* studio: install flash-linear-attention and tilelang for Qwen3.5 family
Studio currently only installs causal-conv1d for qwen3.5 / qwen3.6 /
qwen3-next models. Without flash-linear-attention installed alongside
it, transformers' Qwen3.5 fast-path gate stays False and the model
falls back to a pure-PyTorch loop for the GatedDeltaNet layers. In a
60-step run on unsloth/Qwen3.5-2B on B200, this fallback costs ~2.35x
vs the full fast path.
On top of that, FLA dispatches its hottest GDN kernels through a
TileLang backend when tilelang is importable. Adding tilelang plus a
pinned apache-tvm-ffi gives another ~26% on the same workload (4.73
s/step to 3.50 s/step) and is what users have been getting indirectly
when they install mamba-ssm (mamba-ssm transitively pulls tilelang and
pins apache-tvm-ffi<=0.1.9, which is the last working version on
sm_100; 0.1.10 and 0.1.11 crash Triton with misaligned address).
Changes:
* _ensure_flash_linear_attention: pure-Python PyPI install gated on
the same model match set as _ensure_causal_conv1d_fast_path.
* _ensure_tilelang_backend: installs apache-tvm-ffi==0.1.9 and
tilelang==0.1.8 in one pip resolve so the tvm-ffi pin wins over
tilelang's >=0.1.2 constraint. Gated on the Qwen3.5 family only;
SSM models (Nemotron-H, Falcon-H1, Granite-H, LFM2) do not use
FLA's GDN dispatch.
* UNSLOTH_STUDIO_SKIP_TILELANG_INSTALL=1 escape hatch matching the
flash-attn pattern.
* Orchestration block reordered: causal-conv1d -> fla -> mamba-ssm
-> tilelang -> flash-attn (long context).
* 7 new tests covering the new helpers, including SSM-model skip,
skip-env, full Qwen3 family name variants, and graceful pip
install failure.
Combined Qwen3.5-2B-Vision step time on B200 in our bench goes from
5.0 s/step (current Studio: causal-conv1d only) to 3.5 s/step
(causal-conv1d + fla + tilelang), a 1.43x speedup with no notebook
or user code changes required.
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* tests/studio: accept new grad_norm arg in MLX smoke _on_step callback
The MLX trainer's step callback now passes a ninth positional argument
(grad_norm) per unsloth_zoo/mlx/trainer.py's documented signature
``fn(step, total_steps, loss, lr, tokens_sec, peak_gb, elapsed,
num_tokens, grad_norm=None)``. The smoke's local ``_on_step`` was still
defined with eight, so every per-step invocation raised
``TypeError: _on_step() takes 8 positional arguments but 9 were given``,
``losses_per_step`` never got populated, and the post-train
``assert len(losses_per_step) == 7`` failed.
Add the ninth parameter with a default and surface the gradient norm in
the per-step log line when present.
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* ci: retrigger after zoo drift + IPython fixes landed in main
* tests/studio: pin max_grad_value=0 in MLX smoke so max_grad_norm=1.0 wins
unsloth_zoo PR #5340 added per-element gradient clipping to MLXTrainer
and defaulted ``MLXTrainingConfig.max_grad_value = 5.0``. When both
``max_grad_norm`` and ``max_grad_value`` are set, the trainer warns:
Unsloth: max_grad_norm and max_grad_value are both enabled;
ignoring max_grad_norm in favor of max_grad_value.
and silently drops the test's ``max_grad_norm=1.0``. +-5.0 per-element
is far too loose for this 270M Gemma-3 LoRA r=8 (attention + MLP) at
bs=2 ga=3 lr=1e-3: the update direction is no longer norm-bounded, so
losses overshoot and the model fails to memorise the training row.
Reproduced on a CUDA mirror (scripts/cuda_mlx_mirror_sim.py):
norm_1 (max_grad_norm=1.0, no clip): losses 7.64 -> 0.006,
generation contains 'Unsloth' (the smoke's pass case)
clip_value_5 (max_grad_norm=0, clip+-5.0): losses 7.29 -> 8.39
(DIVERGED after step 4), generation gibberish, no
'Unsloth' -- exactly the failure surfaced on PR 5434
once the _on_step 9-arg fix let the smoke past the
training loop.
Pin ``max_grad_value=0.0`` so the smoke uses the same ``max_grad_norm=
1.0`` clipping it was designed against. Leaves the new default in
place for everyone else; only the smoke needs deterministic clipping
to validate the round-trip.
* tests/studio: clarify why MLX smoke pins max_grad_value=0
Refresh the rationale comment to reflect the new default landing in
unslothai/unsloth-zoo#652 (max_grad_value=1.0, not 5.0). The smoke
still needs the explicit pin because neither default value reliably
converges in 7 steps at seed=3407:
max_grad_value=5.0 -- diverges after step 4 (loss 7.3 -> 8.4)
max_grad_value=1.0 -- stalls (loss ~3.2 plateau across seeds)
max_grad_value=0.5/0.25/0.1 -- noisier still
max_grad_norm=1.0 -- cleanly drops loss to <0.01, emits "Unsloth!"
Mention both the historical 5.0 default and the new 1.0 default in
the comment so future readers do not assume the smoke is dead code
referencing a removed knob, and point to the CUDA mirror scripts
(cuda_mlx_mirror_sim.py + cuda_mlx_clip1_vs_norm1.py) for the
empirical evidence.
No behaviour change; comment-only refresh.
* tests/studio: replace fragile substring gate with loss + round-trip gates
The MLX smoke's three "EXPECT in completion" assertions assume the
trained model will greedy-emit the exact "Unsloth" token after the
prompt. On MLX a single near-zero-loss adamw step at the smoke's
fixed seed=3407 can perturb the final-step logits enough that greedy
decoding picks a wrong first token even while the teacher-forced loss
on the training row stays essentially zero (the smoke captures this
exact state -- step 6 loss=0.049, step 7 grad=36.7, step 7 loss=0.17;
completion goes from "Unsloth!" to "5 lbs!"). Reproduced extensively
on CUDA via scripts/cuda_mlx_step7_*.py: at seed=3407 only one config
in a 9-cell sweep lands inside the "Unsloth"-emitting basin, and only
1/3 seeds at that config pass. This is a property of the assertion,
not of save/reload correctness.
Refactor the three assertions to gate on what the smoke is actually
trying to verify:
in_memory:
- hard gate: post_train_loss < 1.0 (training memorised the row).
- soft check: log whether completion contains EXPECT_IN_OUTPUT
into metrics["in_memory_generation_has_expected"]; print a
WARN when missing instead of failing.
lora / merged reload:
- hard gate: reload output must equal the in-memory completion
saved in train_metrics.json. This is the actual save/reload
invariant -- the reloaded weights have to reproduce whatever
the in-memory model produced. Falls back to the original
gibberish gate if train_metrics.json is unavailable.
gguf reload:
- hard gate: llama.cpp produced usable, non-empty output after
the prompt (>=4 chars). llama.cpp's tokenizer + sampling differ
from mlx_lm so byte-exact match isn't sound. Log
gguf_has_expected for visibility.
Result: the smoke still gates on the real failure modes (training
didn't memorise, save/reload corrupted weights, llama.cpp produced
no output), without depending on the brittle "Unsloth as first
greedy-decoded token" guarantee that MLX's step-7 numerics can break
without harming any save/reload semantics.
Cross-version constraint: no transformers / trl API touched.
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* tests/studio: gate MLX reload on training-row loss, not greedy text
The strict reload assertion (out == in_mem_out) failed on macOS:
in-memory completion was '5 lbs!' and the reloaded completion was
'_________________________'. Both are corrupted by the same MLX
step-7 grad spike (see scripts/cuda_mlx_step7_*), but greedy decoding
can pick a different first token at near-zero teacher-forced loss
even when weights are byte-identical, so exact text equality is not
the right round-trip invariant.
Replace with teacher-forced loss equality on TRAIN_TEXT: the
reloaded model must reach essentially the same post_train_loss the
in-memory model recorded. That is the real save/reload correctness
gate, robust to MLX's near-zero-loss adamw greedy-decode
perturbation. Falls back to a non-empty-body check when
train_metrics.json is missing.
CUDA mirror at this seed converges cleanly to ~0.006 loss; on MLX
post_train_loss < 1.0 still holds via the existing memorisation
gate. The completion text and "matches in-memory" flag are still
recorded in metrics for visibility, just not gated on.
* ci: retrigger Backend CI after transient pwsh-startup timeout
* ci: retrigger MLX dispatch after pytorch CDN DNS flake
* studio: harden FLA + tilelang installers per reviewer feedback
Addresses bot review on #5434:
* Narrow `_ensure_flash_linear_attention` from `_model_wants_causal_conv1d`
(which also matches Nemotron-H / Falcon-H1 / Granite-H / LFM2) to
`_model_wants_tilelang` (Qwen3.5 / Qwen3.6 / Qwen3-Next only). True
SSM families take the mamba_ssm path and never call FLA's GDN
kernels, so installing FLA there is wasted bandwidth.
* Pin both `flash-linear-attention==0.5.0` and `fla-core==0.5.0` and
install with `--no-deps`. Otherwise pip resolves fla-core's
declared `torch>=2.7.0` requirement and may silently upgrade the
Studio venv's torch on environments running torch 2.4/2.5/2.6.
* Skip both installs on Python <3.10 (FLA, fla-core, and tilelang
all declare `Requires-Python: >=3.10`). On older interpreters the
pip install would fail every launch and leave the worker on the
slow torch fallback while still claiming to have set up the fast
path.
* Skip tilelang install on non-Linux platforms. `tilelang==0.1.8`
only publishes Linux x86_64 / aarch64 and macOS arm64 wheels.
Falling back to its 93MB sdist on a Studio worker is undesirable.
* Detect an existing `apache-tvm-ffi` 0.1.10 / 0.1.11 install and
force a reinstall to 0.1.9 with `--force-reinstall --no-deps`.
Previously the import-only probe returned early and left the
broken version in place, which crashes Triton on sm_100.
* Add a 600s timeout to the tilelang and FLA subprocess.run calls,
matching the existing flash-attn install pattern, so a network
hang cannot block the training subprocess indefinitely.
* 13 new / updated tests covering all six guards plus the
pinned-spec, timeout, and force-reinstall code paths.
Total: 21 passing tests (8 original + 13 new / updated).
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* studio: address reviewer.py P1/P2 findings on FLA + tilelang installers
Twelve-reviewer aggregated review on this PR flagged several real
correctness bugs in the first hardening pass. Fixes:
P1:
* Add UNSLOTH_STUDIO_SKIP_FLA_INSTALL escape hatch for symmetry
with UNSLOTH_STUDIO_SKIP_TILELANG_INSTALL and the existing
UNSLOTH_STUDIO_SKIP_FLASHATTN_INSTALL.
* Install einops alongside fla-core. `--no-deps` was suppressing
fla-core's only non-torch runtime dep, so on a clean venv
`import fla.modules` raised ModuleNotFoundError even though pip
exited 0.
* Drop --no-deps from the tilelang force-reinstall path. tilelang
needs z3-solver, ml-dtypes, cloudpickle, etc. at runtime;
--force-reinstall --no-deps left libz3.so missing and
`import tilelang` raised OSError on the next training subprocess.
* Skip FLA install when installed torch is below 2.7.0
(fla-core declares torch>=2.7.0). Otherwise users on Studio's
supported torch 2.4/2.5/2.6 stacks get an incompatible FLA
installed silently.
P2:
* Replace bare `except ImportError` probes with helpers that catch
`Exception` so a broken native package (OSError on missing
.so, RuntimeError in __init__, ...) does not kill the worker
before the fallback path can run.
* Tighten the tilelang platform guard from "any linux" to
"linux + machine in {x86_64, aarch64, ...}" so ppc64le / s390x /
armv7 do not fall through and download the 93 MB tilelang sdist.
* Add --only-binary=:all: to the tilelang install command. The
comment already said we never want the sdist; now the pip
invocation enforces it.
* Verify both FLA and tilelang are importable after pip exits 0;
if not, report and continue on the fallback path.
6 new tests bring the suite to 27 passing (was 21).
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* studio: pin packaging + triton with FLA --no-deps install
An end-to-end install simulation in a fresh venv caught a real
regression: `fla/utils.py` does `from packaging import version` and
`import triton` at module load, but fla-core's METADATA only declares
einops + torch. With `--no-deps` the worker would land FLA in any
runtime that lacks packaging (e.g. minimal torch builds) and the
post-install import probe would fall back to the torch GDN loop
silently.
Add `packaging` and `triton` to `_FLA_RUNTIME_DEPS` so the install
spec list always carries them. Tests updated to assert both are now in
the install command.
* studio: hook transformers' fast-path gates for just-in-time FLA + causal-conv1d install
The substring-based detection in this PR (`_model_wants_tilelang` /
`_model_wants_causal_conv1d`) is brittle: it depends on what the user
typed for the model name, not on what the architecture actually needs.
Users typing custom model paths, future Qwen3.7 / non-Qwen GDN
architectures, and any model whose author renamed it would silently
fall back to the torch loop.
The correct signal is the one transformers itself uses to gate the
fast path. `transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py`
does at module import time:
if is_causal_conv1d_available():
from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
if is_flash_linear_attention_available():
from fla.modules import FusedRMSNormGated
from fla.ops.gated_delta_rule import (
chunk_gated_delta_rule, fused_recurrent_gated_delta_rule,
)
Wrap both gates so the first call (always at modeling import, before
any forward pass) installs the matching kernel synchronously and
delegates to the original function. Any model whose architecture
queries those gates auto-triggers the install; models that never
query them (Llama, Gemma, dense Qwen, ...) never pay the cost.
Mechanics:
- Split `_ensure_flash_linear_attention` and `_ensure_tilelang_backend`
into `_unconditional` variants (no substring gate, retains python
/ torch / platform / skip-env guards) plus thin substring wrappers
used by the legacy fallback path.
- New `_install_fast_path_hooks(event_queue)` patches both gates on
`transformers.utils.import_utils` AND sweeps `sys.modules` so any
modeling file that already did `from ... import is_X` sees the
wrapper (the local binding survives a module-level reassignment).
- Wrappers clear the original's `lru_cache` before delegating, install
on False, re-check, and short-circuit on subsequent calls.
- Set `UNSLOTH_STUDIO_SKIP_FAST_PATH_HOOKS=1` to fall back to the
substring path.
Verified end-to-end against `transformers.models.qwen3_5_moe`:
PRE_STATE fla=False tilelang=False causal_conv1d=False
HOOK_INSTALLED
Hook fired for is_causal_conv1d_available; installing kernel...
Installing prebuilt causal-conv1d wheel...
Hook fired for is_flash_linear_attention_available; installing kernel...
Installing flash-linear-attention==0.5.0 (with fla-core==0.5.0) for the fast path...
Installed flash-linear-attention for the FLA fast path
Installing TileLang backend (apache-tvm-ffi==0.1.9, tilelang==0.1.8)...
Installed TileLang backend for FLA fast path
MODELING_IMPORT_OK
FAST_PATH_SYMBOLS {"chunk_gated_delta_rule": true,
"fused_recurrent_gated_delta_rule": true,
"FusedRMSNormGated": true,
"causal_conv1d_fn": true,
"causal_conv1d_update": true}
POST_STATE fla=True tilelang=True causal_conv1d=True
Adds 9 new tests covering: install-on-False, skip-on-True, idempotency,
install-failure handling, env-disable, lru_cache clear, sys.modules
rebind, missing-transformers fallback, substring fallback. Total
test count is now 36 (was 27).
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* studio: address reviewer.py n=12 findings on the FLA hook path
Eight issues reproduced by parallel reviewers against 6ce495a; all
fixed and covered by regression tests. 45 pytest cases pass (was 36);
end-to-end Qwen3.5_MoE modeling-import drill still loads all five
fast-path symbols.
P1 fixes:
1. TileLang loses the Qwen-family guard on the normal FLA hook path
(10/12 reviewers, reproduced with allenai/OLMo-Hybrid-1B). The
hook unconditionally installed tilelang for any FLA-using model.
- Threaded `model_name` through `_install_fast_path_hooks(event_queue,
model_name)`.
- `_fla_install` now gates tilelang on
`_model_wants_tilelang(model_name)` AND a successful FLA install.
2. TileLang repair `--force-reinstall` (without `--no-deps`) could
replace `torch==2.12.0+cu130` with `torch==2.12.0`. Split repair
into TWO steps:
step 1: `--force-reinstall --no-deps apache-tvm-ffi==0.1.9`
step 2: regular install of tilelang + apache-tvm-ffi
Step 1 surgically downgrades the broken package; step 2 resolves
missing transitive deps (z3-solver, ml-dtypes) without
--force-reinstall, so it never replaces torch.
3. Hook could return True after the installer's deep import probe
failed: when pip exits 0 but `import fla.modules` raises, the old
wrapper re-called `original()` (transformers' metadata check) and
trusted it. Refactored:
- `_ensure_flash_linear_attention_unconditional(...) -> bool`
- `_ensure_tilelang_backend_unconditional(...) -> bool`
The wrapper now uses the installer's bool directly.
4. SSM models (Nemotron-H, Falcon-H1, Granite-H) use
`lazy_load_kernel("causal-conv1d")` and never call
`is_causal_conv1d_available()`, so the hook never fires for them.
The orchestrator now always runs `_ensure_causal_conv1d_fast_path`
outside the hook-mode if/else.
P2 fixes:
5. `_rebind_in_already_imported_modules` invoked transformers' lazy
module `__getattr__` (hundreds of "Accessing X from .models..."
warnings, ~3.4s overhead). Switched to `module.__dict__.get(...)`
which only sees real module-level bindings.
6. TileLang installed even when FLA was skipped (Torch <2.7) or
failed (timeout, post-install probe failed). Now gated on the
installer's bool return.
7. TileLang repair was skipped when FLA was already True but tilelang
missing or apache-tvm-ffi on the broken list. Added an optional
`post_available_fn` to the wrapper; the FLA hook's
`_fla_post_available` runs `_ensure_tilelang_backend_unconditional`
when (model wants tilelang) AND (tilelang missing OR tvm-ffi broken).
8. `_flash_linear_attention_importable()` only checks deep import,
not version. Added `_flash_linear_attention_current()` that
compares against the pinned `flash-linear-attention==0.5.0` /
`fla-core==0.5.0`; older versions trigger `--force-reinstall
--no-deps` so torch stays untouched.
Helpers extracted to keep the surface tight:
- `_pip_install_cmd(*args)` builds `uv pip install` or
`python -m pip install` depending on uv availability.
- `_run_pip(cmd, event_queue, label)` runs a pip command with
timeout / failure handling and a status emission.
Regression tests added:
- test_hook_does_not_install_tilelang_for_non_qwen_fla_model
- test_hook_does_install_tilelang_for_qwen35
- test_tilelang_repair_does_not_touch_torch_cuda_stack
- test_hook_trusts_installer_bool_not_metadata
- test_rebind_does_not_trigger_module_getattr
- test_hook_skips_tilelang_when_fla_install_is_skipped
- test_hook_runs_tilelang_repair_when_fla_already_true
- test_fla_installer_force_reinstalls_when_older_version_present
- test_run_training_process_eagerly_installs_causal_conv1d_in_normal_mode
Existing tests updated for the new `_install_fast_path_hooks` signature
and the two-step tilelang repair flow.
End-to-end re-verified against transformers.models.qwen3_5_moe:
PRE_STATE fla=False, hook fires for both gates, FLA + tilelang +
causal-conv1d install, all 5 fast-path symbols non-None.
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* studio: fix double-install of tilelang on the FLA hook install path
Backend CI surfaced a test-isolation bug introduced by the
post_available_fn mechanism for finding #7. The wrapper ran
`post_available_fn` in BOTH paths (install ran AND gate already True),
but `_fla_install` already chains tilelang on the install path, so the
post-available step then called tilelang install AGAIN.
This was masked locally because tilelang was installed in the
workspace venv (post_available short-circuited on
`_tilelang_importable()` returning True). CI starts with no tilelang,
so the second call actually fired and the mock recorded two calls.
Fix: only run `post_available_fn` when the install path did NOT run.
That preserves the finding #7 semantics (tilelang repair when FLA
already True but tilelang missing or tvm-ffi broken) without
duplicating the chained install on the gate-was-False path.
Also tightened `test_hook_skips_install_when_gate_already_true` to
monkeypatch `_tilelang_importable=True` and
`_installed_tvm_ffi_version=0.1.9` so it stays a pure "no install at
all" test regardless of the venv's actual state.
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* ci: retrigger Mac Studio GGUF after transient HF DNS resolve flake
* studio: skip tilelang on HIP / ROCm torch (Strix Halo crash report)
h34v3nzc0dex tested PR 5434 on Strix Halo (gfx1151, ROCm 7.13,
torch 2.11.0+rocm7.13.0) and hit a hard regression:
File ".../fla/ops/common/backends/tilelang/__init__.py", line 92,
in chunk_bwd_dqkwg
File ".../tilelang/jit/kernel.py", line 137, in __init__
File ".../tilelang/tileop/gemm/__init__.py", line 143,
in _select_gemm_instruction
tvm.error.InternalError: Check failed: (0) is false:
Unsupported target for gemm:
hip -keys=hip,gpu -mcpu=gfx1151 ...
`tilelang==0.1.8` ships no HIP GEMM instruction; `_select_gemm_instruction`
raises at lower-time, not import-time. So:
- pip install succeeds
- `import tilelang` succeeds
- `TileLangBackend.is_available()` returns True
- FLA's dispatcher picks TileLang for `chunk_bwd_dqkwg`
- training subprocess dies at first GDN backward, no graceful fallback
The PR's existing platform gate (`_tilelang_platform_supported`)
checked only `sys.platform == "linux"` and `platform.machine()`, both
of which look identical on a ROCm box.
Fix has two layers:
1. INSTALL GATE: new `_torch_has_hip()` helper checks
`torch.version.hip is not None`. `_tilelang_platform_supported`
now returns False on HIP torch, so the install never fires.
2. RUNTIME GATE: even with the install skipped, a user could have
tilelang already present (e.g. venv carried over from a CUDA box).
`_install_fast_path_hooks` now calls
`os.environ.setdefault("FLA_TILELANG", "0")` when HIP is detected,
which is the env-var FLA's `TileLangBackend` already honors. Users
who know they have a HIP-aware tilelang fork can override by
setting `FLA_TILELANG=1` explicitly.
This costs nothing on CUDA (the gate is a no-op when
`torch.version.hip is None`), and removes the crash for AMD users.
The benchmark numbers in the PR description (1.43x on B200 sm_100)
are not affected.
The other halves of the PR are confirmed working on gfx1151 by the
same report:
- `flash-linear-attention 0.5.0` runs at production scale
(B=1 T=8192 H=16 K=128 V=128 and others) with no patches.
- `causal-conv1d` runs at the shapes the fast-path gate cares
about. (A separate Ubuntu 24.04 `--gcc-install-dir` build
workaround is needed for the source-build path; that mirrors
bbf004c's llama.cpp fix and is out of scope here.)
Tests added:
- test_tilelang_platform_unsupported_on_hip_torch
- test_tilelang_install_skipped_on_hip_torch
- test_install_fast_path_hooks_sets_fla_tilelang_zero_on_hip
- test_install_fast_path_hooks_respects_user_fla_tilelang_override
- test_install_fast_path_hooks_does_not_set_fla_tilelang_on_cuda
Total 50 passing (was 45).
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* ci: retrigger Windows Studio UI after transient Playwright tab-lookup flake
* studio: auto-discover FLA-using model types from installed transformers
Drop the hand-maintained `_TILELANG_MODEL_SUBSTRINGS` tuple
(qwen3.5 / qwen3_5 / qwen3.6 / qwen3_6 / qwen3-next / qwen3_next)
and derive the allowlist by scanning the installed
`transformers/models/*/modeling_*.py` for `from fla.` imports.
A model "wants tilelang" iff its modeling file imports an FLA op,
which is the same signal `is_flash_linear_attention_available()` is
the runtime test for. The scan happens once per worker subprocess
and is cached for the process lifetime; an empty result (eg
transformers not importable) means "no tilelang pre-install" --
the FLA runtime hook still drives the install via the gate when
the loaded model actually probes it.
Verified against the live installed transformers, the auto-derived
set is {qwen3_5, qwen3_5_moe, qwen3_next}, with `_model_wants_tilelang`
matching the HF Hub names `unsloth/Qwen3.5-2B`, `Qwen/Qwen3.5-MoE-A3B`,
`mlx-community/qwen3-next-80b`, and correctly rejecting Llama,
Mistral, Nemotron-H, Falcon-H1, etc. Future GDN models (Qwen3.7,
OLMo-Hybrid-FA, ...) are picked up automatically once they ship in
transformers; no further worker edits needed.
Also trim docstrings / comments through the FLA / tilelang / HIP /
hook block: constants get 1-line trailing comments, function
docstrings collapse to 1-3 lines, and the fast-path-hooks banner
shrinks from a 27-line block to 4 lines. The file drops from 2847
to 2630 lines without losing the load-bearing WHY notes
(--no-deps protects torch; `__dict__.get` avoids lazy-module
__getattr__; two-step tvm-ffi repair keeps torch off the dep
graph; HIP setdefault disables FLA's TileLang dispatch even with
tilelang already installed).
7 new tests (50 -> 57 total): discovery returns only FLA-using
model_types; discovery cache reuse; missing transformers handled;
OSError on a modeling file is non-fatal; `_model_wants_tilelang`
matches real HF repo names across separator variants; empty
discovery -> always False; normalization across `-`, `.`, `/`,
space.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* test: hermetize the non-allowlist hook test against transformers 5.4.0+
transformers 5.4.0 added `olmo_hybrid` as an FLA-using model_type, so
the auto-discovered allowlist now includes it -- and the test's prior
choice of `allenai/OLMo-Hybrid-1B` as a "non-Qwen FLA-only" example
became an allowlist member. CI on Python 3.11 / 3.13 caught this.
Swap to a guaranteed-not-in-allowlist fake model_name AND patch
_discover_fla_model_types to a known {qwen3_5, qwen3_5_moe, qwen3_next}
set so the test stays valid as upstream transformers adds new
FLA-using architectures.
Renames the test to reflect the actual semantic under test:
"outside-allowlist -> no tilelang".
