The web_search tool now accepts an optional `url` parameter that
fetches a specific page directly (skipping the search step). This
lets the model follow URLs it found in earlier search results instead
of re-searching with increasingly desperate queries.
Also updated the tool description to mention that page content is
returned, so models know they will get more than just snippets.
The web_search tool previously returned only titles, URLs, and short
snippets from DuckDuckGo. Small models would loop repeatedly trying
different search queries because the snippets never contained enough
data to answer the question.
Now fetches the actual page content for the top 1-2 search results
(best-effort, capped at 6000 chars and 10s timeout per fetch) and
includes it in the tool response. This gives the model real data to
work with on the first search instead of endlessly retrying.
Uses only stdlib (urllib + re) so no new dependencies are needed.
Small models (e.g. Qwen3.5-4B) frequently skip tool calls when tools
are enabled, defaulting to plain text answers instead. This adds a
short, model-agnostic system prompt nudge when tools are active that
encourages the model to use them for math/code/search tasks. The nudge
varies depending on which tools are enabled (code, web, or both).
Also fixes tool-call XML leaking into visible chat output:
- Strip <tool_call> / <function=...> tags from outgoing SSE content
deltas before they reach the frontend.
- Sanitize stale <tool_call> XML from conversation history messages
so prior leaked text does not re-enter the model context.
- Strip raw content_accum in the false-positive DRAINING path that
previously yielded unprocessed text.
Some models like unsloth/Qwen3-0.6B have no safetensors metadata
on Hugging Face, so the training model selector showed no parameter
size badge. The chat model picker already had extractParamLabel()
as a fallback that parses sizes like "0.6B" from the model name.
Add the same fallback to the training model selector and the
onboarding model selection step.
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
* Detect always-on reasoning models and show Think button as locked-on
Models with hardcoded <think>/<think> tags or reasoning_content in
their chat template (e.g. distilled reasoning models) always produce
thinking output regardless of any toggle. Previously these models
were not detected as reasoning-capable at all, so the Think button
was grayed out even though the model was actively reasoning.
Backend:
- Detect <think>/<think> and reasoning_content in GGUF chat templates
as a fallback when enable_thinking is not present
- Add reasoning_always_on flag to LoadResponse and InferenceStatusResponse
- Pass the flag through all GGUF load and status response paths
Frontend:
- Add reasoningAlwaysOn to the chat runtime store and API types
- When reasoning_always_on is true, show the Think button as lit
(active) but not clickable, with a tooltip explaining the model
always uses thinking
- Force reasoningEnabled=true when the model always reasons
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Use pointer-events-none instead of disabled for always-on Think button
The HTML disabled attribute was not fully blocking clicks on the Think
button for always-on reasoning models. Switch to pointer-events-none
CSS class which prevents all mouse interaction at the CSS level.
* Use a static span instead of disabled button for always-on Think
Replace the button element with a plain span when reasoning is
always on. This makes it physically impossible to toggle since
there is no clickable element at all, avoiding any CSS or
disabled-attribute edge cases.
* Simplify always-on Think button to stay lit and remain toggleable
Keep the Think button as a normal toggleable button but ensure it
shows as lit when reasoning_always_on is true. The model always
reasons regardless of the toggle state so there is no need to
block interaction.
---------
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Use --no-deps for ALL packages (unsloth, unsloth-zoo, and runtime deps)
since the current PyPI metadata for unsloth still declares torch as a
hard dependency. Runtime deps (typer, pydantic, safetensors,
transformers, etc.) are installed from no-torch-runtime.txt with
--no-deps to prevent transitive torch resolution from accelerate, peft,
trl, and sentence-transformers.
no-torch-runtime.txt now includes unsloth's own direct deps (typer,
pydantic, pyyaml, nest-asyncio) since --no-deps skips those too.
install.sh installs no-torch-runtime.txt directly (via helper function
_find_no_torch_runtime). install.ps1 does the same via
Find-NoTorchRuntimeFile. SKIP_STUDIO_BASE stays at 1 to avoid setup.sh
fast-path issues.
install_python_stack.py NO_TORCH branch does the same for unsloth
studio update, using package_name instead of hardcoded "unsloth".
* Fix inference failing for transformers 5.x models (trust_remote_code)
The training worker in core/training/worker.py auto-enables
trust_remote_code for unsloth/* models that need transformers 5.x
(e.g. NVIDIA-Nemotron-3-Nano-4B). The inference worker did not have
the same logic, so loading these models for chat would fail with
"No config file found" while training worked fine.
Add the same auto-detection to the inference worker so
trust_remote_code is set automatically when needed.
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---------
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio shutdown button
* fix: add auth to shutdown endpoint and improve UX
- Add JWT auth (Depends(get_current_subject)) to POST /api/shutdown
- Use authFetch instead of bare fetch in shutdown dialog
- Only show beforeunload prompt when training is running
- Remove Ctrl+W/Cmd+W interception (browsers don't allow it)
- Store shutdown task on app.state to prevent GC
---------
Co-authored-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* fix: only kill studio-managed llama-server processes, not user's own servers
_kill_orphaned_servers() checked for "unsloth" anywhere in the process
cmdline, which matched the user's own llama-server when serving models
from unsloth/ HF repos (the model path in -m contains "unsloth"). This
caused the user's server to get SIGKILLed on Studio startup, destroying
their prompt cache and forcing full model re-loads.
Narrow the check to only match processes whose binary path lives under
~/.unsloth/llama.cpp/ (the Studio install directory).
* Address review: cover env var paths, move Path.home() inside try block
- Also check LLAMA_SERVER_PATH and UNSLOTH_LLAMA_CPP_PATH so orphans
from custom install locations are still cleaned up.
- Move studio_dirs construction inside the try/except so a Path.home()
failure (containers without HOME) does not crash the constructor.
* Address reviewer feedback: proper path ancestry, /proc/pid/exe, legacy paths
Changes based on 10-reviewer consensus:
- Use Path.is_relative_to() instead of substring matching to prevent
false positives on sibling paths like ~/.unsloth/llama.cpp-backup/.
- Use /proc/<pid>/exe (symlink to real binary) instead of parsing the
first cmdline token, which breaks on paths with spaces. Falls back
to cmdline parsing on non-Linux or when /proc is unavailable.
