropic top_k drop to Claude 4.7 only
Previous commit (b5aa6ffd) dropped top_k for every Anthropic call,
but only Claude 4.7 (Opus/Sonnet/Haiku) actually rejects it. 4.6, 4.5,
and the 3.x line still accept top_k and use it as documented.
Backend: _stream_anthropic matches the model id against
^claude-(opus|sonnet|haiku)-4-7(-|.|$) and only strips top_k when it
hits. Every other Claude generation continues to receive the value
from the chat settings panel.
Frontend: anthropic.topK is restored to true so the Top K slider is
visible again — the backend handles the per-model drop, and the
4.7 case is silent (request still succeeds without top_k).
* chore: hide dated openai models in provider select
* studio/providers: apply model_id_denylist when listing remote models
The OpenAI registry entry gained a model_id_denylist regex matching
dated snapshot ids (-YYYY-MM-DD) in 048d73bf, but the list-models
route was never consulting it, so the snapshots still showed up
alongside their canonical ids (gpt-5.5 and gpt-5.5-2026-04-23 both
listed). Apply the denylist with .search() right after the allowlist
filter so dated entries are dropped before the response is built.
* studio/chat: seed registry default_models for remote providers in picker
The Anthropic provider runs in remote model-list mode, so the picker
started with an empty availableModels until the user clicked
'Load Models'. If that /api/providers/models call fails (e.g. the
known transient decryption error during key rotation), the user sees
no models at all — claude-haiku-4-5 in particular was missing from
the dialog even though it is seeded in the registry.
Always pre-populate availableModels with the registry's default_models
when a provider type is selected (curated and remote alike), and have
loadModels() return the union of defaults + the live /models response
so registry-seeded ids are reachable regardless of what the provider's
endpoint returns or whether the call succeeds at all.
* studio/backend: diagnostic logging on provider key decryption
Decryption failures currently log just 'Failed to decrypt API key:
Decryption failed', which leaves no way to tell whether the cause is
a stale public key in the browser, a corrupted ciphertext, an
unexpected exception class, or a server-side keypair rotation. That's
the gap the next reproduction needs to close.
- key_exchange now publishes a short SHA256 fingerprint of the public
key PEM. init_key_pair logs the fingerprint on generation and warns
if it is ever called a second time (re-init silently invalidates
every browser that cached the previous public key).
- decrypt_api_key wraps both the base64 decode and the RSA decrypt
in dedicated try/excepts that log exception type, ciphertext byte
length (RSA-2048 should be exactly 256), input string length, and
the current public-key fingerprint.
- GET /api/providers/public-key returns the fingerprint alongside the
PEM so the frontend can correlate a future encrypt-time fingerprint
against the decrypt-time fingerprint and prove or rule out a
keypair rotation as the cause.
- The /test and /models route-level decrypt warnings now include the
exception class name (alongside the existing message).
* studio/providers: hide dated Anthropic snapshots from the model picker
Anthropic's /v1/models returns dated snapshot ids (e.g.
claude-3-5-sonnet-20241022, claude-3-5-haiku-20241022) alongside
the canonical names users actually want to pick. Same intent as
the OpenAI denylist added in 048d73bf, just a different date
format — Anthropic uses -YYYYMMDD (no dashes) while OpenAI uses
-YYYY-MM-DD.
- Add model_id_denylist = re.compile(r'-\d{8}$') to the anthropic
registry entry. The /api/providers/models route already applies
any denylist after fetching, so dated ids drop out automatically.
- Strip the dated 3.5 ids from default_models so the seeded picker
no longer surfaces them; keep claude-opus-4-7 and the 4.5 family
as the curated set.
Net effect: the picker shows opus-4-7 / opus-4-5 / sonnet-4-5 /
haiku-4-5 only, regardless of whether the remote /models call
succeeds or fails.
* fix: provider dialog and mistral short list
* style: fix provider dialog curated list styling
* fix: provider dialog curated model ids placeholder reference
* style: rename Providers to Cloud and tighten dialog header spacing
* UX: rename Providers to Cloud, remove header shortcut
* studio/chat: normalize structured delta.content from reasoning providers
Mistral's magistral (and similarly-shaped reasoning models) stream
chat-completion deltas where choices[0].delta.content is an array of
structured parts rather than a plain string, e.g.