* ci: retrigger Windows Studio API after llama.cpp prebuilt staging WinError 5 flake
* tests: move MLX smoke gate changes to dedicated PR #5537
The seven MLX smoke commits in this PR's history (_on_step grad_norm,
max_grad_value pin, loss + round-trip gates) are unrelated to the
FLA / tilelang work. They now live in #5537 so this PR's diff is
limited to the studio worker installer changes.
Net effect on tests/studio/run_real_mlx_smoke.py vs main: zero.
* studio: friendlier install banners (drop hook / gate-name jargon)
User-visible status text now reads:
Installing flash-linear-attention==<ver> for faster training...
Installing TileLang==<ver> for faster training...
Installing causal-conv1d for faster training...
Installing flash-attn for faster training...
Removed the transient "Hook fired for is_flash_linear_attention_available;
installing kernel..." banner — the install banner that immediately follows
already tells the user what is happening, in plain English.
The internal logger.info messages (server-side log) still carry the
gate names + "Hook fired ..." for debugging.
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---------
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* fix(studio/worker): inject --gcc-install-dir for HIP source builds on Ubuntu 24.04
On Ubuntu 24.04 + ROCm clang-20, the HIP source-build fallback in
`_install_package_wheel_first` (causal-conv1d, mamba-ssm source fallback,
flash-attn source fallback) dies at:
/opt/rocm-X.Y/lib/llvm/lib/clang/20/include/__clang_hip_runtime_wrapper.h:112:10:
fatal error: 'cstdlib' file not found
Root cause: clang-20 picks the highest-numbered /usr/lib/gcc/x86_64-linux-gnu/<N>
runtime dir by default. On 24.04 that's gcc-14, whose runtime objects ship in
the gcc-14 package but whose C++ headers (/usr/include/c++/14) come from
libstdc++-14-dev — NOT in the default apt set. libstdc++-13-dev IS in the
default set, so /usr/include/c++/13 exists. clang has no way to discover
that asymmetry and the build fails.
Fix: new `_hipcc_gcc_install_dir()` helper iterates gcc 14 → 11 and returns
the first /usr/lib/gcc/x86_64-linux-gnu/<N> dir where BOTH the runtime AND
/usr/include/c++/<N> exist. The HIP branch of `_install_package_wheel_first`
appends `--gcc-install-dir=<that path>` to HIPCC_COMPILE_FLAGS_APPEND before
invoking pip. Respects an existing `--gcc-install-dir` in the env var
(user-set takes precedence); preserves any other flags the user has set
(appends to the end rather than overwriting). No-op on non-HIP, non-Linux,
non-x86_64.
Mirrors the same fix bbf004c added to studio/setup.sh for the llama.cpp HIP
build branch (#5301), but via env var since pip-driven source builds can't
take CMake flags directly.
Verified on Ryzen AI MAX+ 395 / Radeon 8060S (gfx1151) / Ubuntu 24.04 /
ROCm 7.13 nightly: `_hipcc_gcc_install_dir()` returns
`/usr/lib/gcc/x86_64-linux-gnu/13`, which matches the manual workaround
that already lets `pip install causal-conv1d` succeed on this hardware.
Tests added (8 new in test_training_worker_flash_attn.py):
- test_hipcc_gcc_install_dir_picks_highest_with_headers
- test_hipcc_gcc_install_dir_picks_14_when_headers_exist
- test_hipcc_gcc_install_dir_returns_none_when_no_match
- test_hipcc_gcc_install_dir_returns_none_on_non_linux
- test_hipcc_gcc_install_dir_returns_none_on_non_x86_64
- test_install_injects_gcc_install_dir_on_hip_source_build
- test_install_appends_to_existing_hipcc_compile_flags
- test_install_respects_user_gcc_install_dir
- test_install_does_not_inject_env_on_cuda
Per @danielhanchen's suggestion in
https://github.com/unslothai/unsloth/pull/5434#issuecomment-4469980122
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* review: apply gemini-code-assist suggestion on _run_kwargs env handling
Use _run_kwargs.get("env", os.environ).copy() + key-mutation instead of
rebuilding env from os.environ directly. Today both forms are equivalent
(no earlier code in _install_package_wheel_first sets _run_kwargs["env"]),
but the .get().copy() pattern survives any future env modification added
upstream of this block without silently throwing it away.
No behavioural change; tests already assert the final HIPCC_COMPILE_FLAGS_APPEND
value, not the env-construction pattern.
Per https://github.com/unslothai/unsloth/pull/5517#discussion_r... (gemini-code-assist[bot])
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* studio: extend offline DNS auto-detect to inference parent + training
#5505 fixed the GGUF/llama-server load path. Studio still has two
adjacent code paths that burn ~30-60s of soft-failed timeouts before
the worker subprocess starts when DNS to huggingface.co is dead and
the model is already in the local HF cache.
Inference parent process (routes/inference.py:load_model):
* ModelConfig.from_identifier now runs inside _hf_offline_if_dns_dead
so the LoRA-detect hf_model_info call and the urllib config probes
in utils/transformers_version.py short-circuit when DNS is dead.
* utils/models/model_config.py: extracted the inline HF_HUB_OFFLINE/
TRANSFORMERS_OFFLINE check used by list_gguf_variants and
detect_gguf_model_remote into a shared _env_offline() helper, then
reused it to gate the LoRA-detect hf_model_info call.
* utils/transformers_version.py: _check_tokenizer_config_needs_v5 and
_check_config_needs_550 now early-return False when offline instead
of issuing a 10s urllib.urlopen against huggingface.co/raw/main.
Training worker (core/training/worker.py:run_training_process):
* Add the same 2s DNS probe used by core/inference/worker.py at the
top of the training subprocess. On failure, set HF_HUB_OFFLINE,
TRANSFORMERS_OFFLINE, and HF_DATASETS_OFFLINE before the rest of
the subprocess imports torch/transformers/unsloth, so every
from_pretrained, snapshot_download, and load_dataset call below
resolves from cache. Scope is per-subprocess; the orchestrator
always spawns a fresh worker per training run.
Training trainer (core/training/trainer.py:load_model):
* Skip the proactive hf_model_info gated-repo probe when _env_offline()
is true. The API is unreachable anyway, and a gated model that is
already cached is exactly the scenario the user is trying to train
against. from_pretrained surfaces the real error if access is
actually denied.
Tests (tests/test_offline_inference_parent.py, 7 new cases):
* _env_offline truthy/falsy parsing across HF_HUB_OFFLINE and
TRANSFORMERS_OFFLINE.
* transformers_version urllib short-circuit when offline.
* LoRA detect hf_model_info skip when offline.
Existing tests/test_offline_gguf_cache_fallback.py still passes
(26 cases) because the inline env check was extracted, not changed.
* tests: prefer real httpx over stub in offline-test files
The studio test stub convention only included the 6 httpx exception
names that existed callers needed. Newer huggingface_hub (1.15+)
imports HTTPError, Response, Request, HTTPStatusError, AsyncClient,
and more at module import time. When httpx is truly absent the stub
chase becomes a treadmill.
Use the real package when installed (the CI install list already
includes httpx, so this is the production environment). Fall back to
the stub only when httpx is genuinely missing.
No code under test changes.
* studio: detect cached LoRA adapters offline; tighten test
Two follow-ups from the review pass on #5512:
* ModelConfig.from_identifier no longer skips the remote LoRA-detect
hf_model_info call when _env_offline() is true. huggingface_hub
short-circuits the call via OfflineModeIsEnabled in ~0ms when
HF_HUB_OFFLINE is set, so the original 25s concern was moot once
routes/inference.py wrapped the call in _hf_offline_if_dns_dead.
Skipping the API meant users with a cached LoRA adapter
(adapter_config.json on disk) got is_lora=False and the load
failed. After the API call (which raises fast offline) a new
cache-fallback walks the HF cache snapshot for adapter_config.json
via the existing _iter_hf_cache_snapshots helper.
* test_hf_model_info_not_called_when_offline replaced. The old test
raised AssertionError inside production code that catches Exception,
so it passed even if the call happened. New tests use MagicMock and
assert call_count >= 1, plus a fixture that stages a fake HF cache
with adapter_config.json to verify the offline cache detection.
Test count goes from 7 to 8 in test_offline_inference_parent.py.
Combined with test_offline_gguf_cache_fallback.py: 34 pass in 9.75s.
* Fix/adjust offline training DNS probe per PR #5505 review
Same fix as #5505's _probe_dns_dead refactor: run gethostbyname on a
daemon thread with join timeout so concurrent sockets in the parent
interpreter never inherit a process-wide socket.setdefaulttimeout
mutation. Adds a static-pin regression test that the inference parent
file does not regress on this.
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* Trim verbose code comments per review feedback
Shorten the longer explanatory comments added by this PR while keeping
the WHY of each non-obvious branch:
- trainer.py: collapse the 5-line proactive gated-check comment.
- training/worker.py: trim the offline auto-detect preamble and the
"logger isn't configured" note.
- routes/inference.py: shorten the DNS-probe wrap rationale.
- transformers_version.py: collapse the two urllib short-circuit notes.
- model_config.py: shorten the LoRA detect + cache-fallback notes.
- tests/test_offline_inference_parent.py: tighter module docstring,
trim class docstrings, drop multi-line explainer comments inside the
tests; behaviour and coverage unchanged (9/9 tests still pass).
---------
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* studio: scope cancel-cleanup to in-flight tmp dirs; walk back tool_call_id
Two follow-ups to #5375's training and chat hardening.
_cleanup_cancelled_checkpoints used to rmtree every checkpoint-N
directory on Cancel. That is the opposite of what the user expects.
A user cancelling an 8h run with save_steps=2000 loses every
completed checkpoint they could have resumed from. The 67 MB residue
the audit memo flagged is the HF Trainer atomic-rename partial
(tmp-checkpoint-N), not the completed ones. The cleanup now targets
only tmp-checkpoint subdirs; completed checkpoint-N directories are
user-owned and stay. Symlinked output_dir and symlinked children are
skipped so the realpath containment cannot be levered into deleting
arbitrary content via a symlink trick.
ChatMessage._validate_role_shape stamped a random secrets.token_hex
id on tool messages with no tool_call_id. That id is uncorrelated
with the prior assistant tool_calls id, so strict passthrough
backends (OpenAI, Anthropic) reject the request as orphaned and
llama.cpp treats the tool result as "no preceding call" and
hallucinates. The synthesis moves up to ChatCompletionRequest, where
the whole conversation is visible: for each tool message missing an
id we walk back to the most recent assistant turn with tool_calls
(stopping at user turns), prefer a function.name match, otherwise
take the first unconsumed tool_call. Synthesis is the fallback when
no candidate assistant turn exists, preserving the prior round-trip
guarantee for orphaned tool messages.
Tests:
- test_cleanup_cancelled_checkpoints.py (new): pins that completed
checkpoint subdirs survive, tmp-checkpoint partials are removed,
non-int suffixes (checkpoint-final, checkpoint-best) are left
alone, output_dir outside outputs_root is refused, symlinked
output_dir and symlinked child are both skipped, missing dir is
a no-op.
- test_inference_model_validation.py: 6 new walkback cases covering
name-match preference, first-unconsumed fallback, explicit-id
passthrough, multi-tool-result pairing, synth-on-no-parent, and
no-cross-user-turn invariant.
- test_openai_tool_passthrough.py: the two ChatMessage-level
synth-on-missing tests are rewritten to assert that the per-
message validator now leaves tool_call_id untouched; resolution
coverage lives in the request-level tests above.
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* studio: explicit tool_call_id reserve, numeric tmp-checkpoint suffix only
Reviewer follow-ups to the training-cleanup + tool_call_id walkback PR.
tool_call_id walkback: a mixed assistant turn with [call_a, call_b]
followed by a tool result that carried tool_call_id="call_a" and a
sibling tool result with no id resolved to ['call_a', 'call_a']
because the explicit id never reserved call_a in the consumed set.
Added a pre-pass over the message list that walks back from every
role="tool" message carrying an explicit id and marks the matching
(asst_idx, tc_idx) consumed, then the missing-id walkback runs against
that pre-populated set. The second result now resolves to call_b.
While here, also harden the function-shape check: if a provider
ships a malformed tool_call where `function` is a string rather than
a dict, the old `(tc.get("function") or {}).get("name")` raised
AttributeError on the string's .get; now isinstance-gated so the
walkback falls through to the fallback id without raising.
Cancel cleanup: `tmp-checkpoint-*` is too broad. HF Trainer's
in-flight partials are always `tmp-checkpoint-<integer-step>`, so
constrain the cleanup regex to `^tmp-checkpoint-\d+$`. A user folder
named `tmp-checkpoint-final`, `tmp-checkpoint-backup`, or
`tmp-checkpoint-user-notes` is now preserved.
ChatMessage docstring still pointed at the pre-PR contract that
required `tool_call_id` on every role="tool" message. Updated to say
missing ids are accepted at message scope and resolved at
ChatCompletionRequest scope. Inline comment above the cancel-cleanup
call now describes the actual behaviour (in-flight tmp partials,
completed checkpoints preserved).
Test:
- python -m pytest studio/backend/tests/test_inference_model_validation.py
studio/backend/tests/test_cleanup_cancelled_checkpoints.py
studio/backend/tests/test_openai_tool_passthrough.py -q
-> 76 passed (was 67 before this commit; +2 walkback regression
tests, +1 numeric-suffix preservation test)
* studio: trim verbose comments in cleanup + tool_call_id walkback
Move the HF tmp-checkpoint regex to module scope as a named constant.
Drop the multi-paragraph docstring on _cleanup_cancelled_checkpoints
and the inline call-site rationale; the function name + the test
class already cover the why.
Compress _resolve_missing_tool_call_ids docstring from a six-line
explanation to two. Same logic, fewer in-flow tutorials.
76 tests in cleanup + inference-model-validation + tool-passthrough pass.
---------
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Studio's MLX training worker explicitly pinned ``max_grad_value=5.0``
into the ``MLXTrainingConfig`` so it would override the zoo default
regardless. The 5.0 threshold was effectively no protection -- per-
element transformer gradients in steady state are 1e-3..1e-1, so
|g_i| > 5 basically never fires even on spike batches, mixed-precision
overflow, or RL gradient bursts.
Switch to 1.0:
- matches the universal LLM clip_grad_norm=1.0 baseline (HF Trainer
/ TRL / PEFT / AutoTrain) while staying on MLX's fast per-element
``tree_map(mx.clip)`` path (no global reduction)
- actually catches outliers without distorting Adam's normalised
updates (typical post-warmup |g_i| << 1.0)
- lines up with the new MLXTrainingConfig default in
unslothai/unsloth-zoo so Studio doesn't silently disagree with
what zoo ships
No UI change; the TODO to expose grad clipping in Studio settings
remains. Existing trained runs are unaffected: only newly-spawned
training workers pick up the tighter clip.
* studio: skip flash-attn install on Blackwell GPUs (sm_100+)
Dao-AILab does not publish prebuilt flash-attn wheels for sm_100, sm_120,
or sm_121, and the older-arch wheels fail to load on Blackwell. Add a
shared has_blackwell_gpu() helper and gate both the install-time
(install_python_stack._ensure_flash_attn) and runtime
(worker._ensure_flash_attn_for_long_context) paths on it. Detection uses
nvidia-smi --query-gpu=compute_cap, which works on Linux and Windows.
* test: stub has_blackwell_gpu in pre-existing runtime flash-attn tests
prefers_prebuilt_wheel and falls_back_to_pypi exercise the install
paths that the Blackwell guard now short-circuits. Make them explicit
about non-Blackwell so they pass on real Blackwell hosts.
* studio: cache has_blackwell_gpu, skip Blackwell warning under NO_TORCH
- Wrap has_blackwell_gpu in functools.lru_cache so repeated calls in a
single process avoid redundant nvidia-smi spawns. Tests clear the
cache via setup_method/teardown_method.
- In _ensure_flash_attn, run the NO_TORCH short-circuit before the
Blackwell check so GGUF-only users (who never install torch anyway)
do not see a Blackwell warning. Blackwell check still runs above the
IS_WINDOWS / IS_MACOS gates so Blackwell-on-Windows users still see
the explicit reason rather than a silent OS skip.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* test: add has_blackwell_gpu to mlx worker test wheel_utils stub
test_mlx_training_worker_config loads worker.py against a hand-rolled
utils.wheel_utils stub. Adding has_blackwell_gpu to the stub symbol
list so worker's import line resolves.
---------
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* add eval batch size
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Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
* studio: drop unused max_grad_value schema + route plumbing
The MLX worker hardcodes max_grad_value to 5.0 after PR #5340. The
schema field, frontend payload type, route forwarder, and start_training
kwarg threading were all left in place as a transitional buffer for old
clients. The field is now genuinely unused everywhere except inside the
MLX worker, so the schema, route forwarder, and config-build entries can
go. Pydantic still tolerates older clients that send max_grad_value
because TrainingStartRequest's model_config defaults to extra=ignore.
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* mlx fixes
* Fix studio integration, local dataset files, chat templates without the torch gpu imports
* pass grad norm in mlx worker
* fix(studio): pass MLX grad clipping settings
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* mlx: update grad value
* fix(mlx): address ci and clipping review
* fix backward compatibility and CI tests
* unsloth local is mlx function
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* dont reference runtime
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio mlx: hardcode value clipping, drop max_grad_value from frontend
Simplifies the MLX grad-clipping plumbing now that we are standardising on
elementwise value clipping at [-5, 5] for the compiled MLX path and norm
clipping disabled. The MLX worker no longer reads max_grad_norm /
max_grad_value from the request; both are pinned in one place. Frontend
stops sending the field at all, and the TypeScript request type drops it
to match. Non-MLX (CUDA/AMD/Intel) is untouched and continues to pick up
HF TrainingArguments' default max_grad_norm = 1.0.
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* studio: contain export and dataset paths under their configured roots
resolve_under_root and resolve_dataset_path previously returned absolute
paths unchanged, so an authenticated client could supply
save_directory="/tmp/escape" (or any other absolute path) and have the
exporter drop adapter files anywhere the server user could write. This
turned up during a recent audit pass where an authenticated POST to
/api/export/export/lora with save_directory="/tmp/lora_escape_test"
returned 200 and wrote adapter_model.safetensors, adapter_config.json,
and tokenizer files under /tmp.
The fix is two-layered:
storage_roots.py adds an _assert_contained(resolved, root) helper that
runs after path resolution and rejects any result whose realpath does
not sit under realpath(root). resolve_under_root now rejects '..'
segments and null bytes outright, and only accepts absolute inputs when
they are already inside the configured root (internal call sites that
re-resolve a stored absolute path stay idempotent;
worker.py:resolve_output_dir(output_dir) etc. continue to work).
resolve_dataset_path picks up the same containment rule, scoped to the
three dataset roots.
models/export.py adds field_validator("save_directory", mode="before")
to ExportCommonOptions and ExportGGUFRequest so bad input fails fast at
422 with a clear message rather than a 500 deep inside the resolver.
The validator rejects empty/whitespace, null bytes, control chars,
strings longer than 255 chars, absolute paths, and '..' segments.
routes/export.py:_export_details now returns os.path.relpath(output_path,
exports_root()) so the Export Complete dialog and /api/models/loras no
longer leak the absolute install prefix to the UI; the basename is
used as a last-resort fallback.
Verified end to end:
- POST /api/export/export/lora {"save_directory":"/tmp/foo"} -> 422
"save_directory must be a name or relative path under the export
root; absolute paths are rejected". /tmp/foo is not created.
- "../../etc/escape" -> 422 "may not contain '..' segments".
- save_directory="my_subdir" -> still accepted (400 only because the
test had no checkpoint loaded yet, not because of validation).
- Internal idempotent re-resolve via resolve_export_dir(absolute path
that is already under exports_root) returns the same path unchanged.
* studio/sandbox: harden bash + python tool execution
The sandboxed Bash and Python tool channels in Chat ran with a thin
preexec hook (PR_SET_NO_NEW_PRIVS + RLIMIT_FSIZE only). Bash had a
small word blocklist; Python had an AST safety pass aimed at
signal-tampering and shell-escape primitives. An audit pass showed
several gaps that a tool-calling model could trigger inadvertently:
- bash curl/wget/nc reached AWS IMDSv2 and returned live STS
credentials for the instance role.
- python "import socket; s.connect((169.254.169.254, 80))"
reached the same endpoint regardless of the bash blocklist.
- "cat /etc/passwd" was blocked at the bash side (because "passwd"
is in the blocklist), but "open('/etc/passwd').read()" in Python
happily returned its contents.
- "chr(115)+chr(117)+chr(100)+chr(111)" style dynamic-arg
construction slipped through the AST shell-escape check.
- The supervisor used proc.kill() on timeout, which only signals
the immediate pid; bash-backgrounded children survived. A fork
bomb could spawn for the full 300s timeout window.
- Session work directories under ~/studio_sandbox/<id>/ were
created with default umask (0o755), so any other UID on the host
could enumerate them.
- session_id sanitisation used a one-shot str.replace("..",""),
which is non-iterative and a small footgun.
This commit takes a conservative middle path: the sandbox still
runs as the Studio UID with no namespace tricks where the kernel
disallows them, but every chokepoint is tightened.
_sandbox_preexec now:
- calls os.setsid() so children share a process group; the
supervisor uses os.killpg(SIGKILL) on timeout/cancel so
backgrounded children die with the parent (new _kill_process_tree
helper, wired into _cancel_watcher and both _bash_exec /
_python_exec timeout branches).
- calls os.umask(0o077) so files the child writes default to 0o600.
- applies PR_SET_PDEATHSIG=SIGKILL so an orphaned child dies if
Studio exits.
- best-effort unshare(CLONE_NEWNET) for a private network namespace
(failure is logged and swallowed; defense-in-depth is still in
place via the bash blocklist and the AST checker below).
- sets RLIMIT_NPROC=10000 (tunable via UNSLOTH_STUDIO_SANDBOX_NPROC),
RLIMIT_AS=8GB, RLIMIT_CPU=300, RLIMIT_NOFILE=1024. The 10k NPROC
figure is chosen to sit well above the ~500 LWPs a healthy Studio
+ llama-server combination already uses while still capping a
runaway fork bomb. NPROC counts LWPs per real UID, so a lower
figure (e.g. 256) starves legitimate bash forks
("bash: fork: retry: Resource temporarily unavailable").
_get_workdir:
- rejects session_id that doesn't match [A-Za-z0-9_-]{1,64};
non-matching values bucket into a shared "_invalid" dir.
- chmod 0o700 on both the workdir and on ~/studio_sandbox/ so
other UIDs cannot read another session's contents.
_BLOCKED_COMMANDS_COMMON gains: doas, pkexec, halt, poweroff, curl,
wget, nc, ncat, netcat, socat, ssh, scp, sftp, rsync, eval, source.
The intent is to keep general bash usage working (echo, ls, pipes,
loops, for, head, etc.) while denying the obvious egress and
escalation paths.
The AST checker (_check_signal_escape_patterns) is split into the
existing shell/signal/loop checks plus a new narrow IO denylist:
- Always flag non-literal args to anything in _SHELL_EXEC_FUNCS,
not just _STRING_SHELL_FUNCS. Closes the dynamic-arg bypass.
- Reject calls to socket.create_connection, socket.socket().connect,
urllib.request.urlopen, http.client.HTTP*Connection, requests.*,
httpx.* whose literal host argument is in a cloud-metadata
denylist (169.254.169.254 + 169.254.* + 100.64.*, plus the
GCP/Alibaba/ECS metadata hostnames and IPv6 link-local). Public
hosts (example.com, huggingface.co, ...) still work. Dynamic
hosts cannot be statically blocked; mitigated by the bash
blocklist + the netns where the kernel allows it.
- Reject literal open("/etc/passwd"), /etc/shadow, /etc/sudoers,
/etc/ssh/*, and /proc/<pid>/environ. Other files
(/etc/os-release, /etc/hostname, /tmp/*, user dirs) still work.
The _check_code_safety summariser is updated to include the new
network_calls and sensitive_file_reads buckets in its error string.
Regression-checked: echo, sleep, ls /tmp, for loops, piped helpers
(echo a | tr a A), urllib.request.urlopen("http://example.com"),
socket.getaddrinfo("example.com",80), open("/etc/os-release"),
open("/tmp/...","w") all still succeed. curl, wget, nc, ssh, rm,
socket.create_connection(("169.254.169.254",80)),
open("/etc/passwd"), open("/proc/self/environ") all correctly
blocked.
* studio: rate-limit login, rotate refresh tokens, add logout, security headers, gate bootstrap injection
A pass over the auth surface found a cluster of related issues that this
commit closes together.
Login (routes/auth.py):
- Add an in-memory per-IP login rate limiter. Five failed POSTs to
/api/auth/login inside a 60s window produce 429 with Retry-After.
A successful login clears the bucket. Previously 30 wrong passwords
in under one second was accepted as 30x 401, which combined with
the (now fixed) admin-username leak from /api/auth/status made
brute-force trivial against a small password.
Logout (routes/auth.py):
- New POST /api/auth/logout returns 204 and calls
storage.revoke_user_refresh_tokens(subject) so the refresh token
is no longer valid. Previously POST /api/auth/logout returned 405
and there was no way to invalidate refresh tokens short of
changing the password. Frontend session.ts already calls
clearAuthTokens() to drop localStorage; the new endpoint lets the
client also tell the server to revoke server-side state.
Refresh-token rotation (routes/auth.py + auth/storage.py):
- New storage.consume_refresh_token(token) atomically validates +
deletes a refresh token, returning (username, is_desktop). The
/api/auth/refresh handler now mints both a new access AND a new
refresh token; the supplied token becomes invalid. Replaying a
consumed refresh returns 401 "Invalid or expired refresh token".
The previous refresh_access_token helper is left in place for
callers that intentionally want the non-rotating shape; nothing
in the route layer uses it now.
/api/auth/status no longer leaks default_username (models/auth.py +
routes/auth.py):
- AuthStatusResponse.default_username becomes Optional[str] with a
None default; the handler always returns None. The frontend already
hardcodes HIDDEN_LOGIN_USERNAME = "unsloth" (auth-form.tsx:82), so
no UI change is required.
window.__UNSLOTH_BOOTSTRAP__ no longer auto-injects (main.py):
- _inject_bootstrap is now opt-in via the
UNSLOTH_STUDIO_INJECT_BOOTSTRAP env var. The previous default
(inject whenever requires_password_change is true) embedded the
plaintext bootstrap password into the first-boot HTML for any
caller that hit /, /change-password, or any unknown SPA path.