- Add legacy in-tree install paths (project_root/llama.cpp/ and
project_root/bin/) so orphans from older setup.sh are still cleaned.
- Treat LLAMA_SERVER_PATH as an exact binary match rather than widening
it to its parent directory, which could match unrelated servers in
shared locations like /usr/local/bin/.
- Keep everything inside the try/except so Path.home() failures in
containers do not crash the constructor.
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* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: add Linux platform guard and log cleanup errors
- Guard pgrep fallback with sys.platform check so it does not crash
on Windows/macOS when psutil is unavailable.
- Replace silent except-pass with logger.warning for observability.
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---------
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The [huggingfacenotorch] extras only exist in pyproject.toml but are
NOT published on PyPI, so uv pip install "unsloth[huggingfacenotorch]"
fails on fresh installs from the registry.
Fix: add studio/backend/requirements/no-torch-runtime.txt with the
runtime deps (safetensors, transformers, datasets, accelerate, etc.)
that mirror [huggingfacenotorch] from pyproject.toml. In no-torch mode:
1. install.sh/ps1 install unsloth + unsloth-zoo with --no-deps
2. SKIP_STUDIO_BASE=0 so install_python_stack.py's NO_TORCH branch runs
3. install_python_stack.py installs no-torch-runtime.txt
* Guard against late tool_calls after visible content, filter incomplete fragments
1. If visible content was already emitted (_last_emitted is non-empty)
when delta.tool_calls arrives, ignore the tool_calls instead of
reclassifying the turn as a tool call. llama-server never
interleaves content and tool_calls (they are mutually exclusive),
but this guard is defensive for other OpenAI-compatible backends.
2. Filter out incomplete structured tool_calls fragments before
execution. Entries with empty function.name (from truncation by
max_tokens, disconnect, or interruption) are skipped instead of
being passed to execute_tool().
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* fix: account for KV cache in GGUF GPU fit check and auto-cap context length
The GPU fit check only compared GGUF file size against free VRAM,
ignoring KV cache memory. Models with large native context lengths
(e.g. Qwen3.5-9B at 262k) would pass the fit check since the GGUF
is only 5.6 GB, but the KV cache at 262k context needs ~40 GB at
f16. This caused llama-server to silently fall back to CPU inference.
Changes:
- Parse block_count, head_count_kv, head_count, and embedding_length
from GGUF metadata alongside context_length
- Add KV cache VRAM estimation based on architecture params and the
selected cache quantization type (f16, q8_0, q4_0, etc.)
- Auto-reduce context length to the maximum that fits in available
GPU VRAM when the native context would exceed it
- Include estimated KV cache size in the _select_gpus total so the
fit decision reflects actual runtime memory, not just file size
For the reported scenario (Qwen3.5-9B on RTX 3090 with 22415 MiB
free), context is auto-reduced from 262144 to ~63k with f16 KV cache,
keeping the model fully on GPU. With q4_0 KV cache quantization the
context can reach ~226k.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: resolve 6 bugs in KV cache VRAM estimation and add test harness
- Fix q8_0 BPE constant: 1.125 -> 34/32 (1.0625) to match llama.cpp block size
- Fix _fit_context_to_vram returning min_ctx when weights exceed budget
(should return requested_ctx unchanged, let --fit handle it)
- Fix binary search inflating below-2048 requests (lo=min_ctx=2048 > hi)
- Fix n_ctx=0 regressing to 4096 when metadata unavailable (preserve sentinel)
- Fix multi-GPU auto-cap using single-GPU budget instead of aggregate
- Fix _context_length being overwritten with capped effective value
Add tests/test_gguf_kv_vram.py: 43 cross-platform pytest tests covering
pure logic, integration (monkeypatched load_model), and real GGUF parsing.
Runs in an isolated uv venv with only pytest -- no GPU/torch/structlog needed.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: complete _effective_context_length lifecycle
- Initialize _effective_context_length in __init__ (prevents AttributeError)
- Reset _effective_context_length in unload_model (prevents stale values)
- Update context_length property to return effective (capped) value for
the UI/API, falling back to native _context_length if not set
* fix: multi-GPU selection tries smallest subset first
The previous approach summed all GPUs' memory to cap context, then
selected GPUs afterward. This was overly optimistic for heterogeneous
setups (e.g., 48 GiB + 4 GiB): the context was inflated by the tiny
GPU's contribution, then both GPUs were dragged in.
Now we try GPU subsets from smallest (1 GPU) to largest, capping
context for each. We pick the smallest subset where the model+KV
fits. This prefers single-GPU when possible (simpler, no tensor
split overhead) and avoids pulling in GPUs that barely help.
Add tests: test_multi_gpu_prefers_fewer_gpus,
test_multi_gpu_heterogeneous.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: prefer fewer GPUs over higher context in GPU selection
Multi-GPU inference is slower due to tensor-split overhead, so we
should prefer fewer GPUs with reduced context over more GPUs with
full context. Now the loop stops at the first GPU subset where the
model fits, rather than continuing to find subsets that allow higher
context. Only if the model can't fit on N GPUs do we try N+1.
This preserves the original behavior: use multi-GPU only when the
model doesn't fit on a single GPU.
* fix: make _kill_orphaned_servers cross-platform via psutil
Replace pgrep + os.kill(SIGKILL) with psutil.process_iter() and
proc.kill(), which work on Linux, macOS, and Windows. Build an
allowlist of install roots matching _find_llama_server_binary so
only studio-managed servers are killed.
* fix: skip KV estimation loop when effective context is unknown
When n_ctx=0 and GGUF metadata lacks context_length, effective_ctx
stays 0. _estimate_kv_cache_bytes(0) returns 0, so a GPU could be
selected with no KV headroom. Guard the loop with effective_ctx > 0
to fall back to file-size-only GPU selection in this case.
* chore: temporarily remove test harness (will add back separately)
* refactor: deduplicate UINT32/UINT64 handling in GGUF parser
Replace duplicated if/elif chains for vtype 4 and 10 with a single
block using setattr. No behavioral change.
* fix: honor explicit n_ctx by using multi-GPU before capping
When the user explicitly sets n_ctx, try to fit the full requested
context using _select_gpus (which adds GPUs as needed). Only cap
context if it doesn't fit on any GPU combination.