[{ type: 'text', text: '...' }, { type: 'thinking', thinking: '...' }]
The accumulator did 'cumulativeText += delta', which coerced each
part to '[object Object]' and produced output like
'[object Object][object Object]...Hey there!'.
Add extractDeltaText() to normalize delta.content before append:
- string → returned as-is
- array of parts → text/output_text parts contribute their .text or
.content; thinking/reasoning parts are re-wrapped inline as
<think>...</think> so the downstream parseAssistantContent lifts
them into a reasoning part the same way it does for providers that
emit thinking inline. magistral keeps its thinking panel; no other
provider's output shape changes.
- unknown shapes → dropped rather than stringified, so a stray field
cannot pollute the rendered chat with '[object Object]'.
* Studio: restore Cloud icon shortcut in chat header
Brings back the header chip that opens Settings -> Cloud (external
providers) directly from the chat view. Same button as before the
bf24e604 removal: single-mode only, opens useSettingsDialogStore on
the 'connections' tab, tooltip 'API providers'.
* studio/chat: strip trailing template literal from external provider streams
Mistral's magistral occasionally appends a literal '${response}' token
after its actual answer — likely a training-format artifact, since it
keeps happening with an empty system prompt and only on that model.
Apply a tight strip in the chat-adapter SSE accumulator: when the
active provider is external, drop a trailing '${...}' template literal
(with optional whitespace) from cumulativeText after each chunk. The
regex anchors to end-of-string, so mid-stream fragments ('${re')
remain untouched and only collapse once the closing brace arrives.
Local-model output is unaffected.
* studio/providers: scope Kimi picker to kimi-k2.6 / kimi-k2.5
Mirror what the live Kimi docs surface as the current models
(https://platform.kimi.ai/docs/models). Everything else the
remote /v1/models call returns — moonshot-v1-* legacy ids and
dated k2 previews like kimi-k2-0711-preview — is filtered out.
- default_models: ['kimi-k2.6', 'kimi-k2.5'] (was four
legacy moonshot-v1 ids plus the dated k2 preview)
- model_id_allowlist: ^kimi-k2\.[56]$ applied in the
/api/providers/models route after the live fetch
- doc-link comments point at platform.kimi.ai overview /
models / list-models for the next refresh
* studio: drop temperature/top_p for Kimi reasoning models
Kimi k2.5/k2.6 are reasoning-class. The API locks temperature and
top_p to fixed defaults and 400s on any other value with
'invalid temperature: only 1 is allowed for this model'.
The frontend capability map already gated these knobs out of the
external request body, but the OpenAI-compat path on the backend
unconditionally re-adds them from the pydantic ChatCompletionRequest
defaults (temperature=0.7 etc), so the gate was bypassed end-to-end.
Add a generic body_omit hook on the provider registry that
stream_chat_completion consults after building the body, and use it
to strip temperature/top_p for Kimi. Frontend provider-capabilities
flips kimi.temperature and kimi.topP to false so the sliders are
hidden in the chat settings panel as well.
* studio/providers: scope Gemini picker to current 3.x + *-latest aliases
Google's /v1beta/openai/models returns dozens of historical,
experimental, and non-chat ids that we never want in the chat UI.
Cap the picker to the current curated set:
- gemini-3.1-pro-preview
- gemini-3.1-flash-lite
- gemini-3-flash-preview
- gemini-pro-latest
- gemini-flash-latest
- gemini-flash-lite-latest
Default_models seeded with these, model_id_allowlist applied in
the /api/providers/models route to drop anything else the live
fetch returns.
* studio/providers: switch Hugging Face to remote model listing
Per the Inference Providers docs
(https://huggingface.co/docs/inference-providers/index),
GET https://router.huggingface.co/v1/models returns the full
chat-model catalog across all providers, including per-provider
metadata. The OpenAI-compatible endpoint we already use for
chat completions accepts the same Bearer token, so flipping
model_list_mode from 'curated' to 'remote' lets users discover
models via the existing list_models() path without any new
wiring.
