* feat: inject local model provider into recipe jobs via JWT
* feat: auto-generate JWT for local model providers in recipes
* feat: add is_local flag to model provider config types and utils
* fix(studio): skip endpoint validation for local providers
* feat(studio): add local/external model source toggle to provider dialog
* feat(studio): thread localProviderNames through model config dialog chain
* feat(studio): show 'Local model (Chat)' label for local model_provider configs
* fix: hardcode loopback for local endpoint, clear stale creds on toggle
* fix: document TOCTOU/JWT rotation, add deferred import comments, fix is_local serialization
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix(studio): clear stale local model state on provider toggle and validation
* fix(studio): override empty local endpoint in validation and skip model gate for unused providers
* fix(studio): resolve loopback port from app.state, clear stale local provider fields, sync model id on toggle
Address review feedback on the local-model-provider flow:
- Backend (jobs.py): _resolve_local_v1_endpoint now reads the actual bound
port from app.state.server_port (set in run.py after binding) instead of
parsing it out of request.base_url, which is wrong behind any reverse
proxy or non-default port. The two duplicated urlparse blocks are gone.
- Backend (jobs.py): defensively pop api_key_env, extra_headers, extra_body
from local providers so a previously external provider that flipped to
local cannot leak invalid JSON or rogue auth headers into the local /v1
call. Also dedupe the post-loop assignment and tighten the local-name
intersection so empty names cannot match.
- Backend (jobs.py): hoist datetime and urllib.parse imports to the top
import block for consistency with the rest of the file.
- Backend (run.py): expose the bound port on app.state.server_port after
the uvicorn server is constructed.
- Frontend (model-provider-dialog.tsx): clear extra_headers and extra_body
when toggling to local mode. Hidden inputs would otherwise keep stale
JSON blocking validate/run.
- Frontend (model-config-dialog.tsx): factor the local-aware provider
selection logic into applyProviderChange and call it from both
onValueChange and onBlur, so manually typing a provider name and tabbing
away keeps the model field consistent.
- Frontend (recipe-studio.ts store): handle both directions of the
is_local toggle in the cascade. external -> local now backfills
model: "local" on already-linked model_configs so they pass validation
immediately, mirroring the existing local -> external clear path.
- Frontend (validate.ts + build-payload.ts): thread localProviderNames
into validateModelConfigProviders and skip the "model is required"
check for local-linked configs. Local providers do not need a real
model id since the inference endpoint uses the loaded Chat model.
* fix(studio): narrow store cascade types, sync model placeholder on graph relink and node removal, harden ephemeral port path
Loop 2 review fixes:
- recipe-studio.ts: type-narrow next.is_local by also checking
next.kind === "model_provider". TS otherwise raised TS2339 because
next was typed as the union NodeConfig after the spread. The behavior
is unchanged but the code now compiles cleanly.
- model-config-dialog.tsx: convert the lastProviderRef / providerInputRef
ref-during-render pattern (pre-existing react-hooks/refs lint error)
to a useEffect that syncs providerInputRef from config.provider. The
combobox blur path still uses applyProviderChange and remains stable.
- recipe-graph-connection.ts: when a graph drag links a model_provider
to a model_config, mirror the dialog applyProviderChange behavior:
fill model: "local" if the new provider is local and the model field
is blank, clear model when relinking from a local placeholder to an
external provider, otherwise leave the model alone.
- reference-sync.ts: when a referenced provider node is removed, clear
the synthetic model: "local" placeholder along with the provider
field, so a future relink to an external provider does not pass
validation with a stale value that fails at runtime.
- run.py: only publish app.state.server_port when the bound port is a
real positive integer; for ephemeral binds (port==0) leave it unset
and let request handlers fall back to request.base_url.
- jobs.py: _resolve_local_v1_endpoint also falls back when
app.state.server_port is non-positive, and uses `is None` instead of
the truthy fallback so a literal 0 is handled correctly.
* fix(studio): strict is_local check, narrow loaded-model gate to LLM-reachable configs, add scope-server port fallback
Loop 3 review fixes:
- jobs.py, validate.py: require `is_local is True` instead of truthy
check. Malformed payloads such as is_local: "false" or is_local: 1
would otherwise be treated as local and silently rewritten to the
loopback endpoint.
- jobs.py: _resolve_local_v1_endpoint now tries request.scope["server"]
(the actual uvicorn-assigned (host, port) tuple) as a second
resolution step before falling back to parsing request.base_url.
This covers direct-uvicorn startup paths and ephemeral binds that
never publish app.state.server_port.
