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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* 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.
---------
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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
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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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* 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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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.
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* 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.
---------
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Gemma 4 is a native transformers 5.5 model and does not need
trust_remote_code=True. The auto-enable logic (added for NemotronH)
was catching all transformers 5.x models, including Gemma 4.
When trust_remote_code=True, unsloth_compile_transformers() returns
early without running the compiler. This disables the fused cross
entropy patch, causing logged training loss to be inflated by the
gradient_accumulation_steps factor.
Exclude models matching "gemma-4" or "gemma4" from the auto-enable
so the compiler runs and applies fused cross entropy correctly.
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
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* fix windows
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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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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.
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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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* feat(studio): display images from Python tool execution in chat UI
When the model calls the Python tool to create a matplotlib plot or
other image file, the image now displays inline in the chat output
instead of being invisible to the user.
Backend:
- Detect new image files (png/jpg/gif/webp/bmp) after Python subprocess
completes by diffing os.listdir before/after execution
- Append __IMAGES__ sentinel to tool result for frontend consumption
- Strip sentinel before injecting result into LLM context (role: tool)
so the model never sees file paths
- Add GET /sandbox/{session_id}/{filename} endpoint with JWT auth
(header or query param), path traversal protection, extension
allowlist, realpath containment check, and nosniff header
Frontend:
- Parse __IMAGES__ sentinel in tool_end SSE events, create structured
result with text/images/sessionId
- Render <img> tags in Python tool UI pointing at the sandbox endpoint
Also fixes a bug where SyntaxError in user code was misreported as
"unsafe code detected" instead of showing the actual Python traceback.
The _check_code_safety function now lets SyntaxError pass through to
the subprocess for a proper error message.
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* fix(studio): improve SVG detection and strip XML preamble
Handle <?xml ...?> declarations before <svg> tags in code fences,
strip XML declaration from SVGs before data URI rendering, and
update the sloth suggestion prompt to request showing code.
* fix(studio): persist parentId so retries survive reload
The append() handler was destructuring only { message } from
ExportedMessageRepositoryItem and discarding parentId. When loading
a saved thread, load() used ExportedMessageRepository.fromArray()
which chains all messages sequentially, flattening retry branches
into a linear list.
Now append() writes parentId to the MessageRecord, and load()
reconstructs the tree when parentIds are present. Old threads
without parentId fall back to the existing fromArray() behavior.
* fix(studio): address review findings for image display and retry persistence
Image detection:
- Use mtime comparison instead of filename-only diff so overwritten
files (e.g. plt.savefig("chart.png") called twice) are detected
Sentinel parsing:
- Use rsplit/lastIndexOf instead of split/indexOf so user code that
prints __IMAGES__: does not collide with the backend sentinel
Mixed legacy/new threads:
- For old messages without a stored parentId, infer sequential parent
from the previous message instead of null, preventing multiple roots
Sandbox endpoint:
- Change Cache-Control from "public, max-age=3600" to "private,
no-store" since these are authenticated responses
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* fix(frontend): scope sans font overrides to chat thread only
* fix(frontend): use font-sans fallback for heading stack and simplify chat font rules
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* update logic to incorporate custom prebuilt installs
* bug fixes
* update for review comments
* fix tags
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* Separate test changes from main PR
Move test file changes out of this PR to keep the diff focused on
the install_llama_prebuilt.py and setup script changes. Test updates
will be submitted in a follow-up PR.
* Fix branch ref normalization and harden JSON parsing
- Add checkout_friendly_ref() to strip refs/heads/ prefix from branch
refs before emitting them in SourceBuildPlan. git clone --branch does
not accept fully qualified refs like refs/heads/main.
- Apply normalization in source_build_plan_for_release() and the
direct-ref fallback in resolve_source_build_plan().
- Allow validated_checksums_for_bundle() to accept releases that carry
only an exact-commit source archive without the legacy upstream-tag
source tarball.
- Add 2>/dev/null || true guards to all inline python -c JSON parsing
in setup.sh so a malformed payload does not abort the script under
set -e.
* Fix Windows CUDA asset ordering and tag ref normalization
- Reorder windows_cuda_upstream_asset_names to prefer the main binary
archive (llama-{tag}-bin-win-cuda-*) over the cudart sidecar archive
(cudart-llama-bin-win-cuda-*). The cudart ZIP only contains CUDA
runtime DLLs, not llama-server or llama-quantize binaries.
- Extend checkout_friendly_ref to also strip refs/tags/ prefix for tag
refs, matching the refs/heads/ handling for branch refs.
* Simplify JSON parsing consistency in setup.sh
Use json.load(sys.stdin) consistently for all inline JSON parsing
in setup.sh, instead of the more complex json.loads(raw) pattern
on the install-tag resolution path. The 2>/dev/null || true guard
already handles empty/malformed input gracefully.
* Fix source build plan fallback for commit ref kind in PR #4771
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Expand test coverage for install_llama_prebuilt.py:
- Add tests for source build plan resolution with custom repos
- Add tests for branch/commit/PR ref matching and normalization
- Add tests for manifest checksum validation
- Add tests for Windows CUDA upstream asset name patterns
- Update capsys checks to capture stderr after log() redirect
* fix(studio): prevent small models from stalling on tool-calling tasks
Small GGUF models (< 9B params) in "Think, Search, Code" mode would
often describe what they planned to do ("Let me create this dashboard")
and then stop generating without ever calling a tool.
Three changes:
1. Simplify web_tips for small models: remove the "fetch its full content
by calling web_search with the url parameter" guidance for models < 9B.
This multi-step instruction causes small models to plan elaborate
search-then-fetch-then-code sequences they cannot reliably execute.
2. Add "always call tools directly" imperative to the system prompt nudge
so models act immediately instead of narrating their intentions.
3. Add plan-without-action re-prompt in the agentic loop: when the model
emits planning text (matching patterns like "let me", "I'll", etc.)
without calling any tool, inject a nudge asking it to call the tool
and continue the loop. Capped at 2 re-prompts per request.
Benchmarked with Qwen3.5-4B-GGUF (N=5 trials per variant):
- Baseline: 40% of requests had any tool call
- Combined fix: 100% of requests had at least one tool call
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* Fix shell injection in GGML conversion paths
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* Remove test file from security fix PR
Move test_save_shell_injection.py to a separate PR to keep this PR focused on the security fix itself.
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Distinguish between actual network downloads and GPU memory loading for cached LoRA adapters in Studio chat.
- Add isCachedLora detection for local LoRA adapter paths using comprehensive cross-platform regex (Unix, Windows, UNC, WSL, tilde)
- Thread isCachedLora through loadInfo to chat-page inline status for proper 3-way distinction (cached / local LoRA / downloading)
- Skip download progress polling for cached LoRA models (no useless /download-progress API calls)
- Fix initial toast state to use isCachedLoad consistently instead of only checking isDownloaded
- Fix cancelLoading toast to not mention background downloads for cached/local loads
- Keep download-specific text ("Downloading model..." / "Download complete") inside the download-only polling block
- Add min-w-0 guards to thread/message/markdown containers to prevent
content overflow past the composer width
- Unify chat typography from Hellix/Space Grotesk to the sans stack,
keeping monospace for code blocks and inline code
- Restructure desktop navbar right-side controls with shrink-0 wrappers
for consistent spacing across HoverCard roots
- Soften tool-call label styling (font-medium + text-foreground/85
instead of bold)
- Add responsive code block sizing via @container queries
- Add horizontal scrolling for wide code blocks within the thread column
- Scope list-item code block alignment CSS to .aui-thread-root
- Preserve useScrollLock in tool-fallback and tool-group collapsibles
- Fall back to bg-background on ViewportFooter when hideComposer is true
- Widen inline code monospace selector to cover th, blockquote, and
heading elements
- Remove unused @fontsource-variable/space-grotesk import
* Fix script unbound variable error
* remove stale test script, add llama.cpp metal source builds, update tests
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* Fix Metal precedence, test sync, and add behavioral tests
- Move macOS arm64 Metal check before CUDA/ROCm in GPU backend
decision chain so Metal is not bypassed when nvcc is in PATH
- Remove RPATH flags from CPU fallback CMAKE_ARGS (only needed
for Metal library linking)
- Update test_llama_pr_force_and_source.py to match _CLONE_ARGS
rename from _CLONE_BRANCH_ARGS in setup.sh
- Add confirm_install_tree guard test for
existing_install_matches_choice
- Add TestMacOSMetalBuildLogic bash subprocess tests verifying
Metal flag selection, nvcc precedence, and CPU fallback behavior
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* Fix Metal CPU fallback to also cover cmake build failures and update tests
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* 1. _GPU_BACKEND_FRAGMENT synced -- removed dead CPU_FALLBACK_CMAKE_ARGS= init (6/8)
2. RPATH assertion replaced -- new test_macos_arm64_cpu_fallback_args_exclude_rpath checks the actual runtime CPU_FALLBACK_CMAKE_ARGS output for @loader_path and -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON (6/8)
3. _TRY_METAL_CPU_FALLBACK=false reset after both configure-failure and build-failure fallback branches in setup.sh (4/8)
4. macOS test now removes libmtmd.0.dylib instead of the platform-agnostic convert_hf_to_gguf.py (3/8)
5. Empty-string tag test added -- test_empty_tag_omits_branch_flag for resolved_tag= (2/8)
6. RPATH checks on cmake call logs -- both fallback tests now assert @loader_path and -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON are absent from CPU fallback cmake calls, plus baseline flag preservation (multiple)
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* tests clean up
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* fix(studio): allow context length slider to reach model's native limit
The context length slider was hard-capped to the VRAM-estimated maximum,
preventing users from requesting higher context even though the backend
already handles it safely (multi-GPU selection, --fit fallback). Expose
the model's native context length from GGUF metadata as a separate API
field and use it as the slider ceiling instead. Add an amber warning
when the selected context exceeds the estimated VRAM capacity.
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* Raise VRAM budget to 90% and add native_context_length tests
Increase the GPU memory utilization threshold from 70% to 90% across
_select_gpus and _fit_context_to_vram, allowing longer context lengths
before VRAM capping kicks in.
Add 33 tests for the native_context_length feature covering the backend
property, context value separation invariants, Pydantic models, route
completeness, edge cases, and cross-platform binary I/O.
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* fix: add tokenizers to no-torch runtime deps and add TORCH_CONSTRAINT for arm64 macOS py313+
Two installer fixes:
1. Add `tokenizers` to `no-torch-runtime.txt` before `transformers`.
Without it, `from transformers import AutoConfig` crashes on startup
because `--no-deps` skips transitive dependencies.
2. Add `TORCH_CONSTRAINT` variable to `install.sh`. On arm64 macOS with
Python 3.13+, tighten the torch requirement to `>=2.6` since torch
<2.6 has no cp313 arm64 wheels. The variable replaces the previously
hard-coded constraint in the uv pip install line.
Includes 66 tests (42 pytest + 24 bash) covering:
- Structural checks on install.sh, install.ps1, no-torch-runtime.txt
- Shell snippet tests with mocked python for 13 platform/version combos
- Mock uv integration verifying correct constraint string
- E2E venv tests on Python 3.12 and 3.13 confirming AutoConfig works
- Negative control proving AutoConfig fails without tokenizers
- Full no-torch sandbox regression guards (safetensors, huggingface_hub)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix incomplete no-torch manifest and align E2E tests with real --no-deps path
- Add missing transitive deps to no-torch-runtime.txt that are required
under --no-deps: regex, typing_extensions, filelock, httpx, httpcore,
certifi, idna, anyio, sniffio, h11. Without these, `from transformers
import AutoConfig` still fails after install.sh --no-torch.
- Change all E2E tests to use --no-deps (matching what install.sh does)
instead of normal dep resolution. Previous tests passed even with an
incomplete manifest because uv backfilled transitive deps.
- Rewrite negative control to derive from the real no-torch-runtime.txt
with tokenizers stripped, proving the specific fix matters.
- Replace GNU-only sed -i with heredoc in shell test for macOS compat.
- Remove unused os/sys imports from Python test file.
- Quote SKIP_TORCH and mock uv paths in bash -c strings.
* Assert install succeeds before checking import results in E2E tests
Address review feedback: test_torch_not_importable and
test_tokenizers_directly_importable in Group 3 now assert that
uv pip install returns 0 before checking import behavior. This
prevents false positives when the install itself fails silently.
* Assert install succeeds in negative control and tighten error check
- Add missing install-success assertion in test_negative_control_no_tokenizers
to prevent false positives from network/install failures.
- Tighten error message check to look for "tokenizers" in stderr or
ModuleNotFoundError, rather than the generic "No module" substring
which could match unrelated import failures.
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
- Fix SSL handshake failures (SSLV3_ALERT_HANDSHAKE_FAILURE, CERTIFICATE_VERIFY_FAILED) when fetching HTTPS pages by introducing _PinnedHTTPSConnection that separates TCP connect (to pinned IP) from TLS handshake (with real hostname for SNI/cert verification)
- Fix SSRF DNS-rebinding vulnerability: previous impl swapped conn.host before connect(), causing fresh DNS resolution; new subclass keeps TCP pinned to validated IP
- Fix SPA/JS-rendered doc sites returning empty content by rotating real browser User-Agents (Chrome/Firefox/Safari)
- Strip nav/footer from HTML-to-Markdown output so article content is not buried under navigation chrome
- Increase raw fetch cap from 64KB to 512KB so SSR article content is reached on GitBook/Docusaurus/Next.js pages
- Fix IPv6 address bracketing in URL netloc construction
- Hoist SSL context, handler classes, and stdlib imports to module level (created once, not per-call)
- Use consistent UA across redirect hops to avoid breaking session-aware bot detection
Split out from #4741 to keep the main PR focused on installer logic.
- New test_install_llama_prebuilt_logic.py: tests for resolve logic,
fallback behavior, env_int, busy/lock handling
- New test_validate_llama_prebuilt.py: validator tests for staged
release_tag/upstream_tag handling
- New test_llama_pr_force_and_source.py: tests for PR_FORCE and
LLAMA_SOURCE maintainer defaults
- Updated test_selection_logic.py: expanded selection/fallback coverage
- Updated test_pr4562_bugfixes.py: updated bugfix tests for new logic
- Updated smoke_test_llama_prebuilt.py: minor update
Replaces the fixed prebuilt llama.cpp tag with dynamic published-release
resolution, adds bounded fallback across older published releases, and
introduces maintainer-editable defaults for PR/source overrides.
Changes:
- Resolve latest from the latest usable published release in unslothai/llama.cpp
- Use the selected release upstream_tag as the authoritative llama.cpp version
- Prefer Unsloth-published platform assets when available
- Fall back to same-tag upstream ggml-org/llama.cpp assets where allowed
- Keep Linux CUDA anchored to Unsloth-published CUDA bundles only
- Add bounded fallback across older Unsloth published releases
- Add separate busy/in-use install handling (exit code 3)
- Skip reinstall when the installed bundle already matches the selected candidate
- Add maintainer-editable _DEFAULT_LLAMA_PR_FORCE and _DEFAULT_LLAMA_SOURCE
- Harden env parsing so malformed installer env vars do not crash import-time fallback logic
- Honor UNSLOTH_LLAMA_RELEASE_TAG in all resolve steps
- Always sync git remote URL in existing-checkout path
* Fix save_pretrained_merged for full-finetuned models
save_pretrained_merged and push_to_hub_merged silently do nothing when
the model is not a PeftModel (i.e. full finetuning without LoRA).
merge_and_overwrite_lora returns None immediately for non-PeftModel,
and unsloth_generic_save does not check the return value.
Add a non-PeftModel branch in unsloth_generic_save that falls back to
model.save_pretrained / model.push_to_hub. When save_method contains
"16bit", cast weights to bfloat16 (or float16) via a state_dict copy
to honor the user's intent without mutating the live model.
The existing PeftModel (LoRA) code path is unchanged.
* Forward create_pr and revision to tokenizer.push_to_hub
The tokenizer push_to_hub call was missing create_pr and revision,
which could cause the tokenizer to push to the wrong branch or
bypass PR creation when the model push uses them.
* Honor merged_16bit dtype contract for full-finetuned models
Cast state_dict to bfloat16/float16 when save_method contains "16bit"
to match the documented behavior of save_pretrained_merged. Also pass
state_dict and save kwargs consistently to both save_pretrained and
push_to_hub paths.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Address review feedback for PR #4755
- Simplify PeftModel isinstance check (PeftModelForCausalLM inherits
from PeftModel)
- Add is_main_process guard for distributed training
- Forward variant to save_pretrained
- Set tokenizer padding_side to "left" before saving (matches other
save paths)
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* feat(studio): architecture-aware KV cache VRAM estimation
Replace the single legacy formula (2 * n_kv_heads * head_dim * n_layers
* n_ctx * bpe) with 5-path estimation that reads 8 additional GGUF
metadata fields:
1. MLA (DeepSeek-V2/V3, GLM-4.7, GLM-5, Kimi-K2.5) -- K-only cache
using compressed KV latent + RoPE; no separate V allocation
2. Hybrid Mamba (Qwen3.5-27B, Qwen3.5-35B-A3B) -- only attention
layers (1 in N) carry KV; Mamba layers have none
3. Sliding Window (Gemma-3, gpt-oss) -- SWA layers cache
min(ctx, window) tokens instead of the full context
4. Standard GQA -- uses explicit key_length/value_length from GGUF
instead of embed // n_heads (which is wrong for many models)
5. Legacy fallback -- identical to old formula for old GGUFs
New GGUF fields parsed: attention.key_length, attention.value_length,
attention.sliding_window, full_attention_interval,
attention.kv_lora_rank, attention.key_length_mla, ssm.inner_size,
ssm.state_size.
Validated against 9 real GGUF files (72/72 field checks pass).
The legacy formula was off by +682% for Gemma-3 and -81% for
DeepSeek-V3.1.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix MLA fallback and SWA global/local ratio heuristic
Two fixes based on review findings:
1. MLA fallback now uses key_length_mla from GGUF metadata instead of
hardcoded rope_dim=64. Falls back to 64 only when key_length_mla is
absent. This ensures correct estimates for MLA variants that use
rope dimensions other than 64.
2. SWA global/local layer ratio changed from 50/50 to 1/4 (25% global,
75% SWA). Most sliding window architectures have predominantly local
layers (Gemma-3 uses ~17% global, gpt-oss uses ~50%). The 1/4
heuristic is closer to the common case and still a large improvement
over the legacy formula which ignores SWA entirely.
* Tighten _can_estimate_kv gate and treat sliding_window=0 as disabled
Two additional fixes from review round 1 (5/8 and 4/8 reviewer consensus):
1. _can_estimate_kv now requires BOTH key_length AND value_length for
the explicit-dims path. Previously key_length alone was enough,
which could cause silent fallthrough to the legacy formula with
fabricated defaults (n_kv=1, head_dim=128) when value_length was
absent from the GGUF.
2. SWA path now requires sliding_window > 0. Some GGUFs use 0 as a
disabled sentinel. Without this guard, min(ctx, 0) would zero out
all SWA layer contributions, severely underestimating KV cache.
* Fix MLA n_kv safety and use ceiling division for hybrid path
Addresses Gemini Code Assist review findings:
1. MLA path now uses n_kv_mla = n_kv_heads or 1 (not n_heads). This
prevents a 128x overestimate for DeepSeek-V3 if head_count_kv is
absent from the GGUF (n_heads=128 would have been used instead).
2. Hybrid path now uses ceiling division for attention layer count.
This prevents undercounting by 1 when n_layers is not perfectly
divisible by full_attention_interval.
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* Fix forward compatibility with transformers 5.x
Tested on transformers 4.57.6, 5.3.0, and 5.4.0. All changes are no-ops
on transformers 4.x.
1. Skip exec-based config patching for transformers >= 5.0
Config classes in v5 use @strict, @auto_docstring, and interval()
which break exec(inspect.getsource(...)). Those configs already use
rope_parameters (the v5 replacement for rope_scaling).
2. Slice position_ids to last token in fast_forward_inference
Transformers 5.x generate() accumulates position_ids as
[batch, full_seq_len] across decode steps instead of [batch, 1].
cos[position_ids] then produces the wrong shape for rotary
embeddings. Fixed in llama, qwen3, falcon_h1, gemma2, cohere,
granite. No-op on 4.x since position_ids is already [batch, 1].
3. Handle @strict config kwargs for sequence classification
num_labels, max_position_embeddings, id2label etc. are set on the
config object and passed via config= instead of as kwargs.
AutoModelForSequenceClassification routing added to FastModel loader.
4. Exclude modernbert from flex_attention
ModernBERT with flex_attention hits CUDA illegal memory access in
create_block_mask. Falls back to eager attention safely.
5. Propagate token_type_ids and mm_token_type_ids through GRPO VLM path
Gemma3 Vision requires token_type_ids during training. Qwen3VL
requires mm_token_type_ids for M-RoPE. Extract from inputs in
compute_loss, pass to grpo_accumulated_loss, and extend
mm_token_type_ids for completion tokens in
_generate_and_score_completions.
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* Add try/except safety net around config exec for pre-release transformers versions
* Pop config-level kwargs in seqclass path and use except Exception
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When searching for a specific publisher model (e.g. `openai/gpt-oss-20b`), the
unsloth search used the full `openai/gpt-oss-20b` string with `author=unsloth`,
which returned zero results because no unsloth model contains the publisher
prefix in its name. Users never discovered unsloth variants.
This PR strips the org prefix for publisher-qualified queries so unsloth variants
surface, then pins the original publisher model after a small batch of unsloth
results. Plain queries (no slash) and unsloth-prefixed queries are unchanged.
- Strict regex (`/^([^/\s]+)\/([^/\s]+)$/`) only triggers on valid `owner/repo`
identifiers; incomplete typeahead, multi-slash, and URL-like inputs are rejected
- Queries for `unsloth/...` models (case-insensitive) keep the full 20-result
prefetch and secondary sort
- Pinned model lookup fires in parallel with the unsloth prefetch
- Canonical-name dedup prevents duplicates when HF normalizes casing
- Publisher detection extracted into a single `useMemo` block
Replace strikethrough + opacity-50 OOM styling with gray text and red pill badge across all Studio model selectors (chat, training, onboarding).
- Use gray-500/gray-400 for OOM model names (better contrast than strikethrough)
- Red pill badge for OOM indicator with light/dark mode support
- Scope GGUF gray override to quant name only so downloaded/recommended labels keep colors
- Add !important on TIGHT/OOM badges to resist ComboboxItem hover overrides