- model_list_mode: 'remote' (was 'curated')
- default_models refreshed with current popular ids
(gpt-oss-120b, DeepSeek-V3, Llama-3.3-70B, Qwen2.5-72B) so the
picker still has a sensible seed if /v1/models fails
- notes updated to reference the docs page and clarify the
endpoint is chat-only
* UX: chat cloud icon changed to model select signifier
* studio/providers: org allowlist + count cap for HF Inference picker
The HF /v1/models response is the full cross-provider catalog (hundreds
of ids — community fine-tunes, mirrors, fp8 variants, dated snapshots).
Scope the picker to the first-party org repos worth surfacing and cap
the post-filter list.
- model_id_allowlist matches the org prefixes openai/, deepseek-ai/,
google/, meta-llama/, Qwen/, moonshotai/, mistralai/, zai-org/.
Anything outside those orgs is dropped.
- model_id_limit (new registry field) caps the post-filter list. The
list-models route now slices [:limit] after allowlist/denylist; set
to 15 for HF Inference. Other providers leave it unset and behave
exactly as before.
- default_models stays as the seed so the flagship ids users care
about (gpt-oss-120b, DeepSeek-V3, Llama-3.3-70B, Qwen2.5-72B) are
always reachable regardless of the API's response order.
Dedup is already handled in loadModels() via Set, so no additional
work needed there.
* style: adjust cloud icon right margin with rem spacing
* Studio: cloud openai reasoning level toggle (#5402)
* feat: cloud openai reasoning level toggle
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* fix: honor enable_thinking=false
* fix: prevent local reasoning toggle regressions and align OpenAI effort levels
* fix: isolate external OpenAI reasoning toggle state
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* fix: clamp reasoning effort
* fix: align OpenAI reasoning effort
* fix: clear stale GGUF badge state
* ui: new badge on cloud setting
* fix: separate selected models from cached provider model list
* Studio: anthropic effort by model family (#5412)
* feat: external thinking control and Anthropic effort mapping
* fix: anthropic thinking constraints and 4.6 max effort mapping
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* fix: harden Anthropic thinking params and effort mapping
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* studio/backend: drop top_p from Anthropic body when thinking is enabled
PR 5412 added body['top_p'] = max(0.95, min(top_p, 1.0)) inside the
thinking branch of _stream_anthropic, but Anthropic returns 400 on
extended/adaptive thinking when both temperature and top_p are set:
invalid_request_error: temperature and top_p cannot both be
specified for this model. Please use only one.
(Observed on Claude Opus 4.6.) The contract for thinking-enabled
requests is temperature=1 with neither top_p nor top_k allowed.
Replace the body['top_p'] = ... line with body.pop('top_p', None).
Defensive pop rather than a bare delete: the base body construction
above does not currently set top_p, but a future edit that adds it
would silently reintroduce the regression.
* studio/chat: force reasoningEnabled=true on local reasoning-effort models
Followup to PR 5402 / 5412. The model-status refresh path in
use-chat-model-runtime carried reasoningEnabled forward verbatim for
every reasoning-capable model. That left one observable edge case:
1. user picks an external model that supports Off (gpt-5.x, Claude
4.x), clicks Off — store sets reasoningEnabled=false
2. user switches back to a local reasoning-effort model
(gpt-oss / Harmony-style) which does NOT support Off
3. composer's effectiveReasoningEnabled override paints the UI as
'Think: <level>' (on)
4. chat-adapter sees reasoningEnabled=false on the local branch
and sends '{}', so the backend's _request_reasoning_kwargs
returns None and the Harmony template falls back to its own
default effort instead of the displayed level
Mirror the composer's override in the store on load: for local
reasoning-effort models (where supportsReasoningOff is false), force
reasoningEnabled=true so the store and the UI agree on every send.
Other reasoning styles still inherit prior state — only the
reasoning-effort family changes.