- jobs.py: new _used_llm_model_aliases helper collects the set of
model_aliases that an LLM column actually references, and the
"Chat model loaded" gate is now only triggered when a local
provider is reachable from that set. Orphan model_config nodes on
the canvas no longer block unrelated recipe runs.
* fix(studio): force skip_health_check on local-linked configs, skip JSON parsing for local providers, local-aware inline editor
Loop 4 review fixes:
- jobs.py: after rewriting local providers, also force
skip_health_check: true on any model_config linked to a local
provider. The /v1/models endpoint only advertises the real loaded
model id, so data_designer's default model-availability health check
would otherwise fail against the placeholder "local" id before the
first chat completion call. The inference route already ignores the
model id in chat completions, so skipping the check is safe.
- builders-model.ts: buildModelProvider now short-circuits for local
providers and emits only { name, endpoint: "", provider_type, is_local }
without running parseJsonObject on the hidden extra_headers/extra_body
inputs. Imported or hydrated recipes with stale invalid JSON in those
fields no longer block client-side validate/run.
- inline-model.tsx: the model_config branch now accepts an optional
localProviderNames prop and mirrors the dialog applyProviderChange
behavior. Changing provider to/from a local one auto-fills or clears
the "local" placeholder consistently with the other edit paths.
- recipe-graph-node.tsx: derive localProviderNames from the store via
useMemo (stable identity) and pass it through renderNodeBody to
<InlineModel>. Hooks order is preserved by declaring them above the
early return for markdown_note nodes.
- run.py: minor comment tweak - loop 3 already added the scope-server
fallback path, note that in the comment.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: danielhanchen <info@unsloth.ai>
* 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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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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* fix(chat): prevent implicit empty thread creation and stabilize new-chat flow
* fix(chat): harden compare thread sync and simplify sidebar thread query
* fix(chat): harden new-thread state sync and isolate compare active thread updates
* fix(chat): stabilize new-thread state sync and prevent compare/session bleed
* Fix thread restoration, handleNewThread guard, sidebar filter, and delete flow
- Remove __LOCALID_ filter from getInitialSingleChatView: in this
Dexie-backed adapter, AUI's __LOCALID_ prefixed IDs ARE the real
persistent thread IDs stored by initialize(). Filtering them out
breaks thread restoration on navigation.
- Simplify handleNewThread to synchronous: the async Dexie message
check is redundant (persistence is already deferred to first append)
and strands users on legacy empty threads. Use a simple guard that
checks the store's activeThreadId to detect unsent drafts.
- Add message-count filter to sidebar: filter threads to only show
those with at least one message, hiding legacy empty threads.
- Add store-based sidebar highlighting fallback: use activeThreadId
from the store when view.threadId is not set (nonce-backed chats).
- Fix handleDelete to call onNewThread() instead of onSelect(), and
clear activeThreadId, so the runtime properly resets after deleting
the active thread.
* Fix handleDelete nonce path and restore __LOCALID_ filter
handleDelete was calling onNewThread() after clearing activeThreadId,
but the handleNewThread guard sees !view.threadId && !activeThreadId
and returns early, leaving the UI stuck on the deleted thread.
Fix by directly calling onSelect with a new nonce instead.
Restore __LOCALID_ filter in getInitialSingleChatView to prevent
restoring unpersisted AUI local thread IDs on navigation. Without
this filter, navigating away from /chat before sending a message
would restore a non-existent thread that Dexie cannot fetch.
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Fix custom folder scanning when pointing directly at a model directory.
When a user adds a custom scan folder that points directly at a model
directory (e.g. /path/to/gemma-4-e2b-it-gguf/ containing config.json
and gemma-4-E2B-it-BF16.gguf), the model list previously showed
individual .gguf files as separate entries instead of recognizing the
directory as a single model. Clicking any entry showed "No GGUF
variants found" because list_local_gguf_variants received a file path
and immediately returned empty.
Changes:
- Add _is_model_directory() helper that detects directories with both
config metadata and actual model weight files (excludes mmproj GGUFs
and non-weight .bin files like tokenizer.bin)
- _scan_models_dir: detect self-model and return single directory entry
- _scan_lmstudio_dir: surface model directories directly instead of
descending into them as publisher folders; handle both root and child
model directories
- Add _resolve_gguf_dir() helper for GGUF path resolution that only
falls back to parent directory when parent has model metadata
- list_local_gguf_variants / _find_local_gguf_by_variant: use resolver
so .gguf file paths inside model directories work correctly
* fix: skip redundant HfFileSystem().glob() calls in loader.py
Guard the SUPPORTS_LLAMA32 glob blocks with `is_model and is_peft` so
the HfFileSystem HTTP call is only made when both configs could actually
exist. This prevents indefinite hangs on slow/unreliable networks since
the glob result is redundant when either AutoConfig or PeftConfig
already failed to load.
