Fixes a further batch of P1 bypasses and analysis-time DoS vectors found in review.
Static classifier:
- Decode exec/compile bytes payloads the way CPython does (PEP 263 coding cookie
via tokenize.detect_encoding), then analyze the real source. A bytes payload whose
UTF-8 view is pure comments but whose utf-7 decode runs hidden code no longer slips
through; a payload decoding to a blocked op blocks, a benign one stays allowed.
- Resolve exec-builtin aliases assigned in nested scopes (def f(): e = exec; e(...)),
matching the shell-sink aliasing (stored-once guard keeps it low false-positive).
- Treat deserializer modules (pickle/marshal/dill/...) as dangerous dynamic-import
targets so __import__('pickle').loads(blob) is caught.
- Flag vars(os) / vars(__builtins__) as a module-__dict__ obfuscation, like os.__dict__.
- Inspect the pathlib receiver path for read methods: Path('../../.ssh/id_rsa').read_text()
/ read_bytes() / open() now check the constructor path, not only call args.
- Fold literal os.path.join / posixpath.join so open(os.path.join('/etc','passwd')).read()
is seen by the sensitive-read scanner instead of treated as opaque.
Constant-folder allocation DoS (folding runs in-process, before subprocess rlimits):
- Reject oversized f-string / str.format / %-format widths and precisions before
format() allocates the padded string.
- Reject oversized str padding-method widths (ljust/rjust/center/zfill).
- Cap list/tuple repetition (seq * n) as str/bytes repetition already was.
Runtime realpath backstop:
- Do not publish __wrapped__ on the guard wrappers (functools.wraps would expose the
original unguarded callable, e.g. open.__wrapped__(outside, 'w')).
- Guard the low-level _io.open entry point (io.open / builtins.open originate there).
- Confine os.chdir to the workdir and deny os.fchdir so a cwd escape cannot turn a
later relative read/write into a host-path access.
- Deny fd-based metadata mutators (os.fchmod / os.fchown) that could reuse a read-only
descriptor opened on an outside file.
Adds regression tests across the const-fold, aliasing, exec-recursion and runtime-
backstop suites for every item above.
Harden the code-exec sandbox against bypasses raised in review, keeping the
static gate a pure classifier (it never executes the tool call):
- Shell scan: `timeout` duration/float args (5m, 0.5, 2h) no longer drop the
following command out of command position, and `find -exec CMD ... ;` rescans
the whole slice so a wrapped `env`/`timeout`/`sh -c` target is still caught.
- exec/eval/compile of a bytes payload that is not valid UTF-8 Python now blocks:
those sinks honor PEP 263 coding cookies (e.g. utf-7) that the static UTF-8 view
cannot see; plain ASCII bytes payloads stay allowed.
- Resolve aliased/indirect reaches to exec, dynamic import, sensitive modules and
deserialization sinks: builtins.eval / __builtins__.exec, from builtins import
exec as e, importlib aliases, sys.modules[...] (and the getattr form), os.__dict__,
posix/nt, and pickle/marshal module-and-symbol aliases plus the *.load variants.
- Shell-sink aliasing walks the whole tree, so a function-local `s = os.system`
alias is resolved (the stored-once guard keeps it low false-positive).
- Drop __mro__ / __code__ from the introspection-gadget dunders: on their own they
do not reach an execution primitive and are read by ordinary ML/debug code.
- Refuse folding oversized `bytes(n)` / `bytearray(n)` so static analysis cannot OOM.
Runtime realpath backstop:
- Fail closed on a mutating dir_fd / src_dir_fd / dst_dir_fd (an fd-relative path
cannot be confined by a string realpath) for os and shutil mutators.
- Confine Path.rename/replace/symlink_to/hardlink_to when the destination is passed
as the `target=` keyword, not only positionally.
- Splice the guard after a leading docstring and `from __future__` imports instead
of prepending it, so future-import programs no longer raise SyntaxError while the
sandbox is still established before the first real statement.
Adds regression tests for each gap across the shell, const-fold, exec-recursion,
aliasing and runtime-backstop suites.
On Python <= 3.11, pathlib._NormalAccessor captures io.open (and os.* mutators)
into class attributes at pathlib import time. A C builtin captured there does not
bind on instance access, but a Python wrapper does: self shifts into the next
positional, so Path.open / Path.write_text raised 'open() argument mode must be
str, not PosixPath' once the guard had replaced io.open with a Python wrapper
before pathlib was imported. (3.12+ dropped the accessor, which is why it only
failed on the 3.10 CI leg.)
Import io and pathlib at the top of the guard, before any patching, so the
accessor captures the original builtins, and confine Path.open by wrapping the
public method directly (mode-aware) rather than relying on the io.open patch to
reach it. Direct io.open() writers are still guarded for the zipfile-based cases.
The static filesystem write-confinement (the LOCAL/ESCAPE/UNKNOWN path resolver
_resolve_path / _resolve_path_call plus the _FS_* mutating-op inventory) was the
largest and most complex part of the classifier, and for writes it duplicated the
runtime realpath backstop, which is strictly more robust: it resolves the true
realpath at the syscall boundary, so it also catches dynamic paths, pre-existing
symlinks, and library writers the static pass could not prove.
Make the runtime backstop the single filesystem-write boundary and delete the
static resolver:
- Harden the backstop to close the gaps the static layer used to cover: guard the
low-level os.open (any mutating flag confines the target; a mutating dir_fd fails
closed) and io.open (which also carries pathlib.Path.open('w')), and add
os.mknod / lchmod / lchown / chflags and shutil.chown / copymode / copystat to
the wrapped set. Native-C writers (cv2.imwrite) and the realpath TOCTOU window
remain documented residuals that only OS-level isolation can close.
- Remove _resolve_path, _resolve_path_call, _resolve_join, _classify_path_string,
_is_pathlib_expr and the _FS_* / _PATHLIB_CTORS / _PATH_DEPTH_CAP constants, and
the write half of the filesystem visitor plus the FS_READ_STRICT knob.
- Reads are not confined by the backstop, so keep a small static sensitive-read
scanner (_is_sensitive_abs_path) that still blocks host-secret reads via a
sensitive absolute / ~ literal in any call arg (covers open, os.open, and library
loaders such as pandas.read_csv('/etc/passwd')) and .. / ~ traversal on the
dedicated open/read callees.
Net: about 300 fewer lines in tools.py and one fewer concept to audit; static
analysis now scopes to exec, shell, network and sensitive-reads while writes are
confined at runtime. Rework the filesystem tests around the new contract and add
os.open / io.open / Path.open / dir_fd escape cases to the backstop suite.
* scripts: refresh scan_packages allowlist baseline
Regenerate scripts/scan_packages_baseline.json against the current
resolved dependency set so the blocking pip scan-packages gate matches
what the scanner now finds. Refreshes evidence hashes for benign
findings whose code shifted lines (unsloth-zoo mlx loader, gguf/mlx
test /tmp fixtures) and adds two mainstream-library entries that were
newly surfaced (torch inductor codecache base64+subprocess compile
cache, torch testing common_utils socket import). Stale entries whose
matching code changed and no longer triggers are dropped.
All entries remain CRITICAL/HIGH findings manually judged benign;
matched on (package, file, check, evidence_hash).
* ci(security-audit): re-run scan when the allowlist baseline changes
The security-audit pull_request trigger listed the scanners but not
their allowlist baselines, so a baseline-only edit never re-ran the
scan that consumes it. A refreshed baseline could therefore merge
without CI confirming its evidence hashes match what the scanner finds.
Add scan_packages_baseline.json and scan_npm_packages_baseline.json to
the paths filter so baseline changes are validated on their own PR.
* Keep native RoPE scaling when extending context; carry rope_theta for linear
When max_seq_length exceeds a model's native window, the loader overwrote the
model's rope_scaling with linear scaling. For models that already ship a scaled
RoPE (llama3/yarn/longrope) that is far worse for long context, and on
transformers v5 the linear dict omitted rope_theta (v5 keeps it under
rope_parameters), so the rotary base fell back to 10000 and broke past ~8K tokens.
Keep the native scaling and just widen the window; only synthesize linear for
plain-RoPE models, and carry rope_theta so v5 keeps the real base.
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* Only preserve native llama3 when extending context; keep linear fallback otherwise
The patched attention constructor (patch_llama_rope_scaling) rebuilds only linear,
llama3 and longrope and its longrope branch reads a top-level
original_max_position_embeddings, so preserving yarn or a nested-only longrope config
would raise during construction on transformers <= 4.47.1. Keep only llama3 native;
yarn/longrope/other types fall back to the linear override, still carrying rope_theta.
* Correct long-context extension comment to match llama3-only preservation
---------
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* Fix fast_gemv crash on compressed-tensors FP8 models
Loading a compressed-tensors FP8 checkpoint (for example
unsloth/Llama-3.2-1B-Instruct-FP8-Block) with fast_inference=False and
running a forward crashed with 'Parameter object has no attribute absmax'
inside fast_gemv.
A compressed-tensors CompressedLinear exposes an already dequantized bf16
weight at forward time while keeping a weight_scale Parameter. The quant
state resolution in get_lora_parameters/get_lora_parameters_bias fell back
to that weight_scale, so a bf16 weight was routed into the bitsandbytes
fast_gemv/fast_dequantize path, which expects a bitsandbytes QuantState
with an absmax attribute.
Only fall back to weight_scale_inv/weight_scale when the weight is still
fp8. A decompressed bf16 weight then resolves to no quant state and flows
through the normal bf16 path, which already handles bias and the LoRA
backward. Real fp8 and bitsandbytes 4bit weights are unchanged.
* Skip the fast_gemv dispatch test before importing unsloth when bitsandbytes is absent
* Fix Windows installer torch index override
* Clear inherited uv index env vars for pinned installs in studio/setup.ps1 (#6898)
* Harden setup.ps1 index-var clearing to truly remove vars (#6898)
* Apply UV_DEFAULT_INDEX torch index fix to Linux/Mac install.sh (#6898)
* Neutralize all uv index env vars for pinned torch installs (#6898)
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: add Vulkan llama.cpp support
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address gemini's feedback
* Studio: move the Vulkan VRAM probe into a standalone script
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Improve Vulkan probe error reporting
* Resolve llama-server symlink so Vulkan build is detected
* Drop unreachable Vulkan fallback in GPU free-memory dispatcher
* Skip the Intel GPU probe when NVIDIA or ROCm is present
* Reserve host RAM headroom for Vulkan integrated GPUs
* Add a `UNSLOTH_FORCE_VULKAN` environment variable
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* [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
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* Honor GGML_VK_VISIBLE_DEVICES, reserve discrete Vulkan VRAM headroom, and clear Intel GPU on --cpu-fallback
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Route Intel and forced-Vulkan hosts to the upstream Vulkan prebuilt, add arm64 Vulkan, keep Vulkan out of RAG auto-detect
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Clear the fork release pin when routing a Vulkan host to the upstream repo
* Gate auto-Vulkan routing on no physical NVIDIA so hidden CUDA devices aren't used
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Pin Vulkan launches with --device Vulkan<i> instead of the raw GGML_VK_VISIBLE_DEVICES index space
* Let user --device override the Vulkan pin, and gate direct Vulkan asset picks on no physical NVIDIA
* Update RAG auto-backend test mocks for the _resolve_auto binary and Vulkan probes
* Keep the add_dll_directory handle alive through the Vulkan probe DLL loads
* Revert RAG auto Vulkan guard, guard multi-backend Vulkan detection, and preserve forced Vulkan across updates
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* Use getattr for RTLD_GLOBAL in the Vulkan probe CDLL mode
* Skip CUDA/ROCm APU and datacenter GPU tuning on Vulkan builds
On a Vulkan llama.cpp build gpu_indices are ggml compact ordinals, not
CUDA/ROCm physical ids, so _amd_apu_wants_unified_memory and
_apply_datacenter_env were reading the wrong device. On a mixed AMD APU
plus discrete GPU host that could raise a spurious system-RAM shortfall
and block a valid discrete-GPU load. Gate all three call sites on
not is_vulkan_backend; the Vulkan path already reserves iGPU host
headroom and the backend ignores GGML_CUDA_* anyway.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Tighten Vulkan-guard comment in load_model
* Reduce comments in Vulkan support to be more succinct
* Resolve shell-wrapper llama-server entrypoint to the real lib dir
create_exec_entrypoint falls back to a #!/bin/sh wrapper at the install
root when it cannot symlink into build/bin. _find_llama_server_binary
returns that root entrypoint, but Path.resolve() does not follow a shell
wrapper, so _llama_lib_dir returned the install root and _is_vulkan_backend
missed libggml-vulkan.so -- silently skipping the Vulkan probe and --device
pin on an otherwise valid Vulkan install. Follow the wrapper's exec target
to build/bin. Regression test: test_shell_wrapper_entrypoint_resolves_to_real_lib_dir.
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---------
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Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Studio /v1/messages: accept thinking and unknown content blocks
The Anthropic-compatible /v1/messages endpoint modeled a message's content as
Union[str, list[{text|image|tool_use|tool_result}]], so any other block type
made Pydantic reject the whole request with
`messages.N.content.str: Input should be a valid string`. Resuming a Claude
session commonly replays assistant turns that carry `thinking` (extended
thinking) blocks, and sometimes a null content for a tool-only turn, both of
which tripped this and returned a 400.
Accept them:
- Add a permissive AnthropicUnknownBlock fallback (any block whose type is not
one of the four known ones), so thinking/redacted_thinking/provider-specific/
future blocks validate. A validator keeps known types on their typed models,
so a malformed known block (e.g. a tool_use without id) still fails cleanly.
- Coerce a null message (and tool_result) content to "" so the converter's
`for block in content` stays safe.
The converter already drops block types it does not translate, so a thinking
block is not forwarded to the model.
* Studio /v1/messages: keep user content validation strict
Make the thinking/null leniency role-aware so it never silently drops real
user input. Assistant turns (replayed history) still accept unknown/thinking
blocks and coerce a null tool-only turn to empty. User turns keep the strict
boundary: a null user content is rejected, and a content block the converter
cannot translate is rejected instead of being dropped into an empty prompt.
Also remove an empty file committed by accident.
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* Studio /v1/messages: coalesce resumed user turns and tighten content checks
- The /v1/messages count and generation paths now coalesce the adjacent user
turns that dropping an empty or null assistant turn can leave behind, so a
strict GGUF chat template no longer 400s on non-alternating roles.
- A user content block with a non-string type (list / dict) is rejected as a
clean 400 instead of raising TypeError and escaping as a 500.
- The assistant null-to-empty coercion only applies to an explicit null; an
assistant turn that omits content entirely still fails required-field
validation instead of being silently coerced to an empty string.
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* Studio /v1/messages: tighten comments
---------
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* unsloth start: add --resume to persist and reopen agent sessions
`unsloth start <agent>` launches a coding agent whose home is a throwaway
temp dir wiped on exit, so codex/openclaw/hermes/pi (which relocate their
whole home there) cannot resume a conversation after you quit. opencode and
claude keep their session data in a fixed user dir, so they already resume.