* studio/backend: align Anthropic thinking with the extended-thinking docs
Two compliance fixes against
https://platform.claude.com/docs/en/build-with-claude/extended-thinking
1. Adaptive-mode effort field shape
The docs spell adaptive thinking as:
{'thinking': {'type': 'adaptive'}, 'effort': {'type': '<level>'}}
We had been sending the legacy 'output_config: {effort: <level>}'
shape, which Anthropic appears to silently ignore — adaptive ran
at the server default effort regardless of the user's selection.
Rename to 'effort: {type: <level>}'.
2. thinking_delta event translation
The Messages-API streams reasoning content as
content_block_delta events with delta.type == 'thinking_delta',
which our SSE loop was dropping entirely. On Claude 4.5/4.6 with
display=summarized (the default), the user would see the answer
text but never the reasoning panel. Wrap thinking_delta.thinking
as inline <think>...</think> chunks (same pattern as the OpenAI
Responses path) so the frontend's parseAssistantContent lifts it
into the reasoning channel. The </think> closer fires on the
first text_delta transition, on content_block_stop for the
thinking block, on message_delta, and on message_stop —
whichever arrives first — so no model path can leak an
unclosed <think> into chat output.
signature_delta events are left as no-ops; they carry
verification metadata, not user-visible content.
Adds test_anthropic_thinking_translation.py with httpx.MockTransport
coverage of: effort shape on adaptive (Claude 4.6), budget_tokens
shape on manual (Claude 4.5), thinking_delta wrapping with signature
suppression, and thinking-only turns (display=omitted on Opus 4.7).
* studio/backend: revert Anthropic adaptive effort to output_config nesting
The previous commit (0a664df4) moved the adaptive-thinking effort
field to a top-level 'effort: {type: <level>}' based on a misread of
the docs page. The actual Messages API schema nests it under
output_config:
thinking: optional ThinkingConfigParam ({type: 'adaptive'})
output_config: optional OutputConfig
effort: optional 'low' | 'medium' | 'high' | 'xhigh' | 'max'
Sending the top-level field produced:
400 invalid_request_error: effort: Extra inputs are not permitted
Restore the body to:
body['thinking'] = {'type': 'adaptive'}
body['output_config'] = {'effort': effort}
This was the shape PR 5412 originally shipped (and the author
validated against live APIs). My 'compliance fix' was a regression.
The companion thinking_delta SSE translation added in 0a664df4 stays
— that part WAS missing from the previous shape and is unchanged
by this revert. Test pinning the body shape flipped to assert
output_config.effort, top-level effort is asserted absent.
* studio/backend: opt in to summarized thinking display on adaptive
Per the adaptive-thinking docs, the 'display' field on the thinking
config defaults to 'omitted' on Claude Opus 4.7 (and Mythos Preview).
With 'omitted' the API still emits a thinking content block, but its
'thinking' field is empty — only the signature_delta arrives.
Our SSE handler would then surface a stray '<think></think>' for the
empty block and the reasoning panel would stay blank for the entire
response. Set 'display': 'summarized' explicitly on the adaptive
thinking config so Opus 4.7 emits thinking_delta events the same way
Opus 4.6 / Sonnet 4.6 do (where 'summarized' is the default, making
the explicit setting a no-op there).
The manual-thinking branch (Claude 4.5) is unaffected — its default
is also 'summarized', and we have no reason to override it.
* studio/backend: log Anthropic SSE event counts for thinking diagnostics
Reports of 'no reasoning panel content on Anthropic' have two
distinct causes that produce the same symptom:
1. Anthropic streamed thinking_delta events but our frontend
dropped them somewhere on the rendering side.
2. Anthropic did not emit thinking_delta at all (adaptive mode
can skip thinking for simple prompts even with effort=high,
and display=summarized only re-enables the *content* — it
does not force thinking to happen).
Tally each event type for the duration of one stream and log the
counts in the finally branch, so the next 'no reasoning content'
report shows immediately whether thinking_delta was even on the
wire. Zero counts → upstream (model/effort/prompt choice).