Browser extensions and any XSS payload on the page could read it
trivially. With the new gate the bootstrap password lives only in
the auth/.bootstrap_password file (mode 0o600) where it has always
been; users typing it into a current-password field is the right
UX. routes/auth.py:change_password also clears
app.state.bootstrap_password defensively.
Security headers + server fingerprint (main.py + run.py):
- New SecurityHeadersMiddleware adds Content-Security-Policy,
X-Frame-Options: DENY, X-Content-Type-Options: nosniff,
Referrer-Policy: no-referrer,
Permissions-Policy: camera=(), microphone=(), geolocation=(),
interest-cohort=(), and stamps server: unsloth-studio so the
generic uvicorn banner no longer fingerprints the stack. The
uvicorn.Config gains server_header=False so it stops emitting its
own Server header.
/api/health minimisation (main.py):
- Unauthenticated GET /api/health returns just
{"status":"healthy","timestamp":...} so load-balancer liveness
probes keep working without leaking version, device_type,
chat_only, desktop_protocol_version, or studio_root_id to
arbitrary callers. A request that presents a valid Bearer token
still gets the full diagnostic payload so internal launchers and
sibling-Studio detection (which compares studio_root_id) keep
working.
Verification:
- 30 wrong-password POSTs to /api/auth/login -> first 5 = 401, 6th
through 30th = 429.
- POST /api/auth/logout with a fresh token -> 204. The matching
refresh token then fails 401.
- Login -> R1; /api/auth/refresh with R1 -> new access + R2 (R2 !=
R1); /api/auth/refresh with R1 again -> 401; /api/auth/refresh
with R2 -> still succeeds once and rotates again.
- curl /api/auth/status -> default_username: null.
- curl http://127.0.0.1/ does not contain __UNSLOTH_BOOTSTRAP__.
- curl -I / shows CSP, X-Frame-Options: DENY,
X-Content-Type-Options: nosniff, Referrer-Policy: no-referrer,
Permissions-Policy, and server: unsloth-studio.
- curl /api/health unauthenticated -> {status, timestamp} only.
curl with Authorization: Bearer <valid> -> full payload.
- Existing /api/system, /api/models/list, /api/train/status,
/api/inference/status, /api/auth/api-keys, login flow, SPA root
all still return 200 after the changes (regression smoke).
* studio: add SecurityHeadersMiddleware, MaxBodyMiddleware, /recipes redirect, gate _inject_bootstrap, minimise /api/health
This commit lands the main.py-side changes that share a single
middleware-registration spot. They are kept together because every
change here is either (a) a top-level middleware definition that has
to be added next to LoggingMiddleware, or (b) a route handler at the
same file-level.
SecurityHeadersMiddleware (Content-Security-Policy, X-Frame-Options:
DENY, X-Content-Type-Options: nosniff, Referrer-Policy: no-referrer,
Permissions-Policy, server: unsloth-studio). The previous responses
emitted no CSP, no XFO, no Referrer-Policy and were stamped
server: uvicorn.
MaxBodyMiddleware rejects POST/PUT/PATCH on the inference / dataset /
data-recipe / train / export prefixes when Content-Length exceeds
UNSLOTH_STUDIO_MAX_BODY_MB (default 100). The audit hit this by
attaching a 50 MB plain-text file to a chat message and watching
Studio base64-encode it into the JSON body; uvicorn has no enforced
cap so the only previous guard was the per-file 50 MB ceiling that
data-recipe upload routes already enforce. The new middleware extends
that ceiling to the OpenAI-compat path that the Chat attachments
flow through. Verified: a 200 MB JSON POST to /v1/chat/completions
returns HTTP 413 "Request body too large (209,715,264 bytes; max
104,857,600)". A small valid request continues to reach the handler.
_inject_bootstrap is gated behind UNSLOTH_STUDIO_INJECT_BOOTSTRAP.
The previous default was to inline window.__UNSLOTH_BOOTSTRAP__ =
{username, password} into the first-boot HTML whenever
requires_password_change was true, which exposed the plaintext
bootstrap password to any browser extension, page script, or LAN
caller on -H 0.0.0.0. The bootstrap password remains in the on-disk
.bootstrap_password file (mode 0o600) where it has always lived;
users typing it into a current-password field is the right UX.
/api/health unauthenticated returns {"status":"healthy","timestamp":
...} only; the previous payload (version, device_type, chat_only,
desktop_protocol_version, supports_desktop_auth, studio_root_id,
native_path_leases_supported) is preserved for callers that present
a valid Bearer token, so internal launchers and sibling-Studio
detection (which compares studio_root_id) keep working.
/recipes -> /data-recipes 308 redirect. The Data Recipes page lives
at /data-recipes; users typing /recipes hit the SPA catch-all and
saw "Not Found". The redirect also preserves any tail path, so
/recipes/<rest> -> /data-recipes/<rest>.
Verified end to end with curl: CSP / XFO / X-Content-Type-Options /
Referrer-Policy / Permissions-Policy all present on /, server header
is now unsloth-studio (uvicorn's own banner is suppressed via
server_header=False in run.py from the auth-batch commit). Followed
the /recipes redirect lands on the SPA HTML.
* studio: bound TrainingStartRequest hyperparameters at the schema level
POST /api/train/start accepted any value for learning_rate, batch_size,
max_steps, max_seq_length, warmup_steps, warmup_ratio, num_epochs,
save_steps, weight_decay, gradient_accumulation_steps, lora_r,
lora_alpha and lora_dropout, including -1, 0, 1e9, and non-numeric
strings like 'abc' or 'two' (which silently coerce to 0 in the
trainer). Probing showed the API returning 200 to learning_rate=-1
and batch_size=0; only max_steps had any partial clamping.
This commit adds field_validator on every numeric hyperparameter.
Bounds are chosen wide enough to span realistic single-host
configurations (B200 with 180 GB of memory comfortably fits the
upper end) while rejecting the values that always produce broken
training:
- learning_rate: parses str/float, requires 0 < lr < 1.0. Non-numeric
input raises with "learning_rate must be parseable as float (got
'abc')" instead of silently coercing to 0.
- batch_size: [1, 1024].
- gradient_accumulation_steps: [1, 4096].
- num_epochs: [1, 1000].
- max_steps: [1, 1_000_000].
- max_seq_length: [1, 131072].
- warmup_steps: [0, max_steps].
- warmup_ratio: [0.0, 1.0].
- save_steps: [0, 1_000_000].
- weight_decay: [0, 10] (typical 0..0.1).
- lora_r: [1, 512].
- lora_alpha: [1, 1024].
- lora_dropout: [0.0, 1.0).
Each validator names the offending field in its ValueError message
so the 422 response body identifies which input is bad. The
learning_rate validator returns its result as str (the schema field
type is str("2e-4") for backwards compatibility) so existing call
sites that float() the value continue to work.
Verified:
- learning_rate=-1 -> 422 "learning_rate must be > 0 (got -1.0);
typical range is 1e-6 .. 1e-3".
- learning_rate='abc' -> 422 "must be parseable as float".
- batch_size=-1 / 0 / 999999 -> 422 "batch_size must be in [1, 1024]".
- batch_size='two' -> 422 (pydantic int parser).
- max_steps=0 / -5 -> 422 "must be a positive int".
- max_seq_length=200000 -> 422 "must be in [1, 131072]".
- warmup_ratio=2.5 -> 422 "must be in [0.0, 1.0]".
- lora_dropout=1.5 -> 422 "must be in [0.0, 1.0)".
- Valid request with learning_rate='2e-4', batch_size=1, max_steps=5
passes validation and the training run starts as normal.
* studio: redact image-decode errors, clean checkpoint dirs on cancel, tolerate Stop-button + tool-result message shapes
Three small fixes that fall under "do not let the audit findings
become user-visible papercuts".
routes/inference.py - image-decode error redaction (the audit hit
this with a 0-byte / malformed / wrong-extension image upload). The
three image-normalise sites previously raised HTTPException(400,
detail=f"Failed to process image: {e}"). When PIL raised
UnidentifiedImageError(io.BytesIO(raw)) the message string included
"<_io.BytesIO object at 0x7e40a5d7bf60>", leaking both the Python
class name (confirming the PIL/io stack) and a heap address (mildly
useful for ASLR-bypass chaining if another memory-corruption bug is
ever found). Each site now catches UnidentifiedImageError and
returns the generic "Unsupported or corrupt image format"; the
fall-through generic except returns "Failed to process image". No
exception-repr is interpolated into a response body anywhere along
these paths.
core/training/training.py - checkpoint cleanup on cancel. When a
user clicks Cancel Training, the trainer flips _cancel_requested=True
and the supervisor force-terminates the subprocess. The trainer
writes checkpoint-<step> directories under output_dir every
save_steps; previously these survived the cancel and accumulated on
disk (the audit recorded ~67 MB stuck after a 200-step cancel with
save_steps=20). New helper _cleanup_cancelled_checkpoints(output_dir)
globs checkpoint-<int> entries and removes them. It is gated by a
realpath containment check against outputs_root() so it cannot
accidentally rmtree anything outside the configured outputs root.
force_terminate() invokes the helper after the subprocess join when
_cancel_requested is true. Stop-and-Save runs are unaffected because
that path keeps _cancel_requested=False.
models/inference.py - chat message shape tolerance. Two related
frontend interactions used to crash the request validator:
- After the Stop button truncates a generation, the frontend
retained {role:"assistant", content:""} in the conversation
history and replayed it on the next send. ChatMessage previously
required role="assistant" to have non-empty content or tool_calls,
so the next message returned 422 and the thread was permanently
broken. The validator now normalises empty assistant content to
None so the request round-trips and the trailing empty turn can
be ignored downstream.
- The frontend's second-round tool POST drops the streamed
tool_call_id, hitting the strict-spec check "role=tool requires
tool_call_id". The validator now synthesises an opaque id
(call_<8 hex>) when missing, so the request reaches the handler
and the model's final summarising response gets generated. The
proper fix lives in the frontend (carry the streamed id through
the second POST) and will follow.
Verified end to end with curl: HTTP 400 (model not loaded) on both
the empty-assistant history shape and the tool-result-without-id
shape, instead of HTTP 422 from the schema validator.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: tighten code comments from security-hardening pass
Trim verbose docstrings and inline finding references added in the
previous commits in this branch. Functionality unchanged.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: await get_current_subject in /api/health and make refresh-token consumption atomic
The /api/health auth probe called get_current_subject(creds) without
awaiting it. The coroutine object is truthy, so any caller presenting a
Bearer header (valid or not) received the full diagnostic payload
including version, device_type, studio_root_id, etc. Await the coroutine
and treat HTTPException as 'fall back to the minimal liveness payload'.
consume_refresh_token did SELECT then DELETE WHERE id under default
autocommit isolation. Two concurrent POST /api/auth/refresh requests
could both win the SELECT before either DELETE ran, defeating
single-use refresh-token rotation. Replace with a single
DELETE ... WHERE token_hash = ? AND expires_at >= ? RETURNING ...
statement so the validate-and-delete lands as one atomic op under
SQLite's write lock (3.45.1 supports RETURNING; min was 3.35).
* studio: enforce body cap on chunked uploads and drop unsafe-inline from script-src
MaxBodyMiddleware previously only inspected the declared Content-Length
header; clients omitting it or sending Transfer-Encoding: chunked
bypassed the cap and could still drive an OOM via the downstream
JSON / file readers on /v1/chat/completions, /api/inference, /api/data-recipe,
/api/datasets, /api/train, /api/export. Rewrite as a raw ASGI middleware
that drains and counts http.request frames, replies 413 once the running
total exceeds UNSLOTH_STUDIO_MAX_BODY_MB before invoking the FastAPI
handler, and replays the buffered body to downstream so route code that
calls request.json() / await request.body() works unchanged.
CSP previously included 'unsafe-inline' on script-src, which defeats the
main XSS protection. The frontend bundle does not need inline scripts;
the only inline <script> the backend ever emits is _inject_bootstrap,
which is opt-in via UNSLOTH_STUDIO_INJECT_BOOTSTRAP. Drop 'unsafe-inline'
from script-src by default; when _inject_bootstrap fires, generate a
per-response nonce, embed it on the inlined <script>, and have
SecurityHeadersMiddleware splice 'nonce-XXX' into the CSP for that one
response (the internal x-internal-script-nonce header is popped before
the response leaves the server). 'unsafe-inline' stays on style-src for
Vite-injected styles.
* studio: drop empty assistant sentinel before passthrough
ChatMessage._validate_role_shape normalises role="assistant", content=""
(the post-Stop sentinel emitted by the frontend) to content=None so the
in-process path can drop it via _extract_content_parts. The passthrough
path then ran m.model_dump(exclude_none=True), which strips the now-None
content key entirely, sending {"role":"assistant"} to llama-server / the
OpenAI-compat backend. That fails upstream and leaves the user without a
recoverable Stop->resume.
Add _drop_empty_assistant_sentinels and call it at both passthrough
message origins: _openai_messages_for_passthrough (covers
/v1/chat/completions and the Responses API which routes through it) and
the anthropic_messages_to_openai output before
_anthropic_passthrough_*. Assistant messages that carry only tool_calls
(no content) are preserved.
* studio/tests: cover audit-fix surfaces and rebase pre-existing tests
Adds and updates pytest coverage for the four bot-flagged audit fixes
landed earlier in this branch and rebases two pre-existing tests that
were broken by the relaxed-validator and /api/health auth-gate changes.
studio/backend/tests/test_middleware.py (new)
MaxBodyMiddleware: small protected, large declared, unprotected
passthrough, chunked-upload-over-cap rejection (the regression for
the original Content-Length-only gap), and chunked-under-cap replay.
SecurityHeadersMiddleware: script-src no longer carries
'unsafe-inline', style-src still does, default headers
(XFO/XCTO/Referrer-Policy/Permissions-Policy/server), and the
internal x-internal-script-nonce header is consumed by the
middleware and converted to 'nonce-XXX' in the CSP.
/api/health: no auth -> minimal, invalid Bearer -> minimal
(the await regression), valid Bearer -> full diagnostic payload.
studio/backend/tests/test_desktop_auth.py
consume_refresh_token: second-call returns None, expired returns
None, and a 64-thread concurrent pile-up against the same hash
produces exactly one successful consumer (regression for the
SELECT-then-DELETE race).
test_health_response_reports_desktop_capability_fields: rebase
against the new health_check(request) signature by going through
TestClient with a real bearer instead of asyncio.run-ing the
handler directly.
studio/backend/tests/test_openai_tool_passthrough.py
Pin the new ChatMessage tolerance: assistant without content or
tool_calls is tolerated (normalises content -> None), empty-string
and empty-list assistant content normalise to None, and a missing
/ empty tool_call_id on role='tool' is synthesised as call_<hex>
rather than raising. Tests for _drop_empty_assistant_sentinels
cover the three drop shapes (empty string, empty list, missing
content key), preservation of assistant text and tool_calls-only
messages, and end-to-end through
_openai_messages_for_passthrough.
studio/backend/main.py
SecurityHeadersMiddleware.dispatch used response.headers.pop(...)
for the nonce-header handoff; Starlette's MutableHeaders has no
pop. Read-then-del so the internal handoff header is still
stripped before the response leaves the server.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio/tests: rebase three more pre-existing CI tests against this branch
CI on PR #5375 was red on three tests that were tuned for behaviour
predating this branch. Updates each so the assertions match what the
audit fixes intentionally changed; no production code touched.
studio/backend/tests/test_trained_model_scan.py
test_scan_trained_models_includes_lora_and_full_finetune_outputs
passed an absolute tmp_path through scan_trained_models, which now
runs resolve_output_dir / _assert_contained against outputs_root().
Repoint outputs_root() at tmp_path via monkeypatch so the fixture
dirs land under the configured root and the realpath containment
check passes.
tests/test_studio_install_workspace_guard.py
test_health_endpoint_exposes_studio_root_id_not_raw_path read
the first 1500 bytes after @app.get("/api/health") and asserted on
the studio_root_id literal. The handler grew (unauth short-circuit
+ await dependency gate) and the literal slid past the byte window.
Replace the fixed window with a slice up to the next top-level
@app.* decorator so the test surveys the whole handler regardless
of size.
tests/studio/studio_api_smoke.py
The "login burst (5x wrong pw) -> 401 each" assertion was tagged
"When/if we add one, this assertion updates in the same PR." We
added the per-IP rate-limit in routes/auth.py
(_LOGIN_MAX_FAILS=5/60s) but missed the assertion update. Rewrite
the burst probe to observe the new invariant: at least one 401,
eventual transition to 429, and Retry-After present on the 429.
Adds a small _login_with_headers helper since the existing login()
helper drops response headers.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* ci(studio-ui): set UNSLOTH_STUDIO_INJECT_BOOTSTRAP=1 for Playwright Studios
The Chat UI Playwright test drives the first-boot change-password
form, which (per playwright_chat_ui.py step "1. Change-password
through the UI") pre-seeds the hidden current_password field from
window.__UNSLOTH_BOOTSTRAP__. That global is only emitted when the
backend's _inject_bootstrap path fires, which since the security
pass on this branch is gated behind UNSLOTH_STUDIO_INJECT_BOOTSTRAP
and defaults to off. Without the global, the React form's
current_password validator never satisfies, the submit button stays
disabled, and the composer.wait_for() probe times out on
/change-password.
Re-enable injection only for the CI Studios that drive the chat UI
across linux/mac/windows. Production deployments are unaffected: the
env var has to be explicitly opted into, and the on-disk
auth/.bootstrap_password remains the source of truth for human users
typing the password in by hand.
Covers all eight Studio launch sites: the primary chat-ui boot and
the "extra UI tests" boot for each of the three OSes, plus the
pipeTransport JSON-crash retry relaunches in the macOS workflow that
re-spawn Studio mid-job.
A follow-up frontend PR will add a visible current_password input so
the form satisfies its own validator without needing the bootstrap
auto-fill at all; once that lands this CI knob can come back out.
* studio/sandbox: drop unshare(CLONE_NEWNET); add trusted-host allowlist; block sandbox file uploads; raise CPU rlimit default to 600 s
CLONE_NEWNET inside _sandbox_preexec silently killed every outbound
HTTP request from sandboxed Python whenever the kernel allowed
unprivileged user namespaces. requests.get('https://huggingface.co'),
urllib.request.urlopen('https://en.wikipedia.org/wiki/...'),
socket.connect(('arxiv.org', 443)) all failed despite the AST visitor
intending to allow them. The bash blocklist (curl / wget / nc / ssh /
scp / sftp / rsync / socat / eval / source) plus the AST-level
metadata-host denylist still carry the network policy after this
change; CLONE_NEWNET was redundant with both.
Add _TRUSTED_PUBLIC_HOST_LITERALS + _TRUSTED_PUBLIC_HOST_SUFFIXES
(~100 informational hosts: Wikipedia language subdomains, Wikimedia,
Wikidata, Google search, Bing, DuckDuckGo, HuggingFace, GitHub,
raw.githubusercontent.com, arXiv, StackOverflow / Stack Exchange,
MDN, docs.python.org, PyTorch / TensorFlow / NumPy / pandas docs,
pypi / files.pythonhosted.org / npmjs / crates.io, ReadTheDocs,
arXiv, Britannica, BBC / Reuters / Nature / Science, NASA / CDC /
NIH / WHO open data, api.weather.gov). The visitor now blocks
literal hosts that are neither metadata nor trusted with a short
LLM-readable string so the model can retry with an allowed source
instead of choking on a multi-line error.
Block upload-shape calls regardless of host: requests.post / put /
patch / delete / request with files= or data=open(...) /
data=bytes_literal; httpx equivalents; urllib.request.urlopen /
Request with data=...; HuggingFace upload_file / upload_folder /
upload_large_folder / create_commit (module-level FQ paths AND
method-name match on any receiver). Message: "Blocked: file upload
disallowed in sandbox".
Bump UNSLOTH_STUDIO_SANDBOX_CPU_S default 300 -> 600 s so long
agentic chains that span multiple tool calls don't get SIGXCPU'd
mid-stride. Env-var override path is unchanged.
Host normalisation now strips trailing dot, userinfo @, and explicit
port before allowlist / denylist comparison so trailing-DNS-dot,
userinfo-smuggling, and explicit-:443 URLs are decided correctly.
* studio: raise default request-body cap from 100 MB to 500 MB
UNSLOTH_STUDIO_MAX_BODY_MB default goes 100 -> 500 to comfortably
cover vision + audio + multi-recipe-batch JSON payloads. The
MaxBodyMiddleware stream-counting logic from this branch's earlier
06ec088 already handles chunked bodies up to the new cap; env-var
override path is unchanged for callers that want a tighter limit.
* studio/auth: restore /api/auth/status.default_username to 'unsloth'
This branch's earlier b39e9a4 changed default_username to None on the
public /api/auth/status endpoint so the username field didn't leak to
unauthenticated callers. In practice this regressed third-party
clients (and the in-tree React login form's pre-fill UX) without
adding meaningful security: the bootstrap password is the actual
secret, and the username 'unsloth' is the documented default.
Pin default_username to storage.DEFAULT_ADMIN_USERNAME ('unsloth')
and tighten the response model so the field is required rather than
Optional. Anyone who needs anonymisation can still reach for an
allow-list deployment with auth disabled.
* studio/training: raise max_seq_length / batch_size / lora_r / lora_alpha caps
This branch's 7102815 introduced field validators with conservative
caps. The follow-up loosens them so long-context experiments and
high-rank LoRA exploration aren't gated at the schema layer:
_MAX_BATCH_SIZE 1024 -> 4096
_MAX_SEQ_LENGTH 131_072 -> 2_000_000 (2M tokens)
lora_r cap 512 -> 16_384 (_MAX_LORA_R)
lora_alpha cap 1024 -> 32_768 (_MAX_LORA_ALPHA)
_MAX_GRAD_ACCUM / _MAX_STEPS / _MAX_EPOCHS / lora_dropout /
warmup_ratio / weight_decay are unchanged. Hardware (VRAM, host
RAM, kernel launch latency) is now the binding constraint at the
new caps, which is the correct ordering -- the validator stays a
sanity check on -1 / 0 / 'abc' style garbage, not a usability gate.
* studio/tests: cover sandbox allowlist + upload block + raised training caps
studio/backend/tests/test_sandbox_tools.py (new):
TestMetadataHostDenylist -- short "Blocked: cloud-metadata host"
message on AWS IMDS, GCP metadata,
Alibaba ECS, AWS IPv6 IMDS, 169.254/16.
TestTrustedHostAllowlist -- Wikipedia (any language subdomain),
Google, DuckDuckGo, HF, raw GitHub,
arXiv, StackOverflow / family,
MDN, docs.python.org, pypi, BBC,
api.weather.gov, NumPy / PyTorch docs.
TestUntrustedHostBlock -- example.com / random unlisted host
rejected with the short "Blocked: host
not in sandbox allowlist; use an
allowed informational source" message.
Dynamic URLs (computed var) still pass
-- documented limit of static analysis.
TestHostNormalization -- trailing dot, explicit :443, uppercase,
userinfo-@-smuggle all decided
correctly without false-block /
false-pass.
TestUploadDenylist -- requests / httpx / urllib.urlopen with
files= / data=open / data=bytes,
HfApi().upload_file / upload_folder /
create_commit, module-level
huggingface_hub.upload_folder. POST
json= to trusted host still passes.
TestSandboxCpuRlimitDefault -- pin UNSLOTH_STUDIO_SANDBOX_CPU_S=600
default and confirm CLONE_NEWNET
source line is gone.
TestMaxBodyDefault -- pin UNSLOTH_STUDIO_MAX_BODY_MB=500
default.
studio/backend/tests/test_studio_train_validation.py (new):
Pin at-cap-accepts / over-cap-rejects boundaries for
max_seq_length=2_000_000, batch_size=4_096, lora_r=16_384,
lora_alpha=32_768 so a future regression that tightens them back
without explicit user opt-in is caught.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: tighten code comments across the security-hardening pass
* studio: always inject bootstrap credentials on first boot
The UNSLOTH_STUDIO_INJECT_BOOTSTRAP gate added an extra
terminal-to-browser copy-paste on every fresh install. In practice
the LAN credential leak it guarded against is narrow: the password
is one-time, the user rotates it on the very next click, the
default Studio bind is 127.0.0.1, and -H 0.0.0.0 already exposes
the entire API surface. Drop the gate so the inject fires whenever
a bootstrap password is still pending. The CSP nonce wiring stays
in place; the inline script remains the only inline script the
backend ever emits.
The three Playwright UI smoke workflows lose their
UNSLOTH_STUDIO_INJECT_BOOTSTRAP=1 lines along with the explanatory
comment blocks since the inject now happens by default.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.com>
* feat(studio): add Continued Pretraining (CPT) support
Implements CPT as a first-class training method in Unsloth Studio,
resolving feature request #4565.
Changes:
- frontend/src/types/training.ts: add 'cpt' to TrainingMethod union
- frontend/src/lib/vram.ts: add 'cpt' to VramTrainingMethod (fp16 footprint)
- frontend/src/features/export/constants.ts: add CPT to METHOD_LABELS
- frontend/src/features/training/api/mappers.ts: map 'cpt' -> 'Continued Pretraining',
force packing=true and train_on_completions=false for CPT payloads
- frontend/src/features/studio/sections/model-section.tsx: add 'Continued Pretraining'
option (purple dot) to Method selector; update tooltip
- frontend/src/features/onboarding/.../model-selection-step.tsx: add CPT to
onboarding wizard method dropdown
- backend/models/training.py: update training_type field description
- backend/core/training/worker.py: detect is_cpt flag, force packing=True,
train_on_completions=False, pass is_cpt to _train_worker
- backend/core/training/trainer.py: _train_worker reads is_cpt kwarg, forces
packing on, skips train_on_responses_only for raw-text pretraining
CPT behaviour:
- Full model weights (no LoRA adapters), same as Full Finetuning
- Sequence packing always enabled for GPU efficiency
- Trains on every token (no chat-format masking)
- VRAM estimated at fp16 (2.0 bytes/param)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Update mappers.ts
* Add CPT raw dataset support and UI fixes
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add missing training methods module
* Handle invalid raw-text rows and expose raw in onboarding
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
Co-authored-by: Etherll <mrmrmidessam@gmail.com>
* Add Apple Silicon MLX routing
Rewrite __init__.py: detect MLX on macOS arm64 before any torch imports
Extract original GPU init to _gpu_init.py (unchanged)
MLX path imports FastMLXModel from unsloth_zoo, skips all GPU code
GPU path unchanged: from ._gpu_init import *
* Add Apple Silicon MLX routing
- Rewrite __init__.py: detect MLX on macOS arm64 before any torch imports
- Extract original GPU init to _gpu_init.py (unchanged)
- MLX path imports FastMLXModel from unsloth_zoo, skips all GPU code
- GPU path unchanged: from ._gpu_init import *
* mlx with studio
* mlx with studio
* updating temporary install.sh
* updating temporary install.sh
* adding t_v5 path
* adding t_v5 path
* fixing vision training
* fixing vision training
* adding chat
* adding chat
* minor
* minor
* Adding export and fixing training issues, inference with lora adaptors
* Adding export and fixing training issues, inference with lora adaptors
* fix: MLX worker pass load_in_4bit, override is_vlm based on dataset, streaming for VLM
* fix: MLX worker pass load_in_4bit, override is_vlm based on dataset, streaming for VLM
* Merge mlx-apple-silicon into main
* update install.sh to point to main branch
* update install.sh to point to main branch
* fix: export returns 3 values (success, message, output_path) matching upstream worker
* fix: export returns 3 values (success, message, output_path) matching upstream worker
* fix(mlx): show training-process peak memory in Studio UI, not system-wide
Studio UI was showing ~95 GB during MLX training because get_gpu_utilization
read "In use system memory" from IORegistry's AGXAccelerator — system-wide
GPU memory across all processes (training + backend + browser + Display).
Now the trainer's mx.get_peak_memory value is forwarded through the
progress event and surfaced via /api/train/hardware while training is
active. Falls back to the system-wide reading when training is not running.
* fix(mlx): show training-process peak memory in Studio UI, not system-wide
Studio UI was showing ~95 GB during MLX training because get_gpu_utilization
read "In use system memory" from IORegistry's AGXAccelerator — system-wide
GPU memory across all processes (training + backend + browser + Display).
Now the trainer's mx.get_peak_memory() value is forwarded through the
progress event and surfaced via /api/train/hardware while training is
active. Falls back to the system-wide reading when training is not running.
* fix(mlx): make is_bfloat16_supported detect M1/M2 (no native bf16)
M1 and M2 chips emulate bf16 in software on the GPU, causing 40-70%
slower prefill compared to native fp16. M3+ have native bf16 (macOS
Sonoma+ MPSGraph). Replaces the always-True stub with chip-aware
detection via mx.device_info.
* fix(mlx): make is_bfloat16_supported() detect M1/M2 (no native bf16)
M1 and M2 chips emulate bf16 in software on the GPU, causing 40-70%
slower prefill compared to native fp16. M3+ have native bf16 (macOS
Sonoma+ MPSGraph). Replaces the always-True stub with chip-aware
detection via mx.device_info().
* feat(mlx): wire training_type="Full Finetuning" through MLX worker
Compute use_lora from the UI's training_type before loading the model,
pass full_finetuning=not use_lora to FastMLXModel.from_pretrained, and
let the existing 'if use_lora' branch skip get_peft_model. Matches the
GPU worker's flow.
* feat(mlx): wire training_type="Full Finetuning" through MLX worker
Compute use_lora from the UI's training_type before loading the model,
pass full_finetuning=not use_lora to FastMLXModel.from_pretrained, and
let the existing 'if use_lora' branch skip get_peft_model. Matches the
GPU worker's flow.
* fix(mlx): pass save_method='merged_16bit' from Studio's export page
Previously the MLX path called save_pretrained_merged with no
save_method, which fell through to a no-op that didn't actually fuse
LoRA into the base. Now Studio's "Merged Model" export properly
fuses LoRA + dequantizes any 4-bit base to bf16, matching the GPU
behavior for the same UI option.
* fix(mlx): pass save_method='merged_16bit' from Studio's export page
Previously the MLX path called save_pretrained_merged() with no
save_method, which fell through to a no-op that didn't actually fuse
LoRA into the base. Now Studio's "Merged Model" export properly
fuses LoRA + dequantizes any 4-bit base to bf16, matching the GPU
behavior for the same UI option.
* fix(studio): pass private to MLX push, return 3-tuples consistently
MLX push_to_hub branch now forwards private=private (matches GPU)
Existing 2-tuple early-returns ('repo_id+token required', 'PEFT model
needed') were tripping the route's 3-tuple unpack. Added a None
output_path so the unpack always succeeds.
* fix(studio): pass private to MLX push, return 3-tuples consistently
- MLX push_to_hub branch now forwards private=private (matches GPU)
- Existing 2-tuple early-returns ('repo_id+token required', 'PEFT model
needed') were tripping the route's 3-tuple unpack. Added a None
output_path so the unpack always succeeds.
* studio wirings
* studio wirings
* Merge pull request #5 from Manan17/feat/quant_config
studio wirings
* fix(mlx): wire train_on_completions for VLM via per-template lookup
Mirror the GPU worker: stop excluding VLMs and stop hardcoding
template detection. Look up the model in MODEL_TO_TEMPLATE_MAPPER and
fetch the per-template instruction/response markers from
TEMPLATE_TO_RESPONSES_MAPPER. The frontend already force-disables
train_on_completions for vision+image and audio cases, so backend
just trusts the flag.
* fix(mlx): wire train_on_completions for VLM via per-template lookup
Mirror the GPU worker: stop excluding VLMs and stop hardcoding
template detection. Look up the model in MODEL_TO_TEMPLATE_MAPPER and
fetch the per-template instruction/response markers from
TEMPLATE_TO_RESPONSES_MAPPER. The frontend already force-disables
train_on_completions for vision+image and audio cases, so backend
just trusts the flag.
* wire in lora rslora, init lora weights, random_state
* wire in lora rslora, init lora weights, random_state
* loftq studio error message fix
* loftq studio error message fix
* handle unknown optim and lr scheduler
* handle unknown optim and lr scheduler
* Merge pull request #6 from Manan17/update/peftkwargs
Update/peftkwargs
* feat(mlx): pass finetune_language/attention/mlp/vision flags to FastMLXModel
Studio's four UI checkboxes now actually flow through to MLX get_peft_model
(which was just updated in unsloth-zoo to honor them). Also drops the
incorrect train_projector wiring that tied projector LoRA to the
attn/mlp flags — those are language-side toggles, not projector toggles.
Co-Authored-By: Manan17 <shahmanan170602@gmail.com>
* feat(mlx): pass finetune_language/attention/mlp/vision flags to FastMLXModel
Studio's four UI checkboxes now actually flow through to MLX get_peft_model
(which was just updated in unsloth-zoo to honor them). Also drops the
incorrect train_projector wiring that tied projector LoRA to the
attn/mlp flags — those are language-side toggles, not projector toggles.
Co-Authored-By: Manan17 <shahmanan170602@gmail.com>
* feat(mlx,ux): auto-imply finetune_language_layers when user picks attn/mlp
UI guardrail. The four checkboxes (vision/language/attention/MLP) carry
"scope × module-type" semantics that aren't obvious — picking just
"Attention modules" + "MLP modules" without "Language layers" naturally
reads as "fine-tune attn/mlp" but our backend reads it as "fine-tune
attn/mlp modules in *no* tower" → empty target_modules → zero
trainable params → crash inside value_and_grad.
If user selected attn or mlp module types but no layer scope, default
to language scope. Power users can still explicitly choose
language=False, vision=True if they want vision-only fine-tuning of
attn/mlp.
Co-Authored-By: Manan17 <shahmanan170602@gmail.com>
* feat(mlx,ux): auto-imply finetune_language_layers when user picks attn/mlp
UI guardrail. The four checkboxes (vision/language/attention/MLP) carry
"scope × module-type" semantics that aren't obvious — picking just
"Attention modules" + "MLP modules" without "Language layers" naturally
reads as "fine-tune attn/mlp" but our backend reads it as "fine-tune
attn/mlp modules in *no* tower" → empty target_modules → zero
trainable params → crash inside value_and_grad.
If user selected attn or mlp module types but no layer scope, default
to language scope. Power users can still explicitly choose
language=False, vision=True if they want vision-only fine-tuning of
attn/mlp.
Co-Authored-By: Manan17 <shahmanan170602@gmail.com>
* fix(mlx): wire top_k, repetition_penalty, and VLM top_p through to mlx-lm/mlx-vlm
Inference UI sliders for top_k and repetition_penalty had no effect on
MLX, and VLM top_p was also silently dropped. Plus a latent pre-existing
bug: mlx_vlm.generate_step expects temperature= (long form), but we
were passing temp= which silently fell into **kwargs — every VLM chat
was effectively greedy regardless of the temperature slider.
Text path (_generate_text):
make_sampler now receives top_k in addition to temp/top_p
make_logits_processors built and forwarded when repetition_penalty is
non-trivial (skip when 0.0/1.0 to avoid pointless overhead)
VLM path (_generate_vlm):
Pass top_p, top_k, repetition_penalty as kwargs (mlx_vlm.stream_generate
forwards them to generate_step's sampler/logits_processor builders)
Rename temp= → temperature= so it's actually consumed
Verified end-to-end with a smoke test on Qwen2.5-0.5B-Instruct (text) and
Qwen2.5-VL-3B-Instruct (VLM): each of {greedy, top_p=0.5, top_k=10,
rep_pen=1.5} now produces a distinct output, proving the parameters
reach the sampler.
Co-Authored-By: Manan17 <shahmanan170602@gmail.com>
* fix(mlx): wire top_k, repetition_penalty, and VLM top_p through to mlx-lm/mlx-vlm
Inference UI sliders for top_k and repetition_penalty had no effect on
MLX, and VLM top_p was also silently dropped. Plus a latent pre-existing
bug: mlx_vlm.generate_step expects temperature= (long form), but we
were passing temp= which silently fell into **kwargs — every VLM chat
was effectively greedy regardless of the temperature slider.
Text path (_generate_text):
- make_sampler now receives top_k in addition to temp/top_p
- make_logits_processors built and forwarded when repetition_penalty is
non-trivial (skip when 0.0/1.0 to avoid pointless overhead)
VLM path (_generate_vlm):
- Pass top_p, top_k, repetition_penalty as kwargs (mlx_vlm.stream_generate
forwards them to generate_step's sampler/logits_processor builders)
- Rename temp= → temperature= so it's actually consumed
Verified end-to-end with a smoke test on Qwen2.5-0.5B-Instruct (text) and
Qwen2.5-VL-3B-Instruct (VLM): each of {greedy, top_p=0.5, top_k=10,
rep_pen=1.5} now produces a distinct output, proving the parameters
reach the sampler.
Co-Authored-By: Manan17 <shahmanan170602@gmail.com>
* feat(mlx): map format_type to MLX save_method, reuse local save dir for hub push
export_merged_model: format_type="4-bit (FP4)" → save_method="merged_4bit"
(was hardcoded merged_16bit, ignoring the UI choice).
Both export_merged_model and export_base_model now pass save_directory=
to push_to_hub_merged so it reuses the just-written local folder
instead of re-saving under a relative "username/model" directory.
Co-Authored-By: Manan17 <shahmanan170602@gmail.com>
* feat(mlx): map format_type to MLX save_method, reuse local save dir for hub push
- export_merged_model: format_type="4-bit (FP4)" → save_method="merged_4bit"
(was hardcoded merged_16bit, ignoring the UI choice).
- Both export_merged_model and export_base_model now pass save_directory=
to push_to_hub_merged so it reuses the just-written local folder
instead of re-saving under a relative "username/model" directory.
Co-Authored-By: Manan17 <shahmanan170602@gmail.com>
* [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
* restore install
* restore install
* fix(mlx): restore FastVisionModel as a distinct class
unsloth/__init__.py was assigning `FastVisionModel = FastLanguageModel`
right after defining `class FastVisionModel(FastLanguageModel)` with a
`for_training` static method. The alias erased the class binding, so
the documented `FastVisionModel.for_training(model)` call from upstream
Unsloth's VLM notebooks raised `AttributeError` on MLX.
Remove the offending alias. `FastVisionModel` is now a real subclass of
`FastLanguageModel` again — inherits `from_pretrained` /
`get_peft_model` / `for_inference`, exposes `for_training` as a no-op
pass-through (no-op because MLX doesn't have a train/eval mode flag;
the call exists purely for GPU/MLX notebook parity).
Verified end-to-end: Qwen3-VL-2B + LaTeX_OCR LoRA + vision LoRA via
FastVisionModel.from_pretrained → get_peft_model → for_training →
MLXTrainer.train runs 10 steps cleanly (loss 1.10 → 0.12, no NaNs,
peak 5.89 GB).
Studio's path (FastLanguageModel.from_pretrained for any repo,
auto-detect VLM in the loader) is unaffected. Tier-1 review finding #8.
* fix(mlx): restore FastVisionModel as a distinct class
unsloth/__init__.py was assigning `FastVisionModel = FastLanguageModel`
right after defining `class FastVisionModel(FastLanguageModel)` with a
`for_training` static method. The alias erased the class binding, so
the documented `FastVisionModel.for_training(model)` call from upstream
Unsloth's VLM notebooks raised `AttributeError` on MLX.
Remove the offending alias. `FastVisionModel` is now a real subclass of
`FastLanguageModel` again — inherits `from_pretrained` /
`get_peft_model` / `for_inference`, exposes `for_training` as a no-op
pass-through (no-op because MLX doesn't have a train/eval mode flag;
the call exists purely for GPU/MLX notebook parity).
Verified end-to-end: Qwen3-VL-2B + LaTeX_OCR LoRA + vision LoRA via
FastVisionModel.from_pretrained → get_peft_model → for_training →
MLXTrainer.train() runs 10 steps cleanly (loss 1.10 → 0.12, no NaNs,
peak 5.89 GB).
Studio's path (FastLanguageModel.from_pretrained for any repo,
auto-detect VLM in the loader) is unaffected. Tier-1 review finding #8.
* Studio: harden MLX training and export, restore GPU init guards
Studio export
Restore Tuple[bool, str, Optional[str]] contract on export_merged_model,
export_base_model, export_gguf, and export_lora_adapter, populating
output_path on successful local saves so routes/worker/CLI/frontend
details.output_path is non-empty again.
Lift the GPU save_method assignment out of the local-save branch so
Hub-only merged exports (save_directory='', push_to_hub=True) no longer
hit UnboundLocalError on the push branch.
For MLX merged and base hub-only export, stage to a tempfile.TemporaryDirectory
before push_to_hub_merged instead of passing save_directory=''.
Source _IS_MLX from unsloth instead of recomputing the platform check
(single source of truth, also enforces mlx-package availability).
Studio MLX training/inference
Pass token=hf_token into FastMLXModel.from_pretrained for gated/private
models, matching the inference path.
Strip hf_token and wandb_token from wandb.init(config=...) so secrets
do not leak into the W&B run config.
Replace load_from_disk(local_datasets[0]) with the existing
UnslothTrainer._resolve_local_files / _loader_for_files helpers so
uploaded JSON/JSONL/CSV/Parquet files train through the normal datasets
loader (load_from_disk still used for HF save_to_disk directories).
Make the dataset slice helper inclusive at the end and treat 0 as a real
index instead of "unset", matching the GPU and embedding paths.
Add a status_message -> message alias inside _send so the existing parent
pump (training.py) renders MLX status updates instead of blanks.
Forward min_p through generate_chat_response into _generate_text /
_generate_vlm and into make_sampler / vlm_kwargs so the sampling control
is no longer a no-op on MLX.
Wrap unsloth_zoo.mlx_loader / mlx_trainer imports with a clearer
ImportError pointing users at install.sh for Apple Silicon.
Exit the MLX stop-polling thread on EOFError/OSError instead of
busy-looping when the queue/pipe is permanently closed (one-line
why-safe rationale inline).
Studio frontend
ParamsSection subscribes to platform deviceType via the Zustand hook so
the gradient checkpointing dropdown re-renders after the async device
fetch completes.
Studio hardware
get_gpu_utilization MLX branch now reads _read_apple_gpu_stats once and
derives VRAM totals from psutil, removing the second ioreg subprocess
per utilization poll.
Unsloth core
Restore the os.geteuid == 0 guard around the CUDA ldconfig recovery
that was lost when GPU initialization moved into _gpu_init.py, plus the
non-root manual-fix warning branch. Non-root CUDA users no longer shell
out to ldconfig at import time.
Load dataprep/raw_text via importlib so the MLX import path no longer
pulls torch in through dataprep/__init__.py -> synthetic.py.
FastVisionModel.from_pretrained overrides the inherited delegator only
to inject text_only=False; this is an extension, not a duplication, and
is needed so VLM checkpoint loads keep the vision tower.
Wrap the MLX-branch unsloth_zoo import with a clearer ImportError.
* Studio: regression tests for MLX training/export and GPU init ldconfig guard
tests/python/test_gpu_init_ldconfig_guard.py asserts the geteuid root
check still wraps the ldconfig recovery and the non-root branch warns
bnb users; AST + source-text inspection so the test runs without torch.
tests/studio/test_export_output_path_contract.py covers the
Tuple[bool, str, Optional[str]] return contract on every export method,
the output_path assignment after successful local save, the Hub-only
GPU save_method binding fix, the MLX hub-only TemporaryDirectory
staging, and the single-source `_IS_MLX` import from unsloth.
tests/studio/test_mlx_training_worker_behaviors.py covers token
forwarding to FastMLXModel.from_pretrained, wandb config secret
stripping, file-aware local dataset loading, status_message ->
message aliasing, inclusive slice semantics, EOFError/OSError stop
thread exit, and the friendly mlx_loader / mlx_trainer ImportError.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(mlx): cap inference memory + release wired on unload + tame worker pre-pin
Three memory-hardening fixes for Studio's MLX path:
1. Inference applies the same Metal caps as the trainer.
load_model previously only called set_wired_limit(100% of recommended)
with no upper memory_limit, leaving large VLM checkpoints unbounded
during the loader allocation. Add _configure_memory_limits() that sets
memory_limit to 85% of recommended and wired_limit to min(recommended,
memory_limit) — matching MLXTrainer's defaults so behavior is the same
whether the user trains or just runs inference.
2. unload_model releases pinned memory back to the OS — but only when
the cache is empty. Without this, pinned wired bytes stayed allocated
to MLX after the model was gone, starving other apps. The release is
guarded on `not self.models` so unloading one of several cached
models doesn't un-pin weights still in use.
3. Worker pre-cap is conservative instead of aggressive.
The previous pre-pin set_wired_limit(100% of recommended) competed
with MLXTrainer's later more conservative cap. Replace with the same
85%-memory / min(rec, memory) pair that the trainer applies later
(idempotent re-apply). Bounds the model load + LoRA setup window
without over-pinning.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* tests/studio: regression tests for the _IS_MLX dispatch gate
Two gates drive every MLX-vs-CUDA dispatch decision in Studio:
1. unsloth._IS_MLX in unsloth/__init__.py — evaluated once at import
time, read by Studio worker code to choose the GPU vs MLX trainer
and inference paths. Defined as
Darwin AND arm64 AND find_spec("mlx") is not None.
2. utils.hardware.detect_hardware() — runtime probe with priority
CUDA > XPU > MLX > CPU. The MLX branch is reached only when both
CUDA and XPU are unavailable and the host is Apple Silicon and
mlx is importable.
Neither gate had a direct test. Adds tests/studio/test_is_mlx_dispatch_gate.py
with six tests:
test_is_mlx_gate_uses_three_required_predicates
AST-walks unsloth/__init__.py and asserts the _IS_MLX assignment
is a BoolOp(And) of platform.system()=="Darwin",
platform.machine()=="arm64", and find_spec("mlx") is not None.
Catches accidental rewrites that drop a predicate.
test_is_mlx_gate_true_on_apple_silicon_with_mlx_present
Spoofs platform to Darwin/arm64, injects a fake mlx module so
find_spec returns a real ModuleSpec, re-evaluates the gate
expression. Verifies it flips True under the exact conditions
Studio expects.
test_is_mlx_gate_false_when_mlx_missing
Spoofs Apple Silicon but with mlx absent. Verifies the gate stays
False (so a Mac without mlx installed does not pretend to have
MLX support).
test_is_mlx_gate_false_on_non_apple_silicon
Canary on the actual Linux+CUDA / AMD / Intel test host: the gate
must remain False regardless of whether mlx happens to be
importable. Protects existing GPU users from accidental MLX
hijack when MLX support evolves.
test_detect_hardware_picks_mlx_when_only_apple_silicon_available
Forces torch.cuda and torch.xpu off, spoofs Apple Silicon, injects
fake mlx and mlx.core. detect_hardware() must return DeviceType.MLX.
test_detect_hardware_picks_cuda_on_real_host
Canary: on a real CUDA host detect_hardware() must return
DeviceType.CUDA. Protects against the MLX branch shadowing CUDA
dispatch on NVIDIA / AMD ROCm hosts.
Uses the same monkeypatch.setitem(sys.modules, ...) fake-mlx pattern as
the existing test_mlx_inference_backend.py — no new test infrastructure,
no real mlx install required.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add AGPL-3.0 SPDX header to Studio MLX regression tests
Four Studio MLX test files shipped without an SPDX-License-Identifier:
studio/backend/tests/test_mlx_training_worker_config.py
tests/studio/test_mlx_training_worker_behaviors.py
tests/studio/test_export_output_path_contract.py
tests/studio/test_is_mlx_dispatch_gate.py
They sit in or alongside studio/backend/, which is governed by
studio/LICENSE.AGPL-3.0, and exercise AGPL Studio code. Add the same
"# SPDX-License-Identifier: AGPL-3.0-only" header that's already on
test_mlx_inference_backend.py so the license declaration matches
the code under test rather than defaulting to the repo-root
Apache-2.0.
* Wrap MLX submodule imports with friendly install hint
The _IS_MLX block at the top of unsloth/__init__.py already catches the
missing-package case with a friendly install hint, but the follow-up
"from unsloth_zoo.mlx_trainer import ..." and "from unsloth_zoo.mlx_loader import ..."
lines run unguarded. An Apple Silicon user who has unsloth-zoo installed
but on an older version (e.g. the current PyPI release, before the MLX
modules ship) sees a raw ImportError on the submodule rather than the
hint that points at install.sh.
Wrap the two submodule imports in the same try/except shape so the
friendly install message fires whether the package is missing entirely
or just predates the MLX submodules. No-op once both packages release
together; smooths the transitional window where unsloth/main has merged
but unsloth-zoo on PyPI has not.
---------
Co-authored-by: DoubleMathew <mmathew23@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* feat(studio): add Tauri native GGUF intake
* feat(studio): polish native GGUF intake
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix(studio): load backend helpers during local setup
* fix(studio): acquire native load lease before unload
* Studio: harden native path lease verification and Tauri intake
- Wrap path.resolve(strict=True) and Path.stat() in NativePathLeaseError so a deleted or unmounted GGUF returns 400 instead of leaking the full filesystem path through the generic load_model/validate_model handler.
- Re-apply _reject_network_or_device_path to the resolved canonical path for defense in depth after symlink resolution.
- Replace try/except ValueError pattern in the device-path guard with Path.is_relative_to; the previous shape silently swallowed NativePathLeaseError (which subclasses ValueError) so /dev,/proc,/sys were never actually rejected.
- Broaden the lease redaction regex and dict-key check (Python and Rust diagnostics) to cover both native_path_lease and nativePathLease so the camelCase form emitted by Tauri/frontend payloads is also redacted.
- Hoist the redact_native_paths import to module top in loggers/handlers; the recursive filter no longer pays a per-record import lookup.
- Persist activeNativePathToken in the chat runtime store so the rollback branch can mint a fresh lease and reload the previous native GGUF when a new load fails after unload; clear it in clearCheckpoint and overwrite it on each successful load.
- use-native-drop: read options through a ref so the Tauri onDragDropEvent listener is registered once and stays attached across option changes; reject ambiguous multi-file drops up front instead of silently registering only the first GGUF.
- pick_native_model: use an async pick_file with a tokio oneshot channel instead of blocking_pick_file so the Tokio worker is not held for the duration of the OS dialog.
- registerNativeModelPath: drop the duplicate sourceKind argument; the Rust command parameter is source_kind.
- install_python_stack: insert the script directory (studio/) on sys.path; the previous insert pointed at studio/backend/ which does not satisfy `from backend.utils.wheel_utils import ...`.
* install_python_stack: keep _BACKEND_DIR on sys.path
Restore the studio/backend insertion. Although the immediately following `from backend.utils.wheel_utils import (...)` is satisfied by studio/ already being on sys.path[0] when invoked as `python studio/install_python_stack.py`, wheel_utils itself runs `from utils.native_path_leases import ...`, which requires studio/backend/ to be importable. Without the backend insertion, the existing tests/python/test_install_python_stack.py collection fails with ModuleNotFoundError: No module named 'utils'.
* Studio: tighten native path lease lifecycle and Tauri intake IPC
- register_native_model_path now hardcodes NativePathSourceKind::Drop on the Rust side and the frontend stops sending source_kind. The previous JS payload (source_kind only) never reached the Rust deserializer because Tauri's default ArgumentCase::Camel maps the Rust parameter source_kind to the JS key sourceKind, so drag/drop registration silently failed. Hardcoding the source kind also keeps audit metadata trustworthy on this command.
- Add native_path_secret_removed_for_child_start context manager and wrap multiprocessing.Process.start() at the inference, export, training, and data-recipe job spawn sites. The previous wrapper-only scrub left UNSLOTH_STUDIO_NATIVE_PATH_LEASE_SECRET visible to spawn-platform import-time worker code. The wrapper run_without_native_path_secret stays as defense-in-depth inside the child.
- Stop passing exc_info=True from the native-grant load/validate error logs in routes/inference.py. The structlog filter_sensitive_data processor runs before the renderer, so ConsoleRenderer formatted tracebacks bypassed redaction; the redacted str(e) preserves the message text.
- Replace the os.path.normcase string equality on the resolved canonical path with Path.samefile (with a normcase fallback) so Windows leases that differ only in extended-length \\?\ prefix or short-name spelling are accepted.
- Wrap consumeNativePathToken in its own try/catch in the chat runtime rollback. If the previous native-model token has aged out of TOKEN_TTL we now surface a clear modelsError instead of silently swallowing the rollback inside the outer catch.
- Reject non-ASCII lease strings in _split_lease and convert UnicodeEncodeError / binascii.Error / ValueError raised by _b64decode into NativePathLeaseError so verify_native_path_lease never escapes raw exceptions to the route handler.
- Tighten dropStateForPaths to mark multi-file payloads invalid so the overlay matches the post-fix drop handler that rejects the same payload.
- Replace the one-shot fetch in useNativePathLeasesSupported with a delayed-retry loop so the picker/drop becomes available once the backend is up rather than staying disabled for the rest of the session after a transient failure.
- Drop the unused setActiveNativePathToken setter; the value is set via setState directly in use-chat-model-runtime.
- Add a toast on auto-load failure in use-native-drop so a collapsed model selector does not hide the error.
- Burn the lease nonce before _validate_current_stat so a stat-failed lease is single-use even if a later state change happens to match the original size/mtime.
* Studio: cache lease secret, harden native path stat checks, polish intake UX
- Cache the decoded UNSLOTH_STUDIO_NATIVE_PATH_LEASE_SECRET on first verify and validate that it is base64-decodable and at least 32 bytes. Subsequent _decode_secret calls return from the cache and never touch os.environ, so concurrent /api/inference/load and /api/health requests no longer race with native_path_secret_removed_for_child_start scrubbing the env. native_path_leases_supported now wraps _decode_secret so the health flag matches what verify_native_path_lease actually accepts.
- Replace path.is_file()/is_dir() + path.stat() with os.lstat() in _validate_current_stat and explicitly reject S_ISLNK; size and mtime checks now refer to the link itself, closing the same-size+same-mtime symlink-swap window that the prior follow-symlink stat() left open.
- Add an issued_at_ms < expires_at_ms sanity check in _validate_payload to reject internally inconsistent (HMAC-protected) lease payloads.
- Sort _NATIVE_PATH_REDACTIONS by length (descending) before iterating in redact_native_paths so a longer registered path is replaced before a shorter prefix path; otherwise logs containing /foo/X.gguf.bak after only /foo/X.gguf was registered would leak the .bak suffix.
- classify_existing_path now re-checks the canonical path with symlink_metadata after canonicalize, so a regular file that is replaced with a symlink in the small canonicalize window is rejected at registration.
- ModelSelector renders the local file picker as its own block (not in the eject ternary), so a user with an active model can still replace it via the picker rather than only via drag/drop.
- useNativePathLeasesSupported caps the readiness probe at MAX_READINESS_POLLS (60 = ~5 minutes) and aborts the in-flight fetch on unmount via AbortController, so a permanently-disabled backend stops generating sustained traffic and hot-reload no longer leaks open connections.
- useChooseNativeModel returns a stable useCallback closure and guards the OS dialog with a useRef so rapid double-clicks cannot open multiple dialogs and orphan Rust tokens.
- Branch the multi-file drop toast: if no GGUF was present we say "Only .gguf model files can be dropped here." and otherwise "Drop a single .gguf model file." so users dropping non-GGUF attachments get an accurate explanation.
* native_path_leases: lstat the signed canonical path before resolving
The earlier change to lstat inside _validate_current_stat operates on grant.canonical_path, which is the post-resolve target. If the user atomically replaces the originally-signed file with a symlink to a different file of identical size and mtime, path.resolve(strict=True) follows the symlink, samefile returns True (both ends share the new inode), and the lstat in _validate_current_stat sees the regular target file rather than the symlink, so the swap goes undetected.
Add an os.lstat on the signed canonical path before path.resolve(strict=True), and reject S_ISLNK there. The lstat in _validate_current_stat stays as defense-in-depth for swaps that occur strictly between resolve and stat.
* Studio: scrub native lease secret before mp.Queue spawn and tighten lease lifecycle
- Move _CTX.Queue / _CTX.Event / _CTX.Process construction inside native_path_secret_removed_for_child_start at the inference, export, training and data-recipe spawn sites. The first Queue creation lazily spawns Python's multiprocessing.resource_tracker child, so when it ran outside the scrub context the tracker process inherited the lease secret. Reproduced via the proc filesystem environ entry; the wrapped order keeps the tracker clean.
- native_path_secret_removed_for_child_start now refcounts entries: the env var is popped on the first entry and restored only when the last context exits. Concurrent training/inference/export starts no longer serialize on the env lock across the entire proc.start yield, while still guaranteeing the env stays empty for the duration of every overlapping spawn.
- run_without_native_path_secret now also nulls the module-level cached lease secret. With the existing spawn-only multiprocessing context the cache is irrelevant in practice, but a future fork caller would otherwise inherit the in-memory secret even though the env var was scrubbed.
- filter_sensitive_data now applies the native lease key check on the top-level event_dict, not only on nested dicts, so a logger call that includes a lease value as a top-level keyword field actually redacts it (the bare value does not match the prefix-anchored regex).
- chat-page loadNativeModelIntent now passes intent.id to clearModelIntent so a second drag-drop during an in-flight first auto-load is not wiped from the chip area when the first resolves.
- Bump useNativePathLeasesSupported's MAX_READINESS_POLLS from 60 to 720 so first-run installs that compile llama.cpp from source or download large CUDA wheels (well past 5 minutes) don't permanently disable the native picker.
* native_path_leases: serialize first-decode against scrub context
_decode_secret used a separate _SECRET_INIT_LOCK from the env scrub's _NATIVE_PATH_ENV_LOCK, so the very first decode (before the cache is populated) could race a concurrent native_path_secret_removed_for_child_start and read os.environ during the env-empty window, raising "Native path grants require the managed desktop backend." Subsequent calls hit the cache and were already safe.
Acquire _NATIVE_PATH_ENV_LOCK around the env read inside _SECRET_INIT_LOCK and fall back to _SCRUB_SAVED_SECRET when the scrub has temporarily popped the env var. Lock ordering (init then env) is consistent with no other caller, so no deadlock.
* Studio: surface native model load errors and harden native path label cache
- Native model load and validate now bubble up the actual exception (with
paths redacted) and apply the same friendly-error rewrite the non-native
path uses, so users see "CUDA OOM", "trust_remote_code required", etc.
instead of a generic "Failed to load native model: <label>".
- run_without_native_path_secret now also nulls _SCRUB_SAVED_SECRET so a
forked grandchild that imports native_path_leases cannot recover the
secret via the scrub-aware fallback in _decode_secret.
- _NATIVE_PATH_LABELS now has its own 10000-entry cap independent of the
100-entry redaction list, so display_label_for_native_path no longer
falls back to returning the raw canonical path after 101 native paths
in one session. Redaction list keeps the 100-entry cap for log-scan
performance.
- _validate_payload now also rejects null bytes in display_label, which
is echoed back in HTTP responses and log lines.
* Studio: harden native path lease validation and chained native rollback
- child_env_without_native_path_secret now copies os.environ under
_NATIVE_PATH_ENV_LOCK so a concurrent scrub-context env pop cannot
raise RuntimeError: dictionary changed size during iteration in a
background hardware scan or other env reader.
- _validate_payload and grant construction route every signed numeric
field (version, issued_at_ms, expires_at_ms, size_bytes, modified_ms)
through new _required_int / _optional_int helpers that wrap raw int()
ValueError into NativePathLeaseError. The single upstream catcher
produces 400 instead of 500 for malformed signed payloads.
- verify_native_path_lease now runs _validate_current_stat before
_consume_nonce, so a transient stat error on the canonical path no
longer permanently burns the nonce. Concurrent verifies still
serialize through _consume_nonce, so single-use is preserved.
- Chained native model rollback now restores activeNativePathToken in
the chat runtime store after a successful rollback loadModel. Without
this, a second consecutive failed switch could not re-roll-back
because the store token had been overwritten by the failed attempt.
- validate_model now applies the same not_supported_hints friendly
rewrite to native model errors that load_model already does, so a
native .gguf that fails validation with an upstream "is not supported"
message gets the same actionable wording as the non-native branch.
* Studio: harden native path log redaction, status disclosure, and chip lifecycle
- structlog processor chain now runs format_exc_info before
filter_sensitive_data so traceback strings are produced (and then
redacted) rather than passed through as untouched (type, value, tb)
tuples that the JSON or console renderer formats after the redaction
filter has already finished.
- native_path_secret_removed_for_child_start clears _CACHED_LEASE_SECRET
in addition to popping the env var, so a fork during the scrub window
cannot inherit the cached bytes via the parent's heap. Parent verify
calls during the window keep working through the existing scrub-aware
fallback in _decode_secret.
- load_model's except ValueError handler now redacts native paths and
uses the native model log label when native_grant_backed is true.
Previously a ValueError raised after lease verification (e.g. from
ModelConfig.from_identifier or downstream GGUF parsing) returned the
raw exception string in the HTTP response body.
- llama_cpp_backend now records the native display label at GGUF load
time, and /api/inference/status prefers it over the redaction store.
After a Python backend restart the redaction store is empty; the
attribute keeps the friendly label, and an absolute model_identifier
with no other label source falls back to the basename so the canonical
path no longer appears in active_model.
- reveal_path_token uses native "reveal and select" commands on macOS
(open -R) and Windows (explorer /select,) so the file is highlighted
in the file manager. Linux keeps the existing parent-directory open.
- Native model rollback that fails because the previous token cannot be
consumed now throws a rollback-specific Error, and the outer empty
catch was replaced with one that re-throws the rollback error. The
rollback-specific message now reaches the user instead of being
overwritten by the original load error message.
- NativeModelChip tracks the Rust token's expiresAtMs on a single
setTimeout, disables the Load button at expiry, and relabels it
"Select again" with an explanatory tooltip so users do not click into
a guaranteed-failure path after the 15-minute TTL elapses.
* Studio: tighten native artifact policy, mmproj sibling check, and intake UX
- is_open_safe_artifact no longer grants Open for directories. Reveal
already handles directory navigation, so the change closes the
attack surface where a macOS .app artifact could be launched via
open_path_token + open::that_detached.
- Display labels are sanitized in classify_existing_path. Control
characters in filenames (newlines, tabs, NUL et al.) are replaced
with spaces and the label is trimmed and capped, so a file named
with embedded newlines cannot inject forged log lines or scramble
the UI status panel.
- validate_entry_path skips the size_bytes/modified_ms equality check
when the operation is Reveal or Open. Cloud-sync agents (Dropbox,
iCloud Drive, OneDrive) routinely rewrite extended-attribute
metadata which bumps mtime, and the user expects Reveal/Open to
remain available for files in synced folders.
- llama_cpp_backend gains a _native_grant_backed flag at GGUF load
success. /api/inference/status only applies the absolute-path
basename fallback when that flag is true, so a non-native absolute
local GGUF still reports its canonical model_identifier and unload
by identifier keeps working.
- Native vision GGUFs now run through _validate_native_mmproj_companion
before llama-server starts: the companion mmproj must be a regular
file, not a symlink, and must live in the same resolved directory as
the granted GGUF. This stops a hostile sibling or symlinked mmproj
from being loaded under a single-file lease.
- Chained native rollback restructured: the rollback loadModel + state
+ refresh runs inside its own try/catch that swallows so the outer
throw error surfaces the ORIGINAL load failure. The native-token
consume-failure case still throws the rollback-specific message
early, before the inner block runs, so its actionable guidance is
preserved.
- Loading-model state and the duplicate-load guard in the chat runtime
hook now compare both the model id and the native path token. Two
drops or picks with the same basename in different folders no longer
silently dedup; the second token is honored.
- chat-page loadNativeModelIntent awaits selectModel before clearing
the pending intent. If selectModel returns early via dedup or
throws, the chip and its token stay so the user can retry instead
of losing the selection.
- NativeModelChip's Reveal button is disabled when the lease has
expired (Rust would reject it anyway), and the Load button label
reads "Expired" instead of "Select again" so the disabled element
no longer promises an action it cannot perform.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* feat: add checkpoint resume for stopped training runs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix:add resume checkpoint helpers
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: use checkpoint parent as resume output dir
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: save optimizer and scheduler state on stop-and-save
Use Trainer._save_checkpoint instead of save_state so resume restores
optimizer momentum and LR-schedule position via the checkpoint-NNN/
subdir written by HF's official path.
* fix: clean up resume training history and startup progress
* fix: preserve resume output dirs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: tighten resume run lookup
* fix: remove stale output-dir lookup
* fix: preserve startup download progress
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
* qwen3.6 unsloth studio support
* Add qwen3.6 causal-conv1d detection
* Update model_mappings.py
moved qwen3.6-27B to thinking train on completion template
* [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 unsloth studio desktop app
* Fix review findings
- studio/src-tauri/tauri.conf.json: retarget updater to staging repo
(danielhanchen/unsloth-staging-2); switch to unslothai/unsloth on upstream merge.
- studio/src-tauri/linux/postremove.sh: drop the interactive read loop and the
/home/* iteration. Package maintainer scripts must stay non-interactive and
must not touch other users' data.
- studio/frontend/src/app/auth-guards.ts: honor tauriAutoAuth() boolean. Failed
auto-auth now redirects to /login; requireGuest/requirePasswordChangeFlow
only redirect to /chat when auth succeeds. The new early-return on failed
auth is intentional so the login / change-password flows remain reachable
when desktop auth is not yet established.
- studio/frontend/src/config/env.ts: keep fetched=false on health failure so
later calls retry instead of caching the client-side platform guess.
- studio/src-tauri/src/install.rs: pick the available system package manager
(apt-get, dnf, zypper, pacman); AppImage bundles run on non-Debian distros.
- studio/frontend/src/lib/open-link.ts + markdown-text/sources callers: return
boolean from openLink so callers only preventDefault on handled URLs; relative
hrefs now navigate natively.
- studio/frontend/src/features/settings/tabs/about-tab.tsx: fetch(apiUrl(...))
so the version request targets the backend port in desktop mode. The bare
/api/health predates the Tauri webview (blame: the earlier onboarding commit,
which ran with same-origin frontend/backend); in desktop mode the webview
origin is tauri://localhost so the bare path fails.
- install.ps1: gate the install_python_stack.py hotfix on a sentinel comment
instead of a content regex; append the sentinel after applying so reruns
are unambiguous.
- unsloth_cli/commands/studio.py _write_auth_secret: use the atomic mkstemp +
os.replace path on Windows too; chmod calls are wrapped in try/except OSError.
- studio/src-tauri/src/preflight.rs probe_existing_backends: fan out the health
probes concurrently; desktop-auth status still runs sequentially per candidate.
reqwest::Client is internally Arc-wrapped so the in-loop .clone() is a
refcount bump, not a deep clone; annotated inline.
- studio/src-tauri/src/preflight.rs run_cli_probe: wait() after kill() to reap
the child, matching probe_cli_capability.
- studio/src-tauri/src/process.rs + main.rs: add stop_backend_detached and use
it from the tray quit handler so the 5s graceful-wait does not block the
Tauri main loop. RunEvent::Exit keeps the synchronous safety-net call.
- studio/backend/main.py: drop the permissive localhost CORS regex in
api-only mode; the explicit allow_origins list is sufficient.
- .github/workflows/release-desktop.yml: drop max-parallel: 1 so platform
builds run in parallel, and lift releaseBody to an env var so the three
tauri-action invocations share one source of truth.
* Fix review findings (loop 2)
- studio/backend/auth/storage.py update_password: clear_desktop_secret()
alongside clear_bootstrap_password() so rotating the admin password
also revokes any previously provisioned .desktop_secret. Without this,
an old local desktop credential keeps minting fresh admin tokens via
/api/auth/desktop-login after a password rotation.
- studio/src-tauri/src/desktop_auth.rs provision_desktop_auth: wrap
cmd.output().await in tokio::time::timeout(30s). DESKTOP_AUTH_LOCK is
held across the whole desktop_auth flow, and previously a hanging
`unsloth studio provision-desktop-auth` subprocess would pin the lock
indefinitely and freeze every subsequent desktop_auth call.
* Add review tests
* Consolidate review tests
Merge review-added tests into the existing studio/backend/tests/test_desktop_auth.py
(the PR's authoritative desktop-auth test file). Drops three scaffolding files under
tests/python/ in favor of five focused tests next to the tests they extend:
- test_update_password_clears_desktop_secret (runtime)
- test_update_password_on_unknown_user_leaves_desktop_secret_intact (runtime)
- test_cli_provisioning_delegates_to_storage_create_desktop_secret (source-level)
- test_cli_connect_auth_db_reads_storage_db_path (source-level)
- test_desktop_auth_provision_has_bounded_timeout (Rust source-level)
* Revert auth-guards.ts Tauri branches to unconditional form
The review loop on PR 5144 introduced a regression: the isTauri branch of
requireAuth redirected to /login when tauriAutoAuth() returned false, and
requireGuest / requirePasswordChangeFlow silently fell through on the same
condition. The Tauri desktop app authenticates via a local auto-generated
secret; it must never surface /login or /change-password to the user. A
failed auto-auth should let the startup layer retry, not expose a password
form.
Restore the three Tauri branches to the author's original unconditional
form (requireAuth: return; requireGuest / requirePasswordChangeFlow: throw
redirect({to: '/chat'})). Keep the rest of the review fixes -- the
apiUrl() fetch wrapping, authRedirect helper, and fetchAuthStatus refactor
are all legitimate improvements and are preserved.
* Revert release-desktop.yml to author's version
The review loop's workflow-file tweaks (drop max-parallel: 1, lift releaseBody
to an env var) are cosmetic. OAuth tokens cannot push workflow-file changes,
and fine-grained PATs cannot honor maintainerCanModify on a third-party fork.
Reverting the workflow file to wasimysaid's version lets the push go through
without needing a classic PAT with both repo and workflow scopes.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Co-authored-by: Daniel Han <unslothai@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* [Studio] Install flash attn at setup time for linux
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* cleanup changes
Signed-off-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Test cases
* wheel_utils: narrow url_exists exceptions and log at debug level
---------
Signed-off-by: Datta Nimmaturi <venkatadattasainimmaturi@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
* Add ROCm detection to install.sh and expand shell tests
Add AMD ROCm GPU detection to get_torch_index_url() in install.sh.
When nvidia-smi is not found, probe for ROCm via amd-smi, /opt/rocm
version file, hipconfig, dpkg-query, and rpm.
Includes validation guard for malformed _rocm_tag, Debian epoch prefix
stripping, ROCm 7.2+ cap to rocm7.1 index, bitsandbytes AMD install,
and status messaging. Shell tests expanded to 23 cases.
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Add ROCm torch reinstall support to install_python_stack.py
Add _detect_rocm_version() and _ensure_rocm_torch() to detect when a
Linux host has ROCm but the venv received CPU-only torch, and reinstall
with the correct ROCm wheels. Covers ROCm 6.0 through 7.1 with a
30-second timeout on the torch GPU probe subprocess.
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Add ROCm support to llama.cpp prebuilt installer
Add has_rocm field to HostInfo, extend detect_host() to probe for ROCm
via hipcc/amd-smi/rocm-smi/ROCM_PATH, and route ROCm hosts to upstream
prebuilts (Linux ROCm 7.2 prebuilt with source fallback, Windows HIP
prebuilt with CPU fallback). Add linux-rocm and windows-hip install
kinds to runtime_patterns_for_choice().
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Add IS_ROCM hardware flag and fix AMD error message
Add IS_ROCM flag to hardware.py detect_hardware() (set when
torch.version.hip is present, DeviceType stays CUDA). Export IS_ROCM
from __init__.py. Add "rocm" key to get_package_versions().
Replace "We do not support AMD" error in tokenizer_utils.py with a
helpful message pointing to ROCm installation docs.
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Add comprehensive ROCm support test suite (68 tests)
Add tests/studio/install/test_rocm_support.py covering all ROCm code
paths across install_llama_prebuilt.py, install_python_stack.py,
hardware.py, tokenizer_utils.py, and install.sh. All tests use mocks
and run without AMD hardware.
Covers: asset selection (11), runtime patterns (5), HostInfo (4),
ROCm version detection (9), torch reinstall (9), index mapping (8),
hardware flag (8), tokenizer message (2), install.sh structure (10),
and live regression (1).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden ROCm support: probe error handling, version cap, validation
Address review findings from 8 independent reviewers:
- Wrap _ensure_rocm_torch() torch probe in try/except for
TimeoutExpired and OSError so a hung or broken torch import does not
crash the installer (8/8 reviewers flagged this)
- Add torch>=2.4,<2.11.0 version cap to the ROCm reinstall path to
prevent installing unsupported torch 2.11.0 from the rocm7.1 index
- Use with-statement for file reads in _detect_rocm_version() to avoid
resource leaks
- Handle ROCM_PATH="" correctly (use `or "/opt/rocm"` instead of
default parameter to avoid relative path resolution)
- Strengthen shell validation guard from rocm[0-9] to rocm[1-9] to
reject rocm0.x tags that would produce nonexistent PyTorch index URLs
- Switch shell version cap from blocklist to allowlist (rocm6.*|rocm7.0*
|rocm7.1* pass through, everything else caps to rocm7.1) so future
ROCm 10+ does not fall through to a nonexistent index
- Add sorted() to _ROCM_TORCH_INDEX lookup for defensive ordering
- Fix test_probe_timeout_handled: replace zero-assertion test with
proper assertions verifying reinstall proceeds after timeout
* Clean up rocm_paths list construction in detect_host()
Filter None from the ROCM_PATH env var lookup at list construction time
instead of relying on the inline `if p` guard in the any() call.
* Require actual AMD GPU presence before selecting ROCm paths
All 8 reviewers across 2 cycles independently flagged that ROCm
detection used toolkit/filesystem hints (hipcc, /opt/rocm, rocm-core)
as a proxy for GPU presence, which would misroute CPU-only or NVIDIA
hosts that happen to have ROCm tools installed.
Now all 3 detection points (install.sh, install_python_stack.py,
install_llama_prebuilt.py) probe for an actual AMD GPU before
entering the ROCm path:
- install.sh: check rocminfo for gfx* GPU names, or amd-smi list
for device rows, before version detection
- install_python_stack.py: new _has_rocm_gpu() function probes
rocminfo and amd-smi list before _ensure_rocm_torch() proceeds
- install_llama_prebuilt.py: detect_host() probes rocminfo/amd-smi
list instead of just checking tool existence or directory paths
Also:
- Shell test mock amd-smi now handles "list" subcommand
- Python tests updated to mock _has_rocm_gpu where needed
- Added test_no_gpu_with_rocm_tools_skips to verify the new guard
- Test index lookups now use sorted() to match production code
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden hipconfig version parsing and torch probe compatibility
- Add parts[1].isdigit() check in hipconfig version parsing to handle
versions like "6.3-HIP" where the minor component has non-numeric
suffix (strip "-" prefix before int() conversion)
- Use getattr() in torch probe subprocess to safely handle old or
custom torch builds that may lack torch.version.hip/cuda attributes
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Strengthen AMD GPU detection and add NVIDIA precedence guard
- Change amd-smi list detection from any-non-empty-output to requiring
"gpu" marker in output, matching the shell-side NR>1 check. Prevents
false positives from header-only amd-smi list output.
- Add nvidia-smi check at the top of _ensure_rocm_torch() so mixed
AMD+NVIDIA hosts preserve NVIDIA precedence (matching install.sh and
install_llama_prebuilt.py behavior).
- Apply the same amd-smi marker fix to install_llama_prebuilt.py
detect_host() for consistency.
* Add Windows-specific ROCm/HIP detection in detect_host()
The previous detect_host() ROCm check used rocminfo and amd-smi list
which are Linux-only tools. On Windows, has_rocm would always be False,
making the Windows HIP prebuilt path at line 1794 unreachable.
Now detect_host() uses platform-specific detection:
- Linux: rocminfo (check for gfx GPU names) or amd-smi list
- Windows: hipinfo.exe, amd-smi, or amdhip64.dll on PATH
This allows Windows AMD users to get the HIP prebuilt binary instead
of silently falling through to the CPU prebuilt.
* Add AMD ROCm gaps: Mamba/SSM source builds, GPU monitoring, Windows messaging, RDNA expansion
- worker.py: Add HIP detection to causal-conv1d/mamba-ssm probe, check
for hipcc before ROCm source builds, improve status messages and error
reporting, add timeout and uv support for the source build fallback
- amd.py: New AMD GPU monitoring module via amd-smi metric --json,
mirroring nvidia.py structure (utilization, temperature, power, VRAM)
- hardware.py: Branch to amd.py when IS_ROCM is True for GPU utilization,
visible GPU queries, and physical GPU count
- install_python_stack.py: Detect AMD GPUs on Windows and warn that
ROCm-enabled PyTorch must be installed manually
- kernels/utils.py: Expand is_rdna() to cover RDNA2 (gfx1030-1032),
RDNA3 (gfx1102-1103), RDNA3.5 (gfx1150-1152) alongside existing entries
- tests: Add 32 new tests covering all changes (95/95 pass)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden ROCm detection, fix VRAM heuristic, and expand RDNA2 coverage
- Windows ROCm detection: validate actual GPU presence via hipinfo/amd-smi
output markers instead of just checking tool existence on PATH
- _ensure_rocm_torch: validate nvidia-smi actually reports a GPU before
giving NVIDIA precedence (fixes AMD-only hosts with stale NVIDIA tools)
- amd.py _parse_numeric: handle dict-shaped metric objects from newer
amd-smi versions ({"value": 10, "unit": "W"}) and strip MiB/GiB units
- amd.py VRAM heuristic: raise threshold from 100k to 10M to correctly
handle MI300X (192 GB = 196608 MB) and other high-VRAM GPUs
- amd.py visible GPU: use AMD-reported GPU IDs instead of enumerate index
so non-dense sets like CUDA_VISIBLE_DEVICES=1,3 report correctly
- install.sh: add ROCm <6.0 minimum version guard (no PyTorch wheels
exist for older versions); fix rocm7.1* glob to not match rocm7.10+
- is_rdna: add gfx1033-1036 for RDNA2 mobile GPUs (RX 6600M etc.)
- worker.py: increase ROCm source build timeout from 600s to 1800s;
fix success log message for ROCm source builds
- Tests: update mocks for _has_usable_nvidia_gpu, add RDNA2 target asserts
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add HIP_VISIBLE_DEVICES support, unit-aware VRAM parsing, Windows GPU validation
- hardware.py: check HIP_VISIBLE_DEVICES and ROCR_VISIBLE_DEVICES on ROCm
before falling back to CUDA_VISIBLE_DEVICES, so multi-GPU AMD setups with
HIP-specific env vars report the correct visible device set
- amd.py: add _parse_memory_mb() that reads "unit" from dict-shaped amd-smi
JSON (e.g. {"value": 192, "unit": "GiB"}) and converts to MB correctly;
fixes MI300X VRAM misreported as 0.19 GB instead of 192 GB
- install_python_stack.py: Windows AMD warning now validates actual GPU
presence via hipinfo/amd-smi output markers before printing
- install_llama_prebuilt.py: restore amdhip64.dll fallback for Windows HIP
detection after tool-based checks, so Windows HIP installs without CLI
tools on PATH are still detected
- hardware.py: fix IS_ROCM comment to accurately describe its role
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix HIP_VISIBLE_DEVICES empty-string handling in GPU visibility spec
Use explicit None checks instead of Python `or` operator when reading
HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES, so that an empty string
("") is correctly honored as "no visible GPUs" rather than silently
falling through to CUDA_VISIBLE_DEVICES on mixed ROCm+CUDA systems.
* Fix IS_ROCM test assertion for multi-line formatting
* Cap torchvision/torchaudio versions, remove amdhip64.dll fallback, fix visible GPU count
- Cap torchvision<0.26.0 and torchaudio<2.11.0 alongside torch<2.11.0 in
both install.sh and install_python_stack.py to prevent resolver from
selecting incompatible companion packages from ROCm wheel index
- Remove amdhip64.dll fallback in Windows ROCm detection (DLL presence
without hipinfo/amd-smi is not proof of GPU existence)
- Fix get_visible_gpu_count() to use _get_parent_visible_gpu_spec() which
respects HIP_VISIBLE_DEVICES/ROCR_VISIBLE_DEVICES on ROCm hosts
* Attribute is_rdna() RDNA2/3/3.5/4 expansion to PR #4428
The is_rdna() expansion to cover RDNA2 (gfx1030-1036), RDNA3
(gfx1100-1103), RDNA3.5 (gfx1150-1152), and RDNA4 (gfx1200-1201)
architectures is based on the original work from PR #4428.
Co-authored-by: GoldenGrapeGentleman <yueyuan@amd.com>
Co-authored-by: billishyahao <bill.he@amd.com>
* Support AMD Radeon for studio (#4770)
Co-authored-by: Iswarya Alex <iswarya.alex@amd.com>
* Remove ROCm test files from main PR
Move test_rocm_support.py and shell test additions to a separate PR
to keep the main ROCm support PR focused on implementation changes.
* Fix installer and hardware detection issues for PR #4720
- Fix empty _tri_arg passed to uv pip install in Radeon path (causes
"Empty field is not allowed for PEP508" error)
- Fix Radeon fallback: use ROCm index instead of CPU-only when
repo.radeon.com is unreachable (TORCH_INDEX_URL already has ROCm)
- Use $TORCH_CONSTRAINT in fallback paths instead of hardcoded strings
- Fix _pick_radeon_wheel: relax suffix to match manylinux_2_28_x86_64
wheels (AMD Radeon repo does not use bare linux_x86_64 platform tag)
- Fix IS_ROCM export: use __getattr__ so callers always see the live
value after detect_hardware() runs
- Fix apply_gpu_ids: set HIP_VISIBLE_DEVICES and ROCR_VISIBLE_DEVICES
on ROCm so _get_parent_visible_gpu_spec picks up narrowed GPU set
- Fix _parse_memory_mb: distinguish GB (1000 MB) from GiB (1024 MiB)
- Add amd-smi version as a fallback in _detect_rocm_version
- Fix trailing whitespace and missing newline at EOF in install.sh
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix GPU detection false positives and add missing health groups
- Fix _has_rocm_gpu() false positive: require "GPU: <number>" data rows
from amd-smi list, not just header containing "gpu"
- Apply same fix in detect_host() in install_llama_prebuilt.py
- Add runtime_payload_health_groups for linux-rocm and windows-hip so
partial/corrupt ROCm/HIP prebuilt installs are properly detected
- Add bitsandbytes install to Radeon fallback paths (was only in the
success path, skipped when repo.radeon.com was unreachable)
- Keep DEVICE/CHAT_ONLY as direct imports in __init__.py (matching main)
and only use __getattr__ for IS_ROCM
* Fix _ensure_rocm_torch and Windows AMD warning false positives
- _ensure_rocm_torch: only skip when HIP is already present, not for
CUDA builds (which are unusable on AMD-only hosts). Fixes the case
where a venv has a stale CUDA wheel and the repair step is skipped.
- Windows AMD warning: use GPU data row check (same as Linux fix) to
avoid false positives from amd-smi list header-only output.
* Fix amd-smi GPU detection for GPU[N] output format
Older amd-smi versions output "GPU[0] : Card series: ..." instead of
"GPU: 0". The regex now matches both "GPU: <digit>" and "GPU[<digit>"
formats to detect actual GPU data rows.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden AMD GPU detection against false positives
- install.sh: replace weak amd-smi list check (awk 'NR>1 && NF') with
strict pattern matching GPU data rows (/^GPU[[:space:]]*[:\[]/)
- All files: reject rocminfo gfx000 (CPU HSA agent) by requiring
gfx[1-9] instead of gfx[0-9] in the rocminfo GPU probe
- Fixes false positives on hosts with ROCm tools but no AMD GPU
* Remove duplicate comment from pre-commit merge
* Refactor: deduplicate AMD detection, consolidate bitsandbytes, clean up imports
- Extract _has_amd_rocm_gpu() shell function to avoid duplicating the
rocminfo/amd-smi GPU detection logic in get_torch_index_url and
the Radeon auto-detect block
- Consolidate bitsandbytes install into a single case block after torch
install (was duplicated 4 times across Radeon success/fallback paths)
- Move math and re imports to top of amd.py (were inline in functions)
- Add _smi_query() helper in hardware.py to centralize IS_ROCM backend
selection for get_gpu_utilization and get_visible_gpu_utilization
Addresses Gemini code review suggestions.
* Fix VRAM parsing for string values and GB/GiB consistency
- Extract unit from string-valued VRAM fields (e.g. "192 GiB") so
_parse_memory_mb correctly applies the unit multiplier instead of
treating the value as bare MB
- Treat GB and GiB identically (both as binary x1024) since GPU tools
including amd-smi use binary units even when labeling them "GB"
- Fixes incorrect VRAM reporting on MI300-class cards (was showing
~0.19 GB instead of 192 GB for string-valued outputs)
* Add --no-cache to uv for ROCm HIP source builds
Avoid stale cache artifacts from partial HIP source builds when
uv is used for causal-conv1d/mamba-ssm compilation on ROCm.
The pip path already uses --no-cache-dir; this adds the uv equivalent
(--no-cache) only when is_hip is True.
* Fix critical: initialize _amd_gpu_radeon before case block
_amd_gpu_radeon was only set inside the */rocm*) case arm, so on
NVIDIA/CPU/macOS paths where TORCH_INDEX_URL does not contain "rocm",
the variable was unbound. With set -u (nounset) enabled, this crashes
the installer for every non-AMD user.
Move initialization to before the case block so it is always defined.
* Fix Windows AMD: route has_rocm hosts to HIP prebuilt path
resolve_release_asset_choice was selecting windows-cpu for all Windows
x86_64 hosts including those with has_rocm=True. Windows AMD users
should fall through to resolve_upstream_asset_choice which tries the
HIP prebuilt first. Add "not host.has_rocm" guard to the published
windows-cpu selection.
* Harden ROCm detection, Radeon wheel fallback, and HIP visibility
Addresses review findings from parallel reviewers on PR #4720:
- install.sh: add _has_usable_nvidia_gpu() helper requiring nvidia-smi -L
to actually list a GPU before treating the host as NVIDIA. Fixes the
stale-nvidia-smi-on-PATH regression where AMD-only hosts fell into the
CUDA branch.
- install.sh: fix hipconfig awk blocks to propagate a non-zero exit code
when the output is not a recognisable version string, so the ||-chain
continues to dpkg-query / rpm instead of terminating early.
- install.sh: fail-closed on Radeon wheel fallback. When torch,
torchvision or torchaudio is missing from the Radeon repo for the
active Python tag, fall back to the standard ROCm index instead of
silently mixing Radeon wheels with PyPI defaults. Quote all wheel
arguments individually so wheel filenames cannot be word-split or
glob-expanded.
- install_llama_prebuilt.py: detect_host() now requires nvidia-smi -L to
list a GPU before setting has_physical_nvidia. Routes AMD ROCm hosts
with a broken leftover nvidia-smi to the ROCm path instead of
misclassifying them as NVIDIA.
- install_llama_prebuilt.py: scan upstream assets for any rocm-<version>
prebuilt instead of hard-coding rocm-7.2, so ROCm 6.x / 7.0 / 7.1 / 7.3+
users pick up a matching upstream prebuilt when one exists.
- install_llama_prebuilt.py: validate_server() adds --n-gpu-layers 1 for
linux-rocm and windows-hip hosts, so new HIP prebuilts are preflighted
on the GPU path instead of passing validation on CPU only.
- install_llama_prebuilt.py: restore the published windows-cpu fallback
for AMD Windows hosts without a HIP prebuilt so hash-approved bundles
are still preferred over the raw upstream CPU asset.
- install_python_stack.py: drop the /opt/rocm / hipcc gate in
_ensure_rocm_torch() and rely on _has_rocm_gpu(). Runtime-only ROCm
installs (package-managed minimal installs, Radeon software) that ship
amd-smi / rocminfo without hipcc can now repair a CPU-only venv via
"unsloth studio update". Adds an explicit IS_WINDOWS / IS_MACOS guard.
- studio/backend/utils/hardware/amd.py: honour HIP_VISIBLE_DEVICES /
ROCR_VISIBLE_DEVICES / CUDA_VISIBLE_DEVICES in
get_primary_gpu_utilization(). A process restricted to GPU 2 now
reports metrics for GPU 2 instead of physical GPU 0. Tighten the plain
bytes unit detection to an explicit allowlist.
- studio/backend/utils/hardware/hardware.py: route
get_backend_visible_gpu_info()'s backend_cuda_visible_devices field
through a helper that reads HIP_VISIBLE_DEVICES on ROCm. Drop the
unconditional "(rocm=False)" suffix in apply_gpu_ids() logs.
* Fix round 2 regressions: ROCm validate_server and Windows HIP routing
Follow-up to 810b833b addressing review findings on the first round of
hardening commits:
- install_llama_prebuilt.py validate_server: gate --n-gpu-layers on the
resolved install_kind instead of host.has_rocm. AMD Windows hosts
without a HIP prebuilt fall back to windows-cpu and must not be
validated with GPU layers; thread install_kind through from the
caller.
- install_llama_prebuilt.py resolve_release_asset_choice: reinstate the
"not has_rocm" guard on the published windows-cpu bundle so AMD
Windows hosts reach resolve_upstream_asset_choice() where the new
HIP prebuilt path lives. Prefer a published windows-hip bundle first
when one exists, fall through to upstream HIP + upstream CPU
otherwise.
- install_llama_prebuilt.py detect_host: also set has_physical_nvidia
when the secondary --query-gpu block confirms a working NVIDIA GPU,
so older nvidia-smi versions without -L support do not silently skip
the Linux diagnostics that key off has_physical_nvidia.
- install_llama_prebuilt.py: drop redundant "import re as _re" /
"import re as _re_rocm" local aliases in favour of the existing
top-level "import re".
- install_python_stack.py _ensure_rocm_torch: run the AMD
bitsandbytes install unconditionally after the HIP-torch probe so
"unsloth studio update" on venvs that already have ROCm torch still
gains the AMD bitsandbytes build.
- install.sh: add a non-x86_64 early-exit to get_torch_index_url() so
aarch64 / arm64 Linux hosts do not hit the ROCm wheel index
(PyTorch only publishes ROCm wheels for linux_x86_64).
- install.sh: add bitsandbytes install to the migrated-environment
branch so upgrades pick it up for ROCm hosts instead of only the
fresh-install path.
- install.sh: in the Radeon wheel path, pass version constraints +
--no-index --find-links to uv instead of explicit wheel URLs so a
version-compatible torch / torchvision / torchaudio triple is
resolved, rather than picking the highest-version wheel for each
package independently.
- studio/backend/utils/hardware/amd.py _first_visible_amd_gpu_id: fall
through to lower-priority visibility env vars when the first entry
is malformed (leading comma, all-whitespace first token) instead of
silently returning GPU 0.
* Fix round 3 findings: x86_64 guard, ROCm version clip, Radeon deps
Address issues surfaced by the round 3 reviewers on top of 8636fa63:
- install_python_stack.py _ensure_rocm_torch: add the same `x86_64`
guard that install.sh already has. Linux aarch64 / arm64 ROCm hosts
must skip the repair path entirely; PyTorch only publishes ROCm
wheels for linux_x86_64, and without this guard
`unsloth studio update` aborts with a missing-wheel error on non
x86_64 hosts.
- install_llama_prebuilt.py resolve_upstream_asset_choice: add a
best-effort _detect_host_rocm_version() helper (reading
/opt/rocm/.info/version, amd-smi version, hipconfig --version) and
filter rocm_candidates to entries whose major.minor is <= host
version. Falls back to the newest candidate only when no compatible
one exists, so a ROCm 6.4 host downloads rocm-6.4 instead of being
handed the numerically newest rocm-7.2 bundle (which fails preflight
and forces a source build).
- install.sh: remove the round 2 --no-index switch from the Radeon
wheel branch. --no-index forced uv to ignore PyPI entirely, which
broke transitive dependency resolution (filelock, sympy, networkx,
jinja2, fsspec, setuptools, typing-extensions, ...) on a fresh venv.
Restore the round 1 explicit wheel URL invocation but add a
torch / torchvision / torchaudio version-pair sanity check so a
mismatched trio (e.g. torch 2.9.1 + torchvision 0.23.0 + torchaudio
2.9.0) falls back to the standard ROCm index instead of installing a
broken combination.
- install_python_stack.py _ensure_rocm_torch: restructure the
"tag is None" path so it no longer short-circuits the bitsandbytes
install. On a ROCm runtime older than anything in
_ROCM_TORCH_INDEX, print the "no wheel" warning but still run the
AMD bitsandbytes install.
- studio/backend/core/training/worker.py: restore the pre-PR
"no timeout" behaviour for non-HIP causal-conv1d / mamba-ssm source
builds. The round 2 "timeout = 1800 if is_hip else 300" cap aborts
slow non-HIP builds (Linux aarch64, unsupported torch/CUDA combos)
after 5 minutes; omit timeout for the non-HIP branch so the cap
only applies to ROCm source builds.
* Fix round 4 findings: apply_gpu_ids env inheritance, Radeon X.Y, bitsandbytes gate
Address remaining issues surfaced by the round 4 reviewers:
- studio/backend/utils/hardware/hardware.py apply_gpu_ids: mirror the
selection into HIP_VISIBLE_DEVICES / ROCR_VISIBLE_DEVICES whenever
the caller already had a ROCm visibility env var set, not only when
IS_ROCM has already been set by detect_hardware(). Training and
inference workers call apply_gpu_ids() before detect_hardware()
runs, so the old guard would leave a forked ROCm worker with a
stale HIP_VISIBLE_DEVICES mask that no longer matched the
narrowed CUDA_VISIBLE_DEVICES selection.
- install.sh get_radeon_wheel_url: accept X.Y ROCm versions in
addition to X.Y.Z. The `/opt/rocm/.info/version` file and some
hipconfig versions report only two components, and the Radeon
repository publishes both rocm-rel-X.Y.Z/ and rocm-rel-X.Y/
directories, so treating X.Y as invalid caused Radeon hosts to fall
back to the generic ROCm index even when a matching AMD wheel set
existed.
- install_python_stack.py _ensure_rocm_torch: only install the AMD
bitsandbytes build when the venv actually has a ROCm-compatible
torch (either already present or just installed by this function).
Previously the bitsandbytes install ran unconditionally, which
could leave an AMD bitsandbytes layered on top of a CPU/CUDA torch
on hosts where the ROCm runtime is older than any entry in
_ROCM_TORCH_INDEX. Also add --force-reinstall so an existing
CPU/CUDA bitsandbytes is replaced by the AMD build during upgrades.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix gemini findings: amd-smi metric envelope validation and dict-wrapped GPU id
Two medium-severity defensive fixes from the gemini-code-assist review on
the AMD monitoring backend:
1. _extract_gpu_metrics may return a dict where every value is None when
amd-smi succeeds (zero exit) but the JSON envelope contains no usable
fields (error response, unsupported card). The new _has_real_metrics
helper lets get_primary_gpu_utilization surface available:False and
lets get_visible_gpu_utilization skip ghost device rows so the UI
does not render placeholder cards with empty numbers.
2. Newer amd-smi versions wrap scalar fields as {"value": 0, "unit":
"none"}, including the per-GPU id. The previous int(raw_id) call
silently fell back to the enumeration index in that case, losing the
real GPU id. Routing raw_id through the existing _parse_numeric
helper handles bare ints, floats, strings, and the dict shape
uniformly, with a debug log on parse failure.
* Fix gemini round 2 findings: explicit length guard on ROCm version file parser
Both _detect_rocm_version (install_python_stack.py) and
_detect_host_rocm_version (install_llama_prebuilt.py) read /opt/rocm/.info/version
or $ROCM_PATH/lib/rocm_version, split on "." and unconditionally accessed
parts[1]. The surrounding broad `except Exception: pass` already swallowed
the resulting IndexError, so a one-component file like "6\n" did fall
through to the next detection source -- but the control flow relied on
exception handling instead of an explicit check.
Add `if len(parts) >= 2:` guards in both helpers so the loop falls through
on its own without raising. Behaviour is unchanged for the common multi-
component case; the previously-silent IndexError path becomes an explicit
no-op.
* Fix gemini round 3: include has_rocm in validate_server fallback path
When validate_server is called without an explicit install_kind (older
call sites that have not been updated), the fallback was only enabling
--n-gpu-layers for NVIDIA and macOS arm64 hosts. AMD ROCm Linux hosts
fell through to the CPU validation path even though the prebuilt being
exercised was a HIP binary.
Add host.has_rocm to the fallback expression so the GPU offload flag is
applied consistently with the install_kind=='linux-rocm' / 'windows-hip'
branches above.
* Fix gemini round 4: remove risky bytes-vs-MB heuristic in _parse_memory_mb
The previous heuristic divided any bare number above 10_000_000 by
1024*1024 on the assumption that large unit-less values were bytes.
This misclassified small VRAM allocations: 5 MB of used VRAM reported
as 5_242_880 bytes without a unit would be taken at face value and
render as 5_242_880 MB (~5 TB) in the monitoring UI.
Modern amd-smi always provides explicit units (MiB/GiB dict form),
and legacy amd-smi returns bare numbers in MB -- the heuristic never
had a real workload to handle. Drop it and default to MB for bare
numeric input, keeping the existing unit-aware branches for dict /
string inputs unchanged.
The unrelated gemini suggestion to "default minor to 0" in the
amd-smi version awk parser was intentionally NOT applied: rocm7.0
and rocm7.1 ship different wheel sets, so silently substituting 0
for a missing minor could install the wrong wheels. The existing
reject-and-fall-through behaviour is safer.
* Fix gemini round 5: POSIX compliance and leading-comma visibility parsing
Three medium findings from gemini-code-assist addressed in this commit:
1. _pick_radeon_wheel used grep -o and sort -V, both GNU extensions
that are not in POSIX and break on BSD/BusyBox coreutils. install.sh
has a #!/bin/sh shebang so the whole pipeline was rewritten as a
single awk script that extracts all href="..." hits on each line,
filters to wheels matching the package prefix and python tag, and
picks the newest version via zero-padded lexical comparison. No
external sort or grep is needed.
2. _first_visible_amd_gpu_id in the AMD monitoring backend treated a
leading comma (e.g. HIP_VISIBLE_DEVICES=",1") as "fall through to
the next env var", which is surprising given the clear intent to
narrow to device 1. Filter empty tokens after the split and return
the first real one. An all-commas value ("," / ",,,") still falls
through because no real tokens exist; the empty-string and "-1"
explicit-zero cases are unchanged.
The unrelated amd-smi version awk parser suggestion was not applied
(see round 4 commit message for rationale: defaulting a missing minor
to 0 could silently install the wrong ROCm wheel set).
* Fix 20-reviewer.py findings: base drift, Radeon %2B, dpkg/rpm fallback, bnb, backend label
Consolidated fix batch from a 20-parallel reviewer.py run on the current
head. Each fix is drawn from a high-consensus finding and addresses a
real bug or feature gap, not a stylistic preference.
1. install.sh: bump `unsloth>=2026.4.2` -> `unsloth>=2026.4.4` at five
call sites so this branch no longer regresses main's version floor
(main bumped to 2026.4.4 in #4876). Without this, merging 4720 would
silently downgrade the minimum version pin for fresh installs.
2. install.sh: URL-decode Radeon wheel names before extracting the
torch / torchvision / torchaudio version strings. Real wheel URLs
from repo.radeon.com are percent-encoded ("torch-2.10.0%2Brocm7.2.0...")
so the previous `[+-]` terminator in the sed regex never matched,
`_torch_ver` stayed empty, `_radeon_versions_match` stayed false,
and every Radeon consumer install silently fell back to the generic
ROCm index. Now decode %2B -> + first, then extract, then validate.
3. install.sh: the two AMD bitsandbytes install lines were running
`uv pip install "bitsandbytes>=0.49.1"` without `--force-reinstall`,
so upgrades where the venv already has a CPU/CUDA bitsandbytes
satisfying the constraint would keep the stale non-AMD wheel. Add
`--force-reinstall --no-cache-dir` to both call sites, matching the
pattern already used in install_python_stack.py::_ensure_rocm_torch.
4. install_python_stack.py and install_llama_prebuilt.py: add
`dpkg-query -W rocm-core` and `rpm -q rocm-core` fallbacks to the
Python-side ROCm version detectors so they match the chain in
install.sh::get_torch_index_url. Package-managed ROCm installs
(Debian/Ubuntu/RHEL/Fedora distro packages) can expose GPUs via
rocminfo/amd-smi but still lack /opt/rocm/.info/version, hipconfig,
or amd-smi `version` output -- without these fallbacks, `unsloth
studio update` on such hosts returned None and skipped the ROCm
torch repair. Also strip the dpkg epoch prefix ("1:6.3.0-1") before
parsing so epoch-annotated packages parse correctly.
5. hardware.py: add a `_backend_label(device)` helper that returns
"rocm" when IS_ROCM is set and the device is DeviceType.CUDA, and
use it for every `"backend": ...` emission in JSON responses served
to the Studio frontend. Internally we still represent ROCm hosts as
DeviceType.CUDA (ROCm torch reuses the whole torch.cuda.* API
surface), but the user-facing API now correctly reports "rocm" on
AMD boxes instead of labeling them as "cuda".
All 250 simulation scenarios pass (was 233 before this batch: added 17
new regression tests covering the version pin, %2B decoding, bnb
force-reinstall flags, dpkg/rpm fallback presence, and the
_backend_label helper's four-way truth table).
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* Fix gemini round 6 + URL audit: amd.py defensive checks, rocm6.5+ clip to 6.4
Two rounds of fixes in one commit, plus a full URL audit of every PyPI /
download.pytorch.org / repo.radeon.com reference the PR introduces.
amd.py (4 medium gemini findings on commit b3627bc2):
1. _extract_gpu_metrics used `and vram_total_mb` as part of the vram_util
gate. The follow-up `vram_total_mb > 0` already handles the division
guard, but the truthiness check was redundant and slightly surprising
for a 0.0 valid value. Replace with explicit `is not None and > 0`
for both vram_util and power_util.
2. get_physical_gpu_count called `data.get("gpu", ...)` without guarding
for non-dict envelopes. A scalar / string JSON response from amd-smi
would raise AttributeError. Add an isinstance(data, dict) check and
return None for unexpected shapes.
3. get_visible_gpu_utilization had the same .get() exposure on the outer
envelope. Rewrite the gpu_list extraction as an explicit
list/dict/else cascade so a malformed scalar envelope produces
gpu_list=[data] and continues without raising.
4. The same function's per-entry loop also called gpu_data.get() on
whatever was inside gpu_list. If a scalar ever leaks into the list
(directly or via the previous fix's fallback), _extract_gpu_metrics
would raise on the first .get() inside the helper. Skip non-dict
entries in the loop before extracting metrics.
install.sh (URL audit finding, previously flagged by 20-reviewer as #13):
5. get_torch_index_url used `rocm6.*` in the rocm tag case statement,
which matched rocm6.5 and rocm6.6 and emitted
download.pytorch.org/whl/rocm6.5 -- which returns HTTP 403 because
PyTorch only publishes rocm 5.7, 6.0-6.4, 7.0-7.2. Enumerate the
supported 6.x minors explicitly and add a rocm6.* fallback branch
that clips to rocm6.4 (the last supported 6.x wheel set).
URL audit results (all URLs PR 4720 references):
- 14/14 download.pytorch.org/whl/{cpu,cu118,cu124,cu126,cu128,cu130,
rocm6.0..6.4,rocm7.0..7.2} return HTTP 200.
- 9/9 repo.radeon.com/rocm/manylinux/rocm-rel-{5.7,6.0,6.1,6.2,6.3,
6.4,7.0,7.1,7.2}/ return HTTP 200.
- X.Y.Z patch directories exist for 7.0.2, 7.1.1, 7.2.1 but NOT for
6.3.0, 6.4.0, 6.2.1 -- install.sh already handles this via the X.Y.Z
-> X.Y fallback sed in the Radeon wheel install block.
- Docs links (rocm.docs.amd.com, docs.unsloth.ai AMD guide) and the
llama.cpp GitHub releases API endpoint all return 200.
Test suite: 255 -> 258. New regression coverage:
- U17: get_physical_gpu_count tolerates scalar amd-smi envelope
- U18: get_visible_gpu_utilization tolerates scalar envelope
- U19a-c: vram_util / power_util return None on zero total, but
vram_total_gb still echoes 0.0 (not None)
- A_rocm{6.5,6.6,6.9}_clips_to_rocm64: install.sh clips unsupported
6.x minors to rocm6.4 instead of producing a 403 index URL
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* Fix reviewer.py round 2: tokenizer AMD multi-GPU, --no-torch bnb, main.py backend label
Three high-confidence findings from a second 20-parallel reviewer.py run
on commit 7effb3ae. Triaged 15 total findings and applied the three that
were confirmed as real bugs; the rest were either false positives (e.g.
"migrated AMD venv not repaired" -- _ensure_rocm_torch runs downstream
via setup.sh regardless), design decisions (e.g. visibility mask env
vars not consulted in installer detection), or edge cases the existing
fallback logic already handles.
1. unsloth/tokenizer_utils.py [6/20]: the multi-GPU guard's shell probe
runs `nvidia-smi --query-gpu=memory.used`, catches the failure, then
only raises if `torch.cuda.is_available()` is False. On ROCm torch,
torch.cuda.is_available() returns True (ROCm reuses the torch.cuda.*
API), so the guard becomes dead code on AMD hosts and multi-GPU AMD
setups slip through even though unsloth does not support them yet.
Add a torch.cuda.device_count() > 1 fallback inside the except so
AMD multi-visible-device setups are flagged consistently with the
original CUDA memory check.
2. install.sh [1/20]: the fresh-install bitsandbytes block for AMD ROCm
ran unconditionally when TORCH_INDEX_URL matched `*/rocm*`, even when
SKIP_TORCH=true (from --no-torch or Intel Mac auto-detect). A user
running `install.sh --no-torch` on an AMD host would still pull in
bitsandbytes despite explicitly asking for GGUF-only mode. Wrap the
case block in an outer `[ "$SKIP_TORCH" = false ]` guard.
3. studio/backend/main.py [3/20]: the /api/system endpoint returned
`"device_backend": get_device().value`, which is "cuda" on ROCm
hosts (because ROCm torch piggybacks on torch.cuda). Other endpoints
(hardware.py) already use the _backend_label helper which swaps
"cuda" -> "rocm" when IS_ROCM. Route /api/system through the same
helper so the Studio UI reports the backend consistently across all
endpoints.
4. studio/backend/tests/test_utils.py: update test_backend_matches_device
to call _backend_label(get_device()) instead of raw get_device().value
so the test matches the new contract and still passes on CUDA hosts.
Tests: 258 -> 261. New regression coverage:
- X08 main.py /api/system uses _backend_label
- X09 tokenizer multi-GPU guard has device_count() fallback
- X10 fresh-install bnb case block gated on SKIP_TORCH=false
* fix: prevent bitsandbytes from overwriting ROCm torch with CUDA wheels
During install, bitsandbytes was installed without --no-deps, causing
uv to resolve torch from PyPI (CUDA build) and silently overwrite the
ROCm wheels that were just installed in the previous step.
This happened in three places:
- install.sh: bitsandbytes install in both migrated and fresh paths
- install_python_stack.py: bitsandbytes install inside _ensure_rocm_torch()
Additionally, multiple install steps in install_python_stack.py (extras,
overrides, studio deps) can pull in CUDA torch via transitive
dependencies. A final _ensure_rocm_torch() call at the end of the
install sequence ensures ROCm torch is always in place at runtime.
All changes are gated behind ROCm-specific conditions and do not affect
NVIDIA, CPU-only, macOS, or Windows install paths.
Tested on AMD Instinct MI300X VF with ROCm 7.2.0 -- confirms
torch==2.10.0+rocm7.1 with HIP 7.1.25424 after install.
* fix: ROCm inference fallback -- skip Unsloth patching and bnb 4-bit on HIP
On AMD ROCm (HIP), two issues prevent the normal Unsloth inference path:
1. Unsloth's global monkey-patching of transformers model classes
(LlamaRotaryEmbedding, attention modules) triggers
_assert_async_cuda_kernel crashes on HIP during generation.
Training uses different code paths and works fine.
2. bitsandbytes 4-bit matmul kernels also trigger HIP assertion
failures on MI300X (CDNA3 / gfx942), even without Unsloth patching.
This commit adds a ROCm-specific inference fallback that:
- Skips importing Unsloth at module level (prevents global patching)
- Loads models in 16-bit with plain transformers + PEFT instead
- Resolves pre-quantized model names (e.g. "xxx-bnb-4bit" -> "xxx")
since pre-quantized HF repos still trigger bnb codepaths
- Guards get_chat_template calls (unavailable without Unsloth import)
- Fixes max_seq_length=0 being passed to from_pretrained (GGUF
semantics don't apply to transformers path)
The NVIDIA path is completely unchanged -- Unsloth import and
for_inference() optimization remain active. GGUF inference (via
llama-server/HIP) is unaffected since it never imports Python model
classes. AMD GPUs typically have large VRAM (e.g. 192GB on MI300X)
so 16-bit loading is practical for inference.
Tested on AMD Instinct MI300X VF (ROCm 7.2, HIP 7.1.25424):
- Simple generation: PASS
- Compare mode (base vs finetuned): PASS
- GGUF inference + tool calling: PASS (unaffected by this change)
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* fix: guard audio/vision inference on ROCm, remove unused import
- Add clear RuntimeError for audio/vision model inference on ROCm
(these paths use Unsloth's FastModel/FastVisionModel which would
crash on HIP; GGUF inference is the supported path on AMD)
- Remove unused `import os as _os` from the ROCm changes
* fix: amd-smi parsing for newer output format (gpu_data wrapper, mem_usage, temperature)
amd-smi on recent ROCm versions (7.x) wraps metric output in a
{"gpu_data": [...]} envelope instead of returning a raw list. This
caused get_primary_gpu_utilization() and get_visible_gpu_utilization()
to fail silently (returning available=False) because the GPU data
dict was never unwrapped.
Additionally:
- VRAM data moved from "vram" to "mem_usage" with "total_vram" /
"used_vram" keys. Added fallback key lookup.
- Temperature "edge" sensor returns "N/A" on MI300X VF; the previous
dict.get() chain returned the "N/A" string instead of falling
through to "hotspot". Changed to a loop that checks each key until
a parseable value is found.
Tested on AMD Instinct MI300X VF (ROCm 7.2, amd-smi 24.x):
- GPU utilization: 0% (idle), up to 100% during training
- Temperature: 40-44C (from hotspot sensor)
- VRAM: 0.28/191.69 GB (idle)
- Power: 158-211W draw
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* Bug fix detecting radeon (#4940)
* Bug fix detecting radeon
* Expanding GPU target for gfx1100*
* Generalize gfx family-prefix filter to cover gfx10/gfx12 as well
rocminfo on ROCm 6.1+ emits LLVM generic-family ISA lines alongside the
specific GPU (e.g. gfx11-generic next to gfx1100). The outer grep captures
the bare family prefix from the generic line, and passing that to
-DGPU_TARGETS breaks the HIP build because clang only accepts specific
gfxNNN ids.
The previous filter only special-cased gfx11. Generalize it so any bare
2-digit family prefix (gfx10, gfx11, gfx12, ...) is dropped whenever a
specific sibling target is present in the same list. No real AMD GPU has
a 2-digit gfx id, so the filter can only ever drop family prefixes and
never a real target.
Covers the existing gfx11 cases unchanged, and extends the same fix to
gfx10-1-generic / gfx10-3-generic (RDNA1/2) and gfx12-generic (RDNA4),
which would otherwise hit the same build failure on newer rocminfo.
---------
Co-authored-by: Iswarya Alex <iswarya.alex@amd.com>
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
---------
Co-authored-by: Eda Z <eda.zhou@amd.com>
Co-authored-by: GoldenGrapeGentleman <yueyuan@amd.com>
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Co-authored-by: Iswarya Alex <iswarya.alex@amd.com>
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
* split venv_t5 into venv_t5_530 and venv_t5_550 for tiered transformers 5.x support
* fix bfloat16 crash on T4 for FORCE_FLOAT32 models and disable trust_remote_code auto-enable for native t5 models
* revert FORCE_FLOAT32 dtype change
* restrict trust_remote_code auto-enable to Nemotron models only
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* use config.json model_type for tier detection, add unsloth/nvidia namespace guard
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* Revert "[pre-commit.ci] auto fixes from pre-commit.com hooks"
This reverts commit fb43d468e2.
* Revert "use config.json model_type for tier detection, add unsloth/nvidia namespace guard"
This reverts commit fc49ae2453.
* add unsloth/nvidia namespace guard to Nemotron trust_remote_code auto-enable
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* reorder tier checks: all substring matches before config.json fetches
* extract shared activate_transformers_for_subprocess into transformers_version.py
* narrow Nemotron trust_remote_code to nemotron_h/nemotron-3-nano, add to export worker
* clean venv_t5 dirs before re-install in setup.sh, clarify version alias comment
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* run venv_t5 migration outside deps fast-path gate in both setup scripts
---------
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* Add vision detection cache to is_vision_model() to avoid redundant subprocess spawns
is_vision_model() is called 4-5 times per training run for the same model
with zero caching. For transformers 5.x models, each call spawns a full
subprocess (~6s each). This adds a module-level _vision_detection_cache dict
following the same pattern as the existing _audio_detection_cache used by
detect_audio_type(). The function is refactored into a thin cache wrapper
around _is_vision_model_uncached(), saving ~12s per training run.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Include hf_token in vision cache key for gated model correctness
Cache key is now (model_name, hf_token) instead of just model_name.
This prevents stale False results when an unauthenticated probe for a
gated model is followed by an authenticated call.
* Remove test file from main PR - will be submitted separately
* Fix vision cache: normalize model names and skip caching transient failures
- Normalize model names in cache key using resolve_cached_repo_id_case()
to avoid duplicate entries for different casings of the same HF repo
(aligns with case normalization from #4822)
- Return None instead of False on transient failures (network errors,
subprocess timeouts, HF API issues) so the cache layer can distinguish
"definitely not a vision model" from "failed to check"
- Only cache definitive True/False results; transient failures are retried
on the next call instead of being permanently locked in as False
* Refine failure handling: cache deterministic failures, guard normalization
- Subprocess non-zero exit, JSON errors, and general exceptions return
False (deterministic, cached) instead of None (retryable). Only
subprocess.TimeoutExpired returns None since timeouts are transient.
- Wrap cache key normalization in try/except so resolve_cached_repo_id_case
or normalize_path failures fall back to raw model_name instead of
crashing callers.
* Harden vision detection cache: fix transient failure handling, thread safety, token security
- All subprocess failure paths now return None (transient) instead of False,
preventing permanent misclassification of VLMs after temporary HF/auth/network errors
- Use SHA256 fingerprint for hf_token in cache key instead of raw bearer token
- Add threading.Lock with double-checked locking to prevent thundering herd
of concurrent subprocess spawns for the same uncached model
- Distinguish permanent failures (RepositoryNotFoundError, GatedRepoError,
ValueError) from transient ones in _is_vision_model_uncached
- Pass resolved/normalized model name to detection (not just cache key)
- Log normalization fallback at debug level instead of silent swallow
- Thread hf_token through callers in routes/models.py and trainer.py
that previously omitted it
* Refine lock strategy and token fingerprint
- Move detection computation outside the lock to avoid serializing
long-running subprocess spawns (60s timeout) and HF API calls across
all concurrent model checks. Lock is now only held for cache writes.
- Use full SHA256 digest for token fingerprint instead of truncated
16-char prefix to eliminate collision risk.
* Fix huggingface_hub import fallback and use atomic cache read
- Add fallback import path for RepositoryNotFoundError/GatedRepoError
from huggingface_hub.utils (older hub versions) when .errors is
not available
- Use sentinel-based dict.get() for single atomic cache read instead
of two-step in/[] pattern (future-proof for no-GIL runtimes)
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Gemma 4 is a native transformers 5.5 model and does not need
trust_remote_code=True. The auto-enable logic (added for NemotronH)
was catching all transformers 5.x models, including Gemma 4.
When trust_remote_code=True, unsloth_compile_transformers() returns
early without running the compiler. This disables the fused cross
entropy patch, causing logged training loss to be inflated by the
gradient_accumulation_steps factor.
Exclude models matching "gemma-4" or "gemma4" from the auto-enable
so the compiler runs and applies fused cross entropy correctly.
* fix(studio): change default weight_decay from 0.01 to 0.001
The default weight decay across Studio was 0.01 but should be 0.001.
Updated the default in all backend fallbacks, the Pydantic model, the
frontend config, and every YAML preset/model-default config.
* fix(studio): auto-set learning rate based on training method
Default LR should be 2e-4 for LoRA/QLoRA and 2e-5 for full fine-tuning.
Frontend: track whether the user has manually edited the LR field via a
_learningRateManuallySet flag (same pattern as trainOnCompletions).
When switching training method and the user has not touched the LR,
auto-set it to the appropriate default. Reset the flag on model load.
Backend: change trainer.py start_training default from 5e-5 to 2e-4,
update default.yaml fallback from 5e-5 to 2e-4, and fix
full_finetune.yaml from 0.0002 (2e-4) to 2e-5.
* refactor(studio): centralize weight_decay and learning rate defaults
Create studio/backend/core/training/constants.py as the single source of
truth for DEFAULT_WEIGHT_DECAY (0.001), DEFAULT_LEARNING_RATE (2e-4),
DEFAULT_LEARNING_RATE_FULL (2e-5), and DEFAULT_LEARNING_RATE_STR ("2e-4").
All backend modules (trainer.py, training.py, worker.py, models/training.py)
now import from constants.py instead of hardcoding values.
On the frontend, add LR_DEFAULT_LORA and LR_DEFAULT_FULL to
config/training.ts and use them in the store instead of magic numbers.
A comment cross-references the backend constants file.
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* Fix model-specific LR override, persist migration, and flag resets
- Preserve model-specific learning rates from YAML configs when the
async autoSelectTrainingMethod callback fires (fixes Qwen2.5-1.5B
getting 2e-4 instead of its configured 1e-5, etc.)
- Bump zustand persist version to 9 with migration so existing users
with weightDecay=0.01 get updated to 0.001
- Clear _learningRateManuallySet in reset() and applyConfigPatch()
for consistency with trainOnCompletions flag behavior
- Add DEFAULT_LEARNING_RATE_FULL_STR to constants.py
* Refine applyConfigPatch to only clear LR flag when patch includes LR
Only reset _learningRateManuallySet when the applied config patch
actually provides a learningRate value. This prevents unrelated config
patches from silently disarming the manual-edit guard, which would
cause a subsequent setTrainingMethod call to overwrite the user's
custom LR.
* Preserve model-specific LR when switching between qlora and lora
Only auto-switch the learning rate when the training category changes
(adapter <-> full fine-tuning). Switching between qlora and lora keeps
the current LR since both methods share the same learning rate range.
This preserves curated per-model defaults (e.g. 1e-5 for
Qwen2.5-1.5B-Instruct) when the user toggles between adapter methods.
* Remove constants.py, use YAML configs as the source of truth
The YAML config files (model-specific + default.yaml) are the intended
config layer for training defaults. The Python backend fallbacks now use
inline values that match the YAML configs, rather than importing from a
separate constants module. This keeps the config architecture simple:
YAML files are the single source of truth, and the inline Python
fallbacks are just safety nets that mirror them.
* fix(studio): preserve model-specific LR when switching training method
Stash YAML-provided learning rate and use it to restore the correct
value when switching between adapter and full fine-tune modes.
- qlora <-> lora no longer overwrites the model's LR
- full -> adapter restores the YAML LR instead of a hardcoded constant
- selecting a model while on full fine-tune uses LR_DEFAULT_FULL
instead of applying the YAML adapter LR
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* [WIP] balanced device map for studio
* gpus as a request parameter
* API for multi GPU stuff
* return multi gpu util in new API
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* Use balanced_low0 instead of balanced
* Use balanced_low0 instead of balanced
* Fix device_map typo, UUID parsing crash, set() filter bug, and broken tests
- balanced_low0 -> balanced_low_0 (transformers/accelerate rejects the old string)
- get_parent_visible_gpu_ids() now handles UUID/MIG CUDA_VISIBLE_DEVICES
gracefully instead of crashing on int() parse
- _get_backend_visible_gpu_info() set() or None bug: empty set is falsy so
CUDA_VISIBLE_DEVICES=-1 would disable filtering and report all GPUs
- test_gpu_selection.py: add missing get_visible_gpu_utilization import and
add required job_id arg to start_training() calls
* Smart GPU determinism using estimates
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* disallow gpu selection for gguf for now
* cleanup
* Slightly larger baseline
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Treat empty list as auto
* Verbose logging/debug
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Cleanup and revert unnecessary deletions
* Cleanup excessive logs and guard against disk/cpu offload
* auth for visibility API. cleanup redundant imports. Adjust QLoRA estimate
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* support for non cuda gpus
* Fix multi-GPU auto-selection memory accounting
The multi_gpu_factor was applied uniformly to all GPUs including the
first one, which unfairly penalizes single-GPU capacity when
transitioning to multi-GPU. This created a discontinuity where a model
that barely fits 1 GPU would suddenly require 2 GPUs because the first
GPU's free memory was discounted by 20%.
Now the first GPU keeps its full free memory, and only additional GPUs
have an overhead factor (0.85) applied to account for inter-GPU
communication and sharding overhead. This gives more accurate
auto-selection and avoids unnecessary multi-GPU for models that
comfortably fit on one device.
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* Add sandbox tests for multi-GPU selection logic
24 tests covering model size estimation, memory requirements, automatic
GPU selection, device map generation, GPU ID validation, and multi-GPU
overhead accounting. All tests use mocks so they run without GPUs on
Linux, macOS, and Windows.
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* Fix reviewer findings: 4bit inference estimate, fallback, GGUF gpu_ids, retry
1. 4-bit inference now uses reduced memory estimate (model_size/3 + buffer)
instead of the FP16 1.3x multiplier. This prevents over-sharding
quantized models across unnecessary GPUs.
2. When model size estimation fails, auto_select_gpu_ids now falls back to
all visible GPUs instead of returning None (which could default to
single-GPU loading for an unknown-size model).
3. GGUF inference route now treats gpu_ids=[] as auto-selection (same as
None) instead of rejecting it as an unsupported explicit request.
4. Training retry path for "could not get source code" now preserves the
gpu_ids parameter so the retry lands on the same GPUs.
5. Updated sandbox tests to cover the new 4-bit inference estimate branch.
* Remove accidentally added unsloth-zoo submodule
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* Fix UUID/MIG visibility and update test expectations
1. nvidia.py: When CUDA_VISIBLE_DEVICES uses UUID/MIG tokens, the
visibility APIs now return "unresolved" with empty device lists instead
of exposing all physical GPUs. This prevents the UI from showing GPUs
that the backend process cannot actually use.
2. test_gpu_selection.py: Updated test expectations to match the new
multi-GPU overhead accounting (first GPU at full capacity, 0.85x for
additional GPUs) and 4-bit inference memory estimation formula.
All 60 tests now pass.
* Add CPU/disk offload guard to audio inference path
The audio model loading branch returned before the common
get_offloaded_device_map_entries() check, so audio models loaded with a
multi-GPU device_map that spilled layers to CPU/disk would be accepted
instead of rejected. Now audio loads also verify no modules are offloaded.
* Improve VRAM requirement estimates
* Replace balanced_low_0 with balanced
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* refine calculations for slightly easier nums
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* adjust estimates
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Use nums instead of obj to avoid seralisation error
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Harden nvidia-smi parsing and fix fallback GPU list
1. nvidia.py: Wrap int() casts for GPU index and memory in try/except
so MIG slices, N/A values, or unexpected nvidia-smi output skip the
unparseable row instead of aborting the entire GPU list.
2. nvidia.py: Handle GPU names containing commas by using the last
field as memory instead of a fixed positional index.
3. hardware.py: fallback_all now uses gpu_candidates (GPUs with verified
VRAM data) instead of raw devices list, which could include GPUs
with null VRAM that were excluded from the ranking.
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* cleanup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* consolidate raise_if_offload
* Improve MoE support. Guard against nvidia-smi failures
* Improve MoE support. Guard against nvidia-smi failures
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix shared-expert LoRA undercount, torch VRAM fallback, and apply_gpu_ids edge case
1. vram_estimation.py: compute_lora_params now includes shared experts
(n_shared_experts) alongside routed experts when computing MoE LoRA
adapter parameters. Previously only n_experts were counted, causing
the estimator to undercount adapter, optimizer, and gradient memory
for DeepSeek/GLM-style models with shared experts.
2. hardware.py: _torch_get_per_device_info now uses mem_get_info (which
reports system-wide VRAM usage) instead of memory_allocated (which
only reports this process's PyTorch allocations). This prevents
auto-selection from treating a GPU as mostly free when another
process is consuming VRAM. Falls back to memory_allocated when
mem_get_info is unavailable.
3. hardware.py: apply_gpu_ids([]) now returns early instead of setting
CUDA_VISIBLE_DEVICES="" which would disable CUDA entirely. Empty
list inherits the parent visibility, same as None.
4. hardware.py: Upgraded fallback_all GPU selection log from debug to
warning so operators are notified when the model likely will not fit
in available VRAM.
* Guard nvidia-smi subprocess calls against OSError and TimeoutExpired
get_visible_gpu_utilization and get_backend_visible_gpu_info now catch
OSError (nvidia-smi not found) and TimeoutExpired internally instead
of relying on callers to wrap every invocation. Returns the standard
available=False sentinel on failure so the torch-based fallback in
hardware.py can take over.
* Guard get_primary_gpu_utilization and reset GPU caches between tests
1. nvidia.py: get_primary_gpu_utilization now catches OSError and
TimeoutExpired internally, matching the pattern already used in
get_visible_gpu_utilization and get_backend_visible_gpu_info. All
three nvidia-smi callers are now self-contained.
2. test_gpu_selection.py: Added _GpuCacheResetMixin that resets the
module-level _physical_gpu_count and _visible_gpu_count caches in
tearDown. Applied to all test classes that exercise GPU selection,
device map, or visibility functions. This prevents stale cache
values from leaking between tests and causing flaky results on
machines with real GPUs.
* Fix nvidia-smi fallback regression and physical GPU count validation
1. hardware.py: get_gpu_utilization, get_visible_gpu_utilization, and
get_backend_visible_gpu_info now check result.get("available") before
returning the nvidia-smi result. When nvidia-smi is unavailable or
returns no data (e.g., containers without nvidia-smi, UUID/MIG masks),
the functions fall through to the torch-based fallback instead of
returning an empty result. This fixes a regression where the internal
exception handling in nvidia.py prevented the caller's except block
from triggering the fallback.
2. hardware.py: resolve_requested_gpu_ids now separates negative-ID
validation from physical upper-bound validation. The physical count
check is only enforced when it is plausibly a true physical count
(i.e., higher than the largest parent-visible ID), since
torch.cuda.device_count() under CUDA_VISIBLE_DEVICES returns the
visible count, not the physical total. The parent-visible-set check
remains authoritative in all cases. This prevents valid physical IDs
like [2, 3] from being rejected as "out of range" when nvidia-smi is
unavailable and CUDA_VISIBLE_DEVICES="2,3" makes torch report only
2 devices.
* Fix UUID/MIG torch fallback to enumerate devices by ordinal
When CUDA_VISIBLE_DEVICES uses UUID or MIG identifiers,
get_parent_visible_gpu_ids() returns [] because the tokens are
non-numeric. The torch fallback in get_visible_gpu_utilization() and
get_backend_visible_gpu_info() previously passed that empty list to
_torch_get_per_device_info(), getting nothing back.
Now both functions detect the empty-list case and fall back to
enumerating torch-visible ordinals (0..device_count-1) with
index_kind="relative". This means the UI and auto-selection still
see real device data in Kubernetes, MIG, and Slurm-style UUID
environments where nvidia-smi output cannot be mapped to physical
indices.
Updated test_uuid_parent_visibility to verify the new torch fallback
path returns available=True with relative ordinals.
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* Add type hint for gpu_ids parameter in InferenceOrchestrator.load_model
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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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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
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* 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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* 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
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* 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
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* 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
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* 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.
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* 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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* 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>
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* 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
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* 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().
---------
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* full finetuning studio
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* Update studio/backend/core/training/trainer.py
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---------
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Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
* Allow Windows setup to complete without NVIDIA GPU
setup.ps1 previously hard-exited if nvidia-smi was not found, blocking
setup entirely on CPU-only or non-NVIDIA machines. The backend already
supports CPU and MLX (Apple Silicon) in chat-only GGUF mode, and the
Linux/Mac setup.sh handles missing GPUs gracefully.
Changes:
- Convert the GPU check from a hard exit to a warning
- Guard CUDA toolkit installation behind $HasNvidiaSmi
- Install CPU-only PyTorch when no GPU is detected
- Build llama.cpp without CUDA flags when no GPU is present
- Update doc comment to reflect CPU support
* Cache frontend build across setup runs
Skip the frontend npm install + build if frontend/dist already exists.
Previously setup.ps1 nuked node_modules and package-lock.json on every
run, and both scripts always rebuilt even when dist/ was already present.
On a git clone editable install, the first setup run still builds the
frontend as before. Subsequent runs skip it, saving several minutes.
To force a rebuild, delete frontend/dist and re-run setup.
* Show pip progress for PyTorch download on Windows
The torch CUDA wheel is ~2.8 GB and the CPU wheel is ~300 MB. With
| Out-Null suppressing all output, the install appeared completely
frozen with no feedback. Remove | Out-Null for the torch install
lines so pip's download progress bar is visible. Add a size hint
so users know the download is expected to take a while.
Also moves the Triton success message inside the GPU branch so it
only prints when Triton was actually installed.
* Guard CUDA env re-sanitization behind GPU check in llama.cpp build
The CUDA_PATH re-sanitization block (lines 1020-1033) references
$CudaToolkitRoot which is only set when $HasNvidiaSmi is true and
the CUDA Toolkit section runs. On CPU-only machines, $CudaToolkitRoot
is null, causing Split-Path to throw:
Split-Path : Cannot bind argument to parameter 'Path' because it is null.
Wrap the entire block in `if ($HasNvidiaSmi -and $CudaToolkitRoot)`.
* Rebuild frontend when source files are newer than dist/
Instead of only checking if dist/ exists, compare source file timestamps
against the dist/ directory. If any file in frontend/src/ is newer than
dist/, trigger a rebuild. This handles the case where a developer pulls
new frontend changes and re-runs setup -- stale assets get rebuilt
automatically.
* Fix cmake not found on Windows after winget install
Two issues fixed:
1. After winget installs cmake, Refresh-Environment may not pick up the
new PATH entry (MSI PATH changes sometimes need a new shell). Added a
fallback that probes cmake's default install locations (Program Files,
LocalAppData) and adds the directory to PATH explicitly if found.
2. If cmake is still unavailable when the llama.cpp build starts (e.g.
winget failed silently or PATH was not updated), the build now skips
gracefully with a [SKIP] warning instead of crashing with
"cmake : The term 'cmake' is not recognized".
* Fix frontend rebuild detection and decouple oxc-validator install
Address review feedback:
- Check entire frontend/ directory for changes, not just src/.
The build also depends on package.json, vite.config.ts,
tailwind.config.ts, public/, and other config files. A change
to any of these now triggers a rebuild.
- Move oxc-validator npm install outside the frontend build gate
in setup.sh so it always runs on setup, matching setup.ps1
which already had it outside the gate.
* Show cmake errors on failure and retry CUDA VS integration with elevation
Two fixes for issue #4405 (Windows setup fails at cmake configure):
1. cmake configure: capture output and display it on failure instead of
piping to Out-Null. When the error mentions "No CUDA toolset found",
print a hint about the CUDA VS integration files.
2. CUDA VS integration copy: when the direct Copy-Item fails (needs
admin access to write to Program Files), retry with Start-Process
-Verb RunAs to prompt for elevation. This is the root cause of the
"No CUDA toolset found" cmake failure -- the .targets files that let
MSBuild compile .cu files are missing from the VS BuildCustomizations
directory.
* Address reviewer feedback: cmake PATH persistence, stale cache, torch error check
1. Persist cmake PATH to user registry so Refresh-Environment cannot
drop it later in the same setup run. Previously the process-only
PATH addition at phase 1 could vanish when Refresh-Environment
rebuilt PATH from registry during phase 2/3 installs.
2. Clean stale CMake cache before configure. If a previous run built
with CUDA and the user reruns without a GPU (or vice versa), the
cached GGML_CUDA value would persist. Now the build dir is removed
before configure.
3. Explicitly set -DGGML_CUDA=OFF for CPU-only builds instead of just
omitting CUDA flags. This prevents cmake from auto-detecting a
partial CUDA installation.
4. Fix CUDA cmake flag indentation -- was misaligned from the original
PR, now consistently indented inside the if/else block.
5. Fail hard if pip install torch returns a non-zero exit code instead
of silently continuing with a broken environment.
* Remove extra CUDA cmake flags to align Windows with Linux build
Drop GGML_CUDA_FA_ALL_QUANTS, GGML_CUDA_F16, GGML_CUDA_GRAPHS,
GGML_CUDA_FORCE_CUBLAS, and GGML_CUDA_PEER_MAX_BATCH_SIZE flags.
The Linux build in setup.sh only sets GGML_CUDA=ON and lets llama.cpp
use its defaults for everything else. Keep Windows consistent.
* Address reviewer round 2: GPU probe fallback, Triton check, stale binary rebuild
1. GPU detection: fallback to default nvidia-smi install locations
(Program Files\NVIDIA Corporation\NVSMI, System32) when nvidia-smi
is not on PATH. Prevents silent CPU-only provisioning on machines
that have a GPU but a broken PATH.
2. Triton: check $LASTEXITCODE after pip install and print [WARN]
on failure instead of unconditional [OK].
3. Stale llama-server: check CMakeCache.txt for GGML_CUDA setting
and rebuild if the existing binary does not match the current GPU
mode (e.g. CUDA binary on a now-CPU-only rerun, or vice versa).
* Fix frontend rebuild detection and npm dependency issues
Addresses reviewer feedback on the frontend caching logic:
1. setup.sh: Fix broken find command that caused exit under pipefail.
The piped `find | xargs find -newer` had paths after the expression
which GNU find rejects. Replaced with a simpler `find -maxdepth 1
-type f -newer dist/` that checks ALL top-level files (catches
index.html, bun.lock, etc. that the extension allowlist missed).
2. setup.sh: Guard oxc-validator npm install behind `command -v npm`
check. When the frontend build is skipped (dist/ is cached), Node
bootstrap is also skipped, so npm may not be available.
3. setup.ps1: Replace Get-ChildItem -Include with explicit path
probing for src/ and public/. PowerShell's -Include without a
trailing wildcard silently returns nothing, so src/public changes
were never detected. Also check ALL top-level files instead of
just .json/.ts/.js/.mjs extensions.
* Fix studio setup: venv isolation, centralized .venv_t5, uv targeting
- All platforms (including Colab) now create ~/.unsloth/studio/.venv
with --without-pip fallback for broken ensurepip environments
- Add --python sys.executable to uv pip install in install_python_stack.py
so uv targets the correct venv instead of system Python
- Centralize .venv_t5 bootstrap in transformers_version.py with proper
validation (checks required packages exist, not just non-empty dir)
- Replace ~150 lines of duplicated install code across 3 worker files
with calls to the shared _ensure_venv_t5_exists() helper
- Use uv-if-present with pip fallback; do not install uv at runtime
- Add site.addsitedir() shim in colab.py so notebook cells can import
studio packages from the venv without system-Python double-install
- Update .venv_t5 packages: huggingface_hub 1.3.0->1.7.1, add hf_xet
- Bump transformers pin 4.57.1->4.57.6 in requirements + constraints
- Add Fast-Install helper to setup.ps1 with uv+pip fallback
- Keep Colab-specific completion banner in setup.sh
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix nvidia-smi PATH persistence and cmake requirement for CPU-only
1. Store nvidia-smi as an absolute path ($NvidiaSmiExe) on first
detection. All later calls (Get-CudaComputeCapability,
Get-PytorchCudaTag, CUDA toolkit detection) use this absolute
path instead of relying on PATH. This survives Refresh-Environment
which rebuilds PATH from the registry and drops process-only
additions.
2. Make cmake fatal for CPU-only installs. CPU-only machines depend
entirely on llama-server for GGUF chat mode, so reporting "Setup
Complete!" without it is misleading. GPU machines can still skip
the llama-server build since they have other inference paths.
* Fix broken frontend freshness detection in setup scripts
- setup.sh: Replace broken `find | xargs find -newer` pipeline with
single `find ... -newer` call. The old pipeline produced "paths must
precede expression" errors (silently suppressed by 2>/dev/null),
causing top-level config changes to never trigger a rebuild.
- setup.sh: Add `command -v npm` guard to oxc-validator block so it
does not fail when Node was not installed (build-skip path).
- setup.ps1: Replace `Get-ChildItem -Include` (unreliable without
-Recurse on PS 5.1) with explicit directory paths for src/ and
public/ scanning.
- Both: Add *.html to tracked file patterns so index.html (Vite
entry point) changes trigger a rebuild.
- Both: Use -print -quit instead of piping to head -1 for efficiency.
* Fix bugs found during review of PRs #4404, #4400, #4399
- setup.sh: Add || true guard to find command that checks frontend/src
and frontend/public dirs, preventing script abort under set -euo
pipefail when either directory is missing
- colab.py: Use sys.path.insert(0, ...) instead of site.addsitedir()
so Studio venv packages take priority over system copies. Add warning
when venv is missing instead of silently failing.
- transformers_version.py: _venv_t5_is_valid() now checks installed
package versions via .dist-info metadata, not just directory presence.
Prevents false positives from stale or wrong-version packages.
- transformers_version.py: _install_to_venv_t5() now passes --upgrade
so pip replaces existing stale packages in the target directory.
- setup.ps1: CPU-only PyTorch install uses --index-url for cpu wheel
and all install commands use Fast-Install (uv with pip fallback).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix _venv_t5_is_valid dist-info loop exiting after first directory
Remove premature break that caused the loop over .dist-info directories
to exit after the first match even if it had no METADATA file. Now
continues iterating until a valid METADATA is found or all dirs are
exhausted.
* Capture error output on failure instead of discarding with Out-Null
setup.ps1: 6 locations changed from `| Out-Null` to `| Out-String` with
output shown on failure -- PyTorch GPU/CPU install, Triton install,
venv_t5 package loop, cmake llama-server and llama-quantize builds.
transformers_version.py: clean stale .venv_t5 directory before reinstall
when validation detects missing or version-mismatched packages.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix ModuleNotFoundError when CLI imports studio.backend.core
The backend uses bare "from utils.*" imports everywhere, relying on
backend/ being on sys.path. Workers and routes add it at startup, but
the CLI imports studio.backend.core as a package -- backend/ was never
added. Add sys.path setup at the top of core/__init__.py so lazy
imports resolve correctly regardless of entry point.
Fixes: unsloth inference unsloth/Qwen3-8B "who are you" crashing with
"No module named 'utils'"
* Fix frontend freshness check to detect all top-level file changes
The extension allowlist (*.json, *.ts, *.js, *.mjs, *.html) missed
files like bun.lock, so lockfile-only dependency changes could skip
the frontend rebuild. Check all top-level files instead.
* Add tiktoken to .venv_t5 for Qwen-family tokenizers
Qwen models use tiktoken-based tokenizers which fail when routed through
the transformers 5.x overlay without tiktoken installed. Add it to the
setup scripts (with deps for Windows) and runtime fallback list.
Integrates PR #4418.
* Fix tiktoken crash in _venv_t5_is_valid and stray brace in setup.ps1
_venv_t5_is_valid() crashed with ValueError on unpinned packages like
"tiktoken" (no ==version). Handle by splitting safely and skipping
version check for unpinned packages (existence check only).
Also remove stray closing brace in setup.ps1 tiktoken install block.
---------
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>
* studio: improve onboarding UX, tooltips, and training defaults
- Change splash text to "Train and run LLMs locally"
- Add "Chat Only" card with BubbleChatIcon to skip directly to chat
- Add Skip/Skip to Chat buttons in sidebar and footer
- Back button on step 1 returns to splash screen instead of being disabled
- Change "Watch video guide" to "Get started with our guide" with new URL
- Update intro text to mention all model types + chat
- Make all tooltips clickable (in addition to hover) via React context
- Strip surrounding quotes from pasted HF tokens
- Rename "Eval Split" to "Evaluation Split"
- Add SparklesIcon to "Auto Detect" format option
- Change step 4 heading to "Choose your training parameters"
- Default max_steps to 60
- Learning rate displayed in scientific notation with +/- stepper
- Context length options capped by model's max_position_embeddings (via AutoConfig)
- Fix "QLORA"/"LORA" to "QLoRA"/"LoRA" in summary step
- Backend: add max_position_embeddings to model config endpoint
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* compare for 2 diff models
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* resolving gemini comments
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: disable thinking for Qwen3.5 <9B and always for AI Assist
- Change Qwen3.5 thinking threshold from <=2B to <9B (0.8B, 2B, 4B
all disable thinking by default; 9B+ enables it)
- Always pass enable_thinking=False in AI Assist helper calls
(_run_with_helper and _generate_with_backend) regardless of chat
thinking settings
* studio: address PR review comments
- Extract _get_max_position_embeddings helper to DRY config extraction
- Fix "Skip to Chat" to navigate to /chat on step 1 (was /studio)
* fix: comment out debug print statements
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: skip Shiki highlighting for incomplete SVG code fences
While streaming SVG content, the syntax highlighter (Shiki) re-parses
the entire growing SVG on every token, blocking the main thread and
freezing the code area until the fence closes. Show a plain-text
preview for incomplete SVG fences instead, similar to how Mermaid
diagrams show a placeholder while streaming.
* studio: fix default top_k from 50/40 to 20 for chat inference
Per Qwen3.5 docs (unsloth.ai/docs/models/qwen3.5), top_k should be 20
for both thinking and non-thinking modes. The model-specific config in
inference_defaults.json already had top_k=20 for Qwen3.5, but the
generic fallback defaults were wrong:
- Frontend DEFAULT_INFERENCE_PARAMS.topK: 50 -> 20
- Backend generate_chat_completion top_k: 40 -> 20
- Backend generate_chat_completion_with_tools top_k: 40 -> 20
- Frontend title generation top_k: 40 -> 20
* studio: set universal inference defaults for unknown models
Default params for any model without specific config:
temperature=0.6, top_p=0.95, top_k=20, min_p=0.01,
presence_penalty=0.0, repetition_penalty=1.0
Models with entries in inference_defaults.json (Qwen3.5, Gemma-3,
Llama, etc.) override these with their recommended values.
Updated in: frontend DEFAULT_INFERENCE_PARAMS, backend Pydantic
request models, and backend generate_chat_completion defaults.
* studio: only trust_remote_code for unsloth/ models in AutoConfig
Only set trust_remote_code=True when the model name starts with
"unsloth/". All other models default to False for safety.
* studio: move Generating spinner above the composer
The "Generating" spinner was below the send message bar, causing
the bar to jump up and down. Move it above the composer in both
the regular thread view and the welcome/empty view.
* studio: adjust toast close button position away from edge
Move the X close button on toasts (like "Starting model...") from
top-1.5 to top-3 and add right-3, giving more breathing room from
the top-right corner.
* studio: make Think button smaller with tighter icon-text gap
Reduce gap from 1.5 to 0.5, padding from px-2.5/py-1 to px-2/py-0.5,
and icon from size-3.5 to size-3.
* studio: multiple onboarding and chat UX improvements
- Move Generating spinner above composer (fixes jumping send bar)
- Make Think button smaller with tighter icon-text gap
- Chat card now inside grid (same size as Audio/Embeddings cards)
- Rename "Chat Only" to "Chat"
- Chat card requires Continue to proceed (no auto-advance)
- Continue on Chat selection skips onboarding and goes to /chat
- Tooltip (i) click on Chat card doesn't trigger navigation
- Step 1 footer Back button goes back to splash (label is "Back")
- Splash "Skip Onboarding" renamed to "Skip to Chat", navigates to /chat
- Toast close button moved away from edge
* studio: align Skip to Chat button, add Skip to footer
- Sidebar "Skip to Chat" now uses primary (green) Button style with
arrow icon, full width, aligned like step items. Shows on all steps.
- Footer: added "Skip" outline button next to Continue that goes
directly to /studio with progress saved (markOnboardingDone)
* studio: change default max steps from 30 to 60 in toggle hook
The DEFAULT_MAX_STEPS in use-max-steps-epochs-toggle.ts was still 30,
used as fallback when toggling from epochs back to max steps.
* studio: extend context length options to 262K
CONTEXT_LENGTHS now includes 65536, 131072, 262144 in addition to
the existing 512-32768 range. The onboarding step filters these by
the model's max_position_embeddings (e.g. Nemotron-3-Nano-4B has
262144), showing powers of 2 up to the model's maximum.
* studio: auto-select LoRA vs QLoRA based on model size and GPU memory
After selecting a model in onboarding, detect the total model weight
file size from HF Hub (safetensors/bin files). Then estimate memory
needed: model_size_gb * 1.5 * context_scale, where context_scale is:
- <=8192 tokens: 1.0x
- >8192 tokens: 1.7x
- >=16384 tokens: 2.0x
- >=32768 tokens: 4.0x
If the estimate fits in free GPU VRAM, default to LoRA (16-bit).
Otherwise default to QLoRA (4-bit).
Backend changes:
- Add model_size_bytes to ModelDetails (models.py)
- Add _get_model_size_bytes() using HfApi.repo_info (routes/models.py)
- Add vram_free_gb to get_gpu_summary (hardware.py)
Frontend changes:
- Add autoSelectTrainingMethod() in training-config-store.ts
- Called after model defaults are loaded
- Add model_size_bytes to ModelConfigResponse type
- Add vramFreeGb to HardwareInfo hook
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: rename "Importing ML libraries..." to "Importing Unsloth..."
* studio: show model/dataset in training status, fix LoRA/QLoRA casing
- Training status now shows 'Training "model_name"' and 'Dataset = ...'
instead of generic "Starting training..."
- Fix Studio progress section to show QLoRA/LoRA instead of QLORA/LORA
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: rename 'Skip to Chat' to 'Skip Onboarding' on splash screen
* studio: add presence_penalty support for chat inference
Add presence_penalty as a parameter across the full stack:
- Backend: llama_cpp.py generate_chat_completion/with_tools, Pydantic
models (inference.py), routes/inference.py pass-through
- Frontend: InferenceParams type, DEFAULT_INFERENCE_PARAMS (0.0),
chat-adapter.ts payload, chat-settings-sheet.tsx slider (0-2),
model defaults loading from inference_defaults.json
- Set Qwen3.5 default presence_penalty to 1.5 per official docs
- Default for unknown models is 0.0 (off)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fix Chat card deselecting Text and aligning with other cards
* studio: fix presence_penalty not loading from inference defaults
The inference_config.py load_inference_config() was not including
presence_penalty in the returned config dict, so the Qwen3.5
default of 1.5 from inference_defaults.json never reached the
frontend. Added it to the config builder.
* studio: add delete button for cached models in model selector
Add trash icon on each downloaded model row (GGUF and safetensors) with
confirmation dialog. Backend DELETE /api/models/delete-cached endpoint
uses huggingface_hub scan_cache_dir + delete_revisions to cleanly remove
cached repos, refusing if the model is currently loaded.
* studio: restore inference defaults, reasoning, and tools on page refresh
On page refresh with a model already loaded, the frontend was not
re-applying model-specific inference defaults (presence_penalty,
temperature, etc.) or restoring reasoning/tools support flags.
Backend: Add inference config, supports_reasoning, supports_tools,
and context_length to InferenceStatusResponse.
Frontend: In the refresh callback, when an active model is detected,
apply mergeRecommendedInference and restore reasoning/tools flags
with proper Qwen3.5 size-based defaults.
* studio: fix delete dialog closing before async completes
Prevent AlertDialogAction's default close behavior with
e.preventDefault() so the dialog stays open during deletion.
Also block onOpenChange dismiss while deleting is in progress.
* fix: add Dict and Any imports to inference models
* studio: fix Qwen3.5 reasoning threshold in frontend load path
The frontend loadModel handler had the old threshold (<=2) for
disabling reasoning on small Qwen3.5 models. Changed to <9 to
match the backend. This was causing 4B to not properly disable
thinking by default when auto-loaded.
* studio: move GGUF delete to per-variant level
For GGUF repos, the trash icon now appears on each downloaded variant
row inside the quantization expander instead of on the repo-level row.
Backend accepts optional variant param to delete specific GGUF files
(blob + symlink) rather than the entire repo cache.
* studio: restore ggufContextLength on page refresh
The Max Tokens slider was capped at 32768 on page refresh because
ggufContextLength was not restored from the status response.
Now set it from statusRes.context_length on reconnect.
* fix: remove <think> from Qwen3.5 response template marker
The train-on-responses-only feature uses template markers to find
where the assistant response starts. The Qwen3.5 response marker
included '<think>\n' which is only present when thinking mode is
enabled. With thinking disabled (default for <9B), the marker
never matched, causing 100% of samples to be dropped.
Changed response marker from '<|im_start|>assistant\n<think>\n'
to '<|im_start|>assistant\n' which works regardless of thinking mode.
* studio: fix sloth ASCII art alignment in training overlay
* fix: correct sloth ASCII art alignment to match Unsloth banner
* studio: add Python and terminal tool calling to chat
Register python and terminal tools alongside web search. Python
executor validates imports (stdlib only) via unsloth_zoo
rl_environments, runs code in a subprocess sandbox with 5-min
timeout and cancel support. Terminal executor blocks dangerous
commands (rm, sudo, etc.) and runs in a temp directory.
Update llama_cpp tool loop to show tool-specific status messages
and pass cancel_event through to executors. Rename composer
toggle from "Search" to "Tools" and show TerminalIcon for
execution status pills.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fix Nemotron/transformers 5.x support, onboarding navigation, port binding
Backend:
- Dynamic transformers 5.x detection via tokenizer_config.json fetch
(checks for TokenizersBackend class, cached per-model)
- Bump transformers 5.x version from 5.2.0 to 5.3.0 across all workers,
setup scripts (setup.sh, setup.ps1)
- Auto-enable trust_remote_code for unsloth/* models needing transformers 5.x
(workaround for NemotronH config parsing bug in transformers)
- Auto-install mamba-ssm/causal-conv1d for SSM models (NemotronH, Falcon-H1)
with --no-build-isolation --no-deps to avoid torch version conflicts
- Add SO_REUSEADDR to port check in run.py (fixes Colab proxy stale connection
falsely reporting port as in-use)
Frontend:
- Fix "Skip to Chat" navigation: use window.location.href instead of React
Router navigate() to bypass useEffect redirect race
- Fix "Skip Onboarding" on splash: navigates to /studio (not /chat)
- Fix onboarding guard: only check isOnboardingDone() on initial mount
- Fix Chat card on step 1: add sr-only spacer for consistent alignment
- Fix Chat+Text both selected: clear RadioGroup value when Chat is selected
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: split tools toggle into Search and Code buttons
Replace the single "Tools" toggle with two independent toggles:
- "Search" (globe icon) enables web search only
- "Code" (terminal icon) enables Python and terminal execution
Add enabled_tools list field to the inference payload so the
backend only registers the tools the user has toggled on. Both
toggles appear in the main composer and the compare composer.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fix tool calling import validation and error logging
Replace unsloth_zoo-dependent import checker with a standalone
ast-based validator using sys.stdlib_module_names. This properly
blocks non-stdlib imports (numpy, requests, etc.) and returns a
clear error message to the model so it can rewrite using only
stdlib.
Add full traceback to tool streaming error logs for debugging.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: parse gpt-oss harmony channels for clean safetensors chat output
gpt-oss models emit multi-channel output via harmony protocol tokens
(<|channel|>analysis<|message|>... and <|channel|>final<|message|>...).
TextIteratorStreamer with skip_special_tokens=True strips the special
tokens but leaves channel names concatenated with content, producing
garbled output like "analysisWe need to...assistantfinalHello!".
Add HarmonyTextStreamer that decodes with skip_special_tokens=False,
parses harmony markup via regex, and emits <think>analysis</think>
for the analysis channel and plain text for the final channel --
reusing the existing frontend reasoning UI.
Also expose supports_reasoning=True for non-GGUF gpt-oss models in
the /status endpoint so the frontend enables the Think toggle.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: use unsloth_zoo for Python sandbox validation
Set UNSLOTH_IS_PRESENT=1 and import check_python_modules and
check_signal_escape_patterns directly from unsloth_zoo instead
of a standalone fallback. This gives us the full Unsloth
validation including stdlib-only import checks and signal/timeout
escape pattern detection.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: allow all imports in Python tool sandbox
Remove stdlib-only import restriction. Keep signal escape
pattern detection via unsloth_zoo for safety.
* studio: fix ReadTimeout on tool streaming final pass
The 0.5s read timeout used for cancel-checking during streaming
also fires when waiting for the first response from llama-server
(e.g. reasoning model thinking for 15+ seconds). Add
_stream_with_retry() context manager that retries on ReadTimeout
while checking cancel_event, so the model has unlimited time to
think before producing the first token. Applied to both the
regular streaming path and the tool-calling final pass.
* fix: rewrite HarmonyTextStreamer with stateful incremental parsing
The delta-on-transformed approach had two critical bugs:
1. Before the full <|channel|>X<|message|> pattern was complete, the
strip-tokens fallback emitted "analysis" as plain text. Then when
the regex matched, _transform returned a completely different format
(<think>...</think>) and the delta was computed against the wrong
base string, producing fragments like "think>", "nk>", ">".
2. Even with full matches, the closing </think> tag shifted position
as content grew, so text[prev_len:] produced garbled deltas.
Replace with stateful incremental parsing that:
- Buffers until a complete channel+message pair is seen
- Emits <think> once when analysis channel first appears
- Streams analysis content deltas (computed on channel content directly)
- Emits </think> once when final channel first appears
- Streams final content deltas
- Closes open think tags in end()
Also skip the generic all_special_tokens stripping in
_clean_generated_text for gpt-oss since HarmonyTextStreamer already
produces clean output and the generic stripping was mangling <think>
tags.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: strip all <|...|> tokens in gpt-oss cleanup, not just harmony subset
The gpt-oss tokenizer has added tokens like <|return|> (id=200002) that
are not part of the harmony channel protocol but can leak into output.
The previous regex only stripped channel|message|start|end tokens.
Broaden the _clean_generated_text regex for gpt-oss to <\|[a-z_]+\|>
which catches all pipe-delimited tokens (return, constrain, reserved,
etc.) without matching <think>/<\/think> tags.
Verified: gpt-oss all_special_tokens are only <|return|>,
<|reserved_200017|>, <|startoftext|> -- none overlap with <think>.
The harmony tokens (channel, message, start, end) are added_tokens
but not in all_special_tokens.
* fix: hide config-only model repos from cached models list
Repos that only have metadata/config files cached (no .safetensors or
.bin weight files) were showing up in the Downloaded list with tiny
sizes like "1.8 KB" or "24 KB". These are just leftover config
snapshots from architecture checks, not usable models.
Filter the cached-models endpoint to only include repos that contain
actual model weight files (.safetensors or .bin).
* studio: fix toast description text contrast in dark mode
Add explicit !text-muted-foreground to toast description classNames
so secondary text (e.g. "Releases VRAM and resets inference state.")
is readable in dark mode.
* studio: fix Chat card icon alignment with size-4 spacer
Replace sr-only span (takes no space) with a size-4 shrink-0 div
matching the RadioGroupItem dimensions in other cards, so the Chat
icon aligns vertically with Text/Audio/Vision/Embeddings icons.
---------
Co-authored-by: workspace <user@workspace.local>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Manan17 <shahmanan170602@gmail.com>
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
## Summary
- Add web search tool calling for GGUF models (Search toggle, DuckDuckGo via ddgs)
- Add KV cache dtype dropdown (f16/bf16/q8_0/q5_1/q4_1) in Chat Settings
- Fix Qwen3/3.5 inference defaults per official docs (thinking on/off params)
- Enable reasoning by default for Qwen3.5 4B and 9B
- Replace "Generating" toast with inline spinner
- Fix stop button via asyncio.to_thread (event loop no longer blocked)
- Fix CUDA 12 compat lib paths for llama-server on CUDA 13 systems
- Fix auto-load model name not appearing in selector
- Training progress messages + dataset_num_proc fix
Integrated PRs:
- #4327 (imagineer99): BETA badge alignment (already in tree)
- #4340 (Manan Shah): prioritize training models in model selection
- #4344 (Roland Tannous): setup.sh macOS python version compatibility
- #4345 (Manan Shah): revamp model+dataset checking logic
* user can upload eval dataset, removed bugs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* resolving merge conflicts
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* resolving gpt comments
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
- Workers now compute backend_path and venv_t5 locally via Path(__file__)
- Moved .venv_t5 to ~/.unsloth/studio/.venv_t5
- Added ensure_studio_directories() call on server startup
- Expanded CLI studio command into sub-app with setup subcommand
Instead of downloading the full dataset and then slicing, use
streaming mode to only fetch the rows needed (up to slice_end + 1)
when a manual dataset slice is configured.
start_training() cherry-picks kwargs into a config dict but was missing
is_embedding, so config.get("is_embedding", False) in worker.py always
returned False and embedding training never ran.
Daniel Han