PR #6364 added a branch that overrode the unsloth owner avatar with the
bundled circle-logo-small.png sticker. Revert it so unsloth uploads use
the live Hugging Face org avatar again, falling back to the colored
initial tile. Upstream re-uploads are unaffected since they still
resolve through provider logos.
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
* Chat: match reasoning thinking icon to the composer bulb
The reasoning "Thinking..." indicator used lucide's LightbulbIcon while
the composer thinking toggle used a custom bulb glyph, so the two did not
match. Move that glyph into lib/bulb-icon.tsx and use it in both places
so they render the same icon.
* Let BulbIcon take and override svg props
---------
Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local>
* Studio: fall back to anonymous HF browsing on a malformed token
The Discover/Recommended feeds call the Hugging Face JS client
(`listModels`/`listDatasets`) directly from the browser. That client
throws `Your access token must start with 'hf_'` when handed a non-empty
token that isn't a well-formed HF token, instead of falling back to
anonymous access. A single bad value left in the HF token field (e.g. a
placeholder someone typed) therefore takes down the entire discovery feed
even though it works fine with no token at all.
Add `hfApiToken()` to the HF token store, which returns the token only
when it looks like a real `hf_...` credential and `undefined` otherwise,
and route hub-page's four HF call sites through it. Malformed tokens now
degrade to anonymous public browsing rather than erroring. The raw token
is still stored and shown in the settings field unchanged.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Studio: trim hfApiToken comments
Collapse the 11-line JSDoc to a 2-line note and drop the redundant
call-site comment in hub-page. AST signature check confirms code is
unchanged (comments only).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
* Studio: treat data-center Blackwell (sm_100/sm_103) as Blackwell in llama.cpp prebuilt selection
_host_is_blackwell gated on _BLACKWELL_MIN_SM = 120, but data-center Blackwell
parts report a lower compute capability than consumer Blackwell: B100/B200 are
sm_100 and B300/GB300 are sm_103, while RTX 50 is sm_120 and DGX Spark is
sm_121. Because 100 and 103 are both < 120, every data-center Blackwell host was
classified as non-Blackwell, so two GPU-targeting paths never fired for a
B200/B300:
- the Linux blackwell_runtime_override that prefers the highest CUDA-major
runtime line shipping a bundle covering the host SMs (so a cu12x torch could
pin a cuda12 bundle over a native cuda13 one), and
- _drop_blackwell_incapable_windows_cuda, which removes cuda-12.4 builds that
load and validate but run Blackwell on a slow PTX-JIT path.
The result is a B200/B300 being handed a prebuilt that does not natively offload
its SM, i.e. the llama.cpp prebuilt is not really for the GPU. The Blackwell
floor is sm_100, so set _BLACKWELL_MIN_SM = 100. The toolkit floor (12.8) is
unchanged and already correct for sm_100/sm_103.
Surfaced loading unsloth/GLM-5.2-GGUF UD-IQ1_S on 8x B200.
Adds tests covering the sm_100/sm_103 classification, the Linux cuda13
preference for a data-center host, and the Windows cuda-12.4 drop.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Trim comments to be succinct (no behavior change)
* studio: require CUDA 12.9 for sm_103/sm_121 Blackwell prebuilts
sm_103 (B300/GB300) and sm_121 (DGX Spark) have no native compiler
target before CUDA 12.9; the family floor of 12.8 only covers
sm_100/101/120. Make the Windows-CUDA Blackwell filter SM-aware so a
legacy win-cuda-12.8 bundle is dropped on an sm_103/sm_121 host while
sm_100/sm_120 hosts keep the 12.8 floor.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix(studio): sort export checkpoints by step
* [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: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* Withhold HF_TOKEN from pull_request runs of CI workflows
The pull_request-triggered CI workflows check out and execute PR-controlled
code (install.sh, .github/scripts/**, tests/**) with secrets.HF_TOKEN in the
step environment. For a same-repo PR, GitHub provides repository secrets to the
run, so a malicious or compromised branch could modify a checked-out script to
read and exfiltrate HF_TOKEN, including by writing it into the uploaded logs/
artifact. HF_TOKEN is an external Hugging Face credential of unknown scope, so
this is the high-value exposure.
Gate every HF_TOKEN reference in these workflows with
`github.event_name != 'pull_request' && secrets.HF_TOKEN || ''`, so the real
token flows only on the trusted schedule/push/workflow_dispatch runs and PR runs
see an empty string. All model repos used by these jobs are public
(unsloth/*-GGUF), so anonymous download still works on PRs; install_llama_prebuilt.py
only sends HF auth to Hugging Face hosts and tolerates an absent token.
GITHUB_TOKEN (passed as GH_TOKEN) is intentionally left in place: it is the
auto-provisioned, job-scoped, contents:read token that expires with the job and
gives a same-repo PR author nothing they do not already have, and
install_llama_prebuilt.py needs it to authenticate the GitHub releases API or
the prebuilt llama.cpp download hits the anonymous rate-limit bucket and 403s.
* Trim the HF_TOKEN gating comments to one line per site
Comment/whitespace-only: collapse the per-step rationale to a single line and
shorten the local-agent-guides header note. No workflow logic changes (verified
each file's parsed YAML is identical before/after).
* Fix FlashAttention fp32 crash with DoRA (use_dora=True)
DoRA upcasts lora_magnitude_vector to fp32 for the optimizer, which promotes
the q/k/v_proj output to fp32. FlashAttention only accepts fp16/bf16, so the
fp32 q/k/v raised 'FlashAttention only support fp16 and bf16 data type'.
Downcast q/k/v to the compute dtype before the flash kernels.
Fixes#1013
* Apply kwarg-spacing format hook to DoRA dtype test (pre-commit)
* DoRA+FA2: downcast any fp32 among Q/K/V and clamp to a flash-supported dtype
* Tighten code comments (no logic change)
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* Fix misleading 'only for image models' error for Qwen3-VL when torchvision is missing
transformers >= 5.4 hard-requires torchvision for VLM image/video processors and
no longer falls back to a slow processor. Without torchvision the processor load
raises ImportError, unsloth degrades to a text-only tokenizer, and the vision data
collator later fails with 'UnslothVisionDataCollator is only for image models!'.
Detect this case at load time and raise a clear, actionable error pointing at the
missing torchvision dependency instead.
Fixesunslothai/unsloth#4202
* Apply kwarg-spacing format hook to vision torchvision guard (pre-commit)
* Make torchvision-missing detection precise: check availability first, match specific error text
* Tighten code comments (no logic change)
* Make missing-torchvision VLM error version-agnostic
The raise also fires on transformers 4.57.x for VLMs with a video processor
(Qwen2.5-VL, Qwen3-VL), where AutoVideoProcessor requires torchvision. The old
message claimed 'transformers >= 5.4 requires torchvision', which is inaccurate
on 4.57.x. Reword to state torchvision is required for this model's vision
processors without a version-specific claim.
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
* Fix GRPOTrainer evaluate() crash when called without prior training
GRPOTrainer.compute_loss read self.current_gradient_accumulation_steps
directly. That attribute is only set by the transformers training loop, so
calling trainer.evaluate() standalone (no prior trainer.train()) raised
AttributeError. Read it via getattr with a fallback to
args.gradient_accumulation_steps so standalone evaluation works.
* GRPO eval: fall back accumulation steps to 1 so standalone eval_loss is not underreported
* Tighten code comments (no logic change)
* fix: use EMPTY_LOGITS on the fused-CE not-return_dict path (#2068)
CausalLM_fast_forward's fused cross-entropy path (small batch, labels set,
UNSLOTH_RETURN_LOGITS off) computes the loss straight from hidden_states
via unsloth_fused_ce_loss and never materializes `logits`. The
return_dict=True branch returns EMPTY_LOGITS, but the `not return_dict`
branch returned `(logits,) + outputs[1:]`, raising
"UnboundLocalError: cannot access local variable 'logits'" whenever it ran
(e.g. training with return_dict=False). Same bug in the llama and mistral
fast-forward paths.
Return EMPTY_LOGITS on that branch too, matching the adjacent return_dict
output. Verified on GPU: a forward(return_dict=False, labels=...) that
raised UnboundLocalError now returns (loss, EMPTY_LOGITS, ...) and
backward() succeeds.
Adds tests/test_fused_ce_not_return_dict_logits.py, a CPU source-drift guard
(the fused path itself is GPU/triton only) asserting both fast-forward paths
keep using EMPTY_LOGITS there.
* Address review: parse the fused-CE drift line with whitespace-tolerant regexes
The drift detector sliced the source with exact string matching
(source.index("output = (") + the next newline), so a formatter respacing or
rewrapping the assignment would break the parse. Switch to anchored regexes that
tolerate whitespace and line wrapping, keeping the match anchored after the
fused guard so it targets the fused-CE branch and not the normal
output = (logits,) path. Behavior and the two drift assertions are unchanged.
* Tighten code comments (no logic change)
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
* studio/setup.sh: guard empty CUDA arch detection in the source build
PR #5826 hardened setup.sh for fresh CUDA toolkits, but the source build
still set -DCMAKE_CUDA_ARCHITECTURES only when nvidia-smi reported a
compute capability. When that query returns nothing the build proceeded
with no explicit arch list, so llama.cpp built PTX only. On a driver older
than the toolkit that binary fails at runtime with "the provided PTX was
compiled with an unsupported toolchain" - the build succeeds, so neither
the build-time check nor the CPU fallback caught it (issue #5854).
Resolve the arch list before committing to a CUDA build. A new pure helper
_resolve_cuda_archs parses and de-duplicates the nvidia-smi compute_cap
output and honors an explicit UNSLOTH_LLAMA_CUDA_ARCHS override. When the
result is empty, build CPU llama.cpp instead of a PTX-only binary, with a
clear message pointing at the override - so the user still ends up with a
working llama-server. The override also lets advanced users force a native
build on hosts where nvidia-smi cannot report compute_cap.
No behavior change when an arch is detected: -DGGML_CUDA=ON plus the arch,
CUDA flags and NVCC_PREPEND_FLAGS are assembled exactly as before.
Adds tests/sh/test_resolve_cuda_archs.sh (single/multi/dedup/empty/garbage/
whitespace/override cases), wired into tests/run_all.sh and the
studio-backend-ci.yml shell-test loop.
* studio/setup.sh: resolve nvidia-smi via /usr/bin fallback for arch detection
Addresses review feedback on the empty-CUDA-arch guard: _setup_has_usable_nvidia_gpu
classifies a host as NVIDIA-usable using nvidia-smi on PATH OR /usr/bin/nvidia-smi,
but the new arch detection probed only `command -v nvidia-smi`. On a GPU host where
nvidia-smi is off PATH (reachable only at /usr/bin), arch detection returned empty
and the new empty-arch branch dropped the build to CPU, losing CUDA. Mirror the same
PATH-then-/usr/bin resolution so those hosts still get a native CUDA build.
Also scope _resolve_cuda_archs locals with `local` (no behavior change; it already
runs under command substitution).
* tests: update compute_cap-probe assertion for $_smi_bin resolution
The nvidia-smi /usr/bin fallback parameterized the binary in the compute_cap
probe (_setup_run_smi "$_smi_bin" ...), so the literal-string assertion in
test_compute_cap_probe_timeout_wrapped no longer matched. Assert the probe is
preceded by _setup_run_smi (timeout-wrapped) instead, scanning all occurrences
so the comment mention is ignored. Same intent, binary-agnostic.
* tests: ruff-format the compute_cap probe assertion (pre-commit)
Collapse the backslash-continued assert onto one line and normalize slice
spacing so the ruff-format pre-commit hook (0.6.9) is satisfied. Formatting
only; no behavior change.
* Tighten code comments (no logic change)
* studio(windows): build CPU when CUDA arch is undetectable (#5854)
The Windows source build added -DGGML_CUDA=ON unconditionally but only set
-DCMAKE_CUDA_ARCHITECTURES when $CudaArch was detected. With no detectable
compute capability that produced a PTX-only binary, the same hole the Linux
fix closed. Build CPU llama.cpp in that case, and honor UNSLOTH_LLAMA_CUDA_ARCHS
to force a CUDA build, matching setup.sh. Detected-arch builds are unchanged.
* test: anchor NVCC_PREPEND_FLAGS scope check on the final CPU branch
The undetectable-arch CPU fallback adds an earlier -DGGML_CUDA=OFF, so the
ordering check now anchors on -DGGML_CUDA=ON and the last -DGGML_CUDA=OFF
instead of the first.
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
* fix(studio): handle multimodal list content in inference text paths
Studio receives chat message content in two shapes: the legacy string
form, and the OpenAI multimodal list form
([{"type": "text", "text": ...}, {"type": "image_url", ...}]).
Several string-only paths called .strip()/re.sub()/f-string interpolation
on content directly, raising "'list' object has no attribute 'replace'"
for vision models (issue #4383), or rendering the list repr into the
prompt for the manual chat-template formatters.
Add core/inference/message_content.py with content_to_text(), a pure
helper (no heavy imports) that returns strings unchanged and joins the
text parts of a list while dropping image/audio parts. Apply it at every
string-only content site: _generate_vision_response, the audio user-text
extraction, format_chat_prompt, and the llama3/mistral/chatml/alpaca/
generic template formatters. The plain-string path is a no-op, so
existing behavior is unchanged.
Adds tests/test_message_content.py covering str/None/list/tuple,
multimodal drop, multi-part join and empty-part skipping.
* Tighten code comments (no logic change)
* studio: join multimodal text parts with newline for llama.cpp parity
llama.cpp joins multiple text content parts with a newline (common/chat.cpp),
so match that in content_to_text instead of a single space.