Non-zero counts → triage moves to chat-adapter / parse-assistant
-content / the reasoning component.
* studio/backend: route external_provider logs through structlog
The studio backend wires structlog as the active logger (via
LogConfig.setup_logging at main.py:262), but external_provider.py
was using stdlib logging.getLogger(__name__) for every diagnostic.
The stdlib root logger defaults to WARNING with no handlers
attached, so plain logger.info('...') and logger.debug('...') from
this module were being silently dropped — including the
'Proxying chat completion to <url>' and the new
'Anthropic stream event counts' lines. Only WARNING/ERROR survived
(via the implicit fallthrough that the user actually observed
when an Anthropic call 400'd).
Switch the module-level logger to structlog.get_logger(__name__),
matching the routes/providers.py and routes/inference.py pattern.
All existing call sites use printf-style positional args, which
structlog accepts unchanged — no other edits needed.
* studio/backend: disable read timeout on SSE streams to external providers
Anthropic Opus 4.7 (adaptive thinking) and OpenAI gpt-5.x (/v1/responses)
can pause for tens of seconds between bytes while the model is
internally reasoning. httpx's read timeout is the *gap* between
successive reads, not a wall clock on the whole request — so the
shared 120s default was cutting streams mid-response:
log: Anthropic stream event counts (... text_delta: 11)
Read timeout from anthropic
(eleven text deltas in, no content_block_stop, no message_stop)
Add a separate _stream_timeout on ExternalProviderClient with
read = None (no gap timeout) and the same 10s / 120s connect/write/
pool bounds, then use it at the three SSE streaming call sites:
default OpenAI-compat chat completions, _stream_anthropic, and
_stream_openai_responses. Non-streaming call sites (chat_completion,
list_models, verify_models_endpoint_lightweight) keep self._timeout
because a stuck non-streaming response should still fail fast.
* studio/backend: log outbound Anthropic request shape for thinking debug
After bumping to Xhigh effort the user still saw zero thinking_delta
events and only one content_block_start, meaning Anthropic Opus 4.7
opened no thinking block at all. Per the effort docs that should be
impossible — Xhigh always thinks. Two open hypotheses:
1. Our adaptive branch is not wiring output_config.effort onto the
outbound body for this code path (regex miss, frontend never
propagated reasoning_effort, etc).
2. Anthropic is silently accepting output_config as an unknown
field and falling back to high default effort regardless.
Add a single-line structlog INFO right before the stream POST that
echoes the keys actually present on the body (thinking, output_config,
temperature, presence of top_p / top_k, max_tokens). Messages are
deliberately excluded to keep PII out of the log. With this in place
the next 'no thinking on 4.7 at Xhigh' report shows immediately
whether we sent the effort knob — separating client bug from
provider behaviour.
* studio/chat: surface delta.reasoning_content from Kimi / DeepSeek thinking
Kimi (kimi-k2.6, kimi-k2-thinking) and DeepSeek's reasoner stream
their thinking content via a separate top-level field on the
chat-completion delta — choices[0].delta.reasoning_content — rather
than as a structured part inside delta.content. Per Kimi docs:
In streaming output (stream=True), the reasoning_content field
will always appear before the content field.
Our chat-adapter SSE loop only read delta.content (via
extractDeltaText), so the entire reasoning channel from these
providers was being silently dropped — kimi-k2.6 thinks by default
yet the chat UI showed no reasoning panel.
In the adapter:
- Read both delta.content and delta.reasoning_content per chunk
- When reasoning_content arrives, open a <think> block in
cumulativeText (mirrors how the backend wraps Anthropic
thinking_delta and OpenAI Responses reasoning summaries)
- When content arrives after reasoning, close </think> first
- On stream end, force-close any still-open <think> so
parseAssistantContent can lift it into a reasoning part cleanly
Anthropic and OpenAI Responses paths are unaffected — they already
wrap as <think> on the backend and never set reasoning_content.
* studio: Kimi thinking toggle + 16k max_tokens floor
Two coordinated changes so Kimi's thinking is user-controllable and
the response budget meets the docs' floor.
