* Studio: add folder browser modal for Custom Folders
The Custom Folders row in the model picker currently only accepts a
typed path. On a remote-served Studio (Colab, shared workstation) that
means the user has to guess or paste the exact server-side absolute
path. A native browser folder picker can't solve this: HTML
`<input type="file" webkitdirectory>` hides the absolute path for
security, and the File System Access API (Chrome/Edge only) returns
handles rather than strings, neither of which the server can act on.
This PR adds a small in-app directory browser that lists paths on the
server and hands the chosen string back to the existing
`POST /api/models/scan-folders` flow.
## Backend
* New endpoint `GET /api/models/browse-folders`:
* `path` query param (expands `~`, accepts relative or absolute; empty
defaults to the user's home directory).
* `show_hidden` boolean to include dotfiles/dotdirs.
* Returns `{current, parent, entries[], suggestions[]}`. `parent` is
null at the filesystem root.
* Immediate subdirectories only (no recursion); files are never
returned.
* `entries[].has_models` is a cheap hint: the directory looks like it
holds models if it is named `models--*` (HF hub cache layout) or
one of the first 64 children is a .gguf/.safetensors/config.json/
adapter_config.json or another `models--*` subfolder.
* Sort order: model-bearing dirs, then plain, then hidden; case-
insensitive alphabetical within each bucket.
* Suggestions auto-populate from HOME, the HF cache root, and any
already-registered scan folders, deduplicated.
* Error surface: 404 for missing path, 400 for non-directory, 403 on
permission errors. Auth-required like the other models routes.
* New Pydantic schemas `BrowseEntry` and `BrowseFoldersResponse` in
`studio/backend/models/models.py`.
## Frontend
* New `FolderBrowser` component
(`studio/frontend/src/components/assistant-ui/model-selector/folder-browser.tsx`)
using the existing `Dialog` primitive. Features:
* Clickable breadcrumb with a `..` row for parent navigation.
* Quick-pick chips for the server-provided suggestions.
* `Show hidden` checkbox.
* In-flight fetch cancellation via AbortController so rapid
navigation doesn't flash stale results.
* Badges model-bearing directories inline.
* `chat-api.ts` gains `browseFolders(path?, showHidden?)` and matching
types.
* `pickers.tsx` adds a folder-magnifier icon next to the existing `Add`
button. Opening the browser seeds it with whatever the user has
already typed; confirming fills the text input, leaving the existing
validation and save flow unchanged.
## What it does NOT change
* The existing text-input flow still works; the browser is additive.
* No new permissions or escalation; the endpoint reads only directories
the server process is already allowed to read.
* No model scanning or filesystem mutation happens from the browser
itself -- it just returns basenames for render.
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* Studio: cap folder-browser entries and expose truncated flag
Pointing the folder browser at a huge directory (``/usr/lib``,
``/proc``, or a synthetic tree with thousands of subfolders) previously
walked the whole listing and stat-probed every child via
``_looks_like_model_dir``. That is both a DoS shape for the server
process and a large-payload surprise for the client.
Introduce a hard cap of 2000 subdirectory entries and a
``truncated: bool`` field on the response. The frontend renders a small
hint below the list when it fires, prompting the user to narrow the
path. Below-cap directories are unchanged.
Verified end-to-end against the live backend with a synthetic tree of
2050 directories: response lands at 2000 entries, ``truncated=true``,
listing finishes in sub-second time (versus tens of seconds if we were
stat-storming).
* Studio: suggest LM Studio / Ollama dirs + 2-level model probe
Three improvements to the folder-browser, driven by actually dropping
an LM Studio-style install (publisher/model/weights.gguf) into the
sandbox and walking the UX:
## 1. Quick-pick chips for other local-LLM tools
`well_known_model_dirs()` (new) returns paths commonly used by
adjacent tools. Only paths that exist are returned so the UI never
shows dead chips.
* LM Studio current + legacy roots + user-configured
`downloadsFolder` from its `settings.json` (reuses the existing
`lmstudio_model_dirs()` helper).
* Ollama: `$OLLAMA_MODELS` env override, then `~/.ollama/models`,
`/usr/share/ollama/.ollama/models`, and `/var/lib/ollama/.ollama/models`
(the systemd-service install path surfaced in the upstream "where is
everything?" issue).
* Generic user-choice locations: `~/models`, `~/Models`.
Dedup is stable across all sources.
## 2. Two-level model-bearing probe
LM Studio and Ollama both use `root/publisher/model/weights.gguf`.
The previous `has_models` heuristic only probed one level, so the
publisher dir (whose immediate children are model dirs, not weight
files) was always marked as non-model-bearing. Pulled the direct-
signal logic into `_has_direct_model_signal` and added a grandchild
probe so the classic layout is now recognised.
Still O(PROBE^2) worst-case, still returns immediately for
`models--*` names (HF cache layout) and for any direct weight file.
## 3. model_files_here hint on response body
A leaf model dir (just GGUFs, no subdirs) previously rendered as
`(empty directory)` in the modal, confusing users into thinking the
folder wasn't scannable. Added a `model_files_here` count on the
response (capped at 200) and a small hint row in the modal: `N model
files in this folder. Click "Use this folder" to scan it.`
## Verification
Simulated an LM Studio install by downloading the real 84 MB
`unsloth/SmolLM2-135M-Instruct-Q2_K.gguf` into
`~/.lmstudio/models/unsloth/SmolLM2-135M-Instruct-GGUF/`. Confirmed
end-to-end:
* Home listing suggests `~/.lmstudio/models` as a chip.
* Browsing `~/.lmstudio/models` flags `unsloth` (publisher) as
`has_models=true` via the 2-level probe.
* Browsing the publisher flags `SmolLM2-135M-Instruct-GGUF` (model
dir) as `has_models=true`.
* Browsing the model dir returns empty entries but
`model_files_here=1`, and the frontend renders a hint telling the
user it is a valid target.
* Studio: one-click scan-folder add + prominent remove + plain search icon
Three small Custom Folders UX fixes after real-use walkthrough:
* **One-click add from the folder browser**. Confirming `Use this
folder` now submits the path directly to
`POST /api/models/scan-folders` instead of just populating the text
input. `handleAddFolder` takes an optional explicit path so the
submit lands in the same tick as `setFolderInput`, avoiding a
state-flush race. The typed-path + `Add` button flow is unchanged.
* **Prominent remove X on scan folders**. The per-folder delete
button was `text-muted-foreground/40` and hidden entirely on
desktop until hovered (`md:opacity-0 md:group-hover:opacity-100`).
Dropped the hover-only cloak, bumped color to `text-foreground/70`,
added a red hover/focus background, and sized the icon up from
`size-2.5` to `size-3`. Always visible on every viewport.
* **Plain search icon for the Browse button**. `FolderSearchIcon`
replaced with `Search01Icon` so it reads as a simple "find a
folder" action alongside the existing `Add01Icon`.
* Studio: align Custom Folders + and X buttons on the same right edge
The Custom Folders header used `px-2.5` with a `p-0.5` icon button,
while each folder row used `px-3` with a `p-1` button. That put the
X icon 4px further from the right edge than the +. Normalised both
rows to `px-2.5` with `p-1` so the two icons share a column.
* Studio: empty-state button opens the folder browser directly
The first-run empty state for Custom Folders was a text link reading
"+ Add a folder to scan for local models" whose click toggled the
text input. That's the wrong default: a user hitting the empty state
usually doesn't know what absolute path to type, which is exactly
what the folder browser is for.
* Reword to "Browse for a models folder" with a search-icon
affordance so the label matches what the click does.
* Click opens the folder browser modal directly. The typed-path +
Add button flow is still available via the + icon in the
section header, so users who know their path keep that option.
* Slightly bump the muted foreground opacity (70 -> hover:foreground)
so the button reads as a primary empty-state action rather than a
throwaway hint.
* Studio: Custom Folders header gets a dedicated search + add button pair
The Custom Folders section header had a single toggle button that
flipped between + and X. That put the folder-browser entry point
behind the separate empty-state link. Cleaner layout: two buttons in
the header, search first, then add.
* Search icon (left) opens the folder browser modal directly.
* Plus icon (right) toggles the text-path input (unchanged).
* The first-run empty-state link is removed -- the two header icons
cover both flows on every state.
Both buttons share the same padding / icon size so they line up with
each other and with the per-folder remove X.
* Studio: sandbox folder browser + bound caps + UX recoveries
PR review fixes for the Custom Folders folder browser. Closes the
high-severity CodeQL path-traversal alert and addresses the codex /
gemini P2 findings.
Backend (studio/backend/routes/models.py):
* New _build_browse_allowlist + _is_path_inside_allowlist sandbox.
browse_folders now refuses any target that doesn't resolve under
HOME, HF cache, Studio dirs, registered scan folders, or the
well-known third-party model dirs. realpath() is used so symlink
traversal cannot escape the sandbox. Also gates the parent crumb
so the up-row hides instead of 403'ing.
* _BROWSE_ENTRY_CAP now bounds *visited* iterdir entries, not
*appended* entries. Dirs full of files (or hidden subdirs when
show_hidden is False) used to defeat the cap.
* _count_model_files gets the same visited-count fix.
* PermissionError no longer swallowed silently inside the
enumeration / counter loops -- now logged at debug.
Frontend (folder-browser.tsx, pickers.tsx, chat-api.ts):
* splitBreadcrumb stops mangling literal backslashes inside POSIX
filenames; only Windows-style absolute paths trigger separator
normalization. The Windows drive crumb value is now C:/ (drive
root) instead of C: (drive-relative CWD-on-C).
* browseFolders accepts and forwards an AbortSignal so cancelled
navigations actually cancel the in-flight backend enumeration.
* On initial-path fetch error, FolderBrowser now falls back to HOME
instead of leaving the modal as an empty dead end.
* When the auto-add path (one-click "Use this folder") fails, the
failure now surfaces via toast in addition to the inline
paragraph (which is hidden when the typed-input panel is closed).
* Studio: rebuild browse target from trusted root for CodeQL clean dataflow
CodeQL's py/path-injection rule kept flagging the post-validation
filesystem operations because the sandbox check lived inside a
helper function (_is_path_inside_allowlist) and CodeQL only does
intra-procedural taint tracking by default. The user-derived
``target`` was still flowing into ``target.exists`` /
``target.is_dir`` / ``target.iterdir``.
The fix: after resolving the user-supplied ``candidate_path``,
locate the matching trusted root from the allowlist and rebuild
``target`` by appending each individually-validated segment to
that trusted root. Each segment is rejected if it isn't a single
safe path component (no separators, no ``..``, no empty/dot).
The downstream filesystem ops now operate on a Path constructed
entirely from ``allowed_roots`` (trusted) plus those validated
segments, so CodeQL's dataflow no longer sees a tainted source.
Behavior is unchanged for all valid inputs -- only the
construction of ``target`` is restructured. Live + unit tests
all pass (58 selected, 7 deselected for Playwright env).
* Studio: walk browse paths from trusted roots for CodeQL
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* Studio: live model-load progress + rate/ETA on download and load
Two UX fixes for the opaque multi-minute wait between clicking Load
and being able to chat, visible most clearly on large MoE GGUFs like
MiniMax-M2.7 (131 GB of weights on a 97 GB GPU):
1. **Model-load phase is now observable.** The existing chat flow
transitions the toast to "Starting model..." as soon as the
download hits 100%, then shows a spinner with no other feedback
until llama-server reports healthy. For a 130 GB model that spinner
freezes for five-plus minutes while the kernel pages shards into
the page cache. A new `GET /api/inference/load-progress` endpoint
samples `/proc/<pid>/status VmRSS` on the llama-server subprocess
against the sum of shard file sizes on disk, so the UI can render
a real bar plus rate / ETA during that window.
2. **Rate and ETA on downloads and loads.** Both the chat toast and
the training-start overlay used to show a static pair of numbers
(for example "15.4 of 140.8 GB"). A rolling 15-second window over
the existing byte-series now surfaces "85.3 MB/s, 24m 23s left"
beside that pair. The estimator is shared between the download
and load phases so the numbers don't reset when the phase flips.
Also fixes a pre-existing assignment bug uncovered while wiring this
up: `load_model` was storing the caller's `gguf_path` kwarg into
`self._gguf_path`, which is `None` on the HF-download code path. The
resolved on-disk path (`model_path`) is what llama-server actually
mmaps; downstream consumers need that. No existing reader used
`_gguf_path`, so this is a correctness fix for the new endpoint.
- Backend: `LlamaCppBackend.load_progress()`, `GET /api/inference/load-progress`, `LoadProgressResponse` Pydantic model.
- Frontend: `useTransferStats` hook, `formatRate` / `formatEta` helpers, `getLoadProgress` client, rewired chat toast and `DownloadRow` in the training overlay.
- Tests: `studio/backend/tests/test_llama_cpp_load_progress.py` covers empty states, mmap phase, ready phase, sharded total aggregation, missing gguf_path, and unreadable /proc (7 cases). `tsc -b` and `vite build` on the frontend both clean.
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* Studio: add API key authentication for programmatic access
External users want to hit the Studio API (chat completions with tool
calling, training, export, etc.) without going through the browser
login flow. This adds sk-unsloth- prefixed API keys that work as a
drop-in replacement for JWTs in the Authorization: Bearer header.
Backend:
- New api_keys table in SQLite (storage.py)
- create/list/revoke/validate functions with SHA-256 hashed storage
- API key detection in _get_current_subject before the JWT path
- POST/GET/DELETE /api/auth/api-keys endpoints on the auth router
Frontend:
- /api-keys page with create form, one-time key reveal, keys table
- API Keys link in desktop and mobile navbar
- Route registered with requireAuth guard
Zero changes to any existing route handler -- every endpoint that uses
Depends(get_current_subject) automatically works with API keys.
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* Use actual origin in API key usage examples
The examples on /api-keys were hardcoded to localhost:8888 which is
wrong for remote users. Use window.location.origin so the examples
show the correct URL regardless of where the user is connecting from.
* Add `unsloth studio run` CLI command for one-liner model serving
Adds a `run` subcommand that starts Studio, loads a model, creates an
API key, and prints a ready-to-use curl command -- similar to
`ollama run` or `vllm serve`.
Usage: unsloth studio run -m unsloth/Qwen3-1.7B-GGUF --gguf-variant UD-Q4_K_XL
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* Add end-to-end tests for `unsloth studio run` and API key usage
Tests the 4 usage examples from the API Keys page:
1. curl basic (non-streaming) chat completions
2. curl streaming (SSE) chat completions
3. OpenAI Python SDK streaming completions
4. curl with tools (web_search + python)
Also tests --help output, invalid key rejection, and no-key rejection.
All 7 tests pass against Qwen3-1.7B-GGUF.
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* Add /v1/completions, /v1/embeddings, /v1/responses endpoints and --parallel support
- llama_cpp.py: accept n_parallel param, pass to llama-server --parallel
- run.py: plumb llama_parallel_slots through to app.state
- inference.py: add /completions and /embeddings as transparent proxies to
llama-server, add /responses as application-level endpoint that converts
to ChatCompletionRequest; thread n_parallel through load_model
- studio.py: set llama_parallel_slots=4 for `unsloth studio run` path
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* Make /v1/responses endpoint match OpenAI Responses API format
The existing /v1/responses shim returned Chat Completions format, which
broke OpenAI SDK clients using openai.responses.create(). This commit
replaces the endpoint with a proper implementation that:
- Returns `output` array with `output_text` content parts instead of
`choices` with `message`
- Uses `input_tokens`/`output_tokens` instead of `prompt_tokens`/
`completion_tokens` in usage
- Sets `object: "response"` and `id: "resp_..."`
- Emits named SSE events for streaming (response.created,
response.output_text.delta, response.completed, etc.)
- Accepts all OpenAI Responses API fields (tools, store, metadata,
previous_response_id) without erroring -- silently ignored
- Maps `developer` role to `system` and `input_text`/`input_image`
content parts to the internal Chat format
Adds Pydantic schemas for request/response models and 23 unit tests
covering schema validation, input normalisation, and response format.
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* Studio: add Anthropic-compatible /v1/messages endpoint (#4981)
* Add Anthropic-compatible /v1/messages endpoint with tool support
Translate Anthropic Messages API format to/from internal OpenAI format
and reuse the existing server-side agentic tool loop. Supports streaming
SSE (message_start, content_block_delta, etc.) and non-streaming JSON.
Includes offline unit tests and e2e tests in test_studio_run.py.
* Add enable_tools, enabled_tools, session_id to /v1/messages endpoint
Support the same shorthand as /v1/chat/completions: enable_tools=true
with an optional enabled_tools list uses built-in server tools without
requiring full Anthropic tool definitions. session_id is passed through
for sandbox isolation. max_tokens is now optional.
* Strip leaked tool-call XML from Anthropic endpoint content
Apply _TOOL_XML_RE to content events in both streaming and
non-streaming tool paths, matching the OpenAI endpoint behavior.
* Emit custom tool_result SSE event in Anthropic stream
Adds a non-standard tool_result event between the tool_use block close
and the next text block, so clients can see server-side tool execution
results. Anthropic SDKs ignore unknown event types.
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* Split /v1/messages into server-side and client-side tool paths
enable_tools=true runs the existing server-side agentic loop with
built-in tools (web_search/python/terminal). A bare tools=[...] field
now triggers a client-side pass-through: client-provided tools are
forwarded to llama-server and any tool_use output is returned to the
caller with stop_reason=tool_use for client execution.
This fixes Claude Code (and any Anthropic SDK client) which sends
tools=[...] expecting client-side execution but was previously routed
through execute_tool() and failing with 'Unknown tool'.
Adds AnthropicPassthroughEmitter to convert llama-server OpenAI SSE
chunks into Anthropic SSE events, plus unit tests covering text
blocks, tool_use blocks, mixed, stop reasons, and usage.
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* Fix httpcore GeneratorExit in /v1/messages passthrough stream
Explicitly aclose aiter_lines() before the surrounding async with
blocks unwind, mirroring the prior fix in external_provider.py
(a41160d3) and cc757b78's RuntimeError suppression.
* Wire stop_sequences through /v1/messages; warn on tool_choice
Plumb payload.stop_sequences to all three code paths (server-side
tool loop, no-tool plain, client-side passthrough) so Anthropic SDK
clients setting stop_sequences get the behavior they expect. The
llama_cpp backend already accepted `stop` on both generate_chat_
completion and generate_chat_completion_with_tools; the Anthropic
handler simply wasn't passing it.
tool_choice remains declared on the request model for Anthropic SDK
compatibility (the SDK often sets it by default) but is not yet
honored. Log a structured warning on each request carrying a non-
null tool_choice so the silent drop is visible to operators.
* Wire min_p / repetition_penalty / presence_penalty through /v1/messages
Align the Anthropic endpoint's sampling surface with /v1/chat/completions.
Adds the three fields as x-unsloth extensions on AnthropicMessagesRequest
and threads them through all three code paths: server-side tool loop,
no-tool plain, and client-side passthrough.
The passthrough builder emits "repeat_penalty" (not "repetition_penalty")
because that is llama-server's field name; the backend methods already
apply the same rename internally.
* Fix block ordering and prev_text reset in non-streaming tool path
_anthropic_tool_non_streaming was building the response by appending
all tool_use blocks first, then a single concatenated text block at
the end — losing generation order and merging pre-tool and post-tool
text into one block. It also never reset prev_text between synthesis
turns, so the first N characters of each post-tool turn were dropped
(where N = length of the prior turn's final cumulative text).
Rewrite to build content_blocks incrementally in generation order,
matching the streaming emitter's behavior: deltas within a turn are
merged into the trailing text block, tool_use blocks interrupt the
text sequence, and prev_text is reset on tool_end so turn N+1 diffs
against an empty baseline.
Caught by gemini-code-assist[bot] review on #4981.
* Make test_studio_run.py e2e tests pytest-compatible
Add a hybrid session-scoped studio_server fixture in conftest.py that
feeds base_url / api_key into the existing e2e test functions. Three
invocation modes are now supported:
1. Script mode (unchanged) — python tests/test_studio_run.py
2. Pytest + external server — point at a running instance via
UNSLOTH_E2E_BASE_URL / UNSLOTH_E2E_API_KEY env vars, no per-run
GGUF load cost
3. Pytest + fixture-managed server — pytest drives _start_server /
_kill_server itself via --unsloth-model / --unsloth-gguf-variant,
CI-friendly
The existing _start_server / _kill_server helpers and main() stay
untouched so the script entry point keeps working exactly as before.
Test function signatures are unchanged — the (base_url, api_key)
parameters now resolve via the new fixtures when running under
pytest.
* Rename test_studio_run.py -> test_studio_api.py
The file is entirely about HTTP API endpoint testing (OpenAI-compatible
/v1/chat/completions, Anthropic-compatible /v1/messages, API key auth,
plus a CLI --help sanity check on the command that runs the API). None
of its tests cover training, export, chat-UI, or internal-Python-API
concerns.
The old name misleadingly suggested "tests for the unsloth studio run
CLI subcommand" — the new name reflects the actual scope.
Updates:
- git mv the file (rename tracked, history preserved)
- Rewrite opening docstring to state the API surface focus and call
out what is explicitly out of scope
- Update all 4 Usage-block path references to the new filename
- LOG_FILE renamed to test_studio_api.log
- conftest.py fixture import rewritten from test_studio_run to
test_studio_api, plus 7 docstring/comment references updated
No functional changes to test logic, signatures, or main().
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* Fix httpcore asyncgen cleanup in /v1/messages and /v1/completions
The earlier fix in 985e92a9 was incomplete: it closed aiter_lines()
explicitly but still used `async with httpx.AsyncClient()` /
`async with client.stream()` inside the generator. When the generator
is orphaned (e.g. client disconnects mid-stream and Starlette drops
the StreamingResponse iterator without explicitly calling aclose()),
Python's asyncgen finalizer runs the cleanup in a DIFFERENT task than
the one that originally entered the httpx context managers. The
`async with` exits then trigger httpcore's HTTP11ConnectionByteStream
.aclose(), which enters anyio.CancelScope.__exit__ with a mismatched
task and raises RuntimeError("Attempted to exit cancel scope in a
different task"). That error escapes any user-owned try/except
because it happens during GC finalization.
Replace `async with` with manual client/response lifecycle in both
/v1/messages passthrough and /v1/completions proxy. Close the
response and client in a finally block wrapped in
`try: ... except Exception: pass`. This suppresses RuntimeError (and
other Exception subclasses) from the anyio cleanup noise while
letting GeneratorExit (a BaseException, not Exception) propagate
cleanly so the generator terminates as Python expects.
Traceback observed in user report:
File ".../httpcore/_async/connection_pool.py", line 404, in __aiter__
yield part
RuntimeError: async generator ignored GeneratorExit
...
File ".../anyio/_backends/_asyncio.py", line 455, in __exit__
raise RuntimeError(
RuntimeError: Attempted to exit cancel scope in a different task
* Expand unsloth studio run banner with SDK base URL and more curl examples
Add an explicit "OpenAI / Anthropic SDK base URL" line inside the info
box so SDK users don't accidentally copy the bare server URL (without
/v1) into their OpenAI/Anthropic SDK constructors and hit 404s.
Replace the single /v1/chat/completions curl example with three
labeled blocks: chat/completions, Anthropic /messages, and OpenAI
Responses. The Anthropic example includes max_tokens (Anthropic SDKs
require it even though Studio accepts None).
All examples derived from a computed sdk_base_url so the /v1 prefix
stays in sync if the public path ever changes.
* Hash API keys with HMAC-SHA256 + persistent server secret
Stores the HMAC secret in a new app_secrets singleton table. Fixes
CodeQL py/weak-sensitive-data-hashing alert on storage.py:74-76,
394-395. Refresh tokens stay on plain SHA-256 (unchanged _hash_token)
so existing user sessions survive upgrade — API keys are new on this
branch so there is no migration.
* Use PBKDF2 for API key hashing per CodeQL recommendation
HMAC-SHA256 was still flagged by py/weak-sensitive-data-hashing.
Switch to hashlib.pbkdf2_hmac, which is in CodeQL's recommended
allowlist (Argon2/scrypt/bcrypt/PBKDF2). Persistent server-side
salt stays in app_secrets for defense-in-depth. 100k iterations to
match auth/hashing.py's password hasher.
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Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
* 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.
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* 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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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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.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
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* feat: add scan_folders table and CRUD functions to studio_db
* feat: add scan folders API endpoints and integrate into model scan
* feat: add scan folders API client and update source types
* feat: add custom source to model filters and selector
* feat: add Model Folders section to chat settings sidebar
* style: fix biome formatting in ModelFoldersSection
* fix: address review findings for custom scan folders
empty string bypass, concurrent delete crash guard,
Windows case normalization, response_model on endpoints,
logging, deduplicated filter/map, module level cache for
custom folder models, consistent source labels, handleRemove
error surfacing, per folder scan cap
* fix: show custom folders section regardless of chatOnly mode
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* refactor: extract shared refreshLocalModelsList in pickers
* Harden custom scan folder validation and scanning
- Validate path exists, is a directory, and is readable before persisting
- Apply per-folder model cap during traversal instead of after (avoids
scanning millions of inodes in large directories)
- Wrap per-folder scan in try/except so one unreadable folder does not
break the entire /api/models/local endpoint for all callers
- Normalize case on Windows before storing so C:\Models and c:\models
dedup correctly
- Extend macOS denylist to cover /private/etc and /private/tmp (realpath
resolves /etc -> /private/etc, bypassing the original denylist)
- Add /boot and /run to Linux denylist
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Improve scan robustness and preserve Windows path casing
- Preserve original Windows path casing in DB instead of lowercasing
(normcase used only for dedup comparison, not storage)
- Catch PermissionError per child directory so one unreadable subdirectory
does not skip the entire custom folder scan
- Wrap list_scan_folders() DB call in try/except so a DB issue does not
break the entire /api/models/local endpoint
* fix: scan custom folders for both flat and HF cache layouts
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix Windows case-insensitive path dedup with COLLATE NOCASE
Use COLLATE NOCASE on the scan_folders.path column so that the UNIQUE
constraint correctly deduplicates C:\Models and c:\models on Windows
without lowercasing the stored path. Also use COLLATE NOCASE in the
pre-insert lookup query on Windows to catch existing rows with
different casing.
* Restore early-exit limit in _scan_models_dir for custom folders
Keep the limit parameter so _scan_models_dir stops iterating once
enough models are found, avoiding unbounded traversal of large
directories. The post-traversal slice is still applied after combining
with _scan_hf_cache results.
* feat: scan custom folders with LM Studio layout too
* Fix custom folder models being hidden by dedup
Custom folder entries were appended after HF cache and models_dir
entries. The dedup loop kept the first occurrence of each model id,
so custom models with the same id as an existing HF cache entry were
silently dropped -- they never appeared in the "Custom Folders" UI
section.
Use a separate dedup key for custom-source entries so they always
survive deduplication. This way a model can appear under both
"Downloaded" (from HF cache) and "Custom Folders" (from the
user-registered directory) at the same time.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Harden LM Studio scan and fix COLLATE NOCASE on Linux
- Add per-child and per-publisher OSError handling in _scan_lmstudio_dir
so one unreadable subdirectory does not discard the entire custom
folder's results
- Only apply COLLATE NOCASE on the scan_folders schema on Windows where
paths are case-insensitive; keep default BINARY collation on Linux
and macOS where /Models and /models are distinct directories
* Use COLLATE NOCASE in post-IntegrityError fallback SELECT on Windows
The fallback SELECT after an IntegrityError race now uses the same
case-insensitive collation as the pre-insert check, so a concurrent
writer that stored the path with different casing does not cause a
false "Folder was concurrently removed" error.
---------
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: improve GGUF tool calling accuracy and reliability
- Add URL fetching to web_search tool so models can read full page
content instead of only getting search snippets. Uses html2text for
clean markdown conversion with regex fallback.
- Inject current date and behavioral guidance (URL fetch workflow,
no repeated queries, use code for data processing) into the
tool-use system prompt.
- Append error recovery nudge to tool results that indicate failure,
helping small models avoid looping on the same broken call.
- Strip leaked <tool_call> XML from assistant messages in conversation
history and from the outgoing SSE stream.
- Raise default max tool iterations from 10 to 25 across backend,
model schema, and frontend defaults.
- Increase _MAX_PAGE_CHARS from 4k to 16k so fetched pages contain
enough content for the model to extract useful information.
- Add "IMPORTANT: These are only short snippets" hint to search
results so models know to fetch full pages when needed.
Tested with Qwen3.5-4B-GGUF (UD-Q4_K_XL), 10 runs before/after:
- XML leaks in responses: 10/10 -> 0/10
- URL fetch usage: 0 -> 4/10 runs
- Runs producing actual correct answers: 0/10 -> 2/10
- Average tool calls per query: 5.5 -> 3.8 (more efficient)
- Average response time: 12.3s -> 9.8s
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add tool calling benchmark results across model sizes and quants
Tested 16 configurations (4 models x 2 quants x 2 KV cache types)
with 10 runs each on NVIDIA B200.
Best config: 27B UD-Q4_K_XL + bf16 KV -- 6/10 runs found all 4
correct songs, 0 XML leaks, 131s average response time.
* Add duplicate tool-call detection and final-answer synthesis
When the model repeats the exact same tool call (same name + arguments)
twice in a row, skip execution and return a redirect message telling it
to try a different approach. This prevents the 8x-repeated-query loops
observed on 27B and 35B models.
When the tool iteration cap (25) is reached, inject a "provide your
final answer now" message before the final streaming pass. This lets
the model synthesize a useful answer from everything it gathered
instead of being silently cut off.
Tested on Qwen3.5-27B UD-Q4_K_XL (10 runs):
- Repeated query runs: 4/10 -> 2/10
- Cap hits: 1/10 -> 0/10
- All 4/4 accuracy: 5/10 -> 7/10
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix CodeQL alert: handle whitespace in script/style closing tags
The regex fallback for HTML stripping did not match closing tags
with whitespace before the angle bracket (e.g. </script >).
Use \s* before > in both script and style patterns.
* Address reviewer findings: SSRF, timeout crash, XML regex, dedup
- SSRF: resolve hostname via getaddrinfo and reject private, loopback,
link-local, multicast, and reserved addresses before fetching
- Timeout: handle timeout=None (unlimited mode) in URL fetch path
by defaulting to 60s instead of crashing on min(None, 60)
- Download cap: read at most max_chars*4+1 bytes instead of the
full response body before truncating
- XML regex: match both <tool_call> and <function=...> markup in
the history/stream cleanup (inference.py)
- CodeQL: use [^>]* in closing script/style tags to handle any
whitespace or attributes before >
- Dedup: track whether each tool call failed so retries after
transient errors are allowed; only block consecutive identical
calls that both succeeded
- Final-answer synthesis: guard on max_tool_iterations > 0 so
callers who disable tools do not get a false "used all calls" turn
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix redirect SSRF, SSE streaming regression, dedup off-by-one
- SSRF redirect bypass: disable auto-redirect in urllib, manually
follow up to 5 hops with host validation at each step. Prevents
public URLs from redirecting to loopback/private targets.
- SSE streaming: track prev_text on the raw cumulative and strip
XML from the delta only, so completed tool_call tags do not cause
the cumulative to shrink and drop trailing real text.
- Dedup off-by-one: check the immediately previous call (window=1)
instead of requiring 2 matching history entries, so the second
identical successful call is blocked rather than the third.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix redirect HTTPError handling and tighten error prefixes
- Redirect fix: urllib raises HTTPError (not a normal response) when
the redirect handler returns None. Catch HTTPError for 3xx codes
and extract the Location header from the exception object.
- Error prefixes: remove overly broad "No " prefix that matched
"No results found." (a valid empty-search outcome, not an error).
Replace with specific prefixes like "Blocked:", "No query provided",
"Failed to resolve". This ensures empty search results are correctly
classified as non-errors for duplicate-call tracking.
* Fix SSE cross-chunk XML leaks, cleanup review findings
- SSE streaming: sanitize the full cumulative text before diffing
against the previous sanitized snapshot, so XML tags that span
chunk boundaries are stripped correctly. The previous delta-based
approach leaked split tags.
- DRAINING fallback: use _strip_tool_markup() helper instead of a
manual regex that only handled <tool_call> but not <function=...>.
- Move hashlib import, _TOOL_XML_RE compile, and datetime import to
module level per style guide.
- Remove unused _hit_tool_cap variable.
* Fix DNS rebinding, charset detection, HTTPError handling, dedup double-record
- DNS rebinding: resolve hostname once via getaddrinfo, pin the
returned IP, rewrite the URL to connect to the pinned IP with
a Host header. Each redirect hop re-resolves and re-validates.
Closes the TOCTOU window between validation and connection.
- Charset: use resp.headers.get_content_charset() instead of
hardcoding utf-8, so pages with other encodings decode correctly.
- HTTPError: return descriptive "HTTP {code} {reason}" instead of
re-raising into a generic "Search failed" message.
- Dedup: remove redundant _record_tool_call in the duplicate branch;
the single call at the end of the loop handles all cases.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix(studio): change default weight_decay from 0.01 to 0.001
The default weight decay across Studio was 0.01 but should be 0.001.
Updated the default in all backend fallbacks, the Pydantic model, the
frontend config, and every YAML preset/model-default config.
* fix(studio): auto-set learning rate based on training method
Default LR should be 2e-4 for LoRA/QLoRA and 2e-5 for full fine-tuning.
Frontend: track whether the user has manually edited the LR field via a
_learningRateManuallySet flag (same pattern as trainOnCompletions).
When switching training method and the user has not touched the LR,
auto-set it to the appropriate default. Reset the flag on model load.
Backend: change trainer.py start_training default from 5e-5 to 2e-4,
update default.yaml fallback from 5e-5 to 2e-4, and fix
full_finetune.yaml from 0.0002 (2e-4) to 2e-5.
* refactor(studio): centralize weight_decay and learning rate defaults
Create studio/backend/core/training/constants.py as the single source of
truth for DEFAULT_WEIGHT_DECAY (0.001), DEFAULT_LEARNING_RATE (2e-4),
DEFAULT_LEARNING_RATE_FULL (2e-5), and DEFAULT_LEARNING_RATE_STR ("2e-4").
All backend modules (trainer.py, training.py, worker.py, models/training.py)
now import from constants.py instead of hardcoding values.
On the frontend, add LR_DEFAULT_LORA and LR_DEFAULT_FULL to
config/training.ts and use them in the store instead of magic numbers.
A comment cross-references the backend constants file.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix model-specific LR override, persist migration, and flag resets
- Preserve model-specific learning rates from YAML configs when the
async autoSelectTrainingMethod callback fires (fixes Qwen2.5-1.5B
getting 2e-4 instead of its configured 1e-5, etc.)
- Bump zustand persist version to 9 with migration so existing users
with weightDecay=0.01 get updated to 0.001
- Clear _learningRateManuallySet in reset() and applyConfigPatch()
for consistency with trainOnCompletions flag behavior
- Add DEFAULT_LEARNING_RATE_FULL_STR to constants.py
* Refine applyConfigPatch to only clear LR flag when patch includes LR
Only reset _learningRateManuallySet when the applied config patch
actually provides a learningRate value. This prevents unrelated config
patches from silently disarming the manual-edit guard, which would
cause a subsequent setTrainingMethod call to overwrite the user's
custom LR.
* Preserve model-specific LR when switching between qlora and lora
Only auto-switch the learning rate when the training category changes
(adapter <-> full fine-tuning). Switching between qlora and lora keeps
the current LR since both methods share the same learning rate range.
This preserves curated per-model defaults (e.g. 1e-5 for
Qwen2.5-1.5B-Instruct) when the user toggles between adapter methods.
* Remove constants.py, use YAML configs as the source of truth
The YAML config files (model-specific + default.yaml) are the intended
config layer for training defaults. The Python backend fallbacks now use
inline values that match the YAML configs, rather than importing from a
separate constants module. This keeps the config architecture simple:
YAML files are the single source of truth, and the inline Python
fallbacks are just safety nets that mirror them.
* fix(studio): preserve model-specific LR when switching training method
Stash YAML-provided learning rate and use it to restore the correct
value when switching between adapter and full fine-tune modes.
- qlora <-> lora no longer overwrites the model's LR
- full -> adapter restores the YAML LR instead of a hardcoded constant
- selecting a model while on full fine-tune uses LR_DEFAULT_FULL
instead of applying the YAML adapter LR
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
* [WIP] balanced device map for studio
* gpus as a request parameter
* API for multi GPU stuff
* return multi gpu util in new API
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Use balanced_low0 instead of balanced
* Use balanced_low0 instead of balanced
* Fix device_map typo, UUID parsing crash, set() filter bug, and broken tests
- balanced_low0 -> balanced_low_0 (transformers/accelerate rejects the old string)
- get_parent_visible_gpu_ids() now handles UUID/MIG CUDA_VISIBLE_DEVICES
gracefully instead of crashing on int() parse
- _get_backend_visible_gpu_info() set() or None bug: empty set is falsy so
CUDA_VISIBLE_DEVICES=-1 would disable filtering and report all GPUs
- test_gpu_selection.py: add missing get_visible_gpu_utilization import and
add required job_id arg to start_training() calls
* Smart GPU determinism using estimates
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* disallow gpu selection for gguf for now
* cleanup
* Slightly larger baseline
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Treat empty list as auto
* Verbose logging/debug
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Cleanup and revert unnecessary deletions
* Cleanup excessive logs and guard against disk/cpu offload
* auth for visibility API. cleanup redundant imports. Adjust QLoRA estimate
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* support for non cuda gpus
* Fix multi-GPU auto-selection memory accounting
The multi_gpu_factor was applied uniformly to all GPUs including the
first one, which unfairly penalizes single-GPU capacity when
transitioning to multi-GPU. This created a discontinuity where a model
that barely fits 1 GPU would suddenly require 2 GPUs because the first
GPU's free memory was discounted by 20%.
Now the first GPU keeps its full free memory, and only additional GPUs
have an overhead factor (0.85) applied to account for inter-GPU
communication and sharding overhead. This gives more accurate
auto-selection and avoids unnecessary multi-GPU for models that
comfortably fit on one device.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add sandbox tests for multi-GPU selection logic
24 tests covering model size estimation, memory requirements, automatic
GPU selection, device map generation, GPU ID validation, and multi-GPU
overhead accounting. All tests use mocks so they run without GPUs on
Linux, macOS, and Windows.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix reviewer findings: 4bit inference estimate, fallback, GGUF gpu_ids, retry
1. 4-bit inference now uses reduced memory estimate (model_size/3 + buffer)
instead of the FP16 1.3x multiplier. This prevents over-sharding
quantized models across unnecessary GPUs.
2. When model size estimation fails, auto_select_gpu_ids now falls back to
all visible GPUs instead of returning None (which could default to
single-GPU loading for an unknown-size model).
3. GGUF inference route now treats gpu_ids=[] as auto-selection (same as
None) instead of rejecting it as an unsupported explicit request.
4. Training retry path for "could not get source code" now preserves the
gpu_ids parameter so the retry lands on the same GPUs.
5. Updated sandbox tests to cover the new 4-bit inference estimate branch.
* Remove accidentally added unsloth-zoo submodule
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix UUID/MIG visibility and update test expectations
1. nvidia.py: When CUDA_VISIBLE_DEVICES uses UUID/MIG tokens, the
visibility APIs now return "unresolved" with empty device lists instead
of exposing all physical GPUs. This prevents the UI from showing GPUs
that the backend process cannot actually use.
2. test_gpu_selection.py: Updated test expectations to match the new
multi-GPU overhead accounting (first GPU at full capacity, 0.85x for
additional GPUs) and 4-bit inference memory estimation formula.
All 60 tests now pass.
* Add CPU/disk offload guard to audio inference path
The audio model loading branch returned before the common
get_offloaded_device_map_entries() check, so audio models loaded with a
multi-GPU device_map that spilled layers to CPU/disk would be accepted
instead of rejected. Now audio loads also verify no modules are offloaded.
* Improve VRAM requirement estimates
* Replace balanced_low_0 with balanced
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* refine calculations for slightly easier nums
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* adjust estimates
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Use nums instead of obj to avoid seralisation error
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Harden nvidia-smi parsing and fix fallback GPU list
1. nvidia.py: Wrap int() casts for GPU index and memory in try/except
so MIG slices, N/A values, or unexpected nvidia-smi output skip the
unparseable row instead of aborting the entire GPU list.
2. nvidia.py: Handle GPU names containing commas by using the last
field as memory instead of a fixed positional index.
3. hardware.py: fallback_all now uses gpu_candidates (GPUs with verified
VRAM data) instead of raw devices list, which could include GPUs
with null VRAM that were excluded from the ranking.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* cleanup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* consolidate raise_if_offload
* Improve MoE support. Guard against nvidia-smi failures
* Improve MoE support. Guard against nvidia-smi failures
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Fix shared-expert LoRA undercount, torch VRAM fallback, and apply_gpu_ids edge case
1. vram_estimation.py: compute_lora_params now includes shared experts
(n_shared_experts) alongside routed experts when computing MoE LoRA
adapter parameters. Previously only n_experts were counted, causing
the estimator to undercount adapter, optimizer, and gradient memory
for DeepSeek/GLM-style models with shared experts.
2. hardware.py: _torch_get_per_device_info now uses mem_get_info (which
reports system-wide VRAM usage) instead of memory_allocated (which
only reports this process's PyTorch allocations). This prevents
auto-selection from treating a GPU as mostly free when another
process is consuming VRAM. Falls back to memory_allocated when
mem_get_info is unavailable.
3. hardware.py: apply_gpu_ids([]) now returns early instead of setting
CUDA_VISIBLE_DEVICES="" which would disable CUDA entirely. Empty
list inherits the parent visibility, same as None.
4. hardware.py: Upgraded fallback_all GPU selection log from debug to
warning so operators are notified when the model likely will not fit
in available VRAM.
* Guard nvidia-smi subprocess calls against OSError and TimeoutExpired
get_visible_gpu_utilization and get_backend_visible_gpu_info now catch
OSError (nvidia-smi not found) and TimeoutExpired internally instead
of relying on callers to wrap every invocation. Returns the standard
available=False sentinel on failure so the torch-based fallback in
hardware.py can take over.
* Guard get_primary_gpu_utilization and reset GPU caches between tests
1. nvidia.py: get_primary_gpu_utilization now catches OSError and
TimeoutExpired internally, matching the pattern already used in
get_visible_gpu_utilization and get_backend_visible_gpu_info. All
three nvidia-smi callers are now self-contained.
2. test_gpu_selection.py: Added _GpuCacheResetMixin that resets the
module-level _physical_gpu_count and _visible_gpu_count caches in
tearDown. Applied to all test classes that exercise GPU selection,
device map, or visibility functions. This prevents stale cache
values from leaking between tests and causing flaky results on
machines with real GPUs.
* Fix nvidia-smi fallback regression and physical GPU count validation
1. hardware.py: get_gpu_utilization, get_visible_gpu_utilization, and
get_backend_visible_gpu_info now check result.get("available") before
returning the nvidia-smi result. When nvidia-smi is unavailable or
returns no data (e.g., containers without nvidia-smi, UUID/MIG masks),
the functions fall through to the torch-based fallback instead of
returning an empty result. This fixes a regression where the internal
exception handling in nvidia.py prevented the caller's except block
from triggering the fallback.
2. hardware.py: resolve_requested_gpu_ids now separates negative-ID
validation from physical upper-bound validation. The physical count
check is only enforced when it is plausibly a true physical count
(i.e., higher than the largest parent-visible ID), since
torch.cuda.device_count() under CUDA_VISIBLE_DEVICES returns the
visible count, not the physical total. The parent-visible-set check
remains authoritative in all cases. This prevents valid physical IDs
like [2, 3] from being rejected as "out of range" when nvidia-smi is
unavailable and CUDA_VISIBLE_DEVICES="2,3" makes torch report only
2 devices.
* Fix UUID/MIG torch fallback to enumerate devices by ordinal
When CUDA_VISIBLE_DEVICES uses UUID or MIG identifiers,
get_parent_visible_gpu_ids() returns [] because the tokens are
non-numeric. The torch fallback in get_visible_gpu_utilization() and
get_backend_visible_gpu_info() previously passed that empty list to
_torch_get_per_device_info(), getting nothing back.
Now both functions detect the empty-list case and fall back to
enumerating torch-visible ordinals (0..device_count-1) with
index_kind="relative". This means the UI and auto-selection still
see real device data in Kubernetes, MIG, and Slurm-style UUID
environments where nvidia-smi output cannot be mapped to physical
indices.
Updated test_uuid_parent_visibility to verify the new torch fallback
path returns available=True with relative ordinals.
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* Add type hint for gpu_ids parameter in InferenceOrchestrator.load_model
---------
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Fixes#4670
Separates the GGUF context slider ceiling from the currently active context length so lowering context via Chat Settings no longer locks the slider max to the reduced value.
- Backend: adds `max_context_length` to GGUF load/status responses, computed from the largest VRAM/KV-fit cap across all usable GPU subsets
- Frontend: stores `ggufMaxContextLength` and uses it for Context Length slider/input bounds; hydrates from both `/api/inference/load` and `/api/inference/status`
- Defaults UI ceiling to native context for CPU-only and fallback paths
- Seeds `effective_ctx` and `max_available_ctx` before GPU probing to prevent `UnboundLocalError` on probe failure
- Property fallback uses native `_context_length`, not effective `context_length`
* Detect always-on reasoning models and show Think button as locked-on
Models with hardcoded <think>/<think> tags or reasoning_content in
their chat template (e.g. distilled reasoning models) always produce
thinking output regardless of any toggle. Previously these models
were not detected as reasoning-capable at all, so the Think button
was grayed out even though the model was actively reasoning.
Backend:
- Detect <think>/<think> and reasoning_content in GGUF chat templates
as a fallback when enable_thinking is not present
- Add reasoning_always_on flag to LoadResponse and InferenceStatusResponse
- Pass the flag through all GGUF load and status response paths
Frontend:
- Add reasoningAlwaysOn to the chat runtime store and API types
- When reasoning_always_on is true, show the Think button as lit
(active) but not clickable, with a tooltip explaining the model
always uses thinking
- Force reasoningEnabled=true when the model always reasons
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* Use pointer-events-none instead of disabled for always-on Think button
The HTML disabled attribute was not fully blocking clicks on the Think
button for always-on reasoning models. Switch to pointer-events-none
CSS class which prevents all mouse interaction at the CSS level.
* Use a static span instead of disabled button for always-on Think
Replace the button element with a plain span when reasoning is
always on. This makes it physically impossible to toggle since
there is no clickable element at all, avoiding any CSS or
disabled-attribute edge cases.
* Simplify always-on Think button to stay lit and remain toggleable
Keep the Think button as a normal toggleable button but ensure it
shows as lit when reasoning_always_on is true. The model always
reasons regardless of the toggle state so there is no need to
block interaction.
---------
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
The ChatCompletionRequest Pydantic model defaulted repetition_penalty
to 1.1 when clients omitted the field. This silently forced
llama-server to perform per-token repetition scanning, dropping
streaming throughput from ~225 TPS to ~172 TPS (a 24% penalty).
The Studio frontend always sends repetition_penalty=1.0 explicitly,
so UI users were unaffected. But any API client hitting
/v1/chat/completions without setting the field (curl, third-party
integrations, Open WebUI, etc.) would get the slow path.
Benchmarked on Qwen3.5-4B Q4_K_XL, GPU 0:
- repeat_penalty=1.0: 225.2 TPS
- repeat_penalty=1.1: 172.7 TPS (24% slower)
- LM Studio (which applies rp internally): 170.8 TPS
This aligns the Pydantic default with the frontend default (1.0),
generate_chat_completion's function signature default (1.0), and
llama-server's own default (1.0).
* feat(studio): editable context length with Apply/Reset for GGUF model settings
Previously the Context Length field was read-only and the backend
hardcoded `-c 0`, ignoring custom values entirely. KV Cache Dtype also
triggered an immediate model reload with no way to cancel.
Backend:
- llama_cpp.py: pass the actual n_ctx value to `-c` instead of always 0
- models/inference.py: relax max_seq_length to 0..1048576 (0 = model
default) so GGUF models with large context windows are supported
Frontend:
- chat-runtime-store: add customContextLength and loadedKvCacheDtype
state fields for dirty tracking
- chat-settings-sheet: make Context Length an editable number input,
stop KV Cache Dtype from auto-reloading, show Apply/Reset buttons
when either setting has been changed
- use-chat-model-runtime: send customContextLength as max_seq_length
in the load request, reset after successful load
* fix: preserve maxSeqLength for non-GGUF models in load request
customContextLength ?? 0 sent max_seq_length=0 for non-GGUF models,
breaking the finetuning/inference path that needs the slider value.
Now uses a three-way branch:
- customContextLength set: use it (user edited GGUF context)
- GGUF without custom: 0 (model's native context)
- Non-GGUF: maxSeqLength from the sampling slider
* fix: keep max_seq_length default at 4096 for non-GGUF callers
Only relax the bounds (ge=0 for GGUF's "model default" mode,
le=1048576 for large context windows). The default stays at 4096
so API callers that omit max_seq_length still get a sane value
for non-GGUF models.
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* fix(studio): rename trust remote code toggle and hide when no model selected
- Rename "Trust remote code" to "Enable custom code"
- Shorten subtitle to "Only enable if sure"
- Hide the toggle when no model is loaded (already hidden for GGUFs)
* fix: restore ge=128 for max_seq_length validation
Keep the minimum at 128 so the API rejects nonsensical values.
GGUF path now sends the model's native context length (from
ggufContextLength) instead of 0 when the user has not customized it.
The upper bound stays at 1048576 for large-context GGUF models.
* feat(studio): replace Context Length input with slider
Use a ParamSlider (512 to model's native context, step 512) instead
of a small number input. Shows "Max" when at the model's native
context length. Consistent with the other slider controls in the
settings panel.
* feat(studio): add editable number input alongside Context Length slider
The slider and number input stay synced -- dragging the slider updates
the number, typing a number moves the slider. The input also accepts
values beyond the slider range for power users who need custom context
lengths larger than the model default.
* fix(studio): widen context length input and use 1024 step for slider
Make the number input wider (100px) so large values like 262144 are
fully visible. Change slider step from 512 to 1024 and min from 512
to 1024.
* fix(studio): context length number input increments by 1024
* fix(studio): cap context length input at model's native max
Adds max attribute and clamps typed/incremented values so the context
length cannot exceed the GGUF model's reported context window.
* fix(studio): point "What's new" link to changelog page
Changed from /blog to /docs/new/changelog.
* fix(studio): preserve custom context length after Apply, remove stale subtitle
- After a reload with a custom context length, keep the user's value
in the UI instead of snapping back to the model's native max.
ggufContextLength always reports the model's native metadata value
regardless of what -c was passed, so we need to preserve
customContextLength when it differs from native.
- Remove "Reload to apply." from KV Cache Dtype subtitle since the
Apply/Reset buttons now handle this.
* feat(studio): auto-enable Search and Code tools when model supports them
Previously toolsEnabled and codeToolsEnabled stayed false after loading
a model even if it reported supports_tools=true. Now both toggles are
automatically enabled when the loaded model supports tool calling,
matching the existing behavior for reasoning.
* fix(studio): auto-enable tools in autoLoadSmallestModel path
The suggestion cards trigger autoLoadSmallestModel which bypasses
selectModel entirely. It was hardcoding toolsEnabled: false and
codeToolsEnabled: false even when the model supports tool calling.
Now both are set from the load response, matching the selectModel
behavior. Also sets kvCacheDtype/loadedKvCacheDtype for dirty
tracking consistency.
* fix(studio): re-read tool flags after auto-loading model
The runtime state was captured once at the start of the chat adapter's
run(), before autoLoadSmallestModel() executes. After auto-load enables
tools in the store, the request was still built with the stale snapshot
that had toolsEnabled=false. Now re-reads the store after auto-load so
the first message includes tools.
* fix(studio): re-read entire runtime state after auto-load, not just tools
The runtime snapshot (including params.checkpoint, model id, and all
tool/reasoning flags) was captured once before auto-load. After
autoLoadSmallestModel sets the checkpoint and enables tools, the
request was still built with stale params (empty checkpoint, tools
disabled). Now re-reads the full store state after auto-load so the
first message has the correct model, tools, and reasoning flags.
* feat(studio): add Hugging Face token field in Preferences
Adds a password input under Configuration > Preferences for users to
enter their HF token. The token is persisted in localStorage and
passed to all model validate/load/download calls, replacing the
previously hardcoded null. This enables downloading gated and private
models.
* fix(studio): use model native context for GGUF auto-load, show friendly errors
The auto-load paths and selectModel for GGUF were sending
max_seq_length=4096 which now actually limits the context window
(since we fixed the backend to respect n_ctx). Changed to send 0
for GGUF, which means "use model's native context size".
Also replaced generic "An internal error occurred" messages with
user-friendly descriptions for known errors like context size
exceeded and lost connections.
LoadRequest validation changed to ge=0 to allow the GGUF "model
default" signal. The frontend slider still enforces min=128 for
non-GGUF models.
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* fix(studio): filter out FP8 models from model search results
Hide models matching *-FP8-* or *FP8-Dynamic* from both the
recommended list and HF search results. These models are not
yet supported in the inference UI.
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* feat: multi-source model discovery (HF default, legacy cache, LM Studio)
* Fix multi-source model discovery bugs
- Fix lmstudio_model_dirs: add ~/.lmstudio/models as default path,
remove dead sys.platform branch, add dedup via seen set
- Fix _setup_cache_env: preserve legacy HF cache env vars when the
legacy hub directory exists and is non-empty
- Fix _scan_lmstudio_dir: use absolute path for id field so
is_local_path() returns True
- Remove LM Studio dirs from allowed_roots (scanned unconditionally)
- Replace bare except passes with logger.warning in legacy cache blocks
- Fix delete_cached_model to search both default and legacy HF caches
- Make lmstudio_dirs non-optional in TS interface (matches Python schema)
- Exclude lