normalize_path() unconditionally converted Windows paths like
C:\Users\... to WSL format /mnt/c/Users/..., which breaks path
resolution on native Windows. This caused LM Studio GGUF models
to fail detection (detect_gguf_model returned None for the invalid
path), falling through to the Unsloth import path which requires
a GPU.
Now only performs the /mnt/ mapping when actually running under WSL.
On native Windows, drive letters are preserved and backslashes are
normalized to forward slashes.
* fix: default HF cache to standard platform path instead of legacy Unsloth cache
* feat: show LM Studio and local models in chat Fine-tuned tab
* feat: show LM Studio models in Hub models tab
* fix: fetch local models after auth refresh completes
* Revert "fix: fetch local models after auth refresh completes"
This reverts commit cfd61f0ac7.
* fix: increase llama-server health check timeout to 600s for large models
* feat: expandable GGUF variant picker for LM Studio local models
* fix: show GGUF variant label for locally loaded LM Studio models
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: show publisher name in LM Studio model labels
* fix: set model_id for loose GGUF files in LM Studio publisher dirs
* fix: show publisher prefix in Fine-tuned tab LM Studio models
* fix: only use model_id for lmstudio source models
* fix: only show LM Studio models in Hub tab on Mac/chat-only mode
* fix: respect XDG_CACHE_HOME, handle Windows paths in isLocalPath, refresh LM Studio on remount
- _setup_cache_env now reads XDG_CACHE_HOME (falls back to ~/.cache)
instead of hard-coding ~/.cache/huggingface. This follows the standard
HF cache resolution chain and respects distro/container overrides.
- isLocalPath in GgufVariantExpander uses a regex that covers Windows
drive letters (C:\, D:/), UNC paths (\\server\share), relative paths
(./, ../), and tilde (~/) -- not just startsWith("/").
- HubModelPicker.useEffect now calls listLocalModels() before the
alreadyCached early-return gate so LM Studio models are always
refreshed on remount. Also seeds useState from _lmStudioCache for
instant display on re-open.
* fix: add comment explaining isLocalPath regex for Windows/cross-platform paths
* fix: prioritize unsloth publisher in LM Studio model list
* fix: scope unsloth-first sort to LM Studio models on all platforms
* fix: add missing _lmStudioCache module-level declaration
* fix: prioritize unsloth publisher before timestamp sort in LM Studio group
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.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
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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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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
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* 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
for more information, see https://pre-commit.ci
* 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)
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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
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* 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
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* 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.
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* 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
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* 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.
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* 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.
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* 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)
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* 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>
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.
* 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 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
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* 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
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* 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.
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* 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.
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* 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.
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* 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.
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* feat: multi-source model discovery (HF default, legacy cache, LM Studio)
* Fix multi-source model discovery bugs
- Fix lmstudio_model_dirs: add ~/.lmstudio/models as default path,
remove dead sys.platform branch, add dedup via seen set
- Fix _setup_cache_env: preserve legacy HF cache env vars when the
legacy hub directory exists and is non-empty
- Fix _scan_lmstudio_dir: use absolute path for id field so
is_local_path() returns True
- Remove LM Studio dirs from allowed_roots (scanned unconditionally)
- Replace bare except passes with logger.warning in legacy cache blocks
- Fix delete_cached_model to search both default and legacy HF caches
- Make lmstudio_dirs non-optional in TS interface (matches Python schema)
- Exclude lmstudio source from trainable model filter
- Remove unused import sys
* Scan HF default cache alongside legacy and active caches
When _setup_cache_env overrides HF_HUB_CACHE to the legacy Unsloth
path, the standard HF default cache (~/.cache/huggingface/hub) was
never scanned, hiding models downloaded before Unsloth Studio was
installed.
Add hf_default_cache_dir() and _all_hf_cache_scans() helper that
deduplicates and scans all three HF cache locations (active, legacy,
default). Used in list_local_models, list_cached_gguf,
list_cached_models, and delete_cached_model.
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
The prebuilt llama.cpp binary (cuda13-newer) links against
libcudart.so.13 and libcublas.so.13. When torch is installed via pip,
these libraries live in the venv's site-packages under
nvidia/cu13/lib/, not in /usr/local/cuda/.
The existing LD_LIBRARY_PATH logic only searched /usr/local/cuda*
paths (which have CUDA 12.x), so the CUDA backend failed to load
silently and llama-server fell back to CPU -- even with -ngl -1.
This adds a glob scan of the venv's nvidia package directories
(cu*, cudnn, nvjitlink) to LD_LIBRARY_PATH before launching
llama-server, matching where pip puts the CUDA runtime.
Tested on Colab with RTX PRO 6000 Blackwell (CUDA 13.0, pip torch):
before -- 3 MiB GPU, 0% util, CPU inference
after -- 13317 MiB GPU, 77% util, full GPU inference
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
When _select_gpus determines that a GGUF model fits on the selected
GPU(s), the code sets CUDA_VISIBLE_DEVICES but never passes -ngl
(number of GPU layers) to llama-server. Without -ngl or --fit,
llama-server defaults to 0 GPU layers and runs entirely on CPU.
This adds -ngl -1 (offload all layers) in the elif branch where
gpu_indices is set and use_fit is False, so models that fit in VRAM
actually use the GPU for inference.
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
* refactor: consolidate dual venvs into single ~/.unsloth/studio/unsloth_studio
* refactor: separate install.sh (first-time) from setup.sh (smart update with PyPI version check)
* fix: install.sh calls setup.sh directly, keep both setup and update CLI commands
* fix: use importlib.resources.files() directly without _path attribute
* fix: bootstrap uv before pip upgrade to handle uv venvs without pip
* fix: frontend 404 when launched via CLI, add global symlink to ~/.local/bin
* feat: add --local flag to install.sh and unsloth studio update for branch testing
* fix: resolve repo root from script location for --local installs
* feat: add --package flag to install.sh for testing with custom package names
* feat: add --package flag to unsloth studio update
* fix: always nuke venv in install.sh for clean installs
* revert: remove Windows changes, will handle in separate PR
* fix: error when --package is passed without an argument
* revert: restore Windows scripts to current main
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* fix: always explicitly set STUDIO_LOCAL_INSTALL and STUDIO_PACKAGE_NAME env vars
* fix: pass explicit STUDIO_LOCAL_REPO env var for --local installs
* fix: align banner box for Setup vs Update labels
* deprecate: hide 'unsloth studio setup' command, point users to update/install.sh
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: check stdout not stdin for auto-launch detection (curl pipe fix)
* fix: update install URL to unsloth.ai/install.sh
* fix: update install.sh usage comments to unsloth.ai/install.sh
* fix: use --upgrade-package for base deps to preserve existing torch/CUDA installs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: --local install now also installs unsloth-zoo via base.txt before editable overlay
* fix: don't skip base packages for --local installs (editable needs unsloth-zoo)
* refactor: move --local full dep install to install.sh, keep SKIP_STUDIO_BASE for all paths
* feat: add migration support for old .venv and CWD-based installs in setup.sh
* Revert "feat: add migration support for old .venv and CWD-based installs in setup.sh"
This reverts commit 301291d002.
* feat: migrate old .venv layout in install.sh instead of always nuking
* feat: validate old .venv with torch CUDA test before migration, recovery message on launch failure
* fix: try CUDA then fall back to CPU for migration validation
* fix: upgrade unsloth/unsloth-zoo with --reinstall-package on migration to preserve torch
* remove: delete unused unsloth ui command (use unsloth studio instead)
* Fix Windows venv path mismatch between install.ps1, setup.ps1, and studio.py
install.ps1 was creating the venv CWD-relative ($VenvName = "unsloth_studio"),
setup.ps1 was using an absolute path to ".unsloth\studio\.venv", and studio.py
looks for ".unsloth\studio\unsloth_studio". All three paths were different, so
the Windows installer would never produce a working Studio setup.
install.ps1:
- Use absolute $StudioHome + $VenvDir matching the Linux install.sh layout
- Add 3-way migration: old .venv at STUDIO_HOME, CWD-relative ~/unsloth_studio
from the previous install.ps1, or fresh creation with torch validation
- For migrated envs, upgrade unsloth while preserving existing torch/CUDA wheels
- Set SKIP_STUDIO_BASE=1 before calling setup.ps1 (matches install.sh behavior)
- Fix launch instructions to use the absolute venv path
setup.ps1:
- Change $VenvDir from ".unsloth\studio\.venv" to ".unsloth\studio\unsloth_studio"
- Add SKIP_STUDIO_BASE guard: error out if venv is missing when called from
install.ps1 (which should have already created it)
- Differentiate "Setup" vs "Update" in banners based on SKIP_STUDIO_BASE
* setup.ps1: unconditionally error if venv missing, matching setup.sh
setup.sh always errors out if the venv does not exist (line 224-228),
telling the user to run install.sh first. setup.ps1 was conditionally
creating a bare venv with python -m venv when SKIP_STUDIO_BASE was not
set, which would produce an empty venv with no torch or unsloth. Now
setup.ps1 matches setup.sh: always error, always point to install.ps1.
* Fix --torch-backend=auto CPU solver dead-end on Linux, macOS, and Windows
On CPU-only machines, `uv pip install unsloth --torch-backend=auto`
falls back to unsloth==2024.8 because the CPU solver cannot satisfy
newer unsloth's dependencies. install.ps1 already solved this with a
two-step approach; this applies the same fix to install.sh and
install_python_stack.py.
install.sh: add get_torch_index_url() that detects GPU via nvidia-smi
and maps CUDA versions to PyTorch index URLs (matching install.ps1's
Get-TorchIndexUrl). Fresh installs now install torch first via explicit
--index-url, then install unsloth with --upgrade-package to preserve
the pre-installed torch. All 5 --torch-backend=auto removed from
primary paths.
install.ps1: add fallback else-branch when TorchIndexUrl is empty,
using --torch-backend=auto as last resort (matching install.sh).
install_python_stack.py: remove unconditional --torch-backend=auto
from _build_uv_cmd. Torch is pre-installed by install.sh/setup.ps1
by the time this runs. Callers that need it can set UV_TORCH_BACKEND.
Both install.sh and install.ps1 now share the same three-branch logic:
migrated env (upgrade-package only), normal (torch-first + index-url),
and fallback (--torch-backend=auto if URL detection fails).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Use --reinstall-package for migrated envs on both Linux and Windows
For migrated environments (moved from legacy venv location),
--reinstall-package is better than --upgrade-package because it forces
a clean reinstall even if the same version is already installed. This
ensures proper .dist-info and .pyc state in the new venv location.
--upgrade-package remains correct for the fresh install path where
torch is already installed and we just want to add unsloth without
re-resolving torch.
* Address review findings: portability, parity, and stale comments
- Replace grep -oP (GNU Perl regex) with POSIX sed in
get_torch_index_url() so the script works on BSD grep (macOS is
already guarded by the Darwin early-return, but Alpine/BusyBox
would silently get the wrong CUDA tag)
- Add LC_ALL=C before nvidia-smi invocation to prevent locale-dependent
output parsing issues
- Add warning on stderr when nvidia-smi output is unparseable, matching
install.ps1's [WARN] message
- Add explicit unsloth-zoo positional arg to install.ps1 migrated path,
matching install.sh (--reinstall-package alone won't install it if it
was never present in the migrated env)
- Fix stale comment in install_python_stack.py line 392 that still
claimed --torch-backend=auto is added by _build_uv_cmd
- Add sed to test tools directory (function now uses sed instead of grep)
* Add --index-url to migrated env path to prevent CPU torch resolution
The migrated path runs uv pip install with --reinstall-package for
unsloth/unsloth-zoo. While uv should keep existing torch as satisfied,
the resolver could still re-resolve torch as a transitive dependency.
Without --index-url pointing at the correct CUDA wheel index, the
resolver would fall back to plain PyPI and potentially pull CPU-only
torch. Adding --index-url $TORCH_INDEX_URL ensures CUDA wheels are
available if the resolver needs them.
Applied to both install.sh and install.ps1.
* Revert --index-url on migrated env path
The original install.ps1 on main already handles the migrated path
without --index-url and it works correctly. --reinstall-package only
forces reinstall of the named packages while uv keeps existing torch
as satisfied. No need for the extra flag.
* Fix unsloth studio update --local not installing local checkout
studio.py sets STUDIO_LOCAL_REPO when --local is passed, but
install_python_stack.py never read it. The update path always
installed from PyPI regardless of the --local flag.
Add a local_repo branch that first updates deps from base.txt
(with --upgrade-package to preserve torch), then overlays the
local checkout as an editable install with --no-deps.
---------
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* Try installing causal-conv1d from prebuilt wheels if avialable
* Prefer installing mamba-ssm from wheel to speed up things
* undo python stack install changes
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* Revert "undo python stack install changes"
This reverts commit d943551092.
* add comments
* Fix wheel installer: model detection, platform tags, torch pin, error handling
- Add nemotron-h (hyphen) and granite-4.0-h / granitemoehybrid to model
detection for both causal-conv1d and mamba-ssm. These hybrid Mamba models
were silently skipped since nemotron_h (underscore) never matches real
HF model IDs like nvidia/Nemotron-H-8B-Base, and granite was missing
entirely despite being a supported model in model_config.py and loader.py.
- Fix _causal_conv1d_platform_tag to detect linux_aarch64 via
platform.machine() instead of hardcoding linux_x86_64. Both upstream
releases publish aarch64 wheels. Drop win_amd64 since neither repo
publishes Windows wheels (avoids a wasted HTTP probe on every run).
- Pin torch to >=2.6.0,<2.11.0 instead of <=2.10.0 to add a version floor
and document the wheel coverage range with upstream release links.
- Strip non-numeric suffixes from torch minor version so nightly builds
like 2.7a0 correctly resolve to wheel tag torch2.7 instead of torch2.7a0.
- Use stderr=_sp.PIPE instead of stderr=_sp.STDOUT in the env probe so
torch import warnings do not corrupt the JSON output.
- Add timeout=30 to the env probe subprocess to prevent indefinite hangs.
- Catch Exception (not just ImportError) on the existing-install check so
ABI-broken installs with OSError/RuntimeError are retried rather than
silently accepted.
- Guard uv invocation with shutil.which("uv") to prevent FileNotFoundError
crash when uv is not on PATH. Wrap the top-level ensure calls in
try/except so failures do not kill the training worker.
- Hoist _SSM_MODEL_SUBSTRINGS to module level.
- Remove redundant --torch-backend=auto flag from direct wheel URL install.
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* Add LFM2 to causal-conv1d detection; stop training on install failure
- Add "lfm2" to _model_wants_causal_conv1d so Studio picks up the
fast kernel path for Liquid Foundation Model 2.
- Replace silent logger.warning on SSM dependency install failure
with an error event that tells the user to choose another model
and stops the training job immediately.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Catch subprocess timeout in torch probe; narrow import guard to ImportError
- _probe_causal_conv1d_env: wrap subprocess.run in try/except for
TimeoutExpired so a slow torch import returns None (falls back to
PyPI) instead of killing the training job.
- _install_package_wheel_first: narrow except Exception to except
ImportError on the __import__ check so unexpected errors from a
broken module still propagate.
* Remove unconditional torch pin from install_python_stack
The torch>=2.6.0,<2.11.0 pin was added to ensure prebuilt
causal-conv1d / mamba-ssm wheels exist, but it runs at install
time for all users regardless of model choice. This can downgrade
or unnecessarily upgrade torch. The worker already handles wheel
compatibility at training time by probing the environment and
falling back to PyPI, so the install-time pin is not needed.
---------
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* Bump Data Designer to 0.5.4 (removes litellm dependency)
NVIDIA Data Designer v0.5.4 removes litellm entirely and replaces it
with native OpenAI and Anthropic adapters. This follows the litellm
supply chain incident where versions 1.82.7 and 1.82.8 were compromised
with a credential stealer.
Release notes: https://github.com/NVIDIA-NeMo/DataDesigner/releases/tag/v0.5.4
Changes:
- Bump data-designer, data-designer-config, data-designer-engine to 0.5.4
- Sync data-designer-deps.txt with 0.5.4 engine requirements:
- Added: chardet, fsspec, mcp
- Removed: python-json-logger, pymupdf, pymupdf4llm, mammoth
(these remain in the unstructured-seed plugin which still needs them)
- duckdb constraint relaxed from <1.5 to <2 (upstream fixed record_batch)
- Bump plugin lower bound to >=0.5.4
* Keep pymupdf, pymupdf4llm, mammoth in data-designer-deps
The unstructured-seed plugin is installed with --no-deps, so its
pyproject.toml dependencies are not auto-resolved. These three
packages are needed by the seed route (studio/backend/routes/
data_recipe/seed.py) and must remain in the explicit deps list.
* feat(db): add SQLite storage layer for training history
* feat(api): add training history endpoints and response models
* feat(training): integrate DB persistence into training event loop
* feat(ui): add training history views and card grid
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio): address review issues in training history persistence
- Strip hf_token/wandb_token from config before SQLite storage
- Add UUID suffix to job_id for collision resistance
- Use isfinite() for 0.0 metric handling throughout
- Respect _should_stop in error event finalization
- Run schema DDL once per process, not per connection
- Close connection on schema init failure
- Guard cleanup_orphaned_runs at startup
- Cap _metric_buffer at 500 entries
- Make FLUSH_THRESHOLD a class constant
- Map 'running' to 'training' phase in historical view
- Derive LR/GradNorm from history arrays in historical view
- Fix nested button with div[role=button] in history cards
- Guard String(value) against null/undefined in config popover
- Clear selectedHistoryRunId on auto tab switch
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio): address round-2 review findings across training backend and frontend
Backend (training.py):
- Move state mutation after proc.start() so a failed spawn does not wedge
the backend with is_training=True
- Create DB run row eagerly after proc.start() so runs appear in history
during model loading, not after first metric event
- Rewrite _flush_metrics_to_db() with snapshot-before-insert pattern to
preserve metrics arriving during the write and retain buffer on failure
- Guard eval_loss with float() coercion and math.isfinite(), matching the
existing grad_norm guard
- Increase pump thread join timeout from 3s to 8s to cover SQLite's
default 5s lock timeout
Frontend (studio-page.tsx):
- Fix history navigation: check isTrainingRunning instead of
showTrainingView in onSelectRun so completed runs are not misrouted
- Replace activeTab state + auto-switch useEffect with derived tab to
eliminate react-hooks/set-state-in-effect lint violation
Frontend (historical-training-view.tsx):
- Add explicit "running" branch to message ternary so running runs no
longer fall through to "Training errored"
- Derive loading from detail/error state and move cleanup to effect
return to eliminate react-hooks/set-state-in-effect lint violation
Frontend (progress-section.tsx):
- Derive stopRequested from isTrainingRunning && stopRequestedLocal to
eliminate react-hooks/set-state-in-effect lint violation and remove
unused useEffect import
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio): resolve 3 remaining bugs from round-2 review
1. Stuck on Current Run tab [12/20]: Only force "current-run" tab when
isTrainingRunning is true, not when stale completed-run data exists.
After training ends, users can freely navigate to Configure.
2. Incomplete metric sanitization [7/20]: Apply float() coercion and
isfinite() guards to loss and learning_rate, matching the existing
pattern used by grad_norm and eval_loss. Prevents TypeError from
string values and NaN leaks into history arrays.
3. Stop button state leak across runs [10/20]: Add key={runtime.jobId}
to ProgressSection so React remounts it when a new run starts,
resetting stopRequestedLocal state.
* fix(studio): deduplicate loss/lr sanitization in training event handler
Reuse _safe_loss/_safe_lr from the progress update block instead of
re-sanitizing the same raw event values for metric history.
* fix(studio): restore loss > 0 guard to prevent eval steps injecting 0.0 into metric histories
Round-2/3 fixes relaxed the history append guard from `loss > 0` to
`loss is not None`, which let eval-only log events (where loss defaults
to 0.0) append fake zeros into loss_history and lr_history. Restore the
`loss > 0` check to match the worker's own has_train_loss gate. The
float() coercion and isfinite() sanitization from round-3 remain intact.
* fix(studio): resolve training history bugs — nullable loss/lr, tab nav, sparkline
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
litellm has been quarantined on PyPI due to a supply chain attack
in version 1.82.8 (malicious credential-stealing .pth file).
No versions are currently installable, which blocks
`unsloth studio setup` at step 8/11 (data-designer deps).
Remove litellm from the single-env data-designer requirements
so setup completes. litellm can be re-added once PyPI lifts the
quarantine.
Ref: https://github.com/BerriAI/litellm/issues/24512
* fix: system prompt was dropped in unsloth text and vision inference
* refactor: simplify system prompt message construction
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: use multimodal typed content parts for vision system message and add fallback
The system message content must use typed content parts
([{"type": "text", "text": ...}]) instead of a plain string to match
the multimodal processor contract (consistent with the audio path).
Plain strings cause some processors (e.g. LLaVA) to silently drop the
system prompt.
Also wraps processor.apply_chat_template in try/except so models that
reject the system role gracefully fall back to no system message with
a warning log.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: capture and log original exception in vision system prompt fallback
---------
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* Fix port conflict detection when loopback address is held by another process
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Use getaddrinfo for IPv6 host support, restore emojis in terminal output
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Guard against conn.pid being None in _get_pid_on_port
psutil.net_connections() can return entries with pid=None when the
current user lacks privileges to see the owning process (common on
macOS without root, Windows without admin, and some Linux configs).
psutil.Process(None) does not raise -- it silently returns the
current process, which would make the warning incorrectly blame
Unsloth Studio itself for blocking the port.
Skip entries with pid=None so the caller falls back to the generic
"port is already in use" message instead.
* Update studio/backend/run.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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* Fix Studio silently exiting on Windows without error output
On Windows, `unsloth studio` launches a child process via
subprocess.Popen to run the server in the studio venv. If the child
crashes (e.g. due to a missing package), the parent just calls
typer.Exit(rc) with no message -- the user sees "Launching Unsloth
Studio... Please wait..." and then the prompt returns with zero
feedback.
Root cause: `data_designer_unstructured_seed` is imported at the top
level in seed.py. If this package is not installed in the studio venv,
the entire import chain (seed.py -> routes/__init__.py -> main.py ->
run_server()) crashes with ModuleNotFoundError. Since run.py has no
try/except around run_server() and studio.py does not report nonzero
exit codes, the failure is completely silent.
Changes:
- run.py: wrap run_server() in try/except, print clear error with
traceback to stderr. Also reconfigure stderr encoding on Windows so
tracebacks with non-ASCII paths do not cause secondary failures.
- studio.py: print an error message when the child process exits with
a nonzero code on Windows, so the user knows something went wrong.
- seed.py: make data_designer_unstructured_seed import optional with
a try/except fallback. The server starts normally and only returns
HTTP 500 if the unstructured seed endpoints are actually called.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Skip Anaconda/Miniconda Python when creating Studio venv on Windows
Conda-bundled CPython ships modified DLL search paths that prevent
torch from loading c10.dll on Windows. The Studio server fails
silently at startup because the venv was created with conda's Python.
Standalone CPython (python.org, winget, uv) does not have this issue.
Both install.ps1 and setup.ps1 now skip any Python binary whose path
contains conda, miniconda, anaconda, miniforge, or mambaforge when
selecting the interpreter for the studio venv. If only conda Python
is available, the scripts print an error with instructions to install
standalone CPython.
* Fix multi-file preview crash and improve setup.ps1 Python discovery
Addresses review findings [10/10] and [8/10]:
1. seed.py: _read_preview_rows_from_multi_files() had a hard import
of build_multi_file_preview_rows inside the function body, bypassing
the optional-plugin guard. Moved it into the top-level try/except
block and added a None guard matching the other functions.
2. setup.ps1: Python discovery now probes py.exe (Python Launcher)
first, uses Get-Command -All to look past conda entries that shadow
standalone CPython further down PATH, skips WindowsApps stubs, and
resolves the actual executable path so venv creation does not
re-resolve back to a conda interpreter.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Check sys.base_prefix to catch venvs created from conda Python
A venv created from conda Python (e.g. C:\Users\danie\.venv) has a
path that does not contain "conda", but sys.base_prefix still points
to the conda install (e.g. C:\Users\danie\miniconda3). The previous
path-only check missed this case entirely.
Both install.ps1 and setup.ps1 now use a Test-IsConda helper that
checks both the executable path AND sys.base_prefix against the
conda/miniconda/anaconda/miniforge/mambaforge pattern. This catches:
- Direct conda Python executables
- Venvs created from conda Python (base_prefix reveals the origin)
* Fix install.ps1 passing version string to uv venv instead of resolved path
Find-CompatiblePython returned a bare version string (e.g. "3.13")
which was passed to `uv venv --python 3.13`. uv performs its own
interpreter discovery and can resolve that version string back to a
conda Python, defeating the entire conda-skip logic.
Now Find-CompatiblePython returns a hashtable with both .Version (for
display) and .Path (the resolved absolute executable path). The venv
is created with `uv venv --python <absolute-path>`, ensuring uv uses
the exact interpreter we validated.
* Quote resolved Python path in uv venv call for paths with spaces
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix(studio): prevent ModuleNotFoundError in dataset.map() on Windows
On Windows, dataset.map() uses "spawn", which requires workers to
import compiled modules from disk. Previously, clear_unsloth_compiled_cache()
deleted the entire directory, causing workers to crash when looking for
UnslothSFTTrainer.py.
Changes:
1. Added `preserve_patterns` to cache cleanup to keep `Unsloth*Trainer.py`
on Windows while clearing model-specific files.
2. Added the cache directory to PYTHONPATH for spawn workers.
Linux/macOS behavior is unchanged.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix spawn-platform coverage, CWD path mismatch, and race condition for PR #4473
- Extend platform guard from win32-only to include macOS (also uses spawn
since Python 3.8, same ModuleNotFoundError would occur)
- Replace fragile CWD-based PYTHONPATH registration with centralized
register_compiled_cache_on_path() that uses the same __file__-relative
_CACHE_DIRS already used by cache_cleanup -- fixes path mismatch when
studio is launched from a directory other than the repo root
- Move PYTHONPATH registration to the top of _train_worker(), before any
dataset.map() call (previously it ran late in config assembly, after
dataset formatting which also calls dataset.map())
- Update inference.py model-unload to preserve trainer files on spawn
platforms, preventing a race where unloading a model via inference tab
would delete UnslothSFTTrainer.py while training workers are importing it
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix cache-dir precedence reversal in register_compiled_cache_on_path()
Iterating _CACHE_DIRS in forward order while calling insert(0) each time
reverses the declared priority: later entries shadow earlier ones. When
multiple compiled-cache directories exist, spawned workers could import a
stale trainer from the wrong cache.
Fix: iterate in reverse so that the highest-priority entry (first in
_CACHE_DIRS) is inserted last and ends up at position 0 in sys.path and
PYTHONPATH.
* fix: harden worker-count helpers against cpu_count=None and desired<=0
- safe_num_proc: guard os.cpu_count() with `or 1`, clamp multi-GPU
path with max(1, min(4, desired)), clamp return with max(1, desired)
- safe_thread_num_proc: same os.cpu_count() guard and return clamp
- Add regression tests (31 L1 unit + 10 sandbox edge-case tests)
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* remove regression tests from PR
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Fix Studio crash on Anaconda Python due to platform._sys_version() parse failure
Anaconda and conda-forge modify sys.version to include distributor
metadata between pipe characters, e.g.:
3.12.4 | packaged by Anaconda, Inc. | (main, ...) [MSC v.1929 ...]
Python's platform._sys_version() has a hardcoded regex that cannot
parse this format, raising ValueError. CPython closed this as "not
planned" (cpython#102396) since Anaconda modified the binary.
This breaks the import chain: run.py -> structlog -> rich -> attrs,
which calls platform.python_implementation() at module scope.
Fix: before any library imports, strip the pipe segments, parse the
cleaned version string via the standard parser, and cache the result
under the original sys.version key so all subsequent platform calls
hit the cache.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add defensive fallback for unpaired pipe edge cases in version patch
Address Gemini review suggestion: if the paired-pipe regex leaves
residual pipes (hypothetical single-pipe distributor metadata), fall
back to extracting the version number and the parenthesized build
info directly. Wrap the entire patch in try/except so unexpected
version string formats degrade gracefully instead of crashing the
patch itself.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Refactor into shared _platform_compat module, cover colab.py entrypoint
Address reviewer feedback:
1. Extract the Anaconda/conda-forge sys.version fix into a shared
_platform_compat.py module that wraps platform._sys_version() with
a retry-on-ValueError fallback. This is more robust than cache-seeding
because it handles all future platform._sys_version() calls, not just
the first one.
2. Import the fix from both run.py and colab.py entrypoints, so Studio
no longer crashes on Anaconda Python regardless of the launch path.
3. The wrapper is idempotent (guarded by a flag) and handles edge cases:
paired pipes (Anaconda, conda-forge), unpaired pipes (hypothetical),
and standard CPython strings (no-op since ValueError is never raised).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Replace monkey-patch with cache-prime, fix colab.py duplicate sys.path, cover main.py
- Rewrite _platform_compat.py: replace function-wrapping monkey-patch with
one-shot cache seed (_seed_sys_version_cache). Parses cleaned sys.version
once and seeds platform._sys_version_cache so the stdlib parser never sees
the problematic Anaconda/conda-forge pipe-delimited string. No function
replacement, no idempotency flag, no reload edge cases.
- colab.py: remove duplicate backend_path sys.path insertion after
_bootstrap_studio_venv(). The early insertion (before _platform_compat
import) already covers it. This also fixes backend/ ending up behind
venv site-packages in sys.path ordering.
- run.py: move PYTHONWARNINGS=ignore before _platform_compat import to
preserve original intent of suppressing warnings early.
- main.py: add sys.path + _platform_compat import before route imports,
covering the direct `uvicorn main:app` launch path.
- Add test_platform_compat.py with 7 tests covering Anaconda, conda-forge,
and standard CPython version strings, plus the loggers import chain.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Remove test_platform_compat.py from PR
* Handle Format B conda-forge version strings with duplicate paren groups
Some conda-forge builds produce sys.version with the build info both
before and after the pipe label (e.g. "3.9.7 (default, ...) | packaged
by conda-forge | (default, ...) \n[GCC 7.5.0]"). After stripping the
pipe segment, two consecutive (...) groups remain, which still fails
platform._sys_version(). Add a second regex pass to drop the duplicate
paren group.
* Guard _sys_version call with try/except to avoid making things worse
If the cleaned version string is still unparseable by the stdlib regex
(e.g. nested parens, exotic multi-pipe formats), silently give up
instead of letting ValueError propagate at import time -- which would
be a worse crash than the original deferred one.
---------
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* fix: handle Windows subprocess crash during dataset.map()
Windows uses spawn (not fork) for multiprocessing. Spawned workers
cannot resolve Unsloth's dynamically compiled cache modules from
unsloth_compiled_cache/, causing ModuleNotFoundError and RuntimeError
during dataset.map() tokenization.
Add two platform-guarded patches for sys.platform == "win32":
1. Force HF_DATASETS_MULTITHREADING_MAX_WORKERS=1 and set spawn method
2. Monkey-patch Dataset.map() to force num_proc=None
Fixes#4490
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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* address review: extend spawn fix to macOS, add multiprocess fallback
- Change platform checks from sys.platform == "win32" to
sys.platform != "linux" so macOS (also spawn-based) is covered
- Wrap multiprocess import in try/except falling back to stdlib
multiprocessing when the multiprocess package isn't installed
- Rename _win32_safe_map to _spawn_safe_map to reflect broader scope
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: replace global Dataset.map monkey-patch with targeted num_proc routing
The previous approach had issues: Patch 1 set HF_DATASETS_MULTITHREADING_MAX_WORKERS
and forced set_start_method (dead code on platforms already using spawn), and Patch 2
globally monkey-patched Dataset.map() (too broad, missed Dataset.filter()).
Replace with a two-layer fix:
1. Studio layer: Add dataset_map_num_proc() that returns None on spawn platforms
(Windows, macOS). Unlike num_proc=1 which still creates Pool(1) and spawns a
worker, num_proc=None runs Dataset.map()/filter() truly in-process.
Update all dataset.map() callsites to use it. ThreadPoolExecutor callers
(format_conversion.py) keep using safe_num_proc() since threads are unaffected.
2. Root-cause layer: Propagate UNSLOTH_COMPILE_LOCATION via PYTHONPATH on spawn
platforms so spawned workers can import compiled modules. Mirrors the .venv_t5
pattern in worker.py. Does not import unsloth_zoo.compiler (heavy torch/triton
imports). Completely skipped on Linux.
Also extend safe_num_proc() to return 1 on macOS (was only guarding Windows),
and narrow the transformers 5.x dataloader guard from != "linux" to explicit
("win32", "darwin").
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: add safe_thread_num_proc() for ThreadPoolExecutor callsites
safe_num_proc() correctly caps to 1 on macOS/Windows for process-based
multiprocessing, but format_conversion.py reuses it for ThreadPoolExecutor
workers. Threads share address space and are unaffected by spawn, so
capping to 1 makes image URL downloads sequential -- a real regression.
Add safe_thread_num_proc() that skips the platform guard but keeps the
cpu_count heuristic, and switch both ThreadPoolExecutor callsites in
format_conversion.py to use it.
* fix: remove double-wrap in dataset_num_proc + fix num_proc=1 in datasets route
- trainer.py:3009: Replace safe_num_proc(max(1, os.cpu_count() // 4))
with max(1, (os.cpu_count() or 1) // 4) to avoid double-wrapping
inside dataset_map_num_proc which already calls safe_num_proc
- trainer.py:15-20: Clarify comment on PYTHONPATH propagation
- datasets.py:445: Change num_proc=1 to num_proc=None for 10-row
preview slice (avoids unnecessary multiprocessing overhead)
* fix: guard os.cpu_count() against None in worker-count helpers
os.cpu_count() can return None on some platforms. Use (os.cpu_count() or 1)
to prevent TypeError in safe_num_proc() and safe_thread_num_proc().
---------
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* fix: reconfigure stdout UTF-8 on Windows to prevent UnicodeEncodeError from emoji
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: default frontend_path when None to fix blank page when venv is pre-activated
* Restore Windows UTF-8 stdout fix dropped in earlier commit
The cp1252 console encoding on Windows cannot render emoji characters
used in startup messages (e.g. print("✅ Frontend loaded ...")).
This causes UnicodeEncodeError and crashes the server before it starts.
Place sys.stdout.reconfigure(encoding="utf-8", errors="replace") at the
top of run_server(), unconditionally before any print() or structlog
call, so all emoji output is covered -- including the frontend status
messages and silent=True paths that the original placement missed.
Guarded by sys.platform == "win32" and hasattr check, so it is a no-op
on Linux/macOS and safe in non-standard stdout environments (Jupyter,
piped IO).
* fix: preserve run_server(None) as headless, fix CLI frontend kwarg
Remove the frontend_path=None fallback in run_server() that changed
None from "headless/API-only" to "mount bundled frontend", breaking
backwards compatibility for embedders.
The blank-page bug was actually caused by the CLI wrappers always
passing frontend_path=frontend (even when frontend=None), which
overrode run_server()'s default. Fix studio.py and ui.py to only
pass frontend_path when the user explicitly sets --frontend.
* fix: use timeout loop for shutdown event in ui command
Match studio_default()'s shutdown loop that uses a 1-second timeout
on Event.wait(). Without a timeout, the bare wait() blocks at the C
level on Linux, preventing Python from delivering SIGINT (Ctrl+C).
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* feat(chat): add server-side timings and context display for GGUF
Extract timings/usage metadata from llama-server SSE stream and forward
through the full stack. Replace client-side estimates with accurate
server-reported metrics (prompt eval, tok/s, token counts, cache hits).
Add context window usage bar to chat top nav.
* feat(chat): source badges with hover cards and 2-row collapse
- Add hover cards to source badges showing favicon, title, URL and
snippet description on hover
- Limit source badges to 2 rows with +X more expand/collapse
- Parse snippet from web search results for hover card descriptions
- Replace individual Source rendering with grouped SourcesGroup component
* fix(chat): add null guards for server timings edge cases
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(chat): reset contextUsage on thread switch, remove unused context-display
* fix(chat): stop double-counting completion tokens in tool-calling path
* fix(chat): skip metadata events in llm_assist consumers
* fix(chat): hide context usage bar in compare mode
* fix(chat): harden timings pipeline and context usage persistence
Accumulate prompt_ms, predicted_ms, and predicted_n from intermediate
tool-detection passes so the final metadata reflects total server work.
Persist contextUsage in message metadata (Dexie) and restore on thread
load. Add type guard in gguf_stream_chunks for unexpected dict events.
Clear contextUsage when entering compare mode.
* feat(chat): make GGUF stream metadata OpenAI-compatible
* fix(chat): address PR review feedback
* feat(chat): address PR review feedback
* [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(recipe-studio): prevent fitView from zooming to wrong location on recipe load
* feat: add pymupdf/python-docx deps and unstructured uploads storage root
* feat: add POST /seed/upload-unstructured-file endpoint
* feat: add multi-file chunking with source_file column
* feat: update frontend types and API layer for multi-file upload
* feat: round-robin preview rows across source files
Ensures every uploaded file is represented in the preview table
by cycling through sources instead of just taking the first N rows.
* fix: disable OCR, fix auto-load timing, fix persistence on reload
- Disable pymupdf4llm OCR with write_images=False, show_progress=False
- Replace onAllUploaded callback with useEffect that detects uploading→done
transition (avoids stale closure reading empty file IDs)
- Fix importer to preserve file IDs from saved recipes instead of clearing
(clearing only happens at share time via sanitizeSeedForShare)
* fix: harden unstructured upload with input validation and state fixes
Validate block_id/file_id with alphanumeric regex to prevent path
traversal, use exact stem match for file deletion, add error handling
for metadata writes and empty files, fix React stale closures and
object mutations in upload loop, and correct validation logic for
unstructured seed resolved_paths.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: address PR review - legacy path import, share sanitizer, sync effect
Promote legacy source.path into resolved_paths for old unstructured
recipes, clear source.paths in share sanitizer to prevent leaking local
filesystem paths, and gate file sync effect to dialog open transition
so users can actually delete all uploaded files.
* fix: CSV column fix (BOM + whitespace + unnamed index re-save) for #4470
* fix: harden unstructured upload flow and polish dialog UX
* [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: support full model GGUF export, disable incompatible methods in UI
* fix: resolve base model from config.json for venv_t5 export switching
* feat: detect BNB-quantized models and disable all export methods for quantized non-PEFT checkpoints
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: relocate Ollama Modelfile alongside GGUFs during non-PEFT export 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>
* full finetuning studio
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update studio/backend/core/training/trainer.py
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* One liner setup for unsloth studio
* Fix install scripts: system deps, activation bugs, curl/wget support
- install.sh: detect platform (macOS/Linux/WSL) and check for missing
system dependencies (cmake, git, build-essential, libcurl4-openssl-dev).
Prompt user once for permission to install all missing packages via
brew (macOS) or sudo apt-get (Linux/WSL). Add wget fallback via
download() helper since curl is not always present on minimal Linux
installs. Fix nested curl|sh stdin stealing by downloading uv installer
to a tempfile first. Replace venv activation (no-op in a pipe subshell)
with explicit --python flag for uv pip install and direct venv binary
invocation. Add idempotency guard for venv creation. Redirect stdin
on unsloth studio setup to prevent pipe consumption. On macOS, check
for Xcode Command Line Tools and trigger install if missing.
- install.ps1: wrap script body in Install-UnslothStudio function so
that errors use return instead of exit (exit kills the terminal when
run via irm|iex). Remove activate.ps1 invocation entirely -- use
explicit --python path for uv pip install and & $UnslothExe for
studio setup. This avoids both the child-scope activation bug (& vs
dot-source) and the execution policy error on default Windows systems.
Add winget availability check with clear error message. Fix PATH
refresh to append registry paths instead of replacing the session PATH.
Add uv installer fallback via astral.sh PowerShell script if winget
install does not put uv on PATH. Broaden Python version check to
accept 3.11-3.13. Add idempotency guard for venv creation.
- README.md: add wget one-liner alternative for systems without curl.
* Fix Tailwind CSS v4 .gitignore bug on Windows (#4444)
- Add .gitignore hiding workaround to setup.ps1 (matching existing
setup.sh logic) so venv .gitignore files containing "*" don't prevent
Tailwind's oxide scanner from finding .tsx source files
- Add CSS size validation to setup.sh, setup.ps1, and build.sh to catch
truncated Tailwind builds early
- Remove stray force-rebuild overrides that made the "skip build if
current" cache check dead code in both setup scripts
- Add rm -rf dist to build.sh to force clean rebuilds for wheel packaging
* Change default port 8000 to 8888, fix installer bugs, improve UX
- Change default Studio port from 8000 to 8888 across all entry points
(run.py, studio.py, ui.py, colab.py, vite.config.ts, setup scripts)
- Update launch banner: "Launching with studio venv..." to
"Launching Unsloth Studio... Please wait..."
- Add "Open your web browser" banner and rename labels
(Local -> Local Access, External -> Worldwide Web Address)
- Fix venv idempotency: check for bin/python instead of just directory
existence, clean up partial venvs on retry
- Fix build.sh CSS validation: handle empty CSS case that silently
bypassed the check with "integer expression expected"
- Fix install.sh sudo handling: try apt-get without sudo first (works
when root), then escalate with per-package tracking and user prompt
- Fix install.ps1: check exit code from studio setup, fail on error
- Add pciutils to WSL GGUF build dependencies
- Apply same smart apt-get escalation pattern to studio/setup.sh
* Use detected Python version for venv, abort on non-apt Linux
- install.ps1: detect existing Python 3.11/3.12/3.13 and use that
version for venv creation instead of always forcing 3.13
- install.sh: exit with error on non-apt Linux distros when required
packages cannot be auto-installed, instead of silently continuing
* Make sudo permission prompt more prominent with warning banner
* Add Accept [Y/n] sudo prompt to studio/setup.sh for consistency
* Fix native command exit code handling and sudo decline flow
install.ps1: Add $LASTEXITCODE checks after winget (Python), uv venv,
and uv pip install calls. $ErrorActionPreference only catches PowerShell
cmdlet errors, not native executable failures. The Python check also
handles winget returning non-zero for "already installed".
setup.sh: Skip llama-server build when user declines sudo or sudo is
unavailable. Previously the script continued to section 8 which would
fail with confusing errors (e.g. "gcc: command not found") since
build-essential was never installed.
* Move rm -rf llama.cpp inside build branch to preserve existing install
When _SKIP_GGUF_BUILD is set (user declined sudo or sudo unavailable),
the previous rm -rf would destroy an already-working llama-server before
the skip check ran. Move it inside the else branch so existing builds
are preserved when the rebuild is skipped.
---------
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Fixing Qwen3.5 bug and adding Outetts dependencies
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Apply suggestion from @danielhanchen
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Fix studio crash on Mac: vendor check_signal_escape_patterns from unsloth_zoo
Vendor the `check_signal_escape_patterns` function from
`unsloth_zoo.rl_environments` directly into `tools.py`. The function is
pure Python (only uses stdlib `ast`) and has zero GPU dependencies, but
importing it from unsloth_zoo triggers `unsloth_zoo.__init__` which calls
`get_device_type()` at module scope -- raising NotImplementedError on
Apple Silicon Macs.
By vendoring the code, the safety checks still run on all platforms
(Mac, Linux, Windows) without needing unsloth_zoo at all.
* [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>
* Add elapsed timer to tool status pill in Studio
Show a count-up seconds timer (0s, 1s, 2s, ...) next to the tool status
text in the composer area. Helps users gauge how long a tool call (web
search, code execution) has been running. Timer resets when a new tool
starts and disappears when all tools finish.
* Fix tool call parsing, add tool outputs panel and reasoning copy button
Backend:
- Rewrite tool call XML parser to use balanced-brace JSON extraction
instead of greedy regex, fixing truncation on nested braces in
code/JSON arguments
- Handle optional closing tags (</tool_call>, </function>, </parameter>)
that models frequently omit
- Support bare <function=...> tags without <tool_call> wrapper
- Strip tool call markup from streamed content so raw XML never leaks
into the chat UI
- Use a persistent ~/studio_sandbox/ working directory for tool
execution so files persist across calls within a session
- Emit tool_start/tool_end SSE events so the frontend can display
tool inputs and outputs
Frontend:
- Add collapsible "Tool Outputs" panel below assistant messages showing
each tool call's input and output with copy buttons
- Add copy button to reasoning blocks
- Add elapsed timer to tool status pill
- Update project URLs in pyproject.toml (http -> https, add docs link)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add interactive HTML preview with fullscreen toggle for code blocks
HTML code fences now render an interactive sandboxed iframe preview
below the syntax-highlighted code, similar to how SVG fences show
an image preview. The iframe uses sandbox="allow-scripts" to allow
JavaScript execution while blocking access to the parent page.
Includes a fullscreen toggle (enlarge/minimize button) that expands
the preview into a viewport overlay, dismissible via button, Escape
key, or backdrop click. A streaming placeholder prevents partial
HTML from rendering mid-stream.
* Add tool call settings: auto-heal toggle, max iterations, timeout
Add three user-configurable tool call settings to the Studio Settings panel:
- Auto Heal Tool Calls: toggle to control fallback XML parsing of malformed
tool calls from model output (default: on)
- Max Tool Calls Per Message: slider 0-40 + Max to cap tool call iterations
per message (default: 10)
- Max Tool Call Duration: slider 1-30 minutes + Max to set per-tool-call
execution timeout (default: 5 minutes)
All settings persist to localStorage and flow through the full stack:
frontend store -> API request -> Pydantic model -> route -> llama_cpp -> tools.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix tool call timeout: respect no-limit and apply to web search
- Use a sentinel to distinguish timeout=None (no limit) from the default
(300s). Previously None was silently replaced with _EXEC_TIMEOUT.
- Pass the configured timeout to DDGS() for web searches so the setting
applies uniformly to all tool types.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add input validation bounds and per-thread sandbox isolation
- Add ge=0 constraint to max_tool_calls_per_message (rejects negative values)
- Add ge=1 constraint to tool_call_timeout (minimum 1 second)
- Thread session_id from frontend through backend to tool execution
- Scope sandbox directories per conversation: ~/studio_sandbox/{thread_id}/
- Backwards compatible: API callers without session_id use ~/studio_sandbox/
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix non-monotonic streaming and Python temp script path
- Split tool markup stripping into closed-only (mid-stream) and full
(final flush) to prevent cumulative text from shrinking mid-stream
- Enforce monotonicity: only emit when cleaned text grows, so the
proxy's delta logic (cumulative[len(prev_text):]) never breaks
- Place Python temp scripts in the sandbox workdir instead of /tmp so
sys.path[0] points to the sandbox and cross-call imports work
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Sanitize session_id to prevent path traversal in sandbox
Strip path separators and parent-dir references from session_id before
using it as a directory name. Verify the resolved path stays under
~/studio_sandbox/ as a second guard.
* feat(chat): proper assistant-ui tool call UIs with sources
Replace custom metadata-based ToolOutputsGroup with native assistant-ui
tool-call content parts. Backend SSE tool_start/tool_end events now emit
proper { type: "tool-call" } parts from the adapter, enabling per-tool
UIs registered via tools.by_name in MessagePrimitive.Parts.
- Web search: Globe icon, Source badges with favicons, auto-collapse
when LLM starts responding
- Python: Code icon, syntax-highlighted code via Streamdown/shiki,
output block with copy
- Terminal: Terminal icon, command in trigger, output with copy
- ToolGroup wraps consecutive tool calls (skips for single calls)
- Sources component renders URL badges at end of message
- Flattened code block CSS (single border, no nested boxes)
* fix(inference): respect empty enabled_tools allowlist
`if payload.enabled_tools:` is falsy for [], falling through to
ALL_TOOLS. Use `is not None` so an explicit empty list disables
all tools as intended.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Shine1i <wasimysdev@gmail.com>
* Fix inference stall during prefill by removing retry storm
The _stream_with_retry method used a 0.5s read timeout and retried by
sending a brand new POST request each time. During prompt prefill (which
can take 5-30+ seconds for long contexts or reasoning models), this
caused 10-60 duplicate requests that forced llama-server to restart
processing from scratch each time, resulting in 10-20s stalls visible
as "Generating" with no progress in the UI.
Fix: send the request ONCE with a 120s read timeout for the initial
response headers. Cancel support during the prefill wait is handled by
a background thread that monitors cancel_event (checked every 0.3s)
and closes the response to unblock the httpx read immediately. This
preserves the ability to stop/cancel/refresh during generation.
The existing 0.5s timeout on the httpx.Client is still used by
_iter_text_cancellable for per-token cancel checking during streaming
(after prefill), which is unaffected by this change.
* Fix race in cancel watcher when response is not yet created
When cancel_event fires before client.stream() returns (response is
still None), the watcher would hit return and exit without closing
anything. The main thread stays blocked for up to 120s.
Fix: after cancel is requested, keep polling _response_ref every 0.1s
until the response object appears (then close it) or _cancel_closed
is set (main thread finished on its own).
* Minor cleanup: remove redundant None check, add debug logging in cancel watcher
Address Gemini review: cancel_event is guaranteed non-None when the
watcher thread runs, and logging the close exception aids debugging.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Retry r.close() on failure instead of giving up
If r.close() raises, stay in the polling loop and retry rather than
returning and leaving the main thread blocked for up to 120s.
* fix: keep short read timeout during token streaming
The prefill_timeout (read=120s) was passed to client.stream(), which
applied to ALL reads -- not just the initial response headers. This
meant _iter_text_cancellable's ReadTimeout-based cancel checking was
broken during token streaming: the Stop button could take up to 120s
to respond instead of 0.5s.
Fix: keep the client's short read timeout (0.5s) for the stream call.
During prefill, catch ReadTimeout in a loop and re-check cancel_event
instead of re-sending the POST (which was the original retry storm).
Once the first bytes arrive, yield the response with a PrependStream
wrapper so iter_text() sees the buffered first chunk.
This preserves both:
- Fast cancel during prefill (via cancel watcher + ReadTimeout loop)
- Fast cancel during streaming (via _iter_text_cancellable's 0.5s
ReadTimeout, which now fires correctly again)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: swap to short-timeout stream after prefill completes
Address two review issues:
1. _PrependStream did not inherit from httpx.SyncByteStream, so
Response.iter_raw() would raise RuntimeError. Replaced with a
_ShortTimeoutStream that inherits SyncByteStream properly.
2. client.stream() entry itself raises ReadTimeout during slow prefill
(before headers arrive). The previous fix tried to catch this at
the body-read level but missed the connection-level timeout.
New approach: keep the 120s read timeout for client.stream() so the
connection survives long prefills. Once headers arrive, replace the
response stream with _ShortTimeoutStream -- a wrapper that uses a
background reader thread and a Queue with a short get() timeout to
re-raise ReadTimeout at the original 0.5s interval. This way
_iter_text_cancellable's cancel-checking remains responsive during
token streaming while prefill gets the long timeout it needs.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: move _ShortTimeoutStream before LlamaCppBackend class
The class was placed inside LlamaCppBackend's body, splitting the
class in two and making _codec_mgr and other attributes unreachable.
Move it to module level before LlamaCppBackend.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: remove _ShortTimeoutStream, use watcher for all cancel
_ShortTimeoutStream had two critical issues:
1. Raising ReadTimeout from a generator kills it -- Python finalizes
generators after an uncaught exception, so the next next() call
hits StopIteration and streaming ends mid-response.
2. The unbounded Queue in the background reader loses backpressure,
causing memory spikes with slow clients.
Simpler approach: use the 120s read timeout for the entire stream and
rely on the cancel watcher thread for all cancellation (both prefill
and streaming). The watcher closes the response on cancel_event,
which unblocks any blocking httpx read within ~0.3s. This eliminates
the need for short timeout tricks entirely.
Cancel latency:
- Prefill: ~0.3s (watcher polls cancel_event every 0.3s)
- Streaming: ~0.3s (same watcher mechanism)
- Both faster than the old 0.5s ReadTimeout approach
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* docs: clarify cancel limitations in _stream_with_retry
The docstrings claimed ~0.3s cancel in all cases, but httpx cannot
interrupt a blocked read before the response object exists. Update
the docstrings to accurately describe the behavior:
- Cancel during prefill (header wait) is deferred until headers arrive
- Cancel during streaming works via response.close() from the watcher
- _iter_text_cancellable docstring updated to reflect the watcher-based
cancel mechanism instead of the old ReadTimeout polling
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Roland Tannous