The `set -u` (nounset) flag in setup.sh causes `${_HIDDEN_GITIGNORES[@]}`
to fail with "unbound variable" when no parent .gitignore with `*` is
found (common on Mac where the install is not inside a Python venv).
Use the `${arr[@]+"${arr[@]}"}` idiom to safely expand empty arrays
under nounset mode.
Two issues caused the studio frontend to render without any styling
when installed via `pip install` (non-editable):
1. `pyproject.toml` package-data only included `frontend/dist/**/*`.
The `include-package-data = true` setting relies on `git ls-files`,
which fails in isolated builds (pip/uv copy source to a temp dir
without `.git`). This meant `frontend/src/`, `package.json`,
`vite.config.ts`, and other build files were missing from the
installed package. Tailwind had no source files to scan.
2. Python venvs auto-create a `.gitignore` with a bare `*` pattern.
Tailwind v4's oxide scanner walks parent directories and respects
`.gitignore` -- so even when source files are present, the venv's
`*` pattern causes the scanner to skip all `.tsx` files. The result
is a 34KB CSS skeleton with zero utility classes instead of the
expected 265KB.
Additionally, Vite adds `crossorigin` to script/link tags by default.
This forces CORS mode on font subresource loads, which Firefox
HTTPS-Only Mode does not exempt -- causing all @font-face downloads
to fail silently when Studio is served over HTTP.
Changes:
- pyproject.toml: Expand package-data to include frontend source,
config files, setup scripts, and backend requirements using glob
patterns (no node_modules)
- studio/setup.sh: Temporarily hide parent .gitignore files containing
a bare `*` during `npm run build`, with trap-based restoration
- studio/backend/main.py: Strip `crossorigin` attributes from HTML
at serve time so fonts load correctly on any protocol
* feat(studio): switch to password-only login and simplify first-time setup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: align change-password button state with validation rules
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
* fix: graceful shutdown on Windows (signal handlers for Ctrl+C)
* [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>
* fix: update Colab notebook to use public unsloth repo and correct paths
* Update studio/Unsloth_Studio_Colab.ipynb
For efficiency, especially in environments like Colab, it's better to perform a shallow clone of the repository. This fetches only the latest commit from the specified branch, which is significantly faster and uses less disk space than cloning the entire project history.
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Update Unsloth_Studio_Colab.ipynb
* studio: add standard Unsloth header, news, section headings, and footer to Colab notebook
* studio: refine Colab notebook section headings and cell cleanup
---------
Co-authored-by: LeoBorcherding <LeoBorcherding@users.noreply.github.com>
* chat only with gguf for mac devices
* resolving gpt comments
* add change-password for chat only
* hide lora adaptors dropdown
* solving gpt comments
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* addressing the comment
* fixing auth flow
---------
Co-authored-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
- Run GGUF load_model in asyncio.to_thread so the event loop stays free
for progress polling during download (was blocking all requests).
- Extract download phase out of the lock in LlamaCppBackend.load_model
so unload_model/cancel can take effect immediately during download.
- Fix "downloaded" badge for split GGUFs: check total cached bytes
across all shards vs expected size, not just first shard existence.
- Respect CUDA_VISIBLE_DEVICES in /api/system GPU reporting so the
frontend GGUF fit estimation uses actual available VRAM.
- Sort tight variants (need CPU offload) smallest-first instead of
largest-first -- closer to GPU budget = faster inference.
- Fix cancel: use refs instead of React state for abort controller and
toast ID so both cancel buttons (text + toast) work reliably. Make
cancel synchronous (fire-and-forget unload) for instant UI response.
Check abortCtrl.signal.aborted after loadModel returns to prevent
ghost model state. Skip rollback and suppress errors on cancel.
- Dynamic top 4 GGUF models fetched from HF API sorted by downloads,
prepended to the default recommended list.
- Remove turnAnchor="top" for auto-scroll to bottom during generation.
- Set default toast duration to 10s (was infinite for loading toasts).
- Deduplicate cached GGUF repos using scan_cache_dir API (fixes
Qwen/X-GGUF vs qwen/x-gguf duplicates from lowercased HF cache).
- Pre-compile repo_id validation regex to silence CodeQL ReDoS warning.
- Change welcome text and default suggestion text.
_get_gpu_free_memory was filtering by CUDA_VISIBLE_DEVICES, so with
CUDA_VISIBLE_DEVICES='0' set by the training env, llama-server only
saw 1 GPU and used --fit for CPU offloading instead of spreading
across all 8 GPUs.
Since llama-server manages its own GPU allocation (the _select_gpus
method picks GPUs and sets CUDA_VISIBLE_DEVICES for the subprocess),
the query must see ALL physical GPUs to make the right decision.
1. Progress endpoint now takes a variant parameter and only counts
.gguf files matching that variant (not all files in the repo cache,
which would include previously downloaded variants)
2. Tracks .incomplete files in HF blobs dir for in-progress single-shard
downloads, capping at 99% until the file is fully committed
3. Fixed loading text: "Loading model..." for cached, "Downloading
model..." for new downloads, with appropriate descriptions
4. Wording: "Downloading and loading model. Large models can take a
while." instead of "This may include downloading."
1. Loading text: shows "Loading model..." for cached models,
"Downloading model..." for new downloads. Toast description
adapts accordingly.