Add an opt-in --resume/--no-resume flag: it routes the launch to the stable
Unsloth agents dir (the same one --no-launch already uses) so the session
survives the exit, never touching the user's own ~/.<agent>. A bare --resume
also reopens the last conversation via the agent's native flag (codex
`resume --last`, opencode/claude/pi `--continue`). The default is unchanged:
a plain launch still uses a temp dir and persists nothing.
Add a dispatch-only `resume` job to the Local Agent Guides CI that drives the
real launch path and asserts the split: codex/pi are wiped without --resume
and persist with it, while opencode/claude persist either way.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* unsloth start: rename --resume to --persist
The session flag collided with agents' own resume flags. `unsloth start
claude --resume <id>` used to forward `--resume <id>` straight to Claude
(which keeps its history in ~/.claude regardless), so a boolean --resume on
unsloth start would have swallowed the session id and turned it into a stray
prompt. Name the persistence flag --persist instead, so every agent's native
resume flag (claude --resume <id>, codex resume, opencode --continue, ...)
still passes through untouched. Behavior is otherwise identical: --persist
keeps a launched agent's session under the Unsloth agents dir, and a bare
--persist reopens the last conversation.
Add a regression test that `--resume <id>` passes through verbatim, and in the
CI resume experiment skip the redundant second pass for opencode/claude (they
persist either way, and a second CPU turn only risks a timeout).
* unsloth start: correct --persist help and drop the buggy auto-resume
Reword the --persist help to be accurate: claude and opencode keep sessions in
the user's own stores and resume regardless, so --persist only stabilizes the
otherwise-ephemeral relocated home of codex/openclaw/hermes/pi. Drop the
bare-launch auto-append of native resume tokens: it errored on a first launch
with no prior session, and was inconsistent between launch and no-launch.
--persist now only keeps the session dir; resume via the agent's own command
(e.g. `unsloth start codex --persist resume`), which now finds it.
In the CI resume experiment, fail the pass when the launched turn exits
non-zero, so a write-then-error is not misread as PERSISTED.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Silence torch._check_is_size FutureWarning and shim it if torch removes it
bitsandbytes 4-bit dequant calls torch._check_is_size, which torch
deprecated with a FutureWarning ("Use _check(i >= 0) instead") that prints
on every bnb-4bit load. Silence that warning in suppress_cuda_printf, and
add fix_torch_check_is_size so a future torch that removes _check_is_size
gets it shimmed to _check(i >= 0) (honoring the max bound) and bitsandbytes
keeps working.
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* Tighten fix_torch_check_is_size docstring
Lead with what the shim does and drop the redundant line; two lines
instead of three, same intent.
---------
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* Mirror dynamo/inductor config sets into defaults so torch 2.12 worker threads honor them
torch 2.12 stores config user overrides in ContextVars, so direct
assignments like torch._dynamo.config.recompile_limit = 1024 no longer
reach the autograd engine worker threads. Gradient checkpointing
recomputes fullgraph-compiled gpt-oss kernels inside backward on those
threads, which then read the default recompile limit of 8 and raise
FailOnRecompileLimitHit at step 0 of GRPO/SFT. Mirror direct config
assignments into the process-global entry defaults on torch >= 2.12,
restoring the torch <= 2.11 cross-thread semantics while leaving the
context-scoped config.patch API untouched.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Keep config.patch thread-local when mirroring dynamo/inductor sets
config.patch(...) also assigns through ConfigModule.__setattr__, so the
default-mirror was leaking its scoped, thread-local writes into the
process-global entry default. Track patch enter/exit with a per-thread
depth counter (wrapping ConfigModule.patch) and skip mirroring while
inside a patch, so only genuine direct assignments restore the torch
2.11 cross-thread semantics and config.patch stays context-local.
* Also keep config.load_config thread-local when mirroring config sets
load_config restores a saved dynamo/inductor config by calling setattr
per key, which the default-mirror would otherwise leak process-wide just
like config.patch did. Wrap load_config with the same per-thread depth
counter (renamed to _scoped_depth) so both scoped writers skip the mirror
and stay context-local, while genuine direct assignments still restore the
torch 2.11 cross-thread default.
* Drop the pre-existing override replay from the config thread fix
The replay was redundant: this runs from _gpu_init before unsloth sets any
dynamo/inductor config, so the __setattr__ wrapper already mirrors every
later assignment (recompile_limit included). It could also read a value
that belonged to a config.patch context still active at import time and
write that thread-local override into the global default. Removing it keeps
the cross-thread fix and drops the now-unused _inductor.config import.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
When a coding agent is missing, `unsloth start <agent>` offers to run the
vendor's own installer (curl | bash, irm | iex, or npm) after an interactive
confirm. Those installers execute with the user's privileges and there is no
signature or hash check on the fetched content, so a blind "yes" is a
supply-chain risk if the delivery path is compromised.
Keep the auto-install convenience but make consent informed: before the prompt,
name the exact remote source the installer fetches (or the command it runs for a
package installer) and state that nothing verifies a signature or hash. Behavior
is otherwise unchanged: non-interactive stdin still never executes anything, and
the confirm still defaults to no.
* Fix FastSentenceTransformer Qwen embedding preprocessing
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Document Transformer.load embedding modality fix for #6881
* Harden #6881 fix and add forwards/backwards-compatible regression tests
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* Fall back to Transformer constructor on legacy sentence-transformers without Hub-capable load
* Mirror legacy sentence-transformers fallback in embedding-parity tripwire test
* Tighten #6881 comments and docstrings
* Skip embedding-parity test on CPU-only runners since FastSentenceTransformer requires CUDA
* Honor the transformer module's saved subfolder when loading
modules.json records a path for the Transformer module (root for
decoder embedders like Qwen3-Embedding, 0_Transformer for the classic
layout). Pooling/Normalize already load from their saved path; thread the
same path into Transformer.load as subfolder so config and tokenizer
resolve like stock ST. stays a no-op, so single-module models are
unchanged.
* Make embedding-parity test bf16-aware
fp16 overflows to NaN on bf16-native embedders such as EmbeddingGemma
(Gemma3), producing a false parity failure. Prefer bf16 when the GPU
supports it so the tripwire can guard the full documented embedding
matrix (Qwen3-Embedding, EmbeddingGemma, BGE-M3, all-MiniLM, GTE-ModernBERT),
not just fp16-safe models.
---------
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Co-authored-by: danielhanchen <danielhanchen@gmail.com>
* Retry the Studio UI shutdown re-login on transient goto timeout
The Chat UI Playwright smoke intermittently failed at the pre-shutdown
re-login: page.goto('/login') can hit a 60s TimeoutError on a slow runner
even while the server is healthy, and the surrounding except only tolerated
ERR_ABORTED / interrupted-navigation, so a plain timeout hard-failed the job.
Wrap the re-login goto/wait/fill/submit in the same 3-attempt retry the
change-password step already uses (recover_or_replace_page between tries,
per-attempt fail screenshots, wait_for_health pre-gate). The composer wait
stays outside the loop so a retry never re-navigates after login has set
tokens (which would redirect to /chat via the guest guard); it remains the
authoritative confirmation, so a genuinely broken login still fails.
* Catch transient login-request failures and preserve error listeners on recovery
Wait on the /api/auth/login POST inside the retry (via click_and_wait_for_response)
so a transient 4xx/5xx is retried in-loop instead of surfacing only at the
out-of-loop composer wait, matching the change-password step. When
recover_or_replace_page swaps in a fresh page, re-attach the pageerror/console
listeners so error tracking survives the replacement.
Tighten the eval/exec/compile dynamic policy so an executing sink (eval/exec/
runpy) applied to a payload that cannot be statically recovered is refused
unconditionally, not only when an RCE-core module happens to be imported in the
snippet. An un-analyzable executing payload can synthesize any shell, network,
or filesystem escape at runtime, so the prior in-scope-import heuristic left
exec(input()) and eval(user_var) allowed whenever no such import was present.
compile() of the same payload stays allowed since it does not run.
A payload that is fully recovered as a constant but is invalid Python for the
sink's mode (for example eval("data = 1") or eval("not python !!")) is now
allowed: its exact source is known and it raises SyntaxError at runtime, so it
is not an execution vector. Only genuinely opaque, non-recoverable payloads
(for example eval of a runtime-computed f-string) reach the block.
Remove the now-unused _RCE_CORE_MODULES set and _scope_imported_roots helper,
and move the opaque-f-string case in the tests to the blocked set.
* Stabilize floating monitor drag
* Restore floating monitor exit animation
* Harden Windows Studio smoke checks
* Keep API menu badge removed
* Apply no-build-tools env overrides in-script
The runner does not apply step-level env keys containing parentheses,
so ProgramFiles(x86) kept its real value and Find-VsBuildTools still
detected VS through vswhere. Set the overrides inside each pwsh step
instead; child processes inherit them. The resolver step moves to pwsh
because bash cannot export a variable named ProgramFiles(x86).
* Reset chat UI session without a second browser context
macOS runs Chromium with --single-process, where closing the last
context tears down the whole browser, so the shutdown re-login died
with TargetClosedError on new_page. Clear cookies and swap pages
inside the same context instead, opening the replacement page before
closing the old one.
* Keep the no-build-tools Path filtered across session refreshes
install.ps1's Refresh-SessionPath and setup.ps1's Refresh-Environment
rebuild the session Path from the Machine and User registry scopes, so
the process-level filter could be undone mid-install and re-expose
CMake. Filter those scopes in the Prepare step with normalized dir
matching and restore them in cleanup.
* Drop stale localStorage auth tokens before re-login
Auth tokens live in localStorage, not cookies, and the login guest
guard redirects on their mere presence. Remove them during the session
reset so the /login navigation is deterministic instead of relying on
the tolerated redirect bounce.
Add ln to the bash command denylist so a symlink escape cannot be created from
the terminal tool. In the sandboxed (non-bypass) _python_exec path, prepend a
one-line guard to the generated temp module that monkeypatches only MUTATING file
ops (builtins.open in write/append/x/+ modes, os remove/unlink/rmdir/removedirs/
rename/renames/replace/truncate/chmod/chown/mkdir/makedirs/symlink/link, shutil
rmtree/move/copy/copy2/copyfile/copytree, pathlib write_text/write_bytes/unlink/
rename/replace/mkdir/rmdir/chmod/symlink_to/hardlink_to/touch) to resolve the true
os.path.realpath of the target and raise PermissionError unless it lands inside
the injected session workdir. Reads are left unpatched. The guard runs in its own
namespace so helper names never leak into user globals, and it is skipped
entirely under disable_sandbox. This catches what the static gate cannot prove:
pre-existing symlink escapes and dynamic library-writer paths that funnel through
builtins.open. Benign in-workdir relative writes and library imports are
unaffected (importlib swallows out-of-workdir bytecode-cache write failures).
Add pragmatic aliasing so an aliased shell sink with a dangerous argument is
caught: a name stored exactly once and bound to a resolved os/subprocess sink
(s = os.system; s('rm -rf /')) and inline literal-container indexing
([os.system][0](...), (os.system,)[0](...), {'k': os.system}['k'](...)) both feed
the existing _find_blocked_commands argument check. Resolution is deliberately
low-false-positive: only unambiguous single assignments and inline literal
containers, never a flow-insensitive union, so s = os.system; s = print; s('hi')
is not aliased. The shell-sink set is lifted to module scope (_SHELL_SINK_FUNCS)
so the alias pre-pass and the visitor share one definition. Interprocedural and
flow-sensitive taint remain out of scope (deferred to a full fixpoint).
Add a filesystem_violations category backed by _resolve_path, a LOCAL / ESCAPE /
UNKNOWN classifier that constant-folds strings and understands os.path.join,
pathlib Path()/'/'/joinpath, and f-strings with real join plus absolute-reset
semantics. expanduser / expandvars / os.environ / getcwd / dynamic parts collapse
to UNKNOWN. A new _FilesystemPolicyVisitor inventories destructive and mutating
ops (open write/append/x/+, os remove/unlink/rmdir/rename/replace/truncate/chmod/
chown/mkdir/makedirs/mknod/symlink/link/chdir, shutil rmtree/move/copy*, pathlib
write_text/write_bytes/unlink/rename/replace/mkdir/rmdir/chmod/symlink_to/touch,
tempfile dir=, and a curated numpy/pandas/torch/joblib/PIL/matplotlib/cv2 writer
set) and applies prove-or-block: mutating LOCAL allows, UNKNOWN/ESCAPE blocks.
rename/move check src and dst; symlink/link check both target and link path;
chdir must be LOCAL; tempfile dir= must be LOCAL. Reads block only on a provable
escape (sensitive absolute path or ..'/~ traversal), with an FS_READ_STRICT knob
for prove-or-block reads. A callee-independent literal-sensitive-path scan blocks
loaders like pandas.read_csv('/etc/shadow'). Library writers block only on a
provable escape so in-memory buffers are not over-blocked; the Stage 5 runtime
backstop covers the dynamic residual.
Replace the blanket dynamic_exec block with a recursive payload analyzer gated by
UNSLOTH_STUDIO_SINK_ANALYZER (default on; =0 restores the legacy ban). For eval /
exec / compile (and single-assignment aliases like e = exec), constant-fold the
first argument; a recovered source string is bracket-depth pre-scanned, size and
recursion-depth bounded, then re-classified through the full analyzer. An inner
sink blocks and surfaces the inner reason; a clean inner payload allows; a bound
or budget breach fails closed. Non-foldable payloads follow a low-false-positive
dynamic policy: block when assembled from decode / fetch / runtime-assembly
primitives (including nested exec and large string repetition) or when an
RCE-core module is imported in scope, else allow.
Keep the gadget-dunder and dynamic-import blocks but constant-fold import names
so __import__('hugging'+'face_hub') resolves to a real module. Refine getattr /
setattr on a sensitive module so a benign constant attribute (getattr(os,
'getpid')) is allowed while a dynamic or dangerous constant attribute blocks. Add
pickle/marshal/dill.loads as unverifiable code-deserialization sinks. eval('2+2'),
compile('a+b','<s>','eval') and ast.literal_eval now pass; base64/hex/rot13/chr
and gadget-obfuscated escapes still block. Wire a filesystem_violations category
through is_safe, the info dict, and the reason assembly (populated in a later
stage). The three legacy tests that asserted the blanket ban are updated to the
new recurse-the-payload behavior.
Introduce _const_fold, a whitelist-only, bounded, side-effect-free partial
evaluator plus a single-assignment const-prop environment builder. It recomputes
pure transforms on literals only (concat, repeat, join, format, f-strings,
slice/reverse, chr/ord, base64/hex/rot13/zlib decode, pure builtins and string
methods) and never executes, imports, or reflects on user code. Depth, op, size,
and sequence caps guarantee it can only fail to recover a value, never crash or
hang. This is the foundation the later eval/exec unwrapping and filesystem path
resolver build on.
* unstructured block removal
* Enhance unstructured block handling
* Restrict block cleanup to upload UIDs
* cleanup for seed block uploads
* upload cleanup queue for unstructured blocks in recipe studio
* Fix unstructured upload cleanup edge cases
* Fix unstructured upload import ownership
* Fix-unstructured-import-path-ownership
* Guard failed-delete restore against stale block in unstructured drop zone
* Drain queued upload cleanups when autosave is skipped
---------
Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com>
Co-authored-by: imagineer99 <samleejackson0@gmail.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: render thinking blocks for safetensors inference with prefilled <think> templates
Reasoning templates like Qwen3.6 end the generation prompt with an open
<think> tag. skip_prompt streaming drops it, so the frontend never sees
the opening tag and shows reasoning as plain text. Detect the prefill
and re-emit it at the start of the stream on the transformers and MLX
paths. Also stop stripping think tags in _clean_generated_text when a
tokenizer marks them special.
* Studio: guard think re-emit for special close tags, yield prefill early
Address review feedback:
- Guard: skip re-emitting the open <think> when the tokenizer marks </think>
as a special token, since skip_special_tokens would strip the model's close
tag and leave an unclosed block that swallows the answer. Falls back to
plain text (pre-fix behaviour) for those tokenizers.
- Yield the prefilled <think> before the first token so the thinking block
renders during prompt prefill instead of after the first generated token.
- Drop the now-unnecessary _clean_generated_text think-tag exemption; the
guard handles the special-token case at the source.
No mainstream reasoning model (Qwen3.6, Qwen3, DeepSeek-R1, QwQ, GLM-4.6)
marks think tags special, so behaviour is unchanged for them.
* [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: Lyxot <longyixing331@gmail.com>
PyPI release unsloth 2026.7.2 is now live. Bumps the pinned floor in
install.sh and install.ps1 from 2026.7.1 to 2026.7.2 for both unsloth and
unsloth-zoo across all 5 install commands (no-torch / reinstall / upgrade /
local / auto torch backend paths) so fresh installs resolve to the new wheel.
Follows the same pattern as #5716.
* Studio: allow CPU-only DiffusionGemma by granting the diffusion runner the CPU device
* Studio: mark CPU-only DiffusionGemma as non-GPU-resident for training VRAM preflight
* Studio: keep the CPU DiffusionGemma change minimal (revert VRAM-flag tweak; Metal hosts still hold unified memory)
* Studio: keep CPU DiffusionGemma fallback fully CPU-masked so a masked GPU host does not re-expose GPU 0
worker.py imports has_blackwell_gpu from utils.wheel_utils, but _load_worker_module
stubs utils.wheel_utils with a fixed name tuple that omitted it, so loading the worker
raised ImportError (cannot import name 'has_blackwell_gpu') and Backend CI could not
collect test_mlx_training_worker_config.py. Add the name to the stub so it matches
worker.py's imports.
* fix: Remove moot has_blackwell_gpu() function
Fixes unslothai/unsloth#6961. This function skipped flash-attn on Blackwell GPUs because no prebuilt wheel existed;
Dao-AILab now ships one and url_exists() already gates resolution.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* fix: use torchao 0.17.0 for Blackwell
Fixes#6961. Torchao 0.16.0's cpp extensions are built against CUDA 12, so on a CUDA-13
torch (cu130 / Blackwell) they fail to load with "libcudart.so.12: cannot
open shared object file". Select 0.17.0 there instead: its cpp targets torch
2.11, so it is skipped cleanly rather than crashing. CUDA-12 / ROCm / CPU
torch 2.10 keeps 0.16.0 and its working kernels.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
* Condense torchao version-selection comments (no behavior change)
* Support torch 2.11 in the Studio installer via the torch2.10 prebuilt wheels
Map torch 2.11 to the torch2.10 prebuilt wheels for flash-attn, causal-conv1d,
and mamba through wheel_utils.prebuilt_wheel_torch_mm, applied in direct_wheel_url
(filename) and flash_attn_wheel_url (version). Those torch2.10 CUDA wheels load and
pass each project's own test suite on torch 2.11 (verified on B200), so a torch 2.11
environment gets the prebuilt accelerators instead of skipping or building from source.
Raise _CUDA_TORCH_PKG_SPEC to <2.12.0 (torchvision <0.27.0, torchaudio <2.12.0) so
the CUDA torch repair path can install torch 2.11, where torchao 0.17's cpp kernels
load cleanly. Add tests for the mapping.
* Keep has_blackwell_gpu as a False stub for future arch gating
* Restore has_blackwell_gpu as a return-False probe kept for future arch gating
Keep the nvidia-smi compute_cap detection and its two call sites, but short-circuit
with return False at the top so flash-attn is no longer skipped on Blackwell (sm_100+
now has prebuilt wheels and url_exists gates resolution). Drop the early return to
re-enable arch-based detection later.
---------
Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* models: auto-target per-expert Linear MoE experts for LoRA (gpt-oss 4bit)
MoE checkpoints whose experts are stored as per-expert nn.Linear ModuleLists
could not receive expert LoRA. gpt-oss bnb-4bit is the canonical case: its
experts live at mlp.experts.gate_up_projs.<i> and mlp.experts.down_projs.<i> as
per-expert Linear4bit modules, not a fused nn.Parameter. The target_parameters
path only handles the fused nn.Parameter layout, and the plain
gate_proj/up_proj/down_proj leaf names do not match the per-expert indices, so
get_peft_model attached LoRA to attention only and left every expert frozen
(0 of 1536 on gpt-oss-20b) even though the grouped bnb-4bit training forward
exists.
Add get_moe_target_modules, the module-LoRA counterpart of
get_moe_target_parameters: it detects per-expert Linear ModuleLists under an
experts container and returns their suffix target_modules names
(gate_up_projs.<i> / down_projs.<i>). get_peft_model in both llama.py and
vision.py extends target_modules with these, handling the explicit leaf-list
form and the regex form (auto / all-linear / scoped). It is gated on the same
MLP-in-scope condition as the parameter path, so an attention-only request still
skips the experts.
Also gate get_moe_target_parameters on the fused parameter actually existing, so
a per-expert-Linear layout no longer produces a dead target_parameters path or a
misleading "Enabling LoRA on MoE parameters" line; those experts are handled
through target_modules instead.
Validated on gpt-oss-20b-unsloth-bnb-4bit (transformers 5.5.0): experts attach
(1536 modules, trainable 0.036 percent to 1.65 percent) across the default, None
and all-linear paths; training memorizes and the LoRA adapter reproduces exactly
after a cold reload in a fresh process. No regression: fused-parameter MoEs
(Qwen3-30B-A3B-4bit), non-MoE models, and attention-only requests are unaffected
(get_moe_target_modules returns an empty list).
Merging these per-expert adapters into a merged_16bit checkpoint is handled by a
companion unsloth-zoo change (saving_utils folds each per-expert delta into the
fused gate_up_proj / down_proj tensor). With both, the LoRA adapter and the
merged_16bit checkpoint reload the trained behavior identically.
* models: scope per-expert MoE targets, keep repeat get_peft_model idempotent, warn on old zoo
Address review of the per-expert Linear MoE targeting:
- Scope get_moe_target_modules to the requested projection leaves (gate/up map to
the gate_up ModuleList, down maps to the down ModuleList), so a narrowed request
such as target_modules=["down_proj"] no longer also trains gate_up_projs, matching
get_moe_target_parameters.
- Detect experts through a PEFT-wrapped base_layer as well, and recompute the
auto-added expert targets in the llama.py existing-adapter check, so a repeat
get_peft_model call with the same arguments stays idempotent instead of raising on
the saved expert targets.
- Warn when the installed unsloth_zoo cannot fold these per-expert experts into a
merged_16bit checkpoint (older releases keep the fused gate_up_proj / down_proj
tensors and drop the per-expert deltas), so the expert LoRA is not silently lost on
save_pretrained_merged; the fold lands in unsloth-zoo #885. The LoRA adapter itself
is unaffected.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix(studio/hub): apply repo_id length limit per segment, not whole string
is_valid_repo_id() applied the 96-char limit to the full "namespace/repo_name"
string, so a repo with a valid (<=96 char) name but a long combined id was
falsely rejected. Match huggingface_hub.validate_repo_id by checking the length
per segment instead. Fixes#6946.
* Fix long repo id state filenames
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Studio: source CPU llama.cpp prebuilts from the unslothai fork
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: reject unknown Linux CPU arches and keep ROCm-tooling hosts off the CPU prebuilt
* Studio: extend the resolve-prebuilt ROCm-tooling guard to Windows
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: let ROCm-SDK-only CPU hosts take the fork CPU prebuilt
* Studio: accept windows-arm64 prebuilt kind and refresh stale fork-routing comments
* Studio: correct stale fork-routing comments and --resolve-prebuilt help
* Refresh stale ggml-org routing comments
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Studio: keep transformers off sys.modules until the training worker activates the sidecar
The training worker (core/training/worker.py:run_training_process) decides the per-worker
Xet env flip during preflight by importing utils/hf_xet_fallback.py, which eagerly imported
unsloth_zoo at module load. unsloth_zoo's __init__ imports transformers, so the default
transformers 4.57.x was cached in sys.modules before activate_transformers_for_subprocess
prepended the 5.x sidecar to sys.path. Since activation only edits sys.path, the already
cached module won, and 5.x models failed to load their tokenizer or config:
- Qwen3.5 / GLM-4.7 (tokenizer_class TokenizersBackend): "Tokenizer class TokenizersBackend
does not exist or is not currently imported."
- gemma-4: "... is not supported yet in transformers==4.57.6."
Fix: load the shared unsloth_zoo backend lazily (only when a heavy download helper is first
used, which is after activation). child_should_disable_xet and the DEFAULT_* constants are
defined locally so importing the shim stays light. The download wrappers, the DownloadStallError
class, start_watchdog and get_hf_download_state resolve the shared backend on first use, and the
degraded no-unsloth_zoo fallback is preserved.
Tests:
- test_hf_xet_fallback.py: existing suite kept green via the restored _shared_* seam; the
GPU-init retry test now triggers the lazy load explicitly; new guard asserts importing
child_should_disable_xet does not import transformers/unsloth_zoo.
- test_training_worker_import_discipline.py: new invariant test that the worker preflight
imports leave transformers unimported, so this class of regression cannot return silently.
Runs in studio-backend-ci (CPU only, no network/GPU/weights).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Studio: CPU-only guard that activation switches transformers to the model's sidecar version
Adds test_worker_activates_correct_transformers.py: runs the real worker preflight
(from utils.hf_xet_fallback import child_should_disable_xet) plus the real tier
detection and activate_transformers_for_subprocess for a transformers-5.x model
(Qwen3.5, tier 530), then asserts the in-process transformers actually switched to
the 5.x sidecar. A stale pre-activation import leaves 4.57.x pinned and fails the
assertion, which is exactly the TokenizersBackend regression (#6951).
Self-contained CUDA spoof (mirrors tests/_zoo_aggressive_cuda_spoof.py) forces
unsloth_zoo down its full, transformers-importing init path on a GPU-less runner;
without it unsloth_zoo degrades and never preloads transformers, masking the bug.
A one-line stub sidecar stands in for the 5.x venv, so no GPU, network, weights, or
real sidecar are needed. Passes on this fix, fails on buggy main.
* Studio: load the repo's canonical CUDA spoof in the correct-version guard
Load tests/_zoo_aggressive_cuda_spoof.py (the committed spoof the consolidated CI
already relies on) as the single source of truth so the guard matches CI and stays
robust on a CPU-only torch wheel, where a partial hand-rolled spoof could miss a
torch.cuda call and let the unsloth_zoo import raise (masking the bug). Falls back to
a minimal inline spoof for a standalone studio checkout. Verified: passes on this fix,
fails on buggy main, and the fallback path passes when the spoof file is absent.
* Studio: declare the lazily-resolved xet names so ruff F822 stays green
DownloadStallError, start_watchdog and get_hf_download_state are provided via the
module __getattr__ (PEP 562), so ruff F822 flagged them as undefined names in __all__
and the Source-lint / pre-commit checks went red. Add annotation-only declarations
(no value bound, so __getattr__ still resolves them lazily to the shared unsloth_zoo
backend) to mark them defined for the linter while keeping F822 active for the rest
of __all__.
* Studio: tighten comments on the sidecar-activation fix and its tests
* Studio: mirror the new MLX-dispatch preflight import in the import-discipline guard
The worker preflight now also runs 'from core.training.training import
is_apple_silicon_training_platform, should_use_mlx_training_backend' before it
activates the transformers sidecar. Add that import (guarded) to the guard's
preflight snippet so the invariant test stays a faithful mirror: a future change
that makes core.training.training pull transformers/unsloth_zoo eagerly would then
be caught too. Verified clean on the current tree (no leak).
---------
Co-authored-by: danielhanchen <unslothai@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>
* feat: detect installed coding agent CLIs in Studio settings
The API-keys panel only ever showed the "claude" flavor of the
`unsloth start` command, so anyone using Codex, OpenCode, OpenClaw,
Hermes, or Pi had to manually rewrite the copied command by hand.
Add a backend check that looks for each agent's CLI binary on PATH
(shutil.which, mirroring the pattern already used elsewhere in
studio/backend/utils) and expose it as GET /api/settings/coding-agents.
The API-keys panel now renders a picker for all six supported agents,
marks the ones it finds installed, and defaults to one of those instead
of always falling back to claude.
Includes unit tests for the detection helper.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* address review feedback on coding-agent detection
Three fixes from PR review:
- detect_installed_coding_agents now treats a PATH lookup failure as
"not installed" instead of letting it bubble up and break the
settings endpoint; added a regression test for it.
- CodingAgentsResponse.agents is now typed as an immutable tuple
instead of a list built from one, matching CODING_AGENTS itself.
- Fixed a race in the API-keys panel: picking an agent while the
installed-CLI check is still in flight could get silently overwritten
once that check resolved. A ref now tracks whether the user has made
a manual choice, so the auto-detected default only applies before
that happens.
* Address Codex feedback: GGUF gating and remote-detection scope
- codex refuses to launch against a non-GGUF (transformers-backed) model
(unsloth_cli's _require_gguf_for_codex), so auto-defaulting to it produced
a copy-pasteable command that fails immediately whenever the loaded model
isn't GGUF. Add useActiveModelIsGguf() (looks up the active checkpoint in
the chat runtime store) and a correction effect that steers the auto-pick
away from codex unless the loaded model qualifies, without ever touching a
choice the user made by hand.
- Detection runs via shutil.which on the Studio backend host, which isn't
the same machine as the browser in a tunnel/remote session. Reword the
'installed'/'detected' copy to say so explicitly when the tunnel URL is
in use, instead of implying the check ran on the viewer's own device.
* Rework auto-default per review: loopback gating + inline GGUF check
Replaces the previous approach with the exact shape discussed on the PR:
- Export isLoopbackHost/normalizeHost from agent-command.ts. The detection
endpoint runs shutil.which on the Studio backend, which only describes the
browser's own machine when the base this panel targets resolves to
loopback. For a LAN or tunnel/remote base, gate the whole thing off --
don't mark anything as "detected" and don't let it drive the default --
instead of just relabeling the copy.
- Drop the separate GGUF-correction effect and useActiveModelIsGguf hook.
Read useChatRuntimeStore.getState().activeGgufVariant inline inside the
existing detection effect's .then() (so it doesn't need to sit in the
effect's deps), and pick the first detected agent that isn't codex unless
the loaded model is GGUF, leaving the existing default untouched when no
compatible agent is detected.