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* Remove test file from main PR - moved to separate PR
Tests for the glob skip guard belong in their own PR to keep
the loader change minimal and reviewable.
* Harden HfFileSystem glob: fix Windows path splitting, add try/except
- Use str.rsplit("/", 1) instead of os.path.split to extract filenames
from HfFileSystem paths. HfFileSystem always returns POSIX-style paths,
but os.path.split uses the OS separator, so on Windows the entire path
was returned as the "filename" and the config name comparison always
failed.
- Wrap the HfFileSystem().glob() call in try/except to gracefully handle
network failures (offline mode, timeouts, unreachable Hub). On failure
both_exist stays False, which is the safe default.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Remove redundant HfFileSystem().glob() call for remote repos
When is_model and is_peft are both True, AutoConfig and PeftConfig
have already loaded successfully, proving both config.json and
adapter_config.json exist. The HfFileSystem network call to re-verify
this was redundant and could cause hangs on slow networks.
Replace the glob + try/except block with a direct both_exist = True
assignment.
* Remove unused HfFileSystem import
HfFileSystem was only used for the glob() calls that were replaced
with direct both_exist = True assignments in the previous commit.
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Gemma-4 does not need FORCE_FLOAT32. Testing shows that both float16 and
bfloat16 work correctly without the forced float32 override:
- Inference: identical outputs for float16 and bfloat16 (greedy decoding)
- Training (100 steps, 4-bit LoRA, SFT on FineTome-100k):
- float16 final loss: 3.048
- bfloat16 final loss: 3.065
- Losses converge to within 0.02 by step 60
- Grad norms healthy and comparable for both dtypes
The FORCE_FLOAT32 path was actually causing training divergence. With
it enabled, the compiled float32 run diverged at step ~28 with grad norms
collapsing to near zero and loss plateauing at ~12.4. Without it, both
dtypes train normally.
This enables float16 on Tesla T4 and other GPUs without bfloat16 support.
* Add tests for is_vision_model() caching behaviour
* Fix review feedback: remove dead helper, fix exception test
- Remove unused _make_config() helper function (dead code)
- Fix test_exception_result_cached to actually exercise the exception path
by mocking load_model_config to raise OSError instead of using
side_effect=[False] which only tested normal False returns
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* Use strict mock specs so tests exercise intended detection paths
Use MagicMock(spec=[]) for all config mocks so hasattr() only returns
True for explicitly set attributes. Without this, MagicMock defaults
make all hasattr checks truthy, allowing tests to pass via unintended
detection paths (e.g. img_processor instead of vision_config).
---------
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* 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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---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* Add fallback message for Colab Studio button when localhost link doesn't work
* Make fallback message darker grey for better readability
* Make fallback message bold for better visibility
---------
Co-authored-by: LeoBorcherding <LeoBorcherding@users.noreply.github.com>
* studio: add speculative decoding support (ngram-mod, on by default)
Enable n-gram speculative decoding for GGUF models in Unsloth Studio.
Uses llama.cpp's ngram-mod mode which gives 10-40% faster generation
with zero VRAM cost via a 4MB fixed hash table that auto-resets on
low acceptance rates.
Backend:
- Add speculative_type field to LoadRequest, LoadResponse, and
InferenceStatusResponse pydantic models
- Add speculative_type parameter to LlamaCppBackend.load_model()
with allowlist validation (ngram-simple, ngram-mod)
- Pass --spec-type, --spec-ngram-size-n 16, --draft-max 24 flags
to llama-server when ngram-mod is active
- Default to ngram-mod for non-vision GGUF models server-side
- Silently skip speculative decoding for vision models (unsupported
in llama.cpp server-context.cpp)
Frontend:
- Add speculative_type to TS API types
- Add speculativeType/loadedSpeculativeType to chat runtime store
with default value of "ngram-mod"
- Add On/Off toggle in Model settings section (GGUF only, hidden
for vision models), included in dirty check for Apply/Reset
- Wire speculative_type through model load request and response
- Restore speculative type state on page refresh/reconnect
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: remove server-side speculative decoding override
The backend was overriding speculative_type=None to "ngram-mod" for
non-vision GGUF models, which prevented users from disabling spec
decoding via the UI toggle. The frontend store already defaults to
"ngram-mod", so the backend fallback was redundant and blocked the
explicit "Off" setting.