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
Three independent upstream CI fixes that currently fail on every open PR:
verify_import_hoist.py: TARGET-CHANGED only flags a genuine swap (a BEFORE
target no longer reachable in AFTER). A pure superset growth such as adding
import urllib.error next to import urllib.request binds the same top-level
package and loses nothing, so it is no longer a blocker (transformers_version.py).
test_vision_cache.py: run each test from a fresh empty cwd. is_vision_model
calls is_local_path first, and a relative model id that happens to exist on
disk short-circuits before the mocked detection runs; the CI cwd and HF cache
can contain dirs colliding with the synthetic ids, causing 'called 0 times'.
Production code is correct; only the test needed cwd isolation.
consolidated-tests-ci.yml: the llama.cpp smoke probes the first of
llama-cli / llama-mtmd-cli / llama-server that exists instead of hard-requiring
llama-cli, which upstream no longer always builds. llama-cli stays first so it
is preferred when present. Adds Windows .exe + build/bin/Release handling.
* Studio: refresh chat tour for the redesigned model picker
- Pick a model step describes the Recommended and On Device tabs instead of the old Hub and Fine-tuned split
- Find a model step (was Two tabs) covers Unsloth search vs Search Hub, the format and sort filters, and the OOM tag
- Settings step now anchors to the run settings panel on the right. The old anchor sat on the open settings button, which unmounts when settings opens, so the tooltip lost its target and drifted left
* Add guided tour step for the composer + menu
Polish for the in-chat model picker popover and its guided-tour step.
- Search box placeholder reads Search Unsloth models, matching the Unsloth-only listing.
- Search Hub button shows a Search all models tooltip on hover.
- Floating Eject pill moves 1px lower so it sits closer to the bottom edge.
- Results list max height trimmed by 1px (21rem to 335px) from the bottom only.
- Chat guided tour Two tabs step updated to describe Unsloth-scoped search plus Search Hub for all of Hugging Face.
- Discover defaults to the whole Hub instead of the unsloth org; an explicit
Unsloth choice is still remembered
- Discover models placeholder reads Search all models to match
- Give the Unsloth/All scope pill a min width so it stays readable
* Use UTF-8 for Python code-execution subprocess I/O
Studio's code-execution tool already tells the child to emit UTF-8
(PYTHONIOENCODING=utf-8 in _build_safe_env), but _python_exec writes the
temp script and decodes the subprocess pipe with the OS default codec.
On Windows (cp1252), non-ASCII in model-written code or its output --
arrows, CJK, emoji -- raises UnicodeEncodeError / UnicodeDecodeError and
breaks execution.
Complete the UTF-8 wiring in core/inference/tools.py:
- write the temp script with encoding="utf-8"
- decode _python_exec stdout as utf-8, errors="replace"
- set PYTHONIOENCODING=utf-8 in _build_bypass_env too (matches
_build_safe_env, so the bypass path's child also emits utf-8)
The child is python with PYTHONIOENCODING=utf-8, so it emits UTF-8
regardless of the console code page and the decode is always correct.
Shell execution via cmd.exe has a separate console-code-page story and
is left to a follow-up.
Refs unslothai/unsloth#6489
* Scope Python exec UTF-8 env to Python tool
* Make bash bypass test robust to a host-set PYTHONIOENCODING for PR #6548
Bypass mode preserves benign host env vars, so a host-set PYTHONIOENCODING was
inherited into the bash bypass env and tripped the new assertion even though
_bash_exec never adds it. Clear it in the test so the assertion checks _bash_exec,
not the runner environment.
---------
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: correct the anyio<4.14 pin rationale (mixed-install ImportError)
The pin comments said "anyio 4.14+ breaks cancel scope on Python 3.13", but
a clean anyio 4.14.0 works on 3.13 (cancel scopes, Event, and the asyncio
backend import all pass). The actual failure is a half-resolved install:
anyio 4.14 added TaskHandle, imported by __init__.py and _backends/_asyncio
from _core/_tasks. When a stale 4.13 _core/_tasks (no TaskHandle) sits under
4.14's importers, the import raises ImportError and 500s the server. Correct
the rationale; the <4.14 pin still stands as the way to keep one consistent
anyio version.
* Clarify the anyio override comment (mixed-install ImportError, not a 4.14 cancel-scope bug)
* Studio: keep model downloads running across navigation and loads
Downloads started from the chat model selector were tied to the staged
pick lifecycle, so they were cancelled in cases where Hub downloads keep
going. This makes the chat download flow behave like the Hub.
- Leaving the chat route or switching thread/project/new chat now detaches
the staging UI but keeps the in-flight transfer running in the global
download manager (new keepDownload option on abandonStagedModel).
- Staging a second pick no longer cancels the previous pick's download, so
multiple models/variants can download at once.
- Picking a model to download while another model is loading now starts the
download in the background instead of refusing, since a download is
independent of a load.
* Studio: also background-download remote GGUF quants while a model loads
isDownloadableHubRepo (wantManagerDownload) excludes GGUF sources, so an
uncached remote GGUF quant picked from the chat selector while another model
was loading fell through to the 'Another model is already loading' toast
instead of downloading in the background. Treat an uncached remote hub GGUF as
a background download too, matching the staged-pick download path.
Addresses review feedback from gemini-code-assist and codex on PR #6573.
* Studio: only toast a background download once it actually starts
The chat background-download path (used when a model is already loading)
fired the "Downloading in the background" toast unconditionally, but
requestStart can return without starting a job: a cross-transport partial
records a conflict that is only resolvable from the Hub download card, and
a busy sibling variant returns after its own toast. So the user could be
told a download started when none did, with no way to resolve the conflict
from chat.
requestStart now reports an outcome (started/conflict/busy/error). The
chat path only shows the success toast on an actual start and points the
user to the Hub when a transport conflict needs resolving. The Hub card
surface keeps its existing behavior (it renders the conflict resolver, so
it ignores the outcome).
* Studio: report background-download outcome from real job state
The chat background-download toast trusted requestStart's optimistic
"started", but a start can no-op without throwing: startJob finalizes the
job as "error" when the backend refuses or fails apiStart, its peer guard
skips a fresh start, and hasActiveOrPendingStart trips on a snapshot, peer
variant, or pending preflight that is not this request. So the user could
be told a download started when none did.
Derive the outcome from the actual job state of the exact key
(running/cancelling = started, otherwise error/busy), so the toast only
fires for a transfer that is really live.
Also guard against re-downloading the model that is already loading: the
/load flow downloads before it sets the checkpoint, and that fetch is not
a download-manager job, so picking the same id+variant again would start a
second transfer against the same cache. Detect that pick and surface a
"this model is already loading" toast instead.
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Restore sys.modules in test_pre_import_gate_is_transformers_free
The test pops transformers and utils.models.model_config from sys.modules to
assert the pre-import security gate does not re-import them, but never put them
back. A later importer then rebound a fresh utils.models.model_config, so tests
that had captured the original instance missed their patches and hit the real
path: test_vision_cache patches _is_vision_model_uncached on the original
module, but is_vision_model (still bound to that original) ran the real network
lookup instead. This produced 17 spurious failures whenever test_ssm_runtime
ran before test_vision_cache in the same process.
Snapshot the removed modules and restore the original objects in a finally, so
the assertions still run against a clean slate while later tests see the same
module instances they captured at import time.
* [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>
The macOS-arm studio venv still installs anyio 4.14.0 despite the
constraints.txt cap from #6546. mlx-vlm / mlx-lm pull anyio>=4.14, which
conflicts with the anyio<4.14.0 constraint; a uv -c constraint loses that
conflict so 4.14.0 gets installed, reintroducing the cancel-scope
RuntimeError on Python 3.13 (#6483). UV_OVERRIDE is already applied on
macOS-arm via overrides-darwin-arm64.txt and a uv override wins the
conflict, so cap anyio there too. macOS-arm now resolves anyio 4.13.0.
* Studio: hide RAG embedder from the On Device list
The bge-small-en-v1.5 RAG embedder (and other infra models) were already
hidden from Discover but still showed up in the On Device browse list,
cluttering the user's downloaded models. They are now filtered out of On
Device the same way, while a search that matches still reveals the row so
the user can confirm it is already downloaded.
* Studio: also check path/title when hiding infra models from On Device
isHiddenModelId only saw row.id and row.repoId, but local inventory rows can
have a null repoId and an id that is a hash rather than the file path/name, so
the llama.cpp validation probe (stories260K.gguf) could slip into the On Device
list. Pass the local row's path and title too, mirroring the backend's
_is_hidden_model(m.id, m.path).
Addresses review feedback from gemini-code-assist on PR #6572.
* Studio: exclude infra models from On Device count and dataset list
The On Device hidden-model filter was applied to datasets too, so a
dataset whose id/title/path contained an infra needle (bge-small-en-v1.5,
stories260k.gguf) was wrongly hidden. Bypass the filter for datasets, the
same way Discover and the format filter already do.
The On Device header count and the Cache/Local stat pills still used the
unfiltered row counts, so a fresh install with only the bge embedder
cached read 1 over an empty list. Count visible (non-infra) rows instead,
keeping full counts for datasets.
* Studio: count search-revealed infra rows in the On Device tally
The visible-row counts excluded every hidden row unconditionally, but the
On Device list reveals a hidden row when the search query matches it. So
with only the bge embedder cached and a "bge" search, the list showed one
row while the header and Cache stat stayed 0. Reuse isVisibleInventoryRow
for the counts so a query-revealed row is counted, keeping them in step
with the list.
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio Playwright: snooze update banner before sending
The llama.cpp update banner is a fixed bottom-right toast (z-9998). When an
update is available it overlaps the composer's Send button and its subtree
intercepts the click, so send_and_wait times out (flaky; surfaces on the
Windows studio UI smoke, passes otherwise). Snooze the banner if it is
showing before each send, then wait for it to detach.
* Also snooze the web update banner before sending
The web update banner (web-update-banner, z-9999) is a fixed bottom-right
toast like the llama.cpp one and can overlap the Send button too. Loop over
both banners and snooze whichever is showing.
* Studio: honor custom HF_HOME for model download and load
_setup_cache_env always derived HF_HUB_CACHE and HF_XET_CACHE from
XDG_CACHE_HOME / ~/.cache, ignoring a user-set HF_HOME. Because it sets
HF_HUB_CACHE explicitly and that variable takes precedence over HF_HOME
in huggingface_hub, the hub cache was pinned to the standard location: a
model already present under a custom HF_HOME was detected but then
re-downloaded from scratch on load.
Seed HF_HUB_CACHE and HF_XET_CACHE from HF_HOME when the user set it
(HF's own default is $HF_HOME/hub and $HF_HOME/xet), and honor the legacy
HUGGINGFACE_HUB_CACHE alias. The hub download workers call
snapshot_download without a cache_dir for both the Xet and HTTP-fallback
paths, so they follow HF_HUB_CACHE; fixing it here unifies detection and
both transports on one root. Explicit HF_HUB_CACHE / HF_XET_CACHE stay
untouched. Adds tests for the custom-HF_HOME, default, explicit-override,
and legacy-alias cases. Fixes#5182.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: do not crash startup when a custom HF_HOME is not writable
Seeding HF_HUB_CACHE/HF_XET_CACHE from HF_HOME means _setup_cache_env now
mkdir's under a user-controlled path. A non-writable or not-yet-mounted
HF_HOME (typo, offline drive) would raise and crash startup, where the old
code silently fell back. Make the mkdir best-effort; the env var is still
set, so HF reports a clear error at download time. Adds a regression test.
* Studio: strip blank HF_HOME and isolate cache-env tests
Address review: a whitespace-only HF_HOME no longer derives " /hub";
strip it and fall back to the default (matches studio_root). Tests set
UNSLOTH_STUDIO_HOME to a tmp dir so _setup_cache_env's UV/VLLM mkdirs do
not touch the real ~/.unsloth/studio. Adds a whitespace regression test.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Resolve the transformers tier by probing AutoConfig instead of guessing
When the only signal is a 5.x tokenizer class, get_transformers_tier guessed the
lowest 5.x sidecar (530). That misroutes models whose built-in config parser needs
a higher tier: dense NemotronH ships a 5.x tokenizer but its '-' (MLP) layer only
transformers 5.10 can parse, so 5.3/5.5 raise KeyError '-'. The config.json
transformers_version field records the saving version, not the minimum to load, so
it cannot drive routing either.
Replace the weak tokenizer->530 guesses (local and remote) with a probe: parse
config.json with the built-in parser (trust_remote_code=False) in each sidecar,
escalating 530->550->510, and pick the first that succeeds. This generalizes to any
architecture without hardcoded lists. Strong signals stay fast paths (no subprocess);
the probe runs only when the tier is otherwise ambiguous and is cached by (model,
commit sha). It never executes repo code, never downloads weights, never raises, and
falls back to the legacy 530 guess on a transient/auth/offline failure or when no
sidecar is available. UNSLOTH_DISABLE_TIER_PROBE restores the old behavior.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: tier probe fallbacks and cross-platform robustness
Codex:
- Never escalate to 510 on uncertainty. When every sidecar was probed and none
parsed with the built-in parser, the model is a remote-code / custom model_type
that loads via its own code; keep the legacy 530 route instead of jumping to
510 (which would change the behavior of models that worked on the 5.3 stack).
- Only cache the 530 fallback when the result is conclusive (every tier actually
probed). If a sidecar was missing/uninstallable the environment is incomplete,
so return 530 uncached and retry on the next call.
- Do not pin the tier cache under an unknown revision: _resolve_commit_sha no
longer memoizes a None sha (a transient Hub failure is retried), and _probe_tier
only caches a tier when the commit sha is known.
Gemini:
- Wrap Path.exists() in the sha resolver in try/except OSError (a remote repo id
can raise WinError 123 on Windows).
- Probe script writes the error to sys.stderr.buffer as UTF-8 bytes so a non-ASCII
message cannot itself raise UnicodeEncodeError under cp1252.
- subprocess.run decodes stderr with errors="replace" to avoid UnicodeDecodeError
on non-UTF-8 consoles.