Verified both branches (loopback vs LAN/tunnel base, gguf vs non-gguf,
manual pick preserved, no-compatible-agent fallback) with a standalone
port of the .then() logic.
* Address latest Codex findings: stale detection, model swap, cache
- Clear detectedAgents (and skip the network call entirely) when the panel
leaves a loopback base, instead of leaving a previous loopback detection
result marked 'installed' for a command that now targets a LAN/tunnel/
remote host.
- Add a separate, network-free correction effect keyed on the live
activeGgufVariant: if codex was auto-picked while a GGUF model was loaded
and the user then switches to a transformers-backed model while this panel
stays mounted, steer away from codex instead of leaving a command that
unsloth_cli's _require_gguf_for_codex will now reject. Never touches a
manual pick.
- Drop coding-agents.ts's module-lifetime cache. Installed-CLI detection is
environment state, not a persisted setting, so a stale positive/negative
from before the user installed something (or reopened the tab) is worse
than one extra cheap local API call per mount; keep only the in-flight
de-dupe for concurrent callers.
Verified the correction-effect logic (gguf->non-gguf swap with/without a
fallback, still-gguf no-op, manual pick never overridden) with a standalone
port of the effect.
* Make the codex/GGUF auto-pick symmetric in both directions
The correction effect only steered away from codex when the model stopped
being GGUF; it never steered back toward codex if the model became GGUF
*after* a non-GGUF-gated fallback had already picked something else (e.g.
codex is the only detected CLI, a transformers model is loaded so the
selection correctly falls back to the claude default, then the user loads a
GGUF model while the panel stays mounted -- codex never gets reconsidered).
Consolidate into one effect that re-derives the preferred detected agent
from scratch whenever detectedAgents or activeGgufVariant changes, in either
direction, instead of only reacting to the codex-specific downgrade case.
The fetch effect now only populates detectedAgents/availableAgents; this
effect is the single source of truth for what gets auto-picked from that
list. Never overrides a manual choice.
Verified both transition directions plus the manual-pick-survives and
initial-detection cases with a standalone port of the derivation logic.
* Reset the auto-pick to the default when it stops being trustworthy
Two more real gaps from the latest Codex pass on d988f52:
- The unified derivation effect only handled the case where a *different*
detected agent could take over. If codex was the only detected agent and
auto-picked while a GGUF model was loaded, then the model stopped being
GGUF, 'preferred' came back undefined and the effect silently left the
selection on codex -- exactly the command unsloth_cli's
_require_gguf_for_codex now rejects. Fall back to DEFAULT_AGENT in that
case instead of leaving it untouched.
- Leaving a loopback base cleared detectedAgents (so the 'installed' badges
correctly disappear) but left whatever agent had been auto-picked from
that now-stale, server-side-only detection still selected. Reset to
DEFAULT_AGENT there too, unless the user picked by hand.
Introduces a shared DEFAULT_AGENT constant instead of repeating the "claude"
literal at each reset site. Verified all five cases (both new resets, both
manual-pick-survives variants, and the existing multi-detected-agent
fallback still preferring another compatible agent over resetting) with a
standalone port of the effects.
* Derive GGUF-ness from the actual loaded state, not just the variant string
activeGgufVariant only covers an HF-repo GGUF pick (a specific quant
variant string). A direct local .gguf file -- custom folder, LM
Studio, or drag-drop -- is just as much a GGUF the codex preflight
(unsloth_cli's _require_gguf_for_codex) would accept, but it never has
a "variant" to report, so it read as non-GGUF here even though
/api/inference/status correctly reports is_gguf: true for it. That
mismatch could leave a Codex-only install not auto-selected, or reset
an auto-picked Codex, for a model that actually supports it.
Combined activeGgufVariant with activeNativePathToken (covers the
drag-drop/picked-file case) and ggufContextLength (only ever populated
when the backend last reported is_gguf: true for the active model, see
applyActiveModelStatusToStore) so all three paths a model can be GGUF
through are covered, matching the same is_gguf-or-equivalent check
hasGgufSource already applies to a staged pick elsewhere in this
codebase.
* Clear stale native-path token on a non-GGUF status refresh
When a native (drag-dropped or picked) GGUF was loaded and the backend later
switches to a transformers model outside the UI load path, refresh() adopts the
new /api/inference/status via setCheckpoint and applyActiveModelStatusToStore.
Those reset activeGgufVariant and ggufContextLength but never clear
activeNativePathToken, so the isGguf OR stays true after the switch and a
Codex-only detection auto-selects unsloth start codex for a non-GGUF model its
preflight rejects.
Drop activeNativePathToken in applyActiveModelStatusToStore whenever the status
is non-GGUF. A real GGUF load reports is_gguf: true, so its token is preserved
(the load path owns it); only a non-GGUF status clears it.
* Add the AGPL-3.0 header to the new studio contract test
* Fix/adjust agent detection for PR #6909
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* fix: launch OpenClaw local TUI by default
* Fix/adjust OpenClaw launch paths for PR #6937
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* Default OpenClaw to the local TUI only on a bare invocation
The first-arg startswith('-') branch rewrote passthrough globals into a broken
command: OpenClaw's grammar is openclaw [--dev] [--profile <name>] <command>, so
'unsloth start openclaw --profile test' became 'openclaw tui --local --profile
test', but tui does not accept --profile (or --dev), so the invocation failed.
A leading '--flag value' is ambiguous between a global (--profile test) and a tui
option (--message hi), so it cannot be reinterpreted safely. Default to the local
TUI only when no passthrough args are given, and forward everything else verbatim
so OpenClaw parses it under its own grammar. The bare-launch default (the point
of this change) is preserved; explicit subcommands and global flags pass through.
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Co-authored-by: Wasim Yousef Said <wasimysdev@gmail.com>
The python tool's static safety analysis (_check_signal_escape_patterns) was
purely name-based with no attribute visitor, so obfuscated routes to the shell
/ network / file policies it already enforces slipped past: eval / exec /
compile, __import__ / importlib with a computed or dangerous module name,
getattr / setattr aimed at os / subprocess / sys / builtins, and dunder gadget
chains (().__class__.__bases__[0].__subclasses__()).
Add a dynamic_exec category covering those, surfaced through _check_code_safety
alongside the existing categories. Dynamic import stays allowed for a benign
literal module name (huggingface_hub, json, numpy) so real workflows and the HF
upload gate keep working; ordinary getattr(obj, "field") and __class__ access
stay benign. Bypass Permissions (disable_sandbox) still skips the check.
Tests: TestDynamicExecObfuscation in test_sandbox_tools.py with matching benign
cases, giving _check_signal_escape_patterns its first direct coverage.
* version-compat CI: fake CPU training runs for SFT/GRPO/DPO
Adds a runtime layer on top of the patch-run canary: actually runs
trainer.train() for a couple of steps on a CPU-only runner under the CUDA
spoof, wrapping a plain tiny HF model in the Unsloth-patched trainer. Exercises
the real train() loop (collation, generation, the injected
_get_per_token_logps_and_entropies, loss, backward, optimizer) so a TRL or
transformers change that breaks the loop at runtime -- not just the source
structure -- surfaces here. No GPU, no meaningful numerics.
Needs a chain of small CPU shims (eager torch.compile, dynamo suppress, cuda
tensor-alloc redirect to CPU, model.for_training/for_inference equivalents)
documented inline. Does not exercise Unsloth's Triton/GPU kernels (CPU can't).
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* cpu fake-train: force adamw_torch + disable dynamo for CPU runner
On a real CPU-build torch runner (GitHub CI) two things bit that a CUDA-build
torch with GPUs hidden masked locally:
- The default optimizer is adamw_8bit (bitsandbytes), whose is_on_gpu() check
dies on CPU tensors. Force optim=adamw_torch in all three configs.