When n_ctx=0 (auto/native context), keep the existing behavior:
prefer fewer GPUs with reduced context, since multi-GPU is slower
and the user didn't ask for a specific context length.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: context_length property returns native value for frontend slider
The frontend uses context_length as the slider max. Returning the
capped effective value prevented users from requesting higher context
on reload (e.g., after switching to q4_0 KV cache). Revert to
returning the native GGUF metadata value -- the backend auto-caps
at load time regardless.
* revert: context_length returns effective (capped) value
The UI slider should show what the server is actually running at,
not the theoretical maximum. Revert to returning the effective
context length.
* fix: raise minimum context floor from 2048 to 4096
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix ~1.2s TTFT penalty when tools are enabled in Studio
When users enable web search, Python execution, or terminal tools,
every message gets a ~1.2s delay before any text appears -- even when
the model does not call any tool. This happens because
generate_chat_completion_with_tools() does a non-streaming detection
pass (stream: False) first, waits for the complete response, then
checks for tool calls. For the ~90% of messages that don't trigger a
tool call, this blocking wait is entirely wasted.
Root cause: the detection pass payload uses stream: False, forcing
llama-server to generate the entire response before returning any
tokens.
Fix: replace the non-streaming detection pass with a streaming pass
(stream: True) and a speculative buffer state machine that detects
tool signals in the first 1-2 SSE chunks:
- BUFFERING: accumulate content tokens, check first chars for tool
signal prefixes (<tool_call>, <function=)
- STREAMING: no tool detected, yield tokens to caller immediately
- DRAINING: tool signal found, silently accumulate rest of stream
Three detection paths:
1. Structured delta.tool_calls -- detected instantly, transition to
DRAINING, accumulate fragments, assemble at stream end.
2. XML tool markup in content -- buffer holds up to 32 chars checking
for <tool_call> or <function= prefix, then transitions to DRAINING.
3. No tool signal -- first non-whitespace, non-XML char triggers
immediate transition to STREAMING (fast path, ~90% of requests).
Safety net: after any stream ends in STREAMING state, check accumulated
content for XML tool signals. Handles rare "content before tool call"
edge case.
Additional supporting changes:
- Add headers parameter to _stream_with_retry for auth forwarding
- Share _strip_tool_markup and regex patterns between the detection
pass and the final streaming pass (removes duplication)
- Remove the iteration==0 non-streaming content shortcut (no longer
needed since all iterations stream directly)
- Keep the final streaming pass as fallback for max_tool_iterations
exhaustion
Benchmarked on Qwen3.5-4B Q4_K_XL:
- No tools: TTFT ~112ms (unchanged)
- Tools enabled, no call: TTFT ~112ms (was ~1207ms)
- Decode TPS: 226 (unchanged in all cases)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add unit tests for streaming tool detection state machine
16 tests covering every tool call parsing path:
- Plain text (no tool call) streaming
- Structured delta.tool_calls detection and fragment assembly
- XML <tool_call>JSON</tool_call> detection via buffer
- XML <function=name> tag detection via buffer
- Whitespace before tool XML
- Safety net (content then tool XML)
- Parallel multi-tool calls
- Reasoning token bypass (thinking models)
- Reasoning then tool call
- Empty response handling
- Buffer prefix timeout (HTML not mistaken for tool)
- Non-XML first char instant streaming
- False positive rejection (<tool_tip> vs <tool_call>)
- Arguments split across multiple chunks
- auto_heal_tool_calls=False respects the flag
- Metrics accumulation across tool iterations
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix reasoning-only BUFFERING, pre-tool content emission, and code duplication
Addresses review feedback on the streaming tool detection:
1. Reasoning tokens are no longer yielded during BUFFERING/DRAINING
states. The consumer in routes/inference.py tracks prev_text across
tool iterations without resetting it, so yielding reasoning during
a detection pass that resolves to a tool call would corrupt the
delta computation for subsequent iterations. Reasoning is now
silently accumulated during detection (matching the old non-streaming
behavior) and flushed together with content when the buffer resolves
to STREAMING.
2. Handle reasoning-only responses in the BUFFERING resolver. When a
thinking model emits only reasoning_content with no content tokens,
the stream ends while still in BUFFERING state. The resolver now
detects this case and yields reasoning as plain text (without
<think> wrapper), matching the final streaming pass behavior for
models like Qwen3 in always-think mode.
3. Replace duplicated re.sub calls for stripping tool markup with
the existing _strip_tool_markup(content_text, final=True) helper,
removing ~40 lines of redundant regex code.
4. Update tests: adjust reasoning test expectations to match the new
behavior (reasoning batched with content, not streamed individually
during BUFFERING). Add test_reasoning_only_no_content for the
reasoning-only edge case. 17/17 tests pass.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address remaining reviewer findings: late tool_call IDs and XML speculation
1. Late-arriving tool_calls.id: when a provider sends the real ID on a
later delta chunk (after the initial one with index and function
name), the accumulator now updates the ID instead of keeping the
synthetic "call_{idx}" placeholder. (P2, 2/10 reviewers)
2. XML speculation respects auto_heal_tool_calls: when auto_heal is
explicitly disabled, _TOOL_XML_SIGNALS is empty so the BUFFERING
state never speculatively holds content for XML prefix detection.
Content starting with literal "<tool_call>" or "<function=" text
flows straight through without delay. (P2, 1/10 reviewers)
Skipped: finish_reason="tool_calls" without delta.tool_calls fallback
(P1, 1/10 reviewers). llama-server always sends delta.tool_calls
fragments in streaming mode. A non-streaming fallback for this edge
case would add complexity for a scenario that does not occur in
practice with the supported backend.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Check request.is_disconnected() every 20 tokens instead of every token
The disconnect check is an async round-trip that adds overhead on every
loop iteration. Since the cancel watcher in llama_cpp.py already
handles connection teardown (closes the streaming response on cancel),
this route-layer check is a secondary safety net that does not need to
run on every single token.
Check every 20 tokens across all 4 streaming paths:
- gguf_tool_stream (tool-enabled GGUF)
- gguf_stream_chunks (standard GGUF)
- audio_input_generate (audio/whisper input)
- generic backend stream (non-GGUF fallback)
* Fix safety net, DRAINING metadata, and test import path
1. Safety net no longer retroactively executes tools after visible
content was already emitted to the user. Once _last_emitted is
non-empty, the stream is committed to normal content mode.