Tests: 72 passed (added partial-sidecar uncached, sha-unresolved not cached,
all-failed stays 530 + cached, sha resolver retries None / handles OSError).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address review round 2: authenticate tier checks, stop memoizing local sigs
Codex:
- Thread hf_token through _check_config_needs_510/550 and
_check_tokenizer_config_needs_v5 (and the underlying raw fetches). Previously a
gated/private model whose only 5.x signal is tokenizer_config.json never reached
the authenticated probe: the unauthenticated raw fetch failed and cached False,
so the model fell through to the default 4.x tier. The per-check caches are now
keyed by (model, token) so an unauthenticated miss cannot poison a later authed
read, mirroring _load_config_json.
- _resolve_commit_sha no longer memoizes a local directory signature. A local
signature is mutable (size/mtime of config/tokenizer), so a reused/overwritten
checkpoint path would otherwise keep selecting the previous tier; it is now
recomputed every call. Only the immutable remote commit sha is memoized.
Tests: 75 passed (added token-cache isolation + auth header, local signature not
memoized, token threaded into all checks/probe).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review round 3: reach activation with the token, drop SHA tier cache
Codex round 3:
- Thread hf_token into the activation path that actually selects a sidecar. The
token-aware tier checks added last round were unreachable:
activate_transformers_for_subprocess called get_transformers_tier without a
token, and the inference/training/export workers passed only the model name even
though they hold a request-scoped hf_token. activate_transformers_for_subprocess
now takes hf_token and the three workers forward config["hf_token"], so a
gated/private model whose only 5.x signal is an authenticated config/tokenizer is
routed to the right sidecar instead of falling to default 4.x.
- Stop importing huggingface_hub during tier detection. _probe_tier no longer
resolves a commit sha, so it never pulls huggingface_hub into the worker before
the sidecar venv is prepended to sys.path (activation only prepends, never
purges), which would otherwise pin the default-env hub over the sidecar's
pinned huggingface_hub==1.8.0.
- The tier cache is now keyed by model_name for the process lifetime (a model's
required tier is a property of its architecture; cleared on restart). This drops
the mutable-SHA memo that masked remote revision changes and the mutable
local-signature memo, removing _resolve_commit_sha / _local_dir_signature /
_probe_sha_cache entirely.
- Do not cache a probe success that depended on a skipped lower tier: if a lower
sidecar was unavailable, the lowest valid tier may change once it installs, so
the result is returned uncached and re-probed next call.
Tests: 73 passed (probe imports no hub; success uncached when a lower tier is
skipped; activation forwards the token).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Trim comments to be more succinct
* Re-probe overwritten local checkpoints and authenticate the probe child
The AutoConfig tier probe cached its result under the bare model_name, so a
local checkpoint overwritten in place (same path, new config.json) kept serving
the stale sidecar. Fold a cheap config.json signature (size + mtime) into the
cache key for local paths; remote ids stay name-keyed so no huggingface_hub
import lands before the sidecar is activated.
The probe relies on the implicit HF_TOKEN env, so an inherited
HF_HUB_DISABLE_IMPLICIT_TOKEN=1 left it unauthenticated and a gated repo 401ed
into the 530 fail-safe. Clear that flag in the child env when a token is set.
* Keep tier probes off the log-only path and probe new 5.x archs default-first
- get_transformers_tier gains probe=True/False. needs_transformers_5 (a coarse
4-vs-5 boolean used only for a spawn log and a vision-check branch) now passes
probe=False, so a parent/log-only caller never spawns sidecar probes. The real
activation path keeps probe=True and resolves the exact tier in the worker.
- A config.json saved by transformers 5.x but matched by no fast path is now probed
default-first: _probe_tier gains include_default + floor, prepending the ambient
4.57.x tier to the escalation. A model that still parses on the default is left on
it (no mis-route onto a sidecar); only a config the default parser cannot read
escalates to the lowest 5.x tier that parses. The transformers_version field is a
cheap 'worth probing' hint only, read from the already-fetched config (no extra
network); ordinary 4.x configs never probe.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Separate probe cache by mode and keep version-field 5.x visible to needs_transformers_5
- _probe_tier cache was keyed only by config.json signature, so a default-first probe
that returned 'default' could be handed back to a later tokenizer/known-5.x caller
(floor=530), leaving a model with a 5.x-only tokenizer on transformers 4.x. Key the
cache by probe mode (floor + include_default); the legacy 530 mode keeps the bare key.
- The version-field 5.x detection is a cheap config read, not a probe, so run it even
when probe=False: a standard-tokenizer model whose only signal is transformers_version
>= 5 now classifies as 5.x via needs_transformers_5 (returns '530' without spawning a
probe), so the vision-routing fallback uses the 5.x subprocess instead of failing the
default parser and marking it non-vision. The real activation path still probes
default-first and may resolve 'default'.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Don't treat local checkpoints as Hub ids, and fix stale activation test double
- _load_config_json / _check_tokenizer_config_needs_v5: a local checkpoint dir whose
config.json / tokenizer_config.json is not yet present was being fetched from the Hub
as if the path were a repo id, and the 404 miss was cached. A later call after the
file is written (in-progress checkpoint) then served the stale miss, so a
TokenizersBackend checkpoint fell through to the default tier. Skip the Hub fetch for
local dirs and do not cache the miss, so the file is read once it appears.
- test_activate_transformers_version_or_warn_*: the worker now threads hf_token into
_activate_transformers_version (model_name, hf_token); update the one-arg test doubles
to the real two-arg signature so the silent-success path stays silent.
* Tighten comments in the AutoConfig probe and tier-selection paths
* Address review: canonical probe cache key and reuse _token_cache_key
- _probe_cache_key resolves config.json to its absolute realpath before
keying, so a relative path or a changed cwd can't collide with or miss a
prior probe result. Remote ids still fall back to the name (stat raises,
caught).
- _cached_config_json reuses _token_cache_key instead of re-hashing the
token inline, keeping the (model, token) key derivation in one place.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* studio: let users change their password from Settings
The only day-to-day way to change credentials was the destructive console command
'unsloth studio reset-password' (it deletes auth.db); the in-app change-password
page is the forced first-login flow and bounces non-forced users to /login.
Add a Change password control to Settings > General > Account: a small dialog
that takes the current and new password and calls the existing
POST /api/auth/change-password, then stores the rotated tokens it returns.
Username changes remain out of scope.
The dialog uses authFetch, so an expired access token is refreshed and the
request retried instead of failing with a spurious expired-token error for a user
who left Studio open past the token lifetime. The row is hidden in the Tauri
desktop app, which authenticates via desktop auto-auth with a generated secret:
there is no user-entered password to change there, and changing it would clear
the desktop secret.
* studio: harden settings password change
* studio: harden settings password dialog UX
---------
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
* Add HF dataset streaming mode to Studio
* Added default value for datasetStreaming in training-config-store.ts
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Handle None max_steps for streaming validation
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fast-fail streaming validation and guard incompatible modes
Reject dataset_streaming at the API boundary when hf_dataset is empty,
the dataset is vision/audio, or max_steps is not set. Probe eval split
with get_dataset_split_names before the streaming load so typos fail
immediately instead of mid-training. Guard column_names=None after map
on iterables. Hide the UI toggle for non-text configurations and clear
the stale flag when config becomes incompatible.
* studio: add streaming dataset tests, iterable helper, and streaming template/format support (WIP)
Work-in-progress on top of feat/studio-dataset-streaming-mode (PR #4946):
- new test_training_streaming.py and iterable.py dataset helper
- streaming support in chat_templates.py and format_conversion.py
- additional streaming guards in trainer.py / models / routes
- frontend streaming wiring in params-section and training-config-store
Committed to preserve uncommitted work before merging latest main.
* studio: fix review-team findings for streaming + main merge
BLOCKER: streaming + raw-text/CPT crashed on len(IterableDataset). Guard it in the
start route (reject format_type=="raw" or training_type=="Continued Pretraining")
and in isStreamingSupported (datasetFormat !== "raw").
Also:
- models/training.py: validate hf_dataset/subset/split (charset+length, block ..//);
cap dataset slice indices (le=1e9); note validator ordering
- chat_templates.py: guard _apply_custom_mapping .map() for streaming
- trainer.py: warn when packing+streaming
- training-config-store.ts: persist-migration bump to v11 (standalone datasetStreaming
backfill); add isVisionModel to NON_PERSISTED; toast on silent streamingCompatiblePatch
mutations in the 4 indirect setters
- tests: route rejections (max_steps, raw/cpt), slice cap, unsafe hf_dataset
* studio: enable raw-text/CPT dataset streaming + streaming UX polish
- raw_text: keep the lazy filter but skip len()-based row counting for
IterableDatasets so raw-text / CPT can stream; guard the eval-size log
- routes/trainer: drop the raw/CPT streaming block; add a defensive
not-streaming guard on the eval auto-split (train_test_split)
- dataset-section: streaming toggle is visible-but-disabled and lists the
exact unmet requirement(s) in its tooltip; block embedding models
- training-start-overlay: show "streaming (no full download)" instead of a
stuck download bar for streaming runs
- trim the streaming test suite to the high-value cases
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: address streaming review (MLX/embedding guards, sliced eval split, rehydrate timing)
- routes: reject dataset_streaming for embedding training and on Apple Silicon
(MLX); both loaders materialize the full dataset instead of streaming
- trainer: validate the base eval split name so streaming eval accepts HF slice
syntax such as "validation[:1000]"
- training-config-store: defer the onRehydrateStorage setState to a microtask so
it doesn't hit the store's TDZ during synchronous hydration
- test: streaming start rejects embedding models
* studio: harden HF dataset streaming (column_names, split slicing, empty/eval bounds, gating)
Address a deeper streaming review:
- raw_text: resolve_column_names() guards IterableDataset.column_names=None
(from_generator / unresolved features) so raw-text and CPT streaming no longer
raise TypeError before training
- models/routes: reject HF slice syntax in train_split/eval_split when streaming
(load_dataset(streaming=True) raises "Bad split"); reject mixed sources
(local/S3) and embedding/MLX streaming at the API, not just in the UI
- trainer: an empty post-slice/filter stream fails preflight with a clear message;
streaming eval is capped (STREAMING_EVAL_MAX_SAMPLES) so each eval terminates;
the manual-slice shortcut falls back to a regular load when train_split is sliced
- format_conversion: streaming conversions preflight the first mapped row so
format errors surface before training, not mid-iteration
- frontend: block streaming on Apple Silicon; clear datasetStreaming when a
dataset is detected as image/audio at start
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fix CI for streaming PR (lint blocker + no-torch sandbox + preflight test)
- trainer.py: drop unused `IterableDataset` import (hoist safety-net blocker).
- test_training_streaming.py: only select real classes (isinstance type) when
locating the trainer class, so a MagicMock-stubbed global is never passed to
object.__new__ (fixes TypeError on the Python 3.10-3.13 jobs).
- no-torch import sandboxes (test_e2e_no_torch_sandbox.py,
test_studio_import_no_torch.py): teach the chat_templates/format_conversion
exec stubs and the full-import-chain copy list about the new `.iterable`
module so the AFTER/runtime cases import without torch again.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
* Installer: respect a declined Studio auto-start and keep Ctrl+C shutdown logs ordered
The `curl | sh` Studio auto-start prompt had two issues on Linux/macOS/WSL
(install.sh). install.ps1 already gates on input redirection, so Windows is
unaffected.
1. Typing n, or any closed/EOF /dev/tty, still launched Studio. The read
fallbacks defaulted to "y" (read failure, and the no-tty branch), so any
answer other than a cleanly delivered y/n line auto-started a blocking
foreground server. Default those to "n"; a real Enter still counts as yes
via ${_reply:-y}.
2. On Ctrl+C the shell prompt printed in the middle of Studio's shutdown logs.
The non-interactive installer shell took the default SIGINT action and died
before the child finished its graceful shutdown, so the prompt raced ahead
of "All subprocesses cleaned up". trap '' INT in the installer shell so it
waits for Studio's own graceful shutdown.
* Studio: wait for the uvicorn thread before the terminal returns on Ctrl+C
Builds on #6565 by @Imagineer99. The studio server runs uvicorn in a daemon
thread, so on Ctrl+C the process could return to the shell while that thread
was still writing its shutdown logs, interleaving them with the prompt.
Retain the uvicorn thread and join it (flushing stdout/stderr) before terminal
entrypoints return, from run.py's main shutdown path and the CLI shutdown paths.
Refinements over #6565:
- Bound the join at 5s (_SERVER_SHUTDOWN_JOIN_TIMEOUT, matching the existing
_graceful_shutdown subprocess timeouts) so a stalled uvicorn shutdown cannot
hang the terminal; the timeout warning branch is now reachable.
- Restore SIG_DFL for SIGINT/SIGTERM at the start of the signal handler so a
second Ctrl+C force-quits, and drop the redundant in-handler wait (the
post-loop wait already covers the signal path).
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: keep child Ctrl+C working and restore SIGBREAK
- install.sh: run studio in a subshell that resets INT to default
(trap - INT; exec ...) so the foreground child does not inherit the
installer shell's ignored SIGINT, which would otherwise swallow the
studio process's own Ctrl+C and graceful shutdown.
- run.py: also restore SIGBREAK to SIG_DFL in the signal handler so a
second Ctrl+Break force-quits on Windows, matching SIGINT/SIGTERM.
* install.sh: capture studio exit with || under set -e so the migration hint still prints
* Trim shutdown-fix comments to be terser (comments only, no code change)
* Dedup CLI shutdown-wait into finally blocks (review follow-up)
---------
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Add regression tests for the stray-forward compile-cache reset
Follow-up to #6511, which fixed the bug but whose squash merge did not
include the tests. These cover the two issues that fix addressed, under
the GPU-free tests/conftest.py harness:
- _unsloth_reset_stray_compile_cache is an exported module-level symbol in
unsloth.models._utils (it previously lived only inside the RL trainer
template string, so every non-RL import silently no-op'd)
- _unsloth_install_pretrain_detector keeps a recorded "seen" forward on an
idempotent reinstall with a live hook, and only resets it after teardown
- only a grad-enabled pre-train forward marks the cache poisoned
- the reset warns and clears seen when a stray forward was seen, tears the
hook down even on the clean path, and walks the .model/.base_model/.module
wrapper chain to reach a nested marker
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Pin UNSLOTH_COMPILE_DISABLE in the warn-path reset tests
The reset only warns and resets Dynamo when UNSLOTH_COMPILE_DISABLE != "1".
A GPU-free CI env that sets it to "1" would make the warn assertion in
test_reset_clears_seen_and_warns_when_a_stray_forward_was_seen flaky.
monkeypatch it to "0" in both warn-path tests so the warn / no-warn
assertions are deterministic and test the seen flag, not the env.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: accept --not-secure as a back-compat alias for --no-secure
PR #6560 renamed the negative secure flag from --not-secure to --no-secure
to match argparse.BooleanOptionalAction. Re-add --not-secure as a hidden,
deprecated alias at both CLI layers so existing scripts and muscle memory
keep working, while --no-secure stays the documented spelling.
- studio/backend/run.py: extract the CLI parser into _build_arg_parser() so
the flag wiring is unit-testable, and register --not-secure as a hidden
store_false alias for --no-secure. Last flag wins, matching
BooleanOptionalAction semantics.
- unsloth_cli/commands/studio.py: add a hidden --not-secure option to
`unsloth studio` and `unsloth studio run`; it forces secure off and
forwards the canonical --no-secure to the backend.
- Tests at both layers for the alias and its polarity.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: address review on --not-secure alias
- run.py: use argparse.SUPPRESS for the --not-secure default so the alias
never contributes a namespace default (the canonical --secure owns it).
- studio.py: resolve --not-secure last-wins from argv via _resolve_secure()
so `--not-secure --secure` keeps secure on, matching the backend's
BooleanOptionalAction and how --secure/--no-secure already behave.
- Add a CLI last-wins test covering both flag orders.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: show tool-call progress for large GGUF tool arguments
The GGUF agentic tool loop only surfaced an early provisional tool card
for render_html, so any other tool (python, terminal, ...) was invisible
in the UI while its arguments streamed. For a large argument such as a
full HTML or code file this left the chat sitting on "Generating..." with
zero progress for tens of seconds while the model was clearly working.
Generalize the provisional tool_start to any enabled tool once its
streamed arguments grow past a threshold (render_html still surfaces
immediately, small-argument tools are unchanged). The provisional and the
real tool_start share the tool_call_id so the frontend reconciles them
into one card. Close the provisional on no-op, denial, parallel-drop,
post-loop, and on stream errors so a card can never spin forever, surface
each parallel call, and skip the early card while a human confirmation
gate is active. Apply the same confirmation-gate guard to the safetensors
agentic loop.
Additional hardening:
- Only emit a provisional card once a real, non-empty tool_call_id is
known. llama.cpp can stream a tool call with an empty id, and a card
keyed by "" cannot reconcile with the real tool_start (the frontend
mints its own id per event), so it would dangle.
- On a connection drop or other mid-iteration failure, close the dangling
provisional card with an error result instead of an empty success so the
UI renders it as failed rather than completed.
- Mirror the provisional cleanup in the safetensors loop: close a
provisional render_html card if the model generator raises mid-stream or
the controller turns the call into an internal no-op.
Adds regression tests for the empty-id guard, the error-result on a
dropped connection, and the safetensors mid-stream exception cleanup.
* [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: wasimysaid <wasimysdev@gmail.com>
* Studio: detect transformers 5.3.0 tier from config.json for local checkpoints
A local safetensors folder whose config.json did not match the Gemma4 (510/550)
architecture signals short-circuited get_transformers_tier() to "default"
(transformers 4.57.x), never reaching the name-substring check that routes
Qwen3.5 to the 5.3.0 sidecar. So a local Qwen3.5 checkpoint (model_type
"qwen3_5", needs transformers >= 5.2.0) loaded with 4.57.x and failed with
"does not support Qwen3.5". The same model as a remote HF id worked, because it
has no local config.json to trigger the short-circuit.
Detect the 5.3.0 tier from config.json (model_type "qwen3_5" / architecture
Qwen3_5ForCausalLM) in the local-config branch, mirroring the existing Gemma4
510/550 handling. This is a positive config signal, so it fixes local Qwen3.5
without weakening the directory-name false-positive guard (a llama checkpoint
under a "gemma-4-12b-*" parent still resolves to default).
Adds tests for the config-based 530 detection and local-folder tier resolution.
* Studio: suppress false warning when config.json parse fails for sidecar-tier models
* Studio: generalize local-checkpoint tier detection for all 5.3.0 families
Expands the config.json-based tier detection to cover all known 5.3.0-tier
model families (Qwen3 MoE, GLM-4.7-Flash, LFM2.5-VL) and adds a _name_or_path
fallback so renamed local checkpoints with unrecognised model_type values still
route correctly via the HF ID embedded in their config.json.
- Expand _TRANSFORMERS_530_ARCHITECTURES / _MODEL_TYPES with verified entries
from Qwen3MoeForCausalLM, Glm4MoeLiteForCausalLM, Lfm2VlForConditionalGeneration,
and Qwen3_5ForConditionalGeneration (confirmed from local Qwen3.5-2B config.json)
- Extract _tier_from_name() helper, deduplicating the fast-substring logic used
by both the remote-path branch and the new config _name_or_path fallback
- In the local-config branch: after architecture checks, resolve the tier from
cfg._name_or_path / cfg.model_name before returning "default", preserving the
existing directory-name false-positive guard
- 79 tests passing
* Studio: match 510/550 style for 530 config sets (no inline comments)
* Studio: use _resolve_base_model instead of reinlining _name_or_path lookup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: recurse into get_transformers_tier for resolved base model (Gemini suggestion)
* Studio: use _tier_from_name in local-config fallback to avoid network probes
Using get_transformers_tier(resolved) on the _name_or_path fallback would
trigger up to 3 network fetches (config.json + tokenizer_config.json, 10s
each) for every ordinary checkpoint whose _name_or_path is a plain HF ID
like meta-llama/Llama-3-8B. The fallback's purpose is name-based detection
on the resolved HF ID, _tier_from_name covers all known cases without I/O.
* Studio: add _check_config_needs_530 to slow HF-ID fallback path
Private or renamed HF repos whose model IDs lack a 5.3 substring were
silently routed to the default tier. _check_config_needs_530 mirrors the
existing 510/550 pattern: fetches config.json once, caches the result, and
is called after the 550 check in the slow path. Includes 5 unit tests.
* Studio: guard _tier_from_name fallback against local-path false positives
When _name_or_path in config.json is an absolute path to the same checkpoint
passed as a relative path, the textual resolved != model_name check passes
and _tier_from_name would scan the directory path for substrings. Split the
fallback: local directories recurse into get_transformers_tier (config check,
no network I/O); HF Hub IDs use _tier_from_name (name-based, no network).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: separator-norm aliases, model_name/_name_or_path fallback, tests
- _norm_separators(): collapse _ . whitespace to - so underscore/dot model
ID variants (Qwen3_5, Qwen3_Next) match the canonical substring list
- _tier_from_name(): apply norm to both name and each substring so aliases
resolve without duplicating the substring lists
- _resolve_base_model(): try model_name then _name_or_path separately so a
self-referential Unsloth model_name doesn't hide the useful HF ID in
_name_or_path
- Gate get_base_model_from_lora on adapter_cfg_path.is_file() to avoid
eagerly importing transformers before the sidecar venv is on sys.path
- 17 new tests covering _norm_separators, separator-insensitive
_tier_from_name, and the model_name/_name_or_path fallback
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: only pre-resolve LoRA adapters in activation callers
activate_transformers_for_subprocess and ensure_transformers_version were
pre-resolving all local checkpoints via _resolve_base_model before calling
get_transformers_tier. After the model_name/_name_or_path fix, a full
checkpoint with a private/offline _name_or_path and no tier substring would
resolve to that HF ID, which can't be probed, bypassing the local config.json
model_type check entirely. Gate pre-resolution on adapter_config.json so full
checkpoints go straight to get_transformers_tier, which reads config.json
directly. LoRA adapters still pre-resolve as before.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: fix Qwen3.5 MoE/Qwen3.6 tier detection and dot-version false positives
- Add Qwen3.5 MoE (qwen3_5_moe / Qwen3_5MoeForConditionalGeneration) and
Qwen3-Next to the 5.3.0 config sets, so renamed local checkpoints route to
the sidecar instead of default transformers
- Let a 510/550 name match override a 530 config match, so Qwen3.6 (which
reuses qwen3_5 / qwen3_5_moe config ids) still routes to the 5.5.0 sidecar
- Stop normalizing version dots to hyphens so size names like Qwen3-5B and
Qwen3-6B are not promoted to a 5.x sidecar; underscore aliases still match
- Skip name matching for resolved values that look like stale local paths
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: close remaining codex P2s: adapter-only LoRA + 530-override path-hint guard
- adapter_model-only LoRA: add import-light _is_lora_adapter_dir/_has_adapter_weights
and gate activation/export pre-resolve on them, so LoRA dirs with
adapter_model*.safetensors but no adapter_config.json still resolve to their base
model (via _resolve_base_model's new unsloth_<model>_<ts> directory-name parse)
instead of tiering off the adapter folder.
- 530 override: only treat a resolved value as a name hint when it is a real Hub id;
a stale/renamed local path in model_name/_name_or_path can no longer flip a correct
530 config to 550. Current folder basename still allowed.
Added 7 regression tests; suite at 116 passing.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: address review feedback on tier detection
- Add Qwen3.5 text-tower model types (qwen3_5_text / qwen3_5_moe_text) to the
5.3.0 config set so text-only configs with stripped architectures still route
to the sidecar
- Apply the Qwen3.6 name override on the remote slow path too, so a renamed or
private repo whose config reuses qwen3_5 ids but names Qwen3.6 in
_name_or_path selects 5.5.0 instead of 5.3.0
- Treat an existing local path (or empty value) as a path, not a Hub id, in
_looks_like_hf_id so a real local checkpoint folder is not name matched
- Guard _resolve_base_model against non-string config values and compare paths
by realpath so relative or absolute self references resolve correctly
- Keep the LoRA adapter is_file check inside the OSError guard
* Studio: harden tier detection against malformed configs and bad paths
- _config_matches_tier no longer raises TypeError when a malformed config.json
carries a non-string model_type (e.g. a list) or non-list architectures; it
fails open to no-match
- guard the model_name-derived is_file/is_dir probes with _safe_is_file /
_safe_is_dir so a pathological or over-long path (e.g. a Windows long path)
fails open to the default tier instead of raising OSError
No routing changes for any valid model; purely defensive. Verified by a
cross-platform simulation (POSIX + NT path semantics) and a before/after tier
matrix that is unchanged for all previously supported models.
* Studio: trim verbose comments in tier detection
Shorten/remove over-long comments and docstrings, mainly on internal helpers,
without changing behavior. Verified code-only via comment_tools.py check; suite
unchanged at 128 passing.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Reset torch.compile cache poisoned by a stray forward before trainer.train()
A manual forward / forward+backward run under model.train() before
trainer.train() (for example a pre-train grad-norm probe like
out = model(**batch); out.loss.backward()) silently poisons training when
torch.compile is enabled. The stray training-mode pass is the first one in the
process, so it compiles and caches the model forward and, via AOTAutograd, its
backward graph in a one-off context that does not match the real training loop.
When trainer.train() reuses that cached graph the gradients come out NaN/Inf,
the loss never moves, and the run looks like it trains but never learns.
Observed on gpt-oss-20b (loss frozen at ~4.25, grad_norm NaN from step 1) with
both use_gradient_checkpointing="unsloth" and =True. It does not reproduce when
the probe runs under torch.no_grad(), nor with UNSLOTH_COMPILE_DISABLE=1, and a
single torch._dynamo.reset() before training fully cures it (loss 4.29 -> 0.0002,
identical to a run with no probe). Resetting the gradient-checkpointing buffers,
zero_grad, empty_cache, or for_training does not help, confirming the corruption
lives in the torch._dynamo / torch.compile cache.
get_peft_model now attaches a one-shot forward pre-hook that records whether a
forward ran before train(). prepare_for_training_mode checks it at the start of
train() and, if a pre-train forward was seen and torch.compile is enabled, calls
torch._dynamo.reset() (plus a pristine gradient-checkpoint reset and zero_grad)
and warns once. On the normal path (no pre-train forward) it is a strict no-op:
no dynamo reset, no recompilation, identical loss curve.
* Ignore no-grad pre-train probes and detect probes across the wrapper chain
A no-grad forward (with torch.no_grad(): model(**batch)) builds no AOTAutograd
backward graph, so it cannot poison the compiled training graph. Gate the marker
on torch.is_grad_enabled() so such probes no longer trigger a needless dynamo
reset, recompile and warning on an otherwise clean run.
Also walk the model wrapper chain (PeftModel / DDP / base model) when resetting
so a probe that ran on a different wrapper than self.model is still detected, and
tear down every detector hook in the chain. Re-installing the detector is now
idempotent and only re-registers when a prior hook was already removed.
* Walk DDP/FSDP .module when scanning for the pre-train marker
The chain walk followed only .model and .base_model, so a probe that fired on the
model below a DDP/FSDP wrapper (which exposes it via .module) left the marker
undetected and the poisoned compile cache un-reset. Add .module to the walk.
* Install pre-train detector on the full-finetuning path too
get_peft_model returns early when UNSLOTH_ENABLE_FULL_FINETUNING=1, before the
detector was installed, so full-finetuning runs (which still use torch.compile) did
not drop a graph cache poisoned by a stray pre-train forward. Install the detector
before both full-finetuning early returns (FastLlamaModel and FastBaseModel). The
detector is idempotent, so this never stacks duplicate hooks when get_peft_model is
also called on a LoRA model.
* torch.compile stray-forward reset: tighten comments (no code change)
* Wire stray-forward compile-cache reset into SFT path and PEFT pass-through
The pre-train forward detector is installed for plain LoRA/vision models in
get_peft_model, but only RL trainers ran the reset via prepare_for_training_mode.
A grad-enabled probe before SFTTrainer.train() therefore left the poisoned Dynamo
cache in place and the detector hook running on every training forward.
- trainer.py: wrap SFTTrainer.train to run _unsloth_reset_stray_compile_cache,
which both drops the poisoned cache and tears down the detector hook. For
UnslothSFTTrainer the later prepare_for_training_mode assignment supersedes it.
- llama.py: arm the detector before the 'Already have LoRA adapters' early return
so pre-wrapped PEFT models keep the reset capability.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Preserve detector evidence on reinstall + wire reset into plain Trainer path
P2 (_utils.py): _unsloth_install_pretrain_detector cleared marker['seen'] before the
live-hook early return, so a re-entrant get_peft_model/patch_peft_model after a
grad-enabled probe erased the recorded poisoning while leaving the hook installed,
and train() then skipped the Dynamo reset. Only reset seen when (re)installing a
fresh hook; keep it when a live hook is already recording.
P2 (llama.py): the detector is armed for every LoRA model, but only TRL SFT/RL train
wrappers consumed it. Inject _unsloth_reset_stray_compile_cache(self) at the start of
the generated _fast_inner_training_loop so a bare transformers.Trainer.train() also
drops a poisoned cache and tears down the hook. Idempotent with the TRL-wrapper reset.
* Make _unsloth_reset_stray_compile_cache an importable module-level helper
The reset was only defined inside the RLTrainer_replacement template string, so
'from unsloth.models.rl import _unsloth_reset_stray_compile_cache' raised ImportError
(swallowed) on the SFT auto-packing wrapper and the injected plain-Trainer loop -
both paths kept the poisoned Dynamo cache and the dangling detector hook.
Move the canonical implementation to unsloth.models._utils (next to the detector,
exported in __all__). The RL trainer template now imports it (no-op fallback if the
import ever fails), and trainer.py / llama.py import it from _utils too, so every
training entry point actually runs the reset.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
studio.backend.run.__main__ adds "--secure" argument via argparse.BooleanOptionalAction, which automatically creates negative --no-secure, that is with **NO** prefix, instead of **NOT**.
* Studio: persistent per-user trust_remote_code approval cache
The consent gate pins each approval to a content fingerprint (sha256 over every
repo .py), but nothing was persisted, so the dialog reappeared on every fresh
load of the same unchanged repo. This adds an on-disk, per-user approval cache
that lets the gate skip the dialog when the same user reloads the same code,
while keeping the safety guarantees intact.
Two-tier validation, both must hold or the user is re-prompted:
- Commit SHA (cheap, one HfApi.model_info().sha, no download): a match means a
byte-identical tree to the approved revision, so the scan/download is skipped.
- Content fingerprint (authoritative): used whenever the SHA is unavailable
(local path / offline) and always recomputed on a SHA miss. A new or edited
.py changes both the SHA and the fingerprint, so it is caught in every mode.
Safety:
- Keyed per subject; one user's approval never auto-runs code for another.
- CRITICAL is never stored or honored (guarded on both write and read), so a
hand-edited store cannot smuggle in an auto-approval.
- The malware (HF unsafe-file) gate stays unconditional.
- Fail-safe: a corrupt store, an unresolvable SHA, or any error degrades to
"ask again", never to "auto-approve". UNSLOTH_TRC_APPROVAL_CACHE_DISABLE=1
turns the cache off entirely.
New module utils/security/remote_code_approvals.py holds the store
(studio_root()/security/remote_code_approvals.json, atomic write, 0600, RLock)
plus the SHA resolvers. Recording happens at the single gate chokepoint when the
caller supplies the matching fingerprint, so subject is just threaded through
inference/training/export (orchestrators, routes, workers). The scan endpoint
returns already_approved so the frontend can skip the dialog on a cache hit.
Tests: new tests/test_trc_approval_cache.py covers cache miss, SHA-match skip,
SHA-moved re-scan, new-file re-consent, CRITICAL never cached (write + forged
read), disable flag, subject isolation, combined adapter+base key, corrupt
store, and no-subject bypass. Full security suite: 101 passed.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: make the approval cache skip only the prompt, never the scan
Codex found that the SHA "no-scan" fast path could run untrusted code without
re-consent. Removed it; the gate now always re-scans and the cache only seeds the
authoritative fingerprint check, so it can skip the dialog but never the scan.
- CRITICAL is hard-blocked on every load (the scan always runs), so a hand-edited
store that downgrades a CRITICAL repo's severity can no longer auto-run it
(P2: do not trust editable severity for SHA approvals).
- The fingerprint covers external auto_map repos, so changed third-party code
always re-prompts even when the primary commit SHA is unchanged; there is no
longer a SHA path that bypasses the fingerprint (P1: external auto_map repos).
- resolve_commit_sha is resolved fresh on every call (no memoization), so a repo
whose default branch moves after approval re-prompts instead of reusing a stale
cached SHA (P1: revalidate mutable Hub SHAs). The SHA is now only a conservative
secondary gate: a fresh resolvable SHA must match the approved revision, else the
seed is withheld; a None (local/offline) falls back to the fingerprint.
- Approvals record the scanner ruleset version (SCAN_RULES_VERSION); the gate
ignores approvals from an older ruleset so reclassified bytes are re-scanned and
re-shown instead of silently auto-approved (P2: invalidate on scan-policy change).
Tests: test_trc_approval_cache.py rewritten around the prompt-skip semantics
(unchanged repo still scans; SHA move / changed code / scanner-version bump /
disable flag all re-prompt; forged downgraded severity still blocks CRITICAL).
105 passed with test_consent_gate.py.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Trim comments to be more succinct
* Keep run-owner subject out of persisted config; serialize approval writes
Threading subject (the run owner's username / API-key id) into the training
config meant _sanitize_db_config persisted it into config_json, which
training-history GET returns to any authenticated user, leaking who started a run
in multi-user installs. Filter subject alongside the token fields; the worker
still receives it from the live config.
The approval store's RLock only guards one process, but approvals are recorded
from separate inference/export/training subprocesses, so concurrent writers could
clobber each other on os.replace and drop an approval (re-prompt). Hold a
best-effort cross-process file lock around the read-modify-write.
* Fail safe on a malformed approval store
A store with the right version but a non-dict shape (e.g. a hand-edited
"subjects": []) passed _load()'s check, then lookup chained .get() on a list and
raised, breaking every remote-code load until the file was removed. Validate that
subjects is a dict in _load(), and tolerate a non-dict per-subject entry in
lookup/record/forget, so a corrupt store fails safe (re-prompt) instead.
* Keep subject out of the MLX W&B run config
_run_mlx_training uploads the whole training config to W&B minus a sensitive set
that only listed hf_token/wandb_token/s3_config, so the authenticated subject
(username / API-key id) was sent to W&B as run config even though DB history
already strips it. Add subject to the W&B-sensitive filter, mirroring
training._sanitize_db_config.
* Tighten the W&B subject-filter comment
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: add Export to GGUF button on finished training runs
A completed run's 'Current Run' tab greys out, so it was unclear how to
export it: GGUF export lives on the separate Export page and there was no
link to it from a run. Add an 'Export to GGUF' button to the run progress
card (shown for completed/stopped runs) that deep-links to the Export page
with that run preselected via a new ?run= search param. The Export page
reads the param, selects the run, defaults to GGUF, and picks the run's
main checkpoint. No retraining is required to export a finished run.
* Studio: fix export deep-link checkpoint preselect and edge cases
Address review feedback on the Export to GGUF deep link:
- Move the main-checkpoint auto-select effect after the model-change reset
effect so it runs last; previously the reset cleared the checkpoint back to
null in the same commit, leaving the field empty on a deep link.
- Reset the applied-run ref when the ?run= param clears (e.g. navigating to
/export via the sidebar) so a later manual reselect of the same run is not
treated as a deep link.
- Trim trailing slashes before taking the run output-dir basename so a path
like /outputs/run/ still yields a name (the button no longer disappears).
* Studio: hide Export to GGUF on runs superseded by a resume
A stopped run whose output_dir was later reused by a resumed run is marked
resumed_later by the backend; its on-disk contents no longer match the older
run's metrics. Since the export deep link selects by output-dir basename,
showing the button on such a run would export the newer continuation instead
of the run being viewed. Carry resumed_later into the view data and hide the
button when set.
---------
Co-authored-by: danielhanchen <michaelhan2050@gmail.com>
The chat and sidebar scroll-fade overlays ended their gradient at the
`transparent` keyword, which is transparent black. Safari 27 Beta
(Liquid Glass) interpolates an opaque colour to transparent black
through a grey midtone, so the fades render as two solid grey bands
(top of the chat and above the composer).
Fade each gradient to the theme colour at zero alpha instead, so every
step keeps the same hue and no grey can appear. Fixes#6457.
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
* Auto-install SSM kernels (causal-conv1d, mamba-ssm) for inference loads
Mamba/SSM hybrids (Nemotron-H/Nano, Falcon-H1, Granite-4.0-H, ...) lazily import
mamba_ssm / causal_conv1d during from_pretrained, so loading them for chat failed
with 'mamba-ssm is required by the Mamba model but cannot be imported'. The training
worker already wheel-first installs these before a fine-tune; the inference worker
did not. Add utils/ssm_runtime.ensure_ssm_runtime and call it from the inference load
path so the same models load for inference. Training worker is untouched; a drift
test keeps the shared detection and pinned versions in lockstep.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* ssm_runtime: invalidate import caches, skip MLX, cover LoRA base
- Invalidate importlib finder caches in _is_importable and after a successful
wheel install, so a kernel installed earlier in this same process is actually
importable when the modeling code lazy-imports it during from_pretrained.
- Skip the SSM kernel install entirely on the MLX (Apple Silicon) load path:
these are CUDA/ROCm Torch kernels with no MLX use and no macOS prebuilt wheel,
so the source build would fail before the MLX backend loads the model.
- For LoRA loads, also run detection over the resolved base model, since an
adapter id like 'me/my-lora' won't match the SSM heuristics but its SSM base
(Nemotron-H, ...) is what needs the kernels.
Adds tests for cache invalidation and the MLX-skip / LoRA-base worker wiring.
* Tighten SSM autoinstall comments
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* ssm_runtime: verify wheel imports, HIP-aware source build, build heartbeat
Address review feedback:
- Verify a prebuilt wheel actually imports before trusting it; a CUDA/ABI-mismatched
wheel now falls back to a source build instead of returning success and failing later
with the cryptic lazy-import error.
- HIP-aware source build: require hipcc on ROCm, inject clang --gcc-install-dir, and use
the 1800s timeout, mirroring the training worker (ROCm has no prebuilt wheel).
- Emit a status heartbeat every 60s during the source build so a long (ROCm) build does
not trip the orchestrator's 300s inactivity timeout.
Tests cover the wheel-not-importable fallback and the missing-hipcc ROCm bail.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Make causal-conv1d best-effort and harden the SSM source build
- causal-conv1d is a fast path: models that merely want it (Qwen3-Next, LFM2)
fall back to torch, so a failed install must not reject an otherwise loadable
chat model on Windows/CPU/macOS or an ABI without a wheel. Only a true SSM
model's mamba-ssm requirement stays fatal, matching the training worker which
treats causal-conv1d as best-effort.
- The source build is reached only when not importable, including a wheel that
installed but failed to import; add --reinstall/--force-reinstall so it
replaces the broken install instead of no-opping as already satisfied.
- Add --no-cache to the ROCm uv source build to avoid reusing stale artifacts
from a partial HIP build, mirroring the training worker.
* Address review: install SSM kernels before transformers, harden import + Windows
Codex:
- Install the SSM kernels before importing transformers. run_inference_process
imported core.inference.inference (which imports unsloth/transformers) before the
load, and a sidecar transformers can evaluate its optional-backend gates against
the import state; installing causal_conv1d/mamba_ssm afterwards left those gates
unsatisfied and a Nemotron/Falcon/Granite load still failed with "mamba-ssm is
required". The initial model's kernels are now installed in run_inference_process
before the ML import, via a shared _ensure_ssm_kernels helper; _handle_load keeps
calling it (idempotent) for a LoRA's base and for later in-process loads.
- _is_importable now treats any import failure as "not importable", not only
ImportError. An ABI-incompatible native kernel (undefined symbol after a torch/CUDA
upgrade) raises OSError/RuntimeError; letting those escape reported
ssm_runtime_install_failed instead of falling back to reinstall/source build.
- Skip causal-conv1d on Windows (no prebuilt wheel), mirroring the training worker.
A causal-conv1d-only model (Qwen3-Next/LFM2) no longer drops a chat load into a
multi-minute untimed source build; it uses the torch fallback. mamba-ssm is still
attempted for true SSM hybrids.
Tests: test_ssm_runtime.py +5 (broken-kernel exceptions read as not-importable;
causal-conv1d skipped on win32 while mamba-ssm still installs). 36 passed.
* Trim comments to be more succinct
* Run security gates before installing SSM kernels
The SSM kernel auto-install is name-based (model_is_ssm is a substring match, no
config fetch), so a model id merely containing an SSM substring triggered a
native-package install (possibly a slow source build) before the malware and
remote-code consent gates ran. Extract those gates into _run_security_gates and
call it before the kernel install in both the pre-import path of
run_inference_process and in _handle_load, so a blocked or nonexistent model is
refused before any build. The gates are metadata-only and do not import
transformers, so they are safe to run before the pre-import install.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Resolve remote LoRA bases before importing transformers
_resolve_base_model only reads a local adapter_config.json, so a remote LoRA
adapter whose own id has no SSM substring but whose base is a Nemotron/Falcon/
Granite model had its base discovered only by ModelConfig in _handle_load, after
transformers was imported and its optional-backend availability snapshotted, so
the SSM kernel install there was too late. Add _remote_lora_base, a metadata-only
adapter_config.json fetch (no huggingface_hub / transformers import), and use it
in the pre-import path so the base is gated and its kernels pre-installed.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Gate only loaded roots, tier on the resolved base, read offline LoRA cache
Three follow-ups to the pre-import resolution:
- The security gate reused the SSM target list, which for a local full fine-tune
includes the config.json-recorded base. That base is never loaded, so scanning
it could falsely block a safe local checkpoint. Gate only the model plus a
genuine LoRA base (matching _handle_load's mc.is_lora), separate from the
broader SSM-install list.
- Tier activation ran on the raw adapter id, so a remote LoRA whose base needs a
sidecar transformers version imported the default and failed. Resolve the base
once up front and activate on it.
- _remote_lora_base bailed on offline before checking the hub cache, missing a
cached adapter's base. Read the cached adapter_config.json when offline or when
the fetch fails.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Keep the pre-import gate transformers-free; harden remote LoRA resolution
The pre-import security gate called security_load_subdirs, which imports
model_config and thus transformers, snapshotting optional-backend availability
before the SSM kernels are installed and defeating the ordering. Add
compute_subdirs to _run_security_gates and pass False in the preflight so it scans
from the root only (transformers-free); _handle_load still runs the authoritative
gate with full subdir scoping after the import.
_remote_lora_base now skips existing local relative paths (is_local_path) so a
checkpoint like outputs/run1 is never treated as a Hub repo, and distinguishes a
definitive 404 (not a LoRA -> None) from transient/offline failures (read the
cache), so a repo that is now a full model no longer resolves a stale cached base.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Probe a real model id for SSM kernels; respect HF_ENDPOINT
model_is_ssm is a substring match, so an arbitrary name could false-match and
force a mamba-ssm install that fails the load for a non-SSM model:
- a LoRA adapter id like user/falcon-h1-lora (the SSM-relevant code is the base's);
- a local checkpoint under an SSM-named parent dir, e.g. /runs/falcon-h1/llama-ckpt.
Add ssm_probe_identifier, which resolves the base (or a bare local checkpoint's
basename) and feed that to ensure_ssm_runtime from both the pre-import path and
_handle_load, so detection runs against a real model id, never an adapter id or
parent folders.
_remote_lora_base now honors HF_ENDPOINT so enterprise/mirror deployments resolve
the adapter base instead of always hitting huggingface.co.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Tighten comments in the pre-import SSM gate/install path
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
* Route dense NemotronH models to the transformers 5.10 tier
Dense NemotronH models (e.g. unsloth/NVIDIA-Nemotron-3-Nano-4B) describe their
layer stack with a hybrid_override_pattern that includes '-' (MLP) layers.
transformers only learned to parse that ('-' -> 'mlp' in pattern_mapping, 'mlp'
in valid_types and MIXER_TYPES) in 5.10; on 5.3/5.5 the config raises
KeyError: '-'. The model also ships auto_map remote code, so training and
inference that approve trust_remote_code load fine, but a native (TRC=False)
load such as export hits the built-in parser and fails with
'Failed to load checkpoint: -'.
Detect dense NemotronH from config.json (a '-' in hybrid_override_pattern, or
'mlp' in an expanded layers_block_type) and route it to the 5.10 tier, where the
model loads natively without remote code. Pure-MoE NemotronH configs are
unaffected and keep their existing tier.
Covers both the local config.json and the remote HF-id paths, and adds tests for
the detector and the resulting tier selection.
* Tighten _nemotron_h_needs_mlp_support docstring
* Detect dense NemotronH in nested, cached, and resolved-away configs
Three gaps could still route a dense NemotronH (MLP '-' layers) to a tier
below 5.10 and hit KeyError: '-':
- VL wrappers (e.g. NemotronH_Nano_VL_V2) keep the dense language model under
llm_config/text_config; the detector only checked the top-level model_type.
Recurse into nested language configs.
- Offline or blocked config fetches returned None for an already-downloaded
repo. Read config.json from the HF hub cache before any network.
- A local checkpoint resolves to its base before tiering, so an offline/private
base discarded the local config that revealed the dense pattern. Prefer the
higher tier of the resolved base and the original path.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden NemotronH tier detection follow-ups
Address review of the nested/cached/resolved-away detection:
- The local re-check ran the full tier detector on the original path, so a bare
LoRA adapter under e.g. /runs/gemma-4-x/llama-lora could upgrade a default base
via directory-name substrings. Gate the re-check on a real local config.json so
it reads metadata, not path names.
- The HF hub cache was read before any network, so an online tier check could
serve stale config.json after the repo changed upstream. Consult the cache only
offline or after a failed fetch.
- Reading the cache imported huggingface_hub during tier detection, which runs
before a sidecar venv is activated and could pin the default-env hub into
sys.modules. Resolve the cache path with stdlib only.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Trim comments to be more succinct
* Select newest hub-cache snapshot by mtime and retry transient config fetches
The HF cache fallback in tier detection picked the lexicographically-first
snapshot when refs/main was absent (commit-pinned downloads), which can be an
older SHA than the Hub would load. Sort snapshots by mtime instead.
A transient online fetch failure cached the hub-cache fallback under the normal
(model_name, token) key, so a long-lived worker kept serving stale metadata even
after connectivity recovered. Return the fallback without memoizing it so the
next call retries the network.
* Harden config.json tier detection against auth failures and transient blips
- _load_config_json: a 401/403/404 from the raw Hub request is a definitive access
answer, not an outage. Return None instead of falling back to the HF hub cache, so
an unauthenticated or wrong-token request can never read another caller's cached
private metadata.
- _check_config_needs_510/550: only memoize the derived tier when the underlying
config read was definitive (local file, offline cache, or a completed fetch).
A transient fetch fallback is no longer pinned, so the tier is re-evaluated once
connectivity returns instead of staying stuck on the lower tier.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Tighten comments in tier-detection auth/cache paths
---------
Co-authored-by: Daniel Han <michaelhan2050@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
The gpt-oss dtype branch selected the BnB UNSLOTH_FORCE_CUSTOM_DTYPE path on the
raw load_in_4bit flag. A native MXFP4 checkpoint loaded by exact name with the
default load_in_4bit=True (e.g. openai/gpt-oss-20b) keeps the flag set until
check_and_disable_bitsandbytes_loading runs inside FastBaseModel, which is after
this branch sets the env var. So MXFP4 gpt-oss got the BnB down_proj/router
compute-dtype path (which also calls dequantize_module_weight on a non-bnb module)
instead of the MXFP4 bias upcast. Gate on the effective bnb state
(load_in_4bit and _bnb_compatible_quant), mirroring the _load_in_4bit_ token gate
introduced in #6504. BnB-4bit and -BF16 gpt-oss are unchanged.
* Studio: redesign Select model dropdown to match Hub design
Make the chat Select model picker easier to scan by reusing the Hub
on-device card's visual language.
- Rows now split owner/name, add a param chip, a DotTag format pill,
a tabular size, and a Loaded marker on the active model.
- Hub models / Fine-tuned tabs reuse the Hub's exact .hub-tab-toggle
styling (selectors extended in hub.css to the selector menu).
- Add a Downloaded / Recommended / Custom section toggle on the Hub
tab to filter the list.
- Widen the popover and nudge the scrollbar toward the edge.
* Studio: move section toggle below search, size tabs to label
Put Downloaded / Recommended / Custom under the search bar in their own
row so Hub models / Fine-tuned no longer wrap. The section toggle uses a
smaller font and sizes each tab to its label instead of equal widths.
* Studio: extract pure row-meta helpers into their own module
Move splitRepoLabel, classifyMetaToken, and parseMetaTokens out of
pickers.tsx into row-meta.ts. No behaviour change; keeps the presentation
logic free of React/DOM deps so it is easy to test in isolation.
* Studio: content-size the source tabs and add section icons
Size the Hub models / Fine-tuned tabs to their labels (with side
padding) like the section toggle, instead of stretching full width. Add
a leading download, star, and folder icon to Downloaded, Recommended,
and Custom.
* Studio: stop source tabs stretching and hide empty Fine-tuned tab
The popover is a flex column, so the fit toggle stretched full width;
add w-fit/self-start so it sizes to its content. Also hide the
Fine-tuned tab when there are no fine-tuned models, defaulting to Hub
models.
* Studio: keep only fine-tuned models in the Fine-tuned tab
Local models (LM Studio, Ollama, custom folders) carry source "local"
and already show in the Hub tab's Downloaded / Custom sections, so
exclude them from the Fine-tuned tab and from its visibility count.
Extract the tab rules into source-tabs.ts.
* Studio: show local providers under Downloaded, Recommended first
Show LM Studio and other local provider models in the Downloaded
section in all modes (was chat-only). Put Recommended first and make it
the default section. Add a little more space below the search bar.
* Studio: make Recommended a sortable live Unsloth listing
Replace the static Recommended list (and its collapse chevron) with a
sort dropdown over Unsloth's own models: Recommended, Trending, Most
likes, Downloads, Recently updated. Recommended shows recently uploaded
GGUF/MLX models that fit the device (hidden if they do not); the other
sorts list all Unsloth models, badged but never hidden. Adds a sort
option to useHfModelSearch and a pure recommended-fit helper.
* Studio: size Recommended models from the repo name when metadata is missing
GGUF and MLX repos rarely expose safetensors metadata, so a large model
with no size could pass the Recommended fit check because unknown size was
treated as fitting. Parse the parameter count from the repo id, including
the Gemma E series, and hide anything we still cannot size.
* Studio: detect model capabilities and family from HF tags
Thread tags and the pipeline tag through the model search results and add a
pure helper that infers vision, reasoning and audio plus the architecture
family, falling back to repo-name keywords when tags are absent.
* Studio: add row details and inline section sorting to Select model
Give each model row more detail and make the Hub sections easier to scan:
- Show vision, reasoning and audio badges plus the architecture family tag
on each row, alongside the params, format and size.
- Drop the redundant unsloth/ prefix on the Recommended rows.
- Rename the Recommended section tab to Unsloth and enlarge the section tabs.
- Move the sort dropdown inline to the right of the tabs at a fixed width.
- Add Recent, Size and Downloaded sorting to the Downloaded and Custom tabs.
- Remove the header icons, pad the subheadings, and grow the list height.
* Studio: tune the Select model sort dropdown and trim row badges
- Recommended now lists the most recently created Unsloth repos.
- Narrow the sort dropdown, remove its border, and truncate long labels.
- Tighten the gap between the section tab icons and their labels.
- Remove the architecture family tag from rows since it repeats the name.
* Studio: extract the PillTabs toggle into a shared module
Move the segmented pill toggle out of the model selector into its own file so
the Hub picker can reuse it for a format filter without duplicating the markup.
* Studio: fix Recommended infinite scroll and add a format filter
- Re-attach the scroll observer on each loaded page so a filtered Recommended
list keeps paging until the viewport fills instead of spinning forever with
nothing new appearing.
- Add an All / GGUF / MLX / Safetensors toggle on the Unsloth listing that
filters every sort.
* Studio: default Recommended to Trending, rename Downloaded to On Device, and fade the scroll edge
Sort: default the Recommended view to Trending and add a Name option to
the On Device / Custom sort. Recent now orders by last load time while
Downloaded orders by file date, tracked in localStorage (model-usage.ts).
Formats: show the format filter on all three tabs (Unsloth, On Device,
Custom), exclude mobile GGUF builds from Recommended, and flag GGUF rows
that exceed the device with the same OOM badge as safetensors.
Polish: download-icon badge on already-downloaded Recommended rows, the
hugeicons view stroke-rounded vision badge, Search all models placeholder,
matched popover padding, and a top-edge mask fade once the list scrolls.
* Studio: size GGUF repos from gguf metadata so large ones flag OOM
Repos with no <n>B token in the name (Kimi, MiniMax) had no param count
and so never showed an OOM badge. Request the gguf expand field from
Hugging Face and read gguf.total, so those repos get a param chip and an
OOM badge when they exceed the device budget.
Keep the row name full contrast when over budget (the OOM badge already
signals the fit), shorten the format and sort dropdowns, narrow the
popover, and rename Recently updated to Recent and All formats to All.
* Studio: address selector review feedback
Add WAI-ARIA roving tabindex and Arrow Left/Right navigation to the pill
toggle so only the active tab is in the tab order. Keep the chat-only
GGUF/MLX filter for every Recommended sort, not just Recommended, so
chat-only users do not see unrunnable checkpoints under Trending. Feed
both listings' GGUF hints into repo detection so a tag-only GGUF in
Recommended expands variants instead of loading as a checkpoint.
* Studio: scope Select model search per tab and add an MLX tag
Search is now per section. The Unsloth tab searches the Unsloth HF
listing only, On Device filters downloaded and LM Studio models by name,
and Custom filters custom-folder models, each with its own empty state.
MLX repos get an MLX pill mirroring the GGUF tag. Downloaded quants in
the Unsloth and search lists get the same delete action as On Device.
Also: revert the model name to normal weight, narrow the popover to
558px so the format and sort dropdowns sit one gap-2 from the tabs,
tighten the dropdown menus to match the Projects activity Select, and
make the empty On Device state name the active format filter.
* Studio: show local ./models on the On Device tab so they stay selectable
Models under the local models directory (source models_dir) flow in as local
models but were dropped from every list: filtered out of Fine-tuned and never
re-added by the Hub picker, which kept only LM Studio and custom-folder
sources. Capture them in the local refresh and render a Local models group on
the On Device tab, with the same format, search, and chat-only GGUF rules as
the other local groups.
* Studio: add a Hub button beside the Select model search bar
Adds a Hub button next to the search bar that opens the full Hub Discover
page to browse more models. Styled like the section tabs (rounded, no
border, soft shadow with a faint top layer) and darkens on hover. Also
nudges the format and sort dropdown chevrons a touch toward the edge.
* Studio: align Select model padding and tighten the format pills
Sizes the popover to the tab cluster so the left and right padding match,
and drops the top row below the rounded corner so the Hub button lines up
with the Trending dropdown. Gives the Hub button a fixed width, lets the
list scrollbar sit inside the box, and shrinks the format pill dot with a
tighter dot-to-label gap.
* Studio: label the Hub button Search Hub and match the dropdown width
Renames the button to Search Hub, sets its width to the format and sort
dropdown width so it lines up above them, and tightens the icon gap.
* Studio: drop the vision and reasoning row badges to declutter
Removes the vision and reasoning capability icons from the model rows so
they read cleaner. Audio is kept.
* Studio: add a safetensors pill, hide diffusion models, eye on Vision
Gives safetensors rows a format pill and size so their meta matches GGUF
and MLX, drops image and video diffusion models from the listing since they
cannot run in chat, and shows an eye icon next to the Vision tag. Also
removes the em dashes from the Projects export and import labels.
* Studio: gate recommended folders on real weights and polish the selector
Only show a Recommended chip once the well-known dir actually holds
weights, so an empty LM Studio or Ollama scaffold no longer suggests
itself. _dir_has_downloaded_model checks for a GGUF/safetensors file or
a non-empty Ollama manifests store, with a bounded walk.
Selector polish: round the popover and option menus a touch more,
lighten the OOM badge in dark mode, soften the inner dropdown shadow,
even out the padding, and lift the toggle track and field triggers so
their edges read against the popover.
Also catch CogVideoX in the diffusion name fallback.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: align the dark Select model panel with the sidebar
Match the popover, fields, dropdowns, tab toggle and row states to the
sidebar surface and accent so the dropdown reads as one piece in dark
mode. The active tab pill and Search Hub button sit a touch lighter
than the track, and the inner option menus drop their drop shadow for a
flatter look. Light mode is unchanged.
* Studio: re-derive the Select model tab on open
The picker remounts each time the dropdown opens, but the source tab
state did not, so a persisted fine-tuned or connected selection that
only lands in its list after an async load would reopen on Hub. Reset
the active tab to the selection-derived default on the open edge, while
still letting the user switch tabs freely within a session.
* Studio: fold Custom into On Device and polish the picker
Merge the Custom tab into On Device so custom folders sit right below
the downloaded models, with a folder shortcut on the group header.
Rename the first Hub tab to Recommended, give the format dropdown
colored dots, even out the tab row spacing, and tighten the popover
width. Align the folder browser with the app dialogs (soft surface,
roomier padding, green confirm, grey hover).
* Studio: fix On Device controls and nudge the folder browser close
The Hub redesign merge dropped the old Search Hub button styling, so the
On Device search row rendered flat. Point the search input and Search
Hub button at the shared .field-soft surface so they match the rest of
the Hub controls, and lift the folder browser close button slightly.
* Studio: run the Select model search on the Hub search stack
Point the picker at the Hub's useHubModelSearch and useHubInfiniteScroll
instead of its own useHfModelSearch/useInfiniteScroll, scoped to unsloth
so the listing matches the old one. Both the search and the recommended
feed now share the Hub implementation, so there is one search path. The
Hub result folds GGUF params into totalParams, so the dead ggufParams
fallback is dropped.
* Studio: trim the recommended sort to Recommended, Trending, Recent
Drop Downloads and Most likes from the sort dropdown.
* Studio: give the section tabs room off the rounded edge
The fit-mode toggle wrapped the tabs with no inset, so On Device sat
tight against the rounded-full edge. Add a small horizontal inset and
widen the popover a touch to fit it.
* Studio: drop the legacy HF search hooks for the Hub ones
Migrate the training model and dataset sections, export page, onboarding
steps and recipe dataset combobox off useHfModelSearch, useHfDatasetSearch
and useInfiniteScroll onto the Hub equivalents, scoped to unsloth so the
listings match. The picker reads recommended param counts off the search
results it already has instead of a separate fetch. Removes the duplicate
search stack: use-hf-model-search, use-hf-dataset-search,
use-hf-paginated-search, use-infinite-scroll, use-recommended-model-vram
and the old lib/hf-cache.
* Fix model selector section toggle proportions
Remove the fit-mode track inset so the active pill sits flush to the
track edge, matching the Hub's segmented controls.
* Tighten model selector width and tab padding
Reduce the popover width so the right edge aligns with the row, and
widen the fit-mode tab padding so On Device clears the track edge.
* Refine Recommended formats, sort width and tab padding
Recommended now suggests GGUF anywhere and MLX only on Mac, never
safetensors. Size the sort dropdown to its label so Recommended no
longer truncates, and match the On Device trailing gap to the active
pill's leading inset.
* Flush section toggle and match dropdown font to Search Hub
Drop the trailing track pad so the active pill fits the track exactly
at either end. Size the sort and format dropdown text to text-xs like
the Search Hub button, and clip long labels without an ellipsis.
* Fix sort menu checkmark overlap and lock dropdown widths
Keep the option's right padding so the selected checkmark no longer
overlaps the label, and let the open menu expand to fit it. Set the
format and sort triggers to a fixed width matching the Search Hub
button so they always line up.
* Keep section toggle and dropdowns on one row
Drop the wrap and size the Search Hub button, format and sort dropdowns
to a shared 100px so they stay equal width and fit on one row without
widening the box.
* Studio: pre-load inference settings dialog with native context
Add a gear on downloaded GGUF quant rows that opens a settings dialog
to adjust inference parameters before loading a model:
- Context length, KV cache dtype, speculative decoding and tensor
parallelism, all written to the runtime store the load call reads.
- Settings can be remembered per model in localStorage.
- The context slider ceiling and "Model supports up to N tokens" come
from the model's native context, read from GGUF metadata and returned
by /api/models/gguf-variants once a variant is downloaded.
Also drop models Studio can't run for chat (diffusion, image, video)
from the recommended feed and Hub search, plus minor selector polish
on row hover padding, Search Hub and dropdown widths, and tab spacing.
* Studio: model selector polish and memory-aware load warning
Search and listing:
- Drop the "Recommended" and "Hugging Face" section labels while
searching so results read as one list; keep the format and sort
dropdowns visible so search results can still be sorted and filtered.
- Request gguf metadata in the Hub listing so GGUF repos report a
parameter count, restoring the OOM badge for repos without a size
token in the name (Kimi, MiniMax, GLM).
Load settings dialog:
- Warn when weights plus the KV cache at the chosen context exceed
available memory. The KV size is sized by the backend's
architecture-aware estimator via a new kv-cache-estimate endpoint;
the budget uses VRAM plus system RAM. Best-effort, no warning on
failure or on auto context.
- Context Length placeholder reads "auto"; dark background slightly
lighter.
Other:
- Clicking the Custom Folders header opens the folder browser; its
title now reads "Select folder to detect models".
- On Device sort lists Downloaded last.
- Smaller chat template editor font; rounded wrapper clips the prompt
and template editor scrollbars so the right corners stay round.
* Studio: fix load dialog memory warning budget and KV dropdown width
- The memory warning never fired without a discrete GPU. useGpuInfo
returned zero system RAM in that case, so the budget was always zero.
Surface system RAM even when no GPU is present (Mac unified memory),
and have the load dialog read memory directly instead of through props.
- Give the dialog fields shrink-0 so the KV Cache Dtype value (e.g.
q8_0) is not squeezed and clipped by the row.
* Studio: fold fine-tuned models into On Device tab
Remove the Hub models and Fine-tuned source tabs. Fine-tuned models now
show as a section in the Hub tab's On Device view, above Custom Folders,
with the Train icon and a collapse toggle. The section only appears when
the user has fine-tuned models. With no external providers the lone Hub
tab hides its own toggle.
Also: tick-circle Show hidden checkbox and drop the divider above Eject;
keep run settings load params (KV cache dtype, speculative, tensor
parallel) from being clobbered by a mid-load status poll.
* Studio: stage load settings in the sidebar with a Load on selection toggle
Replace the pre-load settings popup with a staging flow in the Run settings
sidebar. The gear on a downloaded quant row now stages the model and opens
Run settings with Load model and Cancel buttons, so options like context
length, KV cache, speculative decoding and tensor parallelism are set before
the model loads. A "Remember these settings" tick reuses them next time.
Add a global Load on selection toggle in Settings, Chat tab (default on).
On: Unsloth auto-picks the best settings for your hardware and loads on
selection. Off: picking a model stages it in Run settings to customize first.
The gear always stages, regardless of the toggle.
Other polish in this change:
- Fine-tuned models live under the On Device tab, with a train icon on the
header that jumps to the Fine-tuned section.
- Default to the On Device tab when downloads exist, otherwise the last used
section.
- Standard Unsloth tooltips on the train, folder and gear icons.
- Request the gguf param count on every Hub listing fetch so Kimi, MiniMax
and GLM show a size badge.
- Search Hub hover state, scrollbar position and minor spacing fixes.
Remove the old inference load settings dialog.
* Studio: always show the fine-tuned shortcut and smooth out the picker
- Fine-tuned section and its train shortcut now always show on On Device,
with an empty state when no fine-tuned models exist yet.
- Folder icon on the header jumps to Custom Folders instead of opening the
browse popup, matching the train shortcut.
- Folder browser keeps the list mounted and dims it while refetching, so
toggling Show hidden or changing folders no longer flashes.
- Drop the tooltip hover grace area in the picker so moving between the
train, folder and gear icons switches the tooltip at once.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Studio: add quantization display options and drop the fine-tuned empty text
- Settings, Chat: 'Expand quantizations' toggle. On expands every On Device
GGUF model's quantizations by default; off keeps them behind a click
(default).
- Settings, Chat: 'Show all quantizations' toggle. On lists every quant
including ones not downloaded (default); off shows downloaded only.
- Remove the empty-state line under the Fine-tuned header; the header still
shows on its own.
* Studio: let expanded quantizations collapse on click and split the On/Off help
- With Expand quantizations on, clicking an On Device model now collapses or
re-expands its quantizations. The collapse state is in memory only, so it
resets on reload and when the setting is toggled.
- Put the Off sentence on its own line in the quantization setting descriptions.
* Studio: reorder chat settings and rename the model section
- Rename the Models section to Select model settings and move it above the
Chat menu section.
- Trim the section and Load on selection descriptions.
* Studio: tighten the On/Off lines in the model setting descriptions
Use a line break instead of separate spans so the On and Off lines sit on
consecutive lines without the extra paragraph gap.
* Studio: top-align the Load on selection toggle
Add an alignTop option to SettingsRow and use it so the toggle sits at the top
of the row next to the label, not centered against the tall description.
* Studio: put the gear hint and example chip on one line
Move the gear example chip inline with its label so it reads as a single line
instead of wrapping onto its own row.
* Studio: move the New badge from API keys to Chat settings
Add the New badge to the Chat settings tab and drop it from API keys.
* Studio: line the Load on selection toggle up with the first description line
Offset the top-aligned control past the label row so it sits next to the On
line instead of the label.
* Studio: label the chat menu item Chat with Files (RAG)
Rename the Chat with Files entry in the chat menu settings to clarify it is RAG.
* Studio: drop the pill around the gear example so it fits on one line
Remove the background and padding from the gear example chip so it sits inline
with its label at a lower height.
* Studio: fold the gear example into the description line spacing
Render the gear example inline in the same text block so its line spacing
matches the On and Off lines instead of an extra flex gap.
* Studio: scope Show all quantizations to On Device only
Gate the downloaded-only filter on an onDevice flag so Recommended and other
browse lists always show every quant, and note On Device in the setting copy.
* Studio: tidy On Device GGUF rows
- Drop the redundant Quantizations subheading under On Device models.
- Relay GGUF vision support up to the model name as a Vision badge instead.
- Drop the repo size from On Device GGUF model rows since the quants already
show their size.
* Studio: pin the eject button and tidy General settings
- Move Eject loaded model out of the scrollable list into a centered footer so
it stays in view no matter how far the list is scrolled.
- Space out and center the gear example in the Load on selection description.
- General: drop the duplicate Unsloth version section, move llama.cpp
notifications above Helper LLM, and note new models in its description.
* Studio: add left padding before the gear example
Nudge the gear example away from its label with a small left margin.
* Studio: make the eject footer a sticky bar over the list
Pin Eject loaded model to the bottom of the scroll area with the menu
background so rows scroll under it, and drop the divider line.
* Studio: drop the eject footer background, keep it a sticky button
Make the sticky eject a centered transparent button so it coexists with the
rows scrolling behind it. The wrapper ignores pointer events so only the button
is clickable.
* Studio: give the eject button a solid background
Add the menu background, a border and a soft shadow to the sticky eject button
so it reads as a floating button over the list.
* Studio: restore the eject footer block, keep hover on the button only
Bring back the full-width menu background behind the sticky eject footer, but
keep the button compact and centered so the hover stays on the button.
* Studio: show the vision badge on On Device rows without expanding
- cached-gguf listing reports has_vision (mmproj present), so the badge shows
on the model name without opening the quantizations.
- Make the vision badge icon-only with a tooltip: "This model can process
image inputs". Falls back to the expander-reported value on older backends.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Make LM Studio and Local models sections collapsible
* Fade the eject footer instead of a solid block
* Wrap the vision badge in a bordered pill
* Taller model list with the eject footer pinned to the bottom
* Use purple for the vision badge to set it apart from GGUF
* Reduce the model list height
* Make the eject button inline with no background block
* Match the vision badge color to the Hub indigo tone
* Shorten the model list and square off the format tags
* Pin the eject button so it floats at the bottom of the list
* Give the floating eject button a tinted background
* Add bottom clearance so the list ends on white space under the eject button
* Match eject button to the menu background and unify the settings gear icon
* Move eject below the list and match its shadow and dark background
* Drop the min height so short model lists leave no white space
* Remove the eject button fill so it never covers the list
* Nest dropdown hover radius inside the menu corners
* Float the eject pill again and fix sort dropdown hover radius
* Make the eject button opaque in both themes on hover and dark
* Trim the model menu bottom padding so it stops clipping the last row
* Match dark eject background to the Search Hub button and pad row indicators
* Fade the model list bottom edge while rows sit below the fold
* Lift the eject button and trim the section toggle right padding
* Nudge the model list taller and run the bottom fade to the box edge
* Nudge the model list slightly taller
* Remove the eject button shadow
* Align the eject button to the right
* Widen the Search Hub and dropdowns and right-align them
* Seat the eject button at the base and restore On Device right padding
* Reduce the Search Hub and dropdown width by 4px
* Widen the model menu so the section toggle keeps its padding
* Make the eject button an icon-only button with shadow
* Tighten section tab padding to cut the grey between tabs
* Revert section tab padding back to px-3
* Remove the section toggle trailing padding
* Add an eject button beside the model selector trigger
* Shrink the in-list eject button to a smaller proportional size
* Raise the in-list eject button
* Make the trigger eject a bare icon next to the dropdown arrow
* Revert eject back to the labeled button on the right
* Place the format and sort dropdowns next to the section toggle
* Raise the eject button and shorten its label to Eject model
* Widen the gap between the toggle and dropdowns slightly
* Align Search Hub with the last dropdown via a shared-width grid
* Narrow the model menu for symmetric padding
* Stretch the search row so Search Hub lines up with the last dropdown
* Inset the list so the right padding matches the left
* Right-align dropdowns and full-width search so Search Hub meets the last dropdown
* Pack section toggle and dropdowns with a uniform gap
* Inset search row so Search Hub aligns with the Trending dropdown
* Trim model menu right padding to match the left
* Nudge model list scrollbar inward
* Move eject button to the bottom left with a light shadow
* Shorten show all quantizations description
* Keep eject button right-aligned, nudged in from the edge
* Move Connected into the section toggle as a cloud-icon tab
* Align eject button with the format tag edge
* Right-align Connected layout so Search Hub meets Trending
* Download selected models through the Hub download manager
* Add Other models section for non-Unsloth downloads
* Add directions icon and shortcut for Other models section
* Space out subheadings and gate Other models on non-Unsloth downloads
* Use direction-right icon for Other models
* Use flag icon for Other models
* Widen Connected menu so dropdowns align with Search Hub
* Model selector: truncate long quant labels and tidy layout
- Hub GGUF card: truncate long file-path quant labels with an ellipsis
instead of overflowing the row.
- Connected layout: left-pack the dropdowns and size the box so the last
dropdown's right gap matches the pill's left gap, with Search Hub on its edge.
- On Device: show MLX/Safetensors with the size on non-GGUF rows.
- Connected list rows use the same grey hover as the tabs; the selected
section tab no longer shows a hover change.
* Model selector: drop stale custom section on restore
A persisted custom section value no longer maps to a tab, so restoring it
opened the picker to an empty view. Fall back to recommended instead.
* Model selector: align the non-connected search bar with the All dropdown
Nudge the non-connected box width so the search bar's right edge meets the
All dropdown, which lands Search Hub on the last dropdown's edge.
* Studio chat model selector: remember last tab, route non-GGUF downloads through Hub, stack overlays
- Restore the last Hub section (Recommended / On Device) on every open instead of always snapping to On Device when downloads exist.
- Route uncached non-GGUF repos (safetensors / MLX) through the Hub download manager via a snapshot download, so every model download shows in the bottom-right indicator and follows Load on selection like GGUF.
- Allow safetensors in Recommended on Mac (they run locally there now), and honor the Safetensors format filter instead of dropping it via the recommendation default.
- Stack bottom-right overlays in one column so the download panel and banners never overlap.
- Add evenly spaced divider lines between the On Device subheadings.
- Pad the bottom of the list so the floating Eject pill never covers the last row.
* Studio downloads panel: widen left padding on header and rows
Bump the left inset to pl-4 while keeping pr-3 so the collapse and cancel buttons stay put.
* Studio: update cached-gguf route tests for the has_vision field
list_cached_gguf now returns has_vision per row (vision badge on On Device);
the expected dicts were missing it. True for the mmproj vision repo, False elsewhere.
* Studio: keep MLX/safetensors selectable in chat-only Mac search
The empty Recommended view allows GGUF plus MLX/safetensors on Mac, but the
curated and HF search lists dropped non-GGUF in chat-only via a GGUF-only filter,
so typing a query hid runnable Mac models. Reuse isRecommendableFormat in both
lists so search matches the empty view (chat-only non-Mac stays GGUF-only).
* Model selector: restore global model search and fix GGUF/device-fit regressions
- Search: training, export and onboarding pickers searched only the unsloth org
on a typed query. Restore the prior behavior (global Hub search with unsloth
floated first when a query is typed, curated unsloth listing when empty).
- Recommended browse: the GGUF/MLX-only gate ran before the format filter, so
the Safetensors filter and the Trending/Recent sorts always came back empty.
Apply that gate only for the Recommended sort and chat-only mode.
- GGUF metadata: request the gguf expand field through listModels so repos with
no size token in the name (Kimi, MiniMax, GLM) report a param count for the
size and OOM badge.
- Local GGUF: custom-folder and standalone ./models/*.gguf files now load
directly with the GGUF marker instead of dead-ending in the variant expander,
and scanned GGUF folders are classified via a backend model_format hint.
- Device fit: use system RAM in the budget on unified-memory hosts, and keep MLX
rows selectable on chat-only Macs.
- kv-cache-estimate: resolve the quant from the snapshot-relative path, skip MTP
drafter files, and prefer the most complete snapshot (mirrors the variant
scanner). Bound the Ollama manifest walk.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Model selector: classify suffixless local GGUF folders consistently
Complete the model_format plumbing so a GGUF folder is detected and loaded
through the same GGUF path that the format filter already uses:
- _scan_models_dir: a config.json no longer disqualifies a folder whose only
weights are .gguf, so HF GGUF repos shipping a config still classify as GGUF.
- _scan_lmstudio_dir: emit model_format for every GGUF row (LM Studio dirs
rarely carry a -GGUF suffix), via a shared _dir_model_format helper.
- Custom Folders and LM Studio rows: use localModelIsGguf (the same helper the
filter uses) so the row label, expand-vs-direct-load, and isGguf flag agree;
a suffixless GGUF folder no longer filters as GGUF but loads as non-GGUF.
Adds tests/test_local_model_format.py covering the classification rule.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio model selector: tighten section spacing
Trim each subheading's gap to its rows (pb-1.5 to pb-1) and pull the On Device
heading block tight to the controls while Recommended keeps a little top room.
* Hub: format filter fix, sort defaults, avatar and layout polish
- Format dropdown now filters the feed's Latest list too, so the default
GGUF hides fp8/safetensors and picking a format changes the rows.
- Latest Unsloth Models sorts by newest created, not recently updated.
- Sort dropdown order: Newest, Trending, Most downloads, Recently
updated, Most likes.
- Unsloth uploads with no upstream provider logo show the Unsloth avatar
instead of a colored initial.
- Owner scope pill gets a little more room before the chevron.
- README detail column lines up with the top bar (both-edges gutter).
- Long file-path quant labels truncate instead of overflowing the row.
- Model list keyboard nav no longer clips the focus ring.
- Run settings sheet: restore the Remember settings toggle and larger
Load/Cancel buttons on the staged load flow.
* Hub: hide the RAG embedding model from browse previews
The Hub discover feed and chat model selector pull from the Hugging Face
listing on the client, which the backend _is_hidden_model filter never
touches, so the RAG embedder (unsloth/bge-small-en-v1.5-GGUF) and the
llama.cpp validation probe leaked into the lists.
Added isHiddenModelId mirroring the backend needles and filtered it out of
the discover rows, the trending feed, and the selector's recommended and
Hugging Face search lists. Per-repo file and download views are untouched,
so the model is never deleted and a reinstall still shows it as already
downloaded.
* Studio: skip hidden dirs when checking a folder for downloaded models
_dir_has_downloaded_model walked the tree with rglob("*") bounded by
max_entries. rglob yields entries in arbitrary order and counts every one, so a
model directory that also holds a large hidden subtree (.git/.cache/venv) could
exhaust the budget before reaching the real weights and falsely report no model,
hiding a valid Recommended-folder chip. Replace the generic-weights pass with a
bounded BFS that skips hidden directories so their entries can't starve the walk.
Adds a regression test (50-entry .git beside the weights, max_entries=10).
* Fix/adjust model selector handling for PR #6364
* Studio: address codex review on the staging/recommended-folder paths
- chat-page auto-load: selectModel only clears pendingSelection on success, so a
failed auto-load left the hidden stage (and its edited load knobs) behind.
Abandon the stage when it still matches the failed pick.
- model picker: count fine-tuned rows in the On Device empty check so a
fine-tuned-only tab no longer shows a false 'No models on device' message
above the Fine-tuned section.
- general settings: add the remembered per-model load settings key to PREFS_KEYS
so 'Reset all local preferences' actually clears it.
- recommended-folders: recognize PyTorch .bin weights (gated by the scanner's
weight-name prefixes) so a .bin-only model folder still earns a chip; add tests.
* Studio: name-gate .bin weight detection and complete selector preference reset
Follow-up to the codex review on the model_format/recommended-folder paths:
- _dir_model_format and _scan_models_dir treated any .bin (incl. tokenizer.bin)
as a non-GGUF weight, so a suffixless GGUF folder shipping a companion .bin was
misclassified as a plain checkpoint and routed through the wrong load path.
Factor the scanner's weight-name gating into shared _is_weight_bin /
_has_non_gguf_weights helpers and use them everywhere (also in
_dir_has_downloaded_model).
- PREFS_KEYS was missing the new 'Select model settings' keys (load on selection,
expand/show-all quantizations), so 'Reset all local preferences' left them set.
- On Device cached search dropped the active format filter while a query was
typed; keep matchesFormatFilter applied so the format dropdown stays consistent.
Adds tests for the tokenizer.bin vs weight-.bin classification.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: validate Ollama blobs, gate staged context, honor RAM budget on no-GPU hosts
- recommended-folders: only count an Ollama dir once its manifest resolves to an
on-disk model blob, so a failed/pruned pull no longer surfaces an empty chip
- GGUF variant click: only seed the staged contextLength for already-downloaded
picks, so choosing an undownloaded quant from a partially cached repo still
starts its download (the staging effect short-circuits on a known context)
- device fit: classify GGUF variants against the system-RAM budget on no-GPU /
unified-memory hosts instead of reporting everything as fits, and pass
systemRamGb to every variant expander regardless of gpu.available
* Studio: scope Hub search to Recommended, fix staged non-GGUF settings, keep local MLX on Mac
- model picker: only run the Hub search hooks on the Recommended section. On
Device / Connected render local data, so typing there no longer fires HF
requests or a spinner and the local/offline flow is preserved
- chat settings: when a pick is staged, decide the GGUF-only controls from the
staged model's type, not the currently loaded model's. A staged non-GGUF Hub
repo no longer inherits a loaded GGUF's context/KV/speculative controls
- On Device: keep local MLX builds in ./models selectable on Mac (chat-only ran
GGUF/MLX only, but the filter dropped MLX before the format toggle)
---------
Co-authored-by: shimmyshimmer <info@unsloth.ai>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: wasimysaid <wasimysdev@gmail.com>
Boot `unsloth run --disable-tools` against a small GGUF and drive each
supported coding agent (claude, codex, hermes, openclaw, opencode, pi)
through its documented `unsloth connect <agent> --no-launch` recipe, so
the connect flow in unsloth_cli/commands/connect.py stays exercised end
to end and regressions surface as a failing check.
Per-agent matrix, three jobs:
- connection: assert a non-empty, error-free reply to a trivial prompt
- file-edit: a two-turn create-and-run hello.py test (dispatch/schedule
only, skipped on pull_request)
- prompt-cache: verify llama.cpp prefix-cache reuse across requests
The GitHub-hosted runners are CPU-only, so each request is trimmed to
the smallest prompt that still drives the recipe: claude with --tools to
drop unused tool schemas (--allowedTools only gates permission, it does
not shrink the prompt), hermes with an empty platform_toolsets.cli, and
openclaw with a minimal agent definition. hermes and openclaw run a
multi-turn tool loop in file-edit that a CPU runner cannot finish in
time, so those two cells are best-effort; their endpoint wiring is still
hard-gated by the connection job.
A preflight step HTTP-checks each agent's API dialect before install so a
server-side contract regression is reported separately from agent or
guide drift.
* studio: persist personalization (profile + theme) server-side
Profile name/nickname/avatar and appearance (theme) were stored only in the
browser's localStorage, so every browser or device that connected to the same
Studio started from defaults and forgot the user's personalization.
Persist them server-side (single-account, stored as one JSON blob in
app_settings) so they follow the account:
- utils/personalization_settings.py + GET/PUT /api/settings/personalization,
with validation (theme/shape enums, avatar must be an image data URL capped at
512 KB) and a 'saved' flag.
- Frontend usePersonalizationSync (mounted in the root layout when signed in)
hydrates the profile + theme stores from the server when a blob exists, and
otherwise migrates the existing local settings up once so nothing is lost;
later changes are written through, debounced. Writers keep using the local
stores unchanged.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix/adjust personalization sync for PR #6516
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix Studio personalization sync edge cases
* [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: wasimysaid <wasimysdev@gmail.com>
* fix: pin anyio to <4.14.0 to fix RuntimeError on Python 3.13
Fixes#6483
anyio 4.14+ introduced cancel scope changes that cause
RuntimeError on Python 3.13. Pin to <4.14.0 until the issue is
resolved upstream.
---
If this helps, consider buying me a coffee: https://buymeacoffee.com/muhamedfazalps
* Cap anyio<4.14.0 in single-env constraints so the pin holds across all install steps (#6483)
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Michael Han