* fix: use recommended ngram-mod params from llama.cpp docs
Update speculative decoding params to match the recommended values
from llama.cpp docs (docs/speculative.md):
--spec-ngram-size-n 24 (was 16, docs say small n not recommended)
--draft-min 48 (was 0)
--draft-max 64 (was 24, docs note MoEs need long drafts)
Also fix comment: ngram-mod uses ~16 MB (4M entries * 4 bytes),
not 4 MB.
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* add benchmark table and references to speculative decoding comment
Include speedup numbers from llama.cpp PRs #18471 and #19164 as an
inline comment so future readers understand the expected gains.
---------
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* fix(studio): harden sandbox security for terminal and python tools
The existing command blocklist used naive str.split() which is trivially
bypassable via quoting, full paths, nested shells, variable expansion,
and cross-tool pivoting through Python os.system/subprocess. Fixes#4818.
Changes:
- Replace str.split() blocklist with shlex.split() + os.path.basename()
tokenization and regex scanning at shell command boundaries
- Add sanitized subprocess environment (_build_safe_env) that strips
credentials (HF_TOKEN, WANDB_API_KEY, GH_TOKEN, AWS_*, etc.) and
restricts PATH to /usr/local/bin:/usr/bin:/bin
- Add PR_SET_NO_NEW_PRIVS via prctl on Linux so sudo/su/pkexec fail
at the kernel level regardless of how they are invoked
- Add RLIMIT_NPROC (256) and RLIMIT_FSIZE (100MB) to prevent fork
bombs and disk filling attacks
- Extend AST safety checker to detect os.system(), os.popen(),
subprocess.run/Popen/call/check_output, os.exec*, os.spawn* calls
containing blocked commands or dynamic (non-literal) arguments
- Add cross-platform support: cmd.exe on Windows, bash on Unix;
CREATE_NO_WINDOW flag on Windows, preexec_fn on Unix
- Expand blocklist from 7 to 14 commands: add su, chown, passwd,
mount, umount, fdisk, kill, killall, pkill
- Apply all layers to both _bash_exec and _python_exec
Zero measurable performance overhead -- shlex parsing and a single
prctl syscall per subprocess fork.
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* Fix review findings: exception_catching dead code, false positives, process substitution
- Include exception_catching reasons in _check_code_safety so bare
except-in-loop timeout evasion is actually blocked (was computed in
_check_signal_escape_patterns but never read by the caller)
- Remove base.split() inner loop that caused false positives on quoted
text arguments containing blocked words (e.g. echo "kill this process")
- Add targeted nested shell detection for bash/sh/zsh -c arguments
instead, which catches bash -c 'sudo whoami' without false positives
- Add <() process substitution to the regex character class so
diff <(rm -rf /path) is also caught
- Fix error message to say "unsafe patterns" instead of specifically
mentioning signal manipulation when other categories trigger
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address review feedback: regex paths, keyword args, list element scanning
- Regex now matches blocked commands after optional path prefix at shell
boundaries (catches ls; /usr/bin/sudo and similar)
- Nested shell detection uses os.path.basename so bash -c "/bin/rm" is
caught
- AST checker now inspects keyword arguments (not just positional) so
subprocess.run(args="sudo ...", shell=True) is detected
- List elements in subprocess calls are now checked via
_find_blocked_commands for consistency (catches subprocess.run(["bash",
"-c", "rm -rf /"]))
- Dynamic argument check uses _is_safe_literal that validates list
contents are all string literals
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix nested shell scan to only check the script body, not positional args
bash -c 'script' arg0 arg1 -- only tokens[i+1] is the script body;
subsequent tokens are $0, $1 positional parameters passed to the script
and are not executed as shell commands. Scanning all remaining tokens
caused false positives.
* Add subshell parentheses to regex command boundary detection
(sudo whoami) was not caught because ( was not in the regex character
class for shell command boundaries. Add ( to the set alongside ;, &,
|, backtick, newline.
* Address high-priority review findings from 7 parallel reviewers
- Track from-imports of dangerous functions (from os import system,
from subprocess import run as r, etc.) via shell_exec_aliases dict
so bare-name calls are detected by the AST checker
- Include the active Python interpreter and virtualenv directories
in the sanitized PATH so pip, uv, and Studio packages remain
accessible in the sandbox
- Add Windows-specific blocked commands (rmdir, takeown, icacls,
runas, powershell, pwsh) only on win32 platform
- Add os.posix_spawn and os.posix_spawnp to _SHELL_EXEC_FUNCS
- Handle tuple literals same as list literals in AST argument
inspection (both _extract_strings_from_list and _is_safe_literal)
* Fix false positive on check=True kwargs and recursive nested shell scanning
- Only inspect command-carrying keyword arguments (args, command,
executable, path, file) in the AST checker, not control flags like
check=True, text=True, capture_output=True which are booleans and
were incorrectly flagged as non-literal dynamic arguments
- Replace split() in nested shell detection with recursive call to
_find_blocked_commands so that quoted commands (bash -c '"sudo"
whoami') and semicolons (bash -c "sudo;ls") within nested shells
are properly detected through the full shlex + regex pipeline
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Move preexec_fn imports to module level and use find_library for libc
Addresses two Gemini review findings:
1. preexec_fn thread safety: _sandbox_preexec previously imported ctypes
and resource inside the function body, which runs between fork() and
exec() in the child process. In a multi-threaded server, this could
deadlock if the import machinery locks were held by another thread at
fork time. Now all imports and the libc handle are resolved once at
module load time, so _sandbox_preexec only calls C-level functions
(prctl, setrlimit) with no Python import activity.
2. Hardcoded libc.so.6 path: replaced with ctypes.util.find_library("c")
which works on glibc (libc.so.6), musl (libc.musl-*.so.1), and other
Linux distributions where libc has a different soname.
* Apply Gemini style suggestions: combined regex, dict.fromkeys, constant hoisting
- Combine per-word regex loop into a single re.findall with alternation
pattern, avoiding repeated regex compilation and searching
- Replace manual dedup loop with dict.fromkeys for PATH entries
- Hoist _CMD_KWARGS frozenset out of visit_Call to avoid recreating it
on every AST node visit
* Add cmd /c nested shell detection for Windows parity
The nested shell scan only checked for Unix shells (bash -c, sh -c, etc).
Add cmd /c and cmd.exe /c detection so that Windows nested shell
invocations are also recursively scanned for blocked commands. The token
scan already catches blocked commands at any position, so this is
defense-in-depth for consistency across platforms.
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* Handle combined shell flags (-lc, -xc) and interleaved flags (--login -c)
The nested shell scan only matched token == "-c" with the immediately
preceding token being a shell name. This missed:
- Combined flags: bash -lc 'rm ...' (-lc ends with c, is a valid
combined flag meaning -l -c)
- Interleaved flags: bash --login -c 'sudo ...' (--login sits between
bash and -c)
Now matches any short flag ending in 'c' (e.g. -lc, -xc, -ic) and
walks backwards past intermediate flags to find the shell binary.
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* Fix /bin/bash bypass, remove RLIMIT_NPROC, reduce AST false positives
Addresses three high-consensus findings from 20-reviewer pass:
1. /bin/bash -c 'sudo whoami' bypassed nested shell scan because the
backwards flag-skip logic treated paths starting with / as flags.
Now only skips tokens starting with - as Unix flags; on Windows
only skips short /X flags (not /bin/bash style paths). [9/20]
2. RLIMIT_NPROC=256 caused subprocess.run to fail with EAGAIN because
Linux enforces NPROC per real UID, not per process tree. Removed
RLIMIT_NPROC entirely; RLIMIT_FSIZE and PR_SET_NO_NEW_PRIVS remain
as the primary resource and privilege controls. [5/20]
3. AST checker rejected safe dynamic subprocess usage like
cmd=["git","status"]; subprocess.run(cmd) as shell_escape_dynamic.
Now only flags dynamic args for shell-string functions (os.system,
os.popen, subprocess.getoutput, etc.) or when shell=True is
explicitly set. List-based subprocess calls with shell=False (the
default) do not pass through a shell and are not flagged. [12/20]
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* Handle Windows drive letter paths and .exe extensions in command detection
Gemini review found that Windows absolute paths (C:\Windows\System32\
shutdown.exe) and executable extensions (.exe, .com, .bat, .cmd) were
not handled:
- Token scan now strips .exe/.com/.bat/.cmd extensions before checking
the blocklist, so sudo.exe matches sudo, shutdown.bat matches shutdown
- Regex pattern now includes optional Windows drive letter prefix
([a-zA-Z]:[/\\]) and optional executable extension suffix, so commands
after shell metacharacters with full Windows paths are also caught
* Handle **kwargs dict expansion, non-literal shell=, and except Exception false positive
Addresses three findings from second 20-reviewer pass:
1. **kwargs dict expansion (9/20): subprocess.run(**{"args": "rm ...",
"shell": True}) bypassed the AST checker because **kwargs were
treated as opaque. Now expands literal dict **kwargs to inspect
their keys, and flags opaque **kwargs (variable dicts) as unsafe.
2. Non-literal shell= values (7/20): shell=variable was treated as
shell=False (safe). Now any shell= value that is not literally
False is treated as potentially True (conservative default).
3. except Exception false positive (1/20): except Exception in a loop
was flagged as timeout evasion, but Exception does not catch
SystemExit or KeyboardInterrupt which are used for timeout
enforcement. Narrowed to only flag except BaseException and
except TimeoutError in loops.