Toggle (frontend + backend):
- getExternalReasoningCapabilities now handles provider=='kimi':
kimi-k2.6 -> reasoning_style=enable_thinking, reasoningOff allowed
kimi-k2-thinking -> always on (reasoningAlwaysOn=true, no off)
kimi-k2.5 (and anything else) -> no reasoning controls
- chat-adapter already forwards enable_thinking on the
enable_thinking-style branch, so the user toggle reaches the
backend without additional wiring there.
- external_provider stream_chat_completion now translates the
boolean into Kimi's wire shape on the default OAI-compat path:
enable_thinking=True -> body['thinking'] = {type: enabled, keep: all}
enable_thinking=False -> body['thinking'] = {type: disabled}
kimi-k2-thinking ignores the toggle so the API never gets a
disabled value it would reject. Other providers on the same
path are unaffected (gated on provider_type == 'kimi').
Max tokens floor:
- New EXTERNAL_MIN_OUTPUT_TOKENS_BY_PROVIDER table and
getExternalMinOutputTokens helper. Kimi entry = 16000 per docs:
'Set max_tokens >= 16,000 to ensure the full reasoning_content
and final content can be returned without truncation.'
- chat-adapter clamps the outbound max_tokens to
min(max(stored, providerMin), EXTERNAL_MAX_OUTPUT_TOKENS),
so a stored value of 4096 still becomes 16000 when sending to
Kimi (other providers unaffected, min stays effectively 64).
- chat-settings-sheet's Max Tokens slider min mirrors the same
floor when an external Kimi model is selected, so the slider
cannot show a value lower than what we'd actually send.
- chat-page threads activeExternalProviderType down to the panel.
* fix: stabilize external reasoning controls for Anthropic 4.6 and OpenAI o3
normalize Anthropic 4.6 reasoning effort handling by accepting max as an alias and mapping it to xhigh, while keeping Sonnet/Opus 4.6 in default model suggestions.
broaden reasoning effort typing across backend/frontend and migrate persisted max selections to xhigh for compatibility.
remove reasoning.summary=\"auto\" from OpenAI /v1/responses payloads to avoid o3 eligibility/gating errors.
tighten provider model filtering to hide retired gpt-5.3 IDs and add exact/prefix filtering support in provider routes.
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* studio: add openrouter/free + full reasoning passthrough on OpenRouter
Four-layer wire-up so the OpenRouter free-router model (which picks
a free model at random per request, filtered by needed capabilities)
shows up in the picker and its reasoning channel surfaces in the
chat UI.
Registry:
- providers.py: openrouter/free seeded at the top of openrouter
default_models. Curated list, so picker shows it immediately.
Frontend capability map:
- provider-capabilities.ts: getExternalReasoningCapabilities now
treats openrouter as enable_thinking style with off support. The
Think dropdown appears for every OpenRouter model; the gateway
silently no-ops the parameter for models that do not reason, so
surfacing one toggle on every model is safe.
Backend reasoning passthrough:
- external_provider.py stream_chat_completion (default OAI-compat
branch): for provider_type=='openrouter', translate the request:
reasoning_effort in {low,medium,high} -> body['reasoning'] =
{'effort': <level>}
enable_thinking=True -> body['reasoning'] = {'enabled': True}
enable_thinking=False -> body['reasoning'] = {'enabled': False}
Matches the documented shape at
https://openrouter.ai/docs/guides/best-practices/reasoning-tokens
with effort and max_tokens mutually exclusive.
Frontend SSE reader:
- chat-adapter.ts: OpenRouter streams reasoning as a third shape we
did not handle yet: delta.reasoning_details is an array of parts
like {type: 'reasoning.text', text: '...'}. Pull text from every
part, merge with the existing delta.reasoning_content channel
used by Kimi/DeepSeek, and feed the combined string through the
same <think>...</think> wrap path so parseAssistantContent lifts
it into the reasoning panel. Anthropic/OpenAI Responses paths
already wrap on the backend, so they never set this field — no
cross-provider interference.