Retroactive tool execution after visible output would violate the
streaming contract and corrupt the route-layer cumulative delta
tracker (prev_text). The tool XML is still stripped by
_strip_tool_markup so the user sees clean content.
2. DRAINING false-positive path now merges accumulated metrics from
prior tool iterations instead of dropping them. Uses the same
merge formula as the STREAMING path.
3. Test import path fixed to use repo root instead of hardcoded
sibling directory. Works in clean checkouts and CI.
4. Renamed test_content_then_tool_xml_safety_net to
test_content_then_tool_xml_no_retroactive_execution to reflect
the corrected behavior.
17/17 tests pass.
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* Redact --api-key value from llama-server startup log
When UNSLOTH_DIRECT_STREAM=1, the generated bearer token was logged
verbatim in the startup command. Replace the secret with <redacted>
before logging.
* Remove test file temporarily
* Revert disconnect throttle, reset prev_text on tool_start, restore XML safety net
Addresses all P1 findings from reviewer round 3 (10 reviewers):
1. Revert disconnect check to every iteration (was every 20th).
All 10 reviewers flagged this as a correctness regression for
short streams and sparse tool event loops. The cancel watcher in
llama_cpp.py is the primary mechanism but the route-layer check
must remain per-iteration for completeness. [10/10]
2. Reset prev_text on tool_start in gguf_tool_stream. When a tool
cycle begins after visible content was already streamed, the
route-layer cumulative delta tracker (prev_text) must be reset
so the post-tool synthesis response is not truncated or dropped.
[9/10]
3. Remove the _last_emitted gate from the XML safety net. The gate
was added to prevent retroactive tool execution after visible
content, but with prev_text now reset on tool_start (#2), the
root cause is fixed and the safety net can correctly handle
content-then-tool-XML responses (matching pre-PR behavior).
[8/10]
* Use None instead of {} for empty auth headers in TTS methods
* Include accumulated metrics in STREAMING metadata check
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* refactor(studio): unify setup terminal output style and add verbose setup mode
* studio(windows): align setup.ps1 banner/steps with setup.sh (ANSI, verbose)
* studio(setup): revert nvcc path reordering to match main
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio(setup): restore fail-fast llama.cpp setup flow
* studio(banner): use IPv6 loopback URL when binding :: or ::1
* Fix IPv6 URL bracketing, try_quiet stderr, _step label clamp
- Bracket IPv6 display_host in external_url to produce clickable URLs
- Redirect try_quiet failure log to stderr instead of stdout
- Clamp _step label to column width to prevent negative padding
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add sandbox integration tests for PR #4494 UX fixes
Simulation harness (tests/simulate_pr4494.py) creates an isolated uv
venv, copies the real source files into it, and runs subprocess tests
for all three fixes with visual before/after demos and edge cases.
Standalone bash test (tests/test_try_quiet.sh) validates try_quiet
stderr redirect across 8 scenarios including broken-version contrast.
39 integration tests total (14 IPv6 + 15 try_quiet + 10 _step), all
existing 75 unit tests still pass.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Truncate step() labels in setup.sh to match PS1 and Python
The %-15s printf format pads short labels but does not truncate long
ones. Change to %-15.15s so labels wider than 15 chars are clipped,
matching the PowerShell .Substring(0,15) and Python label[:15] logic.
* Remove sandbox integration tests from PR
These test files are not part of the styling fix and should not
ship with this PR.
* Show error output on failure instead of suppressing it
- install_python_stack.py: restore _red for patch_package_file
warnings (was downgraded to _dim)
- setup.ps1: capture winget output and show on failure for CUDA,
Node, Python, and OpenSSL installs (was piped to Out-Null)
- setup.ps1: always show git pull failure warning, not just in
verbose mode
* Show winget error output for Git and CMake installs on failure
Same capture-and-print-on-failure pattern already used for
Node, Python, CUDA, and OpenSSL winget installs.
* fix: preserve stderr for _run_quiet error messages in setup.sh
The step() helper writes to stdout, but _run_quiet's error header
was originally sent to stderr (>&2). Without the redirect, callers
that separate stdout/stderr would miss the failure headline while
still seeing the log body on stderr. Add >&2 to both step calls
inside _run_quiet to match main's behavior.
* feat: add --verbose flag to setup and update commands
Wire UNSLOTH_VERBOSE=1 through _run_setup_script() so that
'unsloth studio update --verbose' (and the deprecated 'setup')
passes the flag to setup.sh / setup.ps1 / install_python_stack.py.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Make Studio shortcuts launch in a visible terminal
Studio shortcuts (Desktop/Start Menu) previously launched the server as a
hidden background process. Closing the browser tab did not stop the server,
leaving users with no obvious way to shut it down. This change makes shortcuts
open a visible terminal window so users can see server output and close the
terminal to stop Studio.
Launcher changes (install.sh):
- Add TTY detection in the launcher's main section. When a TTY is present
(foreground mode), the launcher spawns a background browser-opener and then
exec's the studio process directly. This means closing the terminal sends
SIGHUP to studio, stopping it cleanly. When no TTY is present (background
mode, e.g. macOS .app or headless), the existing _spawn_terminal behavior
is preserved.
- Add _open_browser_when_ready helper that polls health on the specific
launch port and opens the browser once ready.
- Add WSL fallback in _open_browser: uses powershell.exe Start-Process or
cmd.exe /c start instead of unreliable xdg-open under WSL.
Linux .desktop shortcut:
- Change Terminal=false to Terminal=true so the desktop environment opens
the user's default terminal emulator for the launcher.
WSL support:
- Remove the early-return that skipped WSL entirely. WSL now gets the
launcher script and studio.conf written.
- Add WSL shortcut creation: generates Windows Desktop and Start Menu .lnk
files via a temp PowerShell script. Targets wt.exe (Windows Terminal) with
automatic fallback to wsl.exe. Uses WSL_DISTRO_NAME for multi-distro setups.
Windows launcher (install.ps1):
- Add Find-FreeLaunchPort function that mirrors the Unix _find_launch_port
logic, scanning Get-NetTCPConnection for busy ports and returning the first
free port in the configured range.