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---------
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Fixes#4809
On a new Studio chat, the first tool call could start before the frontend
initializes the thread ID. That meant the first request could go out without
a session_id, so the backend started the tool in the shared sandbox root
instead of the chat's session sandbox.
Frontend:
- Eagerly initialize the thread when switching to a new chat
- Resolve the thread ID once at request time and keep it stable through
async model-load waits
- Disable ActiveThreadSync during new-chat initialization to prevent
stale thread IDs from being written back
- Add error handling for thread initialization failures
- Clear activeThreadId on all compare-mode entry paths to prevent
cross-session leakage
- Fix exitCompare to restore context usage from the saved view
- Coerce falsy thread IDs to undefined for consistent backend/frontend
fallback behavior
- Use _default as the image sessionId fallback to match the backend
Backend:
- Use ~/studio_sandbox/_default when a request arrives without a session_id
* fix(studio): reuse HF cached repo casing to prevent duplicate downloads
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* Move cache case resolution tests to separate PR
Tests for resolve_cached_repo_id_case and get_model_config case resolution
belong in their own PR to keep this change focused on the runtime fix.
* fix(studio): debug-log HF_HUB_CACHE fallback in path_utils
* Fix stale memoization in resolve_cached_repo_id_case
- Check exact-case path before memo to ensure a newly-appeared exact
match always wins over a previously memoized variant
- Validate memoized entries still exist on disk before returning them
to prevent stale results when cache dirs are deleted/recreated
* Minor cleanups for cache case resolution
- Use .is_dir() instead of .exists() for exact-case cache check
(cache entries are always directories)
- Remove redundant fallback in _detect_audio_from_tokenizer since
get_cache_path already handles case resolution and returns None
when the model is not cached
---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* feat: allow non-LLM recipes to run without provider block
* feat: reorder execution tabs and add generation-aware data tab empty state
* fix: add accessibility attrs to data tab spinner and use literal ellipsis
* fix(studio): use shared spinner, stub provider, and hide unused LLM metrics
Backend: inject stub model provider for sampler-only recipes so
DataDesigner init does not reject empty provider lists.
Frontend: use shared Spinner component, hide LLM columns metric
and model usage card when recipe has no LLM columns.
* Fix tab reset and terminal auto-scroll regressions for PR #4805
Reset detailTab to "data" when switching between executions so
the Data tab default is applied consistently, not only on first
mount. Also add detailTab to the terminal scroll effect deps so
auto-scroll-to-bottom fires when the user opens the Overview tab
after landing on Data.
* Guard terminal scroll reset to only fire on Overview tab
The previous scroll effect ran on every tab switch, which could
reset the user's manual scroll position if they scrolled up in
the terminal and briefly switched tabs. Now the scroll-to-bottom
and sticky-bottom reset only fires when navigating to the
Overview tab.
* Use None for stub provider api_key instead of literal string
The stub ModelProvider that satisfies the DataDesigner registry
for non-LLM recipes should not carry a fake credential string.
Using None avoids sending an Authorization header if the provider
is ever inadvertently invoked.
---------
Co-authored-by: Daniel Han <danielhanchen@gmail.com>
Differentiate web_search query searches from URL fetches in the Studio chat UI.
Backend (llama_cpp.py):
- Emit "Reading: hostname" for URL fetches and "Searching: query" for query searches in SSE status events
- Only show hostname for valid http/https URLs; schemeless/non-http URLs get "Reading page..." generic fallback
- Strip www. prefix for consistency with the frontend
Frontend (tool-ui-web-search.tsx):
- Tool card shows "Read hostname" / "Reading hostname..." for URL fetches
- Shows "Searched query" / "Searching for query..." for query searches
- Uses new URL() with protocol check; falls back to "Read page" / "Reading page..." for non-http URLs
* Simplify llama.cpp install logic
* print release tag
* Retry failed json decode
* don't pull all ggml releases
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* Remove test file changes from main PR
Test changes for test_pr4562_bugfixes.py will be submitted in a separate PR to keep this PR focused on the install path simplification.
* Fix setup.sh executable bit and direct tag lookup for pinned releases
- Restore setup.sh file mode to 100755 (was accidentally changed to 100644)
- Add direct GitHub API tag lookup in iter_release_payloads_by_time for
non-latest requested tags (e.g. b7879) instead of relying on paginated
release scans that may miss older releases beyond the 5-page limit
- Update stale DEFAULT_PUBLISHED_REPO comment to match new value
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix force-compile default ref and remove dead code in setup.ps1
- Change FORCE_COMPILE_DEFAULT_REF from "main" to "master" in all three
files (install_llama_prebuilt.py, setup.sh, setup.ps1) since
ggml-org/llama.cpp uses "master" as its default branch, not "main".