- import unsloth reinstalls the real torch.compile over the eager passthrough,
so the GRPO hot path (chunked_selective_log_softmax) actually compiles and
inductor picks the spoofed CUDA device, crashing on device props
(gcnArchName). Re-apply the eager passthrough after import and flip
torch._dynamo.config.disable so every @torch.compile runs eager at call time.
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* cpu fake-train: write checkpoints under pytest tmp_path
Use pytest's tmp_path for each trainer's output_dir instead of a hardcoded
relative temp/ci_* path, so a local pytest run does not leave untracked dirs in
the repo tree and the tests are CWD-independent.
* version-compat CI: disable dynamo at process level for the fake-run job
Set TORCHDYNAMO_DISABLE / TORCH_COMPILE_DISABLE in the fake-run step env so
dynamo/inductor is off before conftest.py's early import unsloth, not only via
the per-test runtime shim. Defense in depth on the GPU-less runner: the GRPO
hot path never compiles regardless of when its functions were decorated.
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* Fix PEFT replacement for TRL >= 1.7.0, add missing compute_aux_loss for TRL 1.7.0
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix GRPO for TRL >= 1.7.0: PEFT ref-adapter removal and return arity
rl.py: for trl >= 1.7.0, scope the PEFT removal regex to the ref-adapter
block only by anchoring the end on ref_param.data.copy_(param.data), so it
no longer also deletes the following gradient-checkpointing
enable_input_require_grads() block. Neutralize TRL 1.7.0's
`if _is_quantized_model:` bf16 cast the same way the existing
is_loaded_in_4bit cast is handled.
rl_replacements.py: initialize _extra_moe_kwargs before use (it was
referenced before assignment whenever compute_aux_loss was passed) and only
request output_router_logits when the aux loss is actually wanted.
rl_replacements.py: _get_per_token_logps_and_entropies now returns a 3-tuple
(logps, entropies, aux_loss) for trl >= 1.7.0 and a 2-tuple for older TRL,
matching how every TRL call site unpacks the result. Without this, TRL 1.7.x
_generate_and_score_completions unpacks 3 values from a 2-tuple and raises
"not enough values to unpack (expected 3, got 2)".
* Return zero aux_loss placeholder and drop inference-mode aux collection
* GRPO TRL >= 1.7.0: reject router aux-loss opt-in at init; drop zero aux placeholder
Unsloth's optimized GRPO forward cannot compute the MoE router auxiliary loss.
Previously an explicit opt-in (router_aux_loss_coef > 0) returned a fabricated
zero, silently training without the requested load-balancing penalty. Now reject
it at trainer init with a clear NotImplementedError, and return None (not zero)
for the aux slot of TRL's 3-tuple. Default stays off (coef 0), so the common
path is unaffected.
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* GRPO hidden-states fallback: free ModelOutput before chunked log-softmax
The old/ref logprob fallback binds the full ModelOutput (which holds every
layer's hidden_states when output_hidden_states=True) and kept it alive across
chunked_hidden_states_selective_log_softmax, an avoidable OOM on large models.
Extract logits then del outputs in both the text and VLM branches.
* Version-compat CI: proactively catch TRL GRPO breakage
The existing TRL canary is a static symbol/source grep: it verifies symbols
exist but is blind to structural changes (TRL 1.7.0's 2->3-tuple per-token-logps
return arity and restructured PEFT ref-adapter block, which the fix in this PR
addresses, both slipped past it because the methods still existed).
Two additions:
- test_trl_grpo_pinned_symbols.py: extend TRL_TAGS to 1.5/1.6/1.7 and pin the
exact source-string contracts the rl.py / rl_replacements.py transforms depend
on for TRL >= 1.7.0 (PEFT elif ref-adapter block + enable_input_require_grads
survival, if _is_quantized_model, aux_loss_enabled anchor, compute_aux_loss
arity). A future TRL change fails on main a few days before the PyPI release.
- test_trl_grpo_fake_run.py + a version-compat-ci job: fake-CUDA run that drives
the real GRPO/SFT/DPO source-transform patchers against latest + main TRL on a
CPU-only runner (no training) and asserts the generated Unsloth trainer still
satisfies the transform contracts. Catches behavioral regressions the grep
cannot see.
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* fake-run test: use a normal Version import for the aux gate
* version-compat CI: fix fake-run job gate + torch-absent collection
- Drop the invalid job-level matrix if (matrix is not available in
jobs.<id>.if -> 'Unrecognized named-value: matrix' fails the whole
workflow). Use a single job that runs vs TRL latest always and re-runs
vs TRL main only on schedule/dispatch via a step-level github.event_name
guard. Validated with actionlint.
- Module-level skip the fake-run test when torch is absent so
daily-fresh-fetch (pytest-only, collects tests/version_compat/) does not
crash on the top-level spoof import.
* fake-run test: do not skip on import failure
unsloth/trl are installed in the grpo-fake-run job, so a failing import is the
import-time drift this canary must catch. Keep only the not-installed find_spec
skips; let a real import error fail the test.
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* GRPO arity gate: regex downgrade + fail loud + CI coverage
The TRL < 1.7.0 per-token-logps return downgrade was an exact-string replace
anchored on the full return line incl. its comment, so a reformat (e.g.
pre-commit) could silently no-op it and ship a 3-tuple to older TRL. Switch to
a regex tolerant of comment/whitespace drift, and raise if the anchor stops
matching (re.subn count != 1) instead of failing silently. Add a monkeypatched
trl_version unit test asserting both arities, since CI only installs TRL >= 1.7.0
and never exercised the downgrade otherwise.
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* fake-run: give SFT/DPO a real contract, not just ast-parse
The SFT/DPO fake patch runs only checked the generated trainer parses. Also
assert the shared QLoRA _is_quantized_model bf16 cast is neutralized (TRL 1.7's
spelling, present in both sft_trainer and dpo_trainer), so a structural TRL
change to that block is caught for SFT/DPO too, not just GRPO.
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* GRPO PEFT ref-adapter removal: lower gate to the TRL 1.4.0 floor
The elif is_peft_model(model) and args.beta != 0.0: ref-adapter block was
introduced in TRL 1.4.0 and is unchanged through 1.7.x, but the removal was
gated at >= 1.7.0, so for 1.4 <= TRL < 1.7 the transform fell through to the
0.27 branch (which matches the older if is_peft_available()... form) and
silently no-oped: a PEFT + beta != 0 GRPO run then computed the KL reference
from the copied ref adapter instead of the base model. Lower the gate to
1.4.0 and keep the 1.7.0-only router aux-loss fail-fast nested. Widen the
pinned-symbol contract test to run from 1.4.0 so the covered versions are
actually exercised.
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* feat(cli): detect MLX distributed launch context
* feat(mlx): wire distributed inference backend
* feat(cli): broadcast MLX distributed chat turns
* fix(cli): wait indefinitely for distributed chat turns
* fix(cli): report MLX distributed load errors cleanly
* fix(mlx): route distributed vlm through loader
* fix(cli): detect inline MLX host JSON
* fix(studio): harden distributed object sharing
* fix(studio): select JACCL distributed backend
* fix(cli): abort distributed error paths
* Distinguish real stream errors from model text via GenStreamError in distributed CLI
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* Fail loud when MLX distributed init returns a singleton group
The worker only reaches this block when distributed was explicitly
requested. A singleton (size 1) group means the launch failed to form a
real group (MLX built without distributed support, or an invalid launch
env/hostfile); silently continuing leaves nonzero ranks looping forever
on share_distributed_object. Raise instead so the surrounding handler
returns a clear load error.
* Tighten MLX distributed inference comments
---------
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Co-authored-by: danielhanchen <unslothai@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