- Replace the hardcoded $basePort with the dynamic port result, with a
MessageBox error dialog if no free port is found.
* Fix review findings: lock race, WSL quoting, Windows port fallback
Foreground lock race (10/10 reviewers):
The foreground mode released the single-instance lock before exec,
allowing a second launcher to acquire the lock and race for the same
port during startup. Move lock release into the background subshell
so it only happens after the health check passes.
WSL shortcut quoting (10/10 reviewers):
WSL_DISTRO_NAME values with spaces (e.g. "Ubuntu Preview", "Fedora
Remix for WSL") were not quoted, causing the distro name to be split
across multiple arguments. Add double-quoting around the distro name
and launcher path in the generated shortcut arguments.
Windows port fallback (3/10 reviewers):
Find-FreeLaunchPort silently assumed no ports were listening when
Get-NetTCPConnection was unavailable, which could return 8888 even
when busy. Add a Test-PortBusy fallback that probes ports with
TcpListener when Get-NetTCPConnection fails. Also scope the
Get-NetTCPConnection query to only the port range we care about.
* Skip powershell.exe shortcut creation if wslpath fails
If wslpath -w fails (returns empty), do not attempt to pass a Linux-style
path to powershell.exe -- it would always fail. Only run powershell.exe
when we have a valid Windows path for the temp PS1 script.
* Remove dead code and fix background health poll target
- Remove unused _open_browser_when_ready function
- Background mode now polls only the specific _launch_port instead of
scanning all ports via _find_healthy_port, matching foreground behavior
- Add launcher test harness (22 unit + 19 integration tests)
* Fix port probe scope, lock ownership, and T4 test coverage
- Test-PortBusy: bind on Any instead of Loopback to match Studio's
0.0.0.0 bind scope (prevents false-free in fallback path)
- _release_lock: verify PID ownership before removing lock dir
(prevents a timed-out subshell from deleting another launcher's lock)
- T4 test: fail first curl call so the test actually exercises the
lock-contention wait path instead of short-circuiting via fast path
* Temporarily remove launcher test scripts
Tests will be re-added in a follow-up PR to keep this diff focused
on the launcher changes.
* Fix missing num_items_in_batch in unsloth_prediction_step
unsloth_prediction_step calls compute_loss without num_items_in_batch
during evaluation. This causes _unsloth_pre_compute_loss to see
num_items_in_batch=None, which triggers a spurious warning for every
model when gradient_accumulation_steps > 1:
"Unsloth: Not an error, but {model} does not accept num_items_in_batch.
Using gradient accumulation will be very slightly less accurate."
The standard transformers prediction_step computes num_items_in_batch
via _get_num_items_in_batch before passing it to compute_loss. This
patch does the same in unsloth_prediction_step.
Tested on Llama-3.2-1B-Instruct and Olmo-3-7B-Instruct with
gradient_accumulation_steps=3 and eval_steps=3. Warning is gone and
eval loss is computed correctly for both.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Guard _get_num_items_in_batch for older transformers versions
_get_num_items_in_batch was added in transformers 4.46. Wrap the call
in try/except so older versions fall back to num_items_in_batch=None,
which preserves the original behavior of not passing it.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix Gemma3N audio training stride assertion with non-reentrant checkpointing
Gemma3N audio conformer processes variable-length audio tensors
that cause stride mismatches in AOT autograd compiled backward
when non-reentrant gradient checkpointing is used. The error
manifests as:
AssertionError: expected size 2==2, stride 1928==1936 at dim=0
This happens because the audio conformer's conv/norm layers produce
tensors whose strides vary with audio clip duration, but AOT autograd
traces the backward graph assuming fixed strides from the first batch.
The notebook sets gradient_checkpointing_kwargs={"use_reentrant": False}
and TRL 0.27.0+ also forces this. Both override Unsloth's own
use_reentrant=True set during prepare_model_for_training.
Fix: intercept gradient_checkpointing_enable on Gemma3N models to
always force use_reentrant=True, regardless of what the notebook
or TRL passes.
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---------
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The previous --no-deps approach skipped ALL dependencies, not just
torch. This left safetensors, transformers, datasets, accelerate, etc.
missing, causing PackageNotFoundError at runtime.
Fix: in no-torch mode, install unsloth[huggingfacenotorch] (which pulls
all runtime deps except torch), then install unsloth-zoo with --no-deps
(since zoo's published metadata still declares torch as a hard dep).
This gives a working no-torch environment with all non-torch packages.
Applied to all three installer files: install.sh, install.ps1, and
studio/install_python_stack.py.
* fix: install.sh Mac Intel compatibility + Studio no-torch support (#4621)
On Intel Macs (x86_64), PyTorch has no wheels for torch >= 2.3, so the
installer crashes. Even when torch is absent, Studio crashes on startup
because two files have bare top-level torch imports.
Studio's GGUF inference (llama.cpp) does not need PyTorch. Training and
HF-inference already isolate torch to subprocesses. Only 2 files in the
server startup chain had top-level torch imports preventing startup.
Changes:
- install.sh: detect architecture, default to Python 3.12 on Intel Mac,
skip torch install, add Python 3.13.8 guard for arm64, pass
UNSLOTH_NO_TORCH env var to setup.sh
- data_collators.py: remove unused `import torch` (no torch.* refs)
- chat_templates.py: lazy-import IterableDataset into function bodies
- install_python_stack.py: add IS_MACOS/NO_TORCH constants, skip
torch-dependent packages, skip overrides.txt, skip triton on macOS
No existing working flow changes. Linux/WSL and macOS arm64 behavior is
identical.
* tests: add test suite for Mac Intel compat + no-torch mode
Shell tests (test_mac_intel_compat.sh):
- version_ge edge cases (9 tests)
- Architecture detection for Darwin x86_64/arm64, Linux x86_64/aarch64
- get_torch_index_url returns cpu on simulated Darwin
- UNSLOTH_NO_TORCH propagation to both setup.sh branches
Python unit tests (test_no_torch_filtering.py):
- _filter_requirements with NO_TORCH_SKIP_PACKAGES
- NO_TORCH env var parsing (true/1/TRUE/false/0/unset)
- IS_MACOS constant check
- Overrides skip and triton macOS skip guards
Python import tests (test_studio_import_no_torch.py):
- data_collators.py loads in isolated no-torch venv
- chat_templates.py has no top-level torch imports
- Negative control confirms import torch fails without torch
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests: add E2E sandbox tests for Mac Intel no-torch mode
Replace static/synthetic test stubs with real sandbox tests:
- Shell: E2E uv venv creation at Python 3.12, mock uv shim to verify
torch install is skipped when MAC_INTEL=true, dynamic env propagation
test for UNSLOTH_NO_TORCH in both local and non-local install paths
- Python filtering: test real extras.txt and extras-no-deps.txt with
NO_TORCH_SKIP_PACKAGES, subprocess mock of install_python_stack() for
5 platform configs (NO_TORCH+macOS, Windows+NO_TORCH, normal Linux,
Windows-only, macOS-only), VCS URL and env marker edge cases
- Python imports: parametrized Python 3.12+3.13 venv fixture, dataclass
instantiation for all 3 collator classes, chat_templates.py exec with
stubs, negative controls proving import torch and torchao install fail
in no-torch venvs
91 total tests, all passing.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: address reviewer findings for Intel Mac no-torch mode
P1 fixes:
- Auto-infer NO_TORCH in install_python_stack.py via platform.machine()
so `unsloth studio update` preserves GGUF-only mode without needing
the UNSLOTH_NO_TORCH env var (6/10 reviewers)
- Add openai-whisper and transformers-cfg to NO_TORCH_SKIP_PACKAGES
since both have unconditional torch dependencies (4/10 reviewers)
- Skip unsloth-zoo on Intel Mac --local installs (depends on torch)
in both migrated and fresh install paths (1/10)
- Recreate stale 3.13 venvs as 3.12 on Intel Mac re-runs (1/10)
- Detect Apple Silicon under Rosetta via sysctl hw.optional.arm64
and warn user to use native arm64 terminal (1/10)
P2 fixes:
- Wire new test files into tests/run_all.sh (4/10 reviewers)
- Add update-path tests (skip_base=False) for Intel Mac
- Add _infer_no_torch tests for platform auto-detection
P3 fixes:
- Fix macOS progress bar total (triton step skipped but was counted)
- Fix temp file leak when Windows + NO_TORCH filters stack
All tests pass: 30 shell, 66 Python (96 total).
* feat: add --python override flag to install.sh
Lets users force a specific Python version, e.g. ./install.sh --python 3.12.
Addresses M2 Mac users whose systems resolve to a problematic 3.13.x patch.
When --python is set, the Intel Mac stale-venv guard and 3.13.8 auto-downgrade
are skipped so the user's choice is respected.
* tests: add comprehensive E2E sandbox tests for no-torch mode
Add test_e2e_no_torch_sandbox.py with 7 test groups (43 tests total)
covering the full no-torch import chain, edge cases, and install logic:
- Group 1: BEFORE vs AFTER import chain comparison (proves the bug
existed and the fix works by synthetically prepending top-level torch
imports)
- Group 2: Dataclass instantiation without torch
- Group 3: Edge cases with broken/fake torch modules on sys.path
- Group 4: Hardware detection fallback to CPU without torch
- Group 5: install.sh flag parsing, version resolution, arch detection
- Group 6: install_python_stack.py NO_TORCH filtering
- Group 7: Live server startup without torch (marked @server, skipped
when studio venv is unavailable)
All 43 tests pass on both Python 3.12 and 3.13 isolated venvs.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat: add --no-torch flag to install.sh/ps1, fix lazy import bug in dataset formatting
- Fix chat_templates.py: narrow torch IterableDataset import into inner
try/except ImportError so dataset.map() works without torch installed
- Fix format_conversion.py: same lazy import fix for convert_chatml_to_alpaca
and convert_alpaca_to_chatml
- Add --no-torch flag to install.sh with unified SKIP_TORCH variable
(driven by --no-torch flag OR MAC_INTEL auto-detection)
- Add --no-torch flag to install.ps1 with $SkipTorch variable
- Print CPU hint when no GPU detected and --no-torch not set
- Replace MAC_INTEL guards with SKIP_TORCH in torch install sections
- Update shell tests (40 pass) and Python tests (90 pass)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: address reviewer findings for --no-torch installer paths
- Fix migrated-env branch in install.sh and install.ps1: check
SKIP_TORCH first, then branch on STUDIO_LOCAL_INSTALL. Previously
SKIP_TORCH+non-local fell into else and installed unsloth-zoo (which
depends on torch), defeating --no-torch mode.
- Fix $env:UNSLOTH_NO_TORCH leak in install.ps1: always set to "true"
or "false" instead of only setting on the true branch. Prevents stale
no-torch state from leaking across runs in the same PS session.
- Fix install_python_stack.py update path: add NO_TORCH guard around
base.txt install so unsloth studio update does not reinstall
unsloth-zoo (which depends on torch) in no-torch mode.
* fix: install unsloth + unsloth-zoo with --no-deps in no-torch mode
Instead of skipping unsloth-zoo entirely (which breaks unsloth's
dependency on it), install both packages with --no-deps so they are
present but torch is not pulled in transitively. Applied consistently
across all no-torch paths: migrated-env, fresh-local, fresh-non-local
in install.sh, install.ps1, and install_python_stack.py.
* chore: temporarily remove test files (will be added in a follow-up)
* refactor: deduplicate SKIP_TORCH conditional branches in installers
Collapse if/else blocks that differ only by --no-deps into a single
branch with a conditional flag variable. Applied to migrated-env and
fresh-local paths in install.sh, install.ps1, and install_python_stack.py.
* fix: apply --no-deps to fresh non-local --no-torch install path
The non-local else branch was missing $_no_deps_arg/$noDepsArg, so
uv pip install unsloth would resolve torch from PyPI metadata (the
published unsloth package still declares torch as a hard dep). Now
--no-deps is applied consistently to all SKIP_TORCH code paths.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Inline querier identity changed every render, forcing useLiveQuery to
resubscribe continuously causing CPU spikes. Store querier in a ref and
only re-subscribe when explicit deps change.
The ChatCompletionRequest Pydantic model defaulted repetition_penalty
to 1.1 when clients omitted the field. This silently forced
llama-server to perform per-token repetition scanning, dropping
streaming throughput from ~225 TPS to ~172 TPS (a 24% penalty).
The Studio frontend always sends repetition_penalty=1.0 explicitly,
so UI users were unaffected. But any API client hitting
/v1/chat/completions without setting the field (curl, third-party
integrations, Open WebUI, etc.) would get the slow path.
Benchmarked on Qwen3.5-4B Q4_K_XL, GPU 0:
- repeat_penalty=1.0: 225.2 TPS
- repeat_penalty=1.1: 172.7 TPS (24% slower)
- LM Studio (which applies rp internally): 170.8 TPS
This aligns the Pydantic default with the frontend default (1.0),
generate_chat_completion's function signature default (1.0), and
llama-server's own default (1.0).
* Allow install_python_stack to run on Colab
The _COLAB_NO_VENV flag was setting _SKIP_PYTHON_DEPS=true, which
skipped both the PyPI version check (needs $VENV_DIR/bin/python) and
install_python_stack (uses sys.executable, works without a venv).
Introduce a separate _SKIP_VERSION_CHECK flag for the version check,
so install_python_stack still runs on Colab. The _SKIP_PYTHON_DEPS
flag remains available for the "versions match" fast path.
* Remove colab.py workarounds that broke transformers/hf-hub compatibility
PR #4601 added _pip_install_backend_deps(), _bootstrap_studio_venv(),
and _is_colab() to colab.py as workarounds for install_python_stack
being skipped on Colab. These workarounds:
- Stripped version constraints from studio.txt and installed into system Python
- Upgraded huggingface-hub to >=1.0, breaking Colab's pre-installed
transformers which requires huggingface-hub<1.0
With install_python_stack now running on Colab (previous commit), these
workarounds are unnecessary — all deps are properly installed by setup.sh.
Restore colab.py to its original PR #4237 structure: just get_colab_url(),
show_link(), and start().
* Remove --local flag from setup.sh in Colab notebook
The --local flag is not needed for the standard Colab flow since
install_python_stack now runs on Colab and installs deps from PyPI.
* studio: humanize ETA display for long training runs
When training takes hours or days, the ETA displayed raw minutes
(e.g. '560m 50s'). This changes the format to:
- Under 1 hour: Xm Ys (unchanged)
- 1-24 hours: Xh Ym Zs
- Over 24 hours: Xd Xh Xm
* Fix formatDuration edge cases and consolidate duplicate for PR #4608
- Guard NaN/Infinity inputs with Number.isFinite() (matches formatNumber in same file)
- Add sub-minute branch so 30s displays as "30s" instead of "0m 30s"
- Accept undefined in type signature to match formatNumber pattern
- Remove duplicate formatDuration from history-card-grid.tsx and import the shared one
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* fix: avoid _yaml.pyd lock on Windows during dependency overrides
* fix: move pytorch_tokenizers and kernels to no-deps install to avoid Windows _yaml.pyd loc
* fix(studio): align config cards, dynamic height for expanders, LoRA collapsible
* Fix clipping regressions in training, dataset, and params section cards
- training-section: Add hasMessage conditional so the card expands
(min-h) when startError, vision/audio incompatibility, or config
validation messages are present instead of always using fixed height
- dataset-section: Expand card when a local dataset is selected via
upload (datasetSource === "upload" && selectedLocalDataset), not only
when the Advanced panel is open
- params-section: Guard loraOpen behind isLora so switching to full
fine-tune collapses the card instead of staying expanded from stale
React useState
* Fix dataset card clipping for direct file uploads
Use uploadedFile instead of selectedLocalDataset in the card height
condition. selectedLocalDataset is derived from localDatasets.find()
which only resolves for Data Recipe entries, not direct file uploads
(.jsonl, .csv, .parquet, .arrow). The card already renders the Eval
Dataset panel based on uploadedFile (line 750), so the height gate
should match.
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Recommended models matching the query were filtered from HF results but the Recommended section was hidden during search, causing them to vanish entirely.
- Show filtered recommended models during search by introducing `filteredRecommendedIds`
- Switch `recommendedSet` to use filtered IDs when searching so dedup against HF results is correct
- Hide empty "Hugging Face" label when recommended matches cover the query
- Add `normalizeForSearch` helper to strip separators (spaces, hyphens, underscores, dots) so queries like "llama 3" match "Llama-3.2-1B" and "qwen 2.5" matches "Qwen2.5-7B" in both the recommended model filter and the LoRA adapter filter
* Fix Colab setup skipping llama.cpp installation
The early exit 0 in the Colab no-venv path prevented setup.sh from
ever reaching the llama.cpp install section. Remove the early exit
and instead guard only the venv-dependent Python deps section, so
execution continues through to the llama.cpp prebuilt/source install.
* Simplify _SKIP_PYTHON_DEPS initialization
* Add --local flag to setup.sh in Colab notebook
* Fix Colab huggingface-hub conflict, ensurepip fallback, bump to 2026.3.14
- colab.py / setup.sh: relax == pins to >= when installing studio.txt
on Colab so huggingface-hub does not clobber Colab's bundled version
(breaks transformers is_offline_mode import)
- install_python_stack.py: when uv is unavailable and pip is missing
(uv-created venvs), bootstrap via ensurepip before attempting upgrade
- Bump version to 2026.3.14
- Bump installer min version pins to 2026.3.14
* [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 Colab Studio launch and setup.ps1 box alignment
- colab.py: when the Studio venv is missing on Colab, pip-install
backend dependencies (structlog, fastapi, etc.) from studio.txt
into the current Python instead of failing with ModuleNotFoundError
- setup.sh: on Colab without a venv, install backend deps into system
Python and skip venv-dependent sections (Python stack update,
llama.cpp build) that would otherwise fail
- setup.ps1: use PadRight(47) for the done-line so "Setup Complete!"
and "Update Complete!" both align with the box border
* [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>
* feat(studio): editable context length with Apply/Reset for GGUF model settings
Previously the Context Length field was read-only and the backend
hardcoded `-c 0`, ignoring custom values entirely. KV Cache Dtype also
triggered an immediate model reload with no way to cancel.
Backend:
- llama_cpp.py: pass the actual n_ctx value to `-c` instead of always 0
- models/inference.py: relax max_seq_length to 0..1048576 (0 = model
default) so GGUF models with large context windows are supported
Frontend:
- chat-runtime-store: add customContextLength and loadedKvCacheDtype
state fields for dirty tracking
- chat-settings-sheet: make Context Length an editable number input,
stop KV Cache Dtype from auto-reloading, show Apply/Reset buttons
when either setting has been changed
- use-chat-model-runtime: send customContextLength as max_seq_length
in the load request, reset after successful load
* fix: preserve maxSeqLength for non-GGUF models in load request
customContextLength ?? 0 sent max_seq_length=0 for non-GGUF models,
breaking the finetuning/inference path that needs the slider value.
Now uses a three-way branch:
- customContextLength set: use it (user edited GGUF context)
- GGUF without custom: 0 (model's native context)
- Non-GGUF: maxSeqLength from the sampling slider
* fix: keep max_seq_length default at 4096 for non-GGUF callers
Only relax the bounds (ge=0 for GGUF's "model default" mode,
le=1048576 for large context windows). The default stays at 4096
so API callers that omit max_seq_length still get a sane value
for non-GGUF models.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio): rename trust remote code toggle and hide when no model selected
- Rename "Trust remote code" to "Enable custom code"
- Shorten subtitle to "Only enable if sure"
- Hide the toggle when no model is loaded (already hidden for GGUFs)
* fix: restore ge=128 for max_seq_length validation
Keep the minimum at 128 so the API rejects nonsensical values.
GGUF path now sends the model's native context length (from
ggufContextLength) instead of 0 when the user has not customized it.
The upper bound stays at 1048576 for large-context GGUF models.
* feat(studio): replace Context Length input with slider
Use a ParamSlider (512 to model's native context, step 512) instead
of a small number input. Shows "Max" when at the model's native
context length. Consistent with the other slider controls in the
settings panel.
* feat(studio): add editable number input alongside Context Length slider
The slider and number input stay synced -- dragging the slider updates
the number, typing a number moves the slider. The input also accepts
values beyond the slider range for power users who need custom context
lengths larger than the model default.
* fix(studio): widen context length input and use 1024 step for slider
Make the number input wider (100px) so large values like 262144 are
fully visible. Change slider step from 512 to 1024 and min from 512
to 1024.
* fix(studio): context length number input increments by 1024
* fix(studio): cap context length input at model's native max
Adds max attribute and clamps typed/incremented values so the context
length cannot exceed the GGUF model's reported context window.
* fix(studio): point "What's new" link to changelog page
Changed from /blog to /docs/new/changelog.
* fix(studio): preserve custom context length after Apply, remove stale subtitle
- After a reload with a custom context length, keep the user's value
in the UI instead of snapping back to the model's native max.
ggufContextLength always reports the model's native metadata value
regardless of what -c was passed, so we need to preserve
customContextLength when it differs from native.
- Remove "Reload to apply." from KV Cache Dtype subtitle since the
Apply/Reset buttons now handle this.
* feat(studio): auto-enable Search and Code tools when model supports them
Previously toolsEnabled and codeToolsEnabled stayed false after loading
a model even if it reported supports_tools=true. Now both toggles are
automatically enabled when the loaded model supports tool calling,
matching the existing behavior for reasoning.
* fix(studio): auto-enable tools in autoLoadSmallestModel path
The suggestion cards trigger autoLoadSmallestModel which bypasses
selectModel entirely. It was hardcoding toolsEnabled: false and
codeToolsEnabled: false even when the model supports tool calling.
Now both are set from the load response, matching the selectModel
behavior. Also sets kvCacheDtype/loadedKvCacheDtype for dirty
tracking consistency.
* fix(studio): re-read tool flags after auto-loading model
The runtime state was captured once at the start of the chat adapter's
run(), before autoLoadSmallestModel() executes. After auto-load enables
tools in the store, the request was still built with the stale snapshot
that had toolsEnabled=false. Now re-reads the store after auto-load so
the first message includes tools.
* fix(studio): re-read entire runtime state after auto-load, not just tools
The runtime snapshot (including params.checkpoint, model id, and all
tool/reasoning flags) was captured once before auto-load. After
autoLoadSmallestModel sets the checkpoint and enables tools, the
request was still built with stale params (empty checkpoint, tools
disabled). Now re-reads the full store state after auto-load so the
first message has the correct model, tools, and reasoning flags.
* feat(studio): add Hugging Face token field in Preferences
Adds a password input under Configuration > Preferences for users to
enter their HF token. The token is persisted in localStorage and
passed to all model validate/load/download calls, replacing the
previously hardcoded null. This enables downloading gated and private
models.
* fix(studio): use model native context for GGUF auto-load, show friendly errors
The auto-load paths and selectModel for GGUF were sending
max_seq_length=4096 which now actually limits the context window
(since we fixed the backend to respect n_ctx). Changed to send 0
for GGUF, which means "use model's native context size".
Also replaced generic "An internal error occurred" messages with
user-friendly descriptions for known errors like context size
exceeded and lost connections.
LoadRequest validation changed to ge=0 to allow the GGUF "model
default" signal. The frontend slider still enforces min=128 for
non-GGUF models.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio): filter out FP8 models from model search results
Hide models matching *-FP8-* or *FP8-Dynamic* from both the
recommended list and HF search results. These models are not
yet supported in the inference UI.
---------
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Add PID file tracking and `unsloth studio stop` command
On macOS the .app shortcut launches Studio via osascript into a
Terminal window, then the launcher script exits. The server process
runs outside of the launcher's context with no PID file, so there
is no straightforward way to find or stop it.
This adds:
- PID file at ~/.unsloth/studio/studio.pid, written after the
server starts and removed on graceful shutdown or via atexit
- `unsloth studio stop` command that reads the PID file and sends
SIGTERM (or taskkill on Windows) to shut down the server
The PID file is only removed if it still contains the current
process ID, avoiding races when a new server instance replaces
a crashed one.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Move atexit PID cleanup into run_server()
The atexit registration was only in the __main__ block, so it
did not cover the `unsloth studio` CLI path that calls
run_server() directly via studio_default(). Moving it into
run_server() ensures the PID file is cleaned up on unexpected
exit regardless of entry point.
* [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>