Using "main" would cause git clone --branch to fail when
UNSLOTH_LLAMA_FORCE_COMPILE=1 with UNSLOTH_LLAMA_TAG=latest.
- Remove dead if ($SkipPrebuiltInstall) block inside the else branch of
setup.ps1 that could never be reached (the outer elseif already
handles $SkipPrebuiltInstall=true).
- Maintain setup.sh executable bit (100755).
* Improve iter_release_payloads_by_time error handling for direct tag lookup
When a pinned release tag is not found (HTTP 404), fall through to the
paginated release scan instead of silently returning empty results.
Non-404 errors (network failures, rate limits) are propagated to the
caller so users get actionable error messages.
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---------
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Co-authored-by: Daniel Han <danielhanchen@gmail.com>
* fix: patch PEFT for Gemma4ClippableLinear in loader checkpoint path
The same Gemma4ClippableLinear monkey-patch that exists in vision.py
for training is needed in loader.py for loading existing checkpoints
(used by export and inference).
Gemma4ClippableLinear wraps nn.Linear but does not subclass it, so
PEFT's LoRA injection fails with "Target module not supported".
The patch redirects PEFT to target the inner .linear child instead.
Applied only to the vision model PeftModel.from_pretrained path.
Temporary fix until PEFT adds native support (peft#3129).
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* fix: wrap ClippableLinear patch in try/finally to always restore
Ensures _create_and_replace is restored even if PeftModel.from_pretrained
raises, preventing leaked global state across subsequent model loads.
---------
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* fix(studio): lazy-import AutoConfig in model_config.py to fix transformers 5.x version switch
Move `from transformers import AutoConfig` from module level to inside
load_model_config() where it is actually used.
model_config.py is transitively imported at module load time via:
core/inference/__init__ → llama_cpp → utils.models → model_config
In inference subprocesses (mp.spawn), this chain runs before
_activate_transformers_version() can prepend .venv_t5/ to sys.path.
The eager import caches transformers 4.57.6 in sys.modules, and the
subsequent sys.path change has no effect — Python always checks
sys.modules before sys.path.
Making the import lazy ensures transformers is not loaded until after
version activation, so the subprocess picks up the correct version.
* fix(studio): also lazy-import extract_model_size_b in llama_cpp.py
Belt-and-suspenders: make the import that originally triggered the
chain lazy as well, so future module-level AutoConfig additions in
utils.models cannot reintroduce the problem.
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---------
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When DEFAULT_PUBLISHED_REPO is ggml-org/llama.cpp, the prebuilt
resolver raises PrebuiltFallback because ggml-org releases do not
include a llama-prebuilt-manifest.json asset. This was caught by the
generic Exception handler and printed as "fatal helper error" to
stderr, which triggers NativeCommandError on PowerShell.
Catch PrebuiltFallback separately in the top-level __main__ handler
and exit with EXIT_FALLBACK (code 2) instead of EXIT_ERROR (code 1).
The message is still logged but without the "fatal helper error"
prefix. The shell scripts already handle non-zero exits and fall
back to source builds.
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
* fix(studio): revert llama.cpp default tag to latest
The latest ggml-org/llama.cpp release (b8637) now includes Gemma 4
support. Revert the temporary "b8637" pin from #4796 to "latest" so
the prebuilt resolver always picks the newest release automatically
without needing manual tag bumps.
* docs: add comment explaining latest vs master for llama.cpp tag
Document in all three files why "latest" is preferred over "master"
and when "master" should be used as a temporary override.
---------
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
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.
ggml-org/llama.cpp b8637 includes Gemma 4 support (ggml-org/llama.cpp#21309).
Revert the temporary "master" default back to a pinned release tag.
This eliminates the HTTP 422 errors from the prebuilt resolver (which
could not find a release matching "master"), avoids unnecessary source
builds, and restores prebuilt binary downloads on all platforms.
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
* fix windows llama.cpp compile from source issue
* undo local repo usage
* fix llama.cpp install
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix windows
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: route resolve-source-build call through Invoke-LlamaHelper
The --resolve-source-build call at the source-build resolution path
was still calling install_llama_prebuilt.py directly instead of going
through Invoke-LlamaHelper. On PS7+ with ErrorActionPreference=Stop,
stderr from the 422 response (when tag is "master") would trigger a
terminating NativeCommandError and crash setup.
* fix: suppress stderr error records from Invoke-LlamaHelper
ErrorActionPreference=Continue prevents termination but PowerShell
still displays stderr lines as visible ErrorRecord objects. Capture
all output via 2>&1 and split stdout from stderr manually so that
stderr lines never appear on the console. When StderrPath is given
the stderr content is written to that file for diagnostics.
* fix: always rebuild llama.cpp on Windows when tag is master
When the requested llama.cpp tag is "master" (a moving target), skip
the "already built" early exit so the build path runs and syncs to
the latest commit. Without this, existing llama-server binaries from
an older build (e.g. b8635 which lacks Gemma 4 support) are reused
and model loading fails.
Pinned tags (e.g. b8635) still skip the rebuild when the binary
already exists, since the tag is immutable.
---------
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Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
The model list merge order was `top_gguf + top_hub + static_models`,
which meant the HF download-ranked models always came first. New models
like Gemma 4 have low download counts and were not in the HF top-40,
so they got buried after 80 other models despite being at the top of
the curated static defaults in defaults.py.
Flip the merge to `static_models + top_gguf + top_hub` so editorial
picks (new model launches, promoted models) always appear first in the
Recommended section, with HF popularity backfilling after.
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
The latest ggml-org/llama.cpp release (b8635) does not include Gemma 4
support (ggml-org/llama.cpp#21309 merged after the release was cut).
This causes `llama-server` to fail with "unknown model architecture:
gemma4" when loading Gemma 4 GGUFs.
Temporarily default _DEFAULT_LLAMA_TAG to "master" so all new installs
build from the llama.cpp master branch which includes Gemma 4 support.
Once a new upstream release is cut with Gemma 4, this can be reverted
back to "latest".
Changes:
- setup.sh: add _DEFAULT_LLAMA_TAG="master" maintainer default
- setup.ps1: add $DefaultLlamaTag="master" maintainer default
- install_llama_prebuilt.py: change DEFAULT_LLAMA_TAG fallback to "master"
Users can still override via UNSLOTH_LLAMA_TAG env var.
Revert the >= loosening from f9c4b08 back to exact pins.
Using transformers>=4.57.6 allows pip to install 5.x into the main
Studio venv, which breaks huggingface_hub imports
(is_offline_mode removed in newer hub versions).
The main venv must stay on transformers==4.57.6 and
huggingface-hub==0.36.2. The 5.x version lives only in .venv_t5/
and is dynamically switched via sys.path at runtime.
The v5.5-release branch now exists on huggingface/transformers.
Use transformers==5.5.0 for all install paths and
git+transformers.git@v5.5-release for the MLX installer.
Also bumps huggingface_hub from 1.7.1 to 1.8.0 in setup.sh and
setup.ps1 to stay consistent.
Hardcode the release repo to ggml-org/llama.cpp and remove the
UNSLOTH_LLAMA_RELEASE_REPO and UNSLOTH_LLAMA_SOURCE env var overrides
so that all users always build/download from mainline llama.cpp.
Gemma-4 support landed in transformers main
(huggingface/transformers#45192). Update the version pin from
5.5.0.dev0 to 5.5.0 across loader, Studio version switcher,
and the MLX installer. Also fix install_gemma4_mlx.sh which
referenced a non-existent v5.5-release branch -- pin it to
the correct commit (91b1ab1) instead.
Small GGUF models (<9B) frequently generate full code or lengthy
explanations instead of calling tools, bypassing the existing
plan-without-action re-prompt mechanism. Three issues:
1. _REPROMPT_MAX_CHARS=500 was too low -- models that output full
HTML/code responses (often 1000+ chars) never triggered the
re-prompt at all, since it only fires on short responses.
2. _MAX_REPROMPTS=1 gave the model only one chance to comply.
Small models often need 2-3 nudges before switching from
text generation to tool calling.
3. The re-prompt text ("Please use the available tools...") was
too polite for small models to follow reliably.
4. Tool-calling detection missed chat templates using Jinja
whitespace-trimming syntax ({%- if tools -%}) since only
({%- if tools %}) and ({% if tools %}) were checked.
Changes:
- Raise _REPROMPT_MAX_CHARS from 500 to 2000 so longer responses
(code blocks, multi-paragraph plans) still trigger re-prompts
- Raise _MAX_REPROMPTS from 1 to 3 for more retry budget
- Use direct, imperative re-prompt language that small models
follow more reliably ("STOP. You MUST call a tool NOW.")
- Strengthen the system prompt tool nudge to explicitly forbid
outputting code blocks (redirect to the python tool instead)
- Add Jinja whitespace-trimmed variants to the tool_markers
list so all template styles are detected correctly
* UI Changes
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* Remove unrelated test file
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