* studio/backend: surface OpenRouter SSE errors and router-chosen model in logs
The frontend showed 'Provider returned error' for some openrouter/free
requests with nothing on the backend side to triage from — the
existing 4xx error log only fires when the upstream returns a non-200
status code, but OpenRouter (and most OAI-compat providers) return
200 OK and emit the actual failure as an SSE error event mid-stream,
which our default-path stream loop forwarded verbatim without
logging.
Best-effort diagnostics on the default OpenAI-compat stream path:
- Peek at every `data:` line in the inner forward loop, parse JSON
best-effort (silently skip on failure so nothing is dropped).
- Count event types: delta / error / done.
- On any chunk containing an `error` field, emit a structlog WARNING
with the provider type and the error payload — same trail the
user would otherwise have to dig out of browser devtools.
- Latch the first non-empty `chunk.model` field. OpenRouter reports
the router-picked underlying model there per request, so the
finally-block summary log shows which free model handled the call.
In the finally block:
'openrouter stream complete (model=openrouter/free,
chosen=google/gemini-2.5-flash, events={delta: 47, done: 1})'
Zero overhead for non-error streams (a json.loads per chunk +
dict-key lookups). The structlog logger is already configured at
INFO; ERROR and WARNING surface in JSON logs without further setup.
Hoists `import json as _json` to module top so the default path can
reuse it; the existing in-function imports in _stream_anthropic and
_stream_openai_responses are now redundant but harmless.
* studio/chat: show router-picked model after 'openrouter/free:' in chip
When the user picks openrouter/free, the gateway routes each request
to a different underlying free model. Until now there was no way to
tell which one actually replied without reading the backend logs.
Surface the picked model in the active-model chip:
- chat-runtime-store gains lastOpenRouterChosenModel: string|null
plus a setter. Reset on every model switch unless the user stays
on openrouter/free.
- chat-adapter SSE loop latches chunk.model into the store on
every chunk whose top-level model differs from
openrouter/free, gated on the active checkpoint being
openrouter/free under an OpenRouter provider.
- chat-page externalModels useMemo appends :<chosen> to the display
name for the openrouter/free option when the store has a value,
so ModelSelector renders e.g.
'openrouter/free:google/gemini-2.5-flash'
in the chip. Other models unaffected.
- Model-switch callback in chat-page clears the cached value when
the user moves to any model other than openrouter/free, so the
chip never shows a stale suffix from a previous session.
* studio/chat: shorten openrouter/free chip to openrouter:<short-chosen>
The full display name in use was:
openrouter/free:inclusionai/ring-2.6-1t-20260508:free
The `:free` suffix on the underlying id already conveys 'free model',
which made the leading `/free` on the router id redundant, and the
`inclusionai/` org prefix was just noise crowding the chip.
Trim both. Now the chip renders as:
openrouter:ring-2.6-1t-20260508:free
Strictly a display change in chat-page externalModels useMemo — the
backend wire id stays `openrouter/free`, the runtime store still
caches the full `inclusionai/...:free` value, and the model-switch
clearing logic is unchanged.
* studio/providers: switch OpenRouter to remote listing with org allowlist + cap
Same shape as Hugging Face Inference. The curated list had only four
entries; remote listing fetches OpenRouter's full ~300-model
catalog via /v1/models and the new allowlist + limit scope it back
down to a usable picker.
- model_list_mode: remote (was curated)
- model_id_allowlist matches the prefixes:
openrouter | openai | anthropic | google | meta-llama | qwen
| mistralai | deepseek | moonshotai | inclusionai | zai-org
| z-ai
Anything outside drops out.
- model_id_limit: 20 — first 20 post-filter matches from the live
fetch; default_models stays seeded so the most useful canonical
ids are always visible regardless of API response order.
- default_models seed extended from 4 to 6 (openrouter/free,
openai/gpt-4o, anthropic/claude-sonnet-4-5, google/gemini-2.5-flash,
mistralai/mistral-large-2411, deepseek/deepseek-r1).
openrouter/free remains the first entry, so the dialog's
loadModels() union-merge (registryDefaults first, then remote,
deduped via Set) keeps it at the top of the picker.
* feat: external mistral thinking toggle
* studio/chat: fix TS2540 by replacing readonly ContentPart instead of mutating
The ContentPart type from @assistant-ui/react marks `text` as readonly,
so the coalesce-adjacent-same-type-part optimization in
parseAssistantContent failed the tsc build with:
parse-assistant-content.ts(15,10): error TS2540: Cannot assign to
'text' because it is a read-only property.
parse-assistant-content.ts(25,10): error TS2540: ...
This broke npm run build, the Studio installer's `building frontend...`
step, and every downstream CI job that runs against an installed
Studio (Mac/Windows/Linux variants of Studio API CI, GGUF CI, UI CI,
Tauri CI, Wheel CI).
Replace the last element with a fresh merged object instead of
mutating its `text` field. Same allocation profile as the previous
path (one object swap per merge), type-safe under the readonly
declaration. Behaviour unchanged.
* studio/backend: restore summary='auto' on OpenAI Responses reasoning body
A recent refactor dropped the `summary: 'auto'` field from the
reasoning config we send to /v1/responses. Without it OpenAI does
not emit reasoning summary events on most reasoning models, which
means our SSE handler has no <think>…</think> to wrap and the chat
reasoning panel stays blank for any gpt-5.x / o3 response.
The expected wire shape is:
body['reasoning'] = {'effort': '<level>', 'summary': 'auto'}
Two backend tests pin this:
- test_responses_reasoning_effort_included_when_requested (high)
- test_responses_reasoning_effort_xhigh_passthrough (xhigh)
Both were failing with AssertionError because the produced body
omitted `summary: auto`.
Restore the field. Skip it only for the explicit "off" case
(effort: 'none'), where summaries serve no purpose. The
enable_thinking=True fallback (no explicit effort) also pairs
medium effort with summary='auto' so that branch produces
reasoning text too.
* chat: external reasoning, OpenRouter curation, Think toggle fixes
* fix: opus and sonnet 4.6 xhigh --> max
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Copilot Autofix powered by AI <62310815+github-advanced-security[bot]@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: imagineer99 <samleejackson0@gmail.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* studio: allow huggingface.co and datasets-server.huggingface.co in CSP connect-src
The security hardening pass (0881a7a5) added connect-src 'self', which
blocked the Training page's direct browser calls to HuggingFace. Model
search (@huggingface/hub listModels/modelInfo/whoAmI -> huggingface.co)
and dataset subset/split discovery (datasets-server.huggingface.co/splits)
both returned nothing as a result.
Extend connect-src to permit the two HF hosts the SPA actually talks to.
No other directive changes; HF tokens still stay client-side.
* studio: format FastAPI 422 detail arrays in training error messages
readError in train-api.ts stringified payload.detail directly. On a 422
the detail is an array of {loc, msg} objects, which JS coerces to
'[object Object],[object Object]' -- the UI showed that instead of the
actual validator message.
Format the array into 'field.path: msg; ...' so the offending field and
the validator's message surface in the UI and toast.
* studio: allow num_epochs/max_steps = 0 sentinel through TrainingStartRequest
The hyperparameter validators added in the security pass rejected 0 for
both num_epochs and max_steps. But Studio's steps-vs-epochs toggle uses
0 as a sentinel: when training by max_steps the frontend sends
num_epochs=0, and when training by epochs it sends max_steps=0. The
trainer expects this and ignores the zeroed field.
Widen both validators to [0, MAX]. They still catch the actual
out-of-range and non-integer inputs they were added for.
* studio: reject TrainingStartRequest when num_epochs and max_steps are both 0
Each field's validator accepts 0 as a "use the other one" sentinel, but
on their own they don't catch the case where both are 0 (or max_steps
is None and num_epochs is 0). That payload would otherwise produce a
no-op training job. Add a model-level validator that rejects it with a
clear 422 message.
* studio: add Optional[int] type hints to _check_max_steps and _check_warmup_steps
Brings these two validators in line with the rest of the TrainingStartRequest
validators in the same file, which all carry explicit cls/v/return hints.
Daniel Han