2. Download progress: polls /api/models/gguf-download-progress every
2s during downloads, updating the toast with percentage and GB
downloaded. Progress is estimated by checking the HF cache folder
size against the expected total bytes.
3. Passes isDownloaded and expectedBytes through the full chain from
variant click to selectModel for accurate UI state.
1. n_gpu_layers kwarg: accept (and ignore) in load_model signature
so callers like llm_assist.py don't get TypeError
2. mmproj exclusion: filter out mmproj files in _find_smallest_fitting_variant
so fallback doesn't pick a tiny vision projection as the "model"
3. Shard preservation after fallback: re-discover shards for the
fallback variant instead of resetting to empty list, so split
GGUFs download all shards
4. Orphan cleanup safety: only kill llama-server processes whose
cmdline contains ".unsloth/", avoiding termination of unrelated
llama-server instances on the same machine
5. Path expression sanitization: validate repo_id format before using
it in cache directory lookups
The variant filename includes a subfolder prefix (e.g.
UD-Q4_K_XL/Kimi-K2.5-UD-Q4_K_XL-00001-of-00013.gguf) but rglob
returns just the filename. Use Path.name for the comparison.
HF cache dirs use the exact case from the repo_id at download time
(e.g. models--unsloth--kimi-k2.5-gguf) which may differ from the
canonical HF repo_id (unsloth/Kimi-K2.5-GGUF). Use case-insensitive
matching to find the cache directory.
Downloaded variants now take priority over the recommended badge in
sort order. Within the same tier (downloaded+fits, etc.), recommended
still sorts first. Order: downloaded -> recommended -> fits -> tight -> OOM
- Backend: /gguf-variants now checks HF cache for each variant's file
and returns a downloaded flag per variant
- Frontend: downloaded variants sort before non-downloaded (after
recommended), and show a green "downloaded" badge
- Sort order: recommended -> downloaded+fits -> downloaded+tight ->
fits -> tight -> OOM
1. Interruptible downloads: load_model now checks a cancel event
between shard downloads. unload_model sets the event so cancel
stops the download at the next shard boundary.
2. /api/models/cached-gguf endpoint: scans the HF cache for
already-downloaded GGUF repos with their total size and cache path.
3. "Downloaded" section in Hub model picker: shows cached GGUF repos
at the top (before Recommended) so users can quickly re-load
previously downloaded models without re-downloading.
The unload endpoint checked is_loaded (requires healthy=True), but
during initial loading the server is not yet healthy. Cancel had no
effect because the unload route fell through to the Unsloth backend.
Fix: add is_active property (process exists, loading or loaded) and
check it in the unload route so cancel kills llama-server even during
the download/loading phase.
Also: toast cancel button now properly triggers the backend unload.
Replace toast.promise with a manual toast.loading that includes a
Cancel action button. Users can now cancel model downloads/loads from
the toast notification itself, not just from the header bar spinner.
When the studio process is killed (SIGTERM/SIGKILL), atexit handlers
may not run in the subprocess orchestrator, leaving llama-server
processes orphaned and holding GPU memory. This caused OOM errors when
trying to load a new model after a studio restart.
On init, LlamaCppBackend now runs pgrep to find and SIGKILL any stale
llama-server processes before starting fresh.
Updated GGUF fit classification to match llama-server's --fit behavior:
- fits: model <= 70% of total GPU memory (all GPUs)
- tight: model > 70% GPU but <= 70% GPU + 70% available system RAM
(llama-server uses --fit to offload layers to CPU)
- OOM: model exceeds both GPU and system RAM budgets
useGpuInfo now also returns systemRamAvailableGb from /api/system so the
frontend can compute the combined GPU+RAM budget.
Two fixes for accurate GGUF OOM detection:
1. /api/system now uses nvidia-smi to enumerate all physical GPUs
instead of torch.cuda which only sees CUDA_VISIBLE_DEVICES. This
matches llama-server which can use all GPUs regardless of the env
var. Falls back to torch-based detection if nvidia-smi unavailable.
2. Frontend GGUF OOM check now uses 70% of total GPU memory as the
budget, matching the PR's _select_gpus logic (30% reserved for KV
cache and compute buffers). Previously used checkVramFit's 100%
threshold which was too generous.
Adds a Cancel button next to the "Downloading model..." spinner so
users can abort long downloads. Clicking it aborts the in-flight load,
calls unloadModel to kill any running llama-server process, and clears
the loading state.
OOM variants are more useful sorted ascending by size since smaller ones
are more likely to run with --fit. Non-OOM variants remain largest-first
(best quality).
Two fixes for GGUF variant dropdown:
1. useGpuInfo now sums memory across all GPU devices instead of only
reading devices[0]. This matches llama-server's multi-GPU allocation
where models can be split across GPUs.
2. When the backend-recommended variant (e.g. UD-Q4_K_XL) exceeds total
GPU VRAM, the frontend picks the largest variant that fits instead.
If all variants are OOM, it recommends the smallest one (most likely
to work with --fit).
The useMemo for sortedVariants was placed after the loading/error early
returns, which violated React's rules of hooks (hooks must be called in
the same order every render). Move it before the conditional returns.
Fixes: Minified React error #310
Move the sort logic from the backend to the frontend GgufVariantExpander
component where GPU VRAM info is available. The backend now does a simple
size-descending sort. The frontend pins the recommended variant at the
top, pushes OOM variants to the bottom, and sorts the rest by file size
descending (largest/best quality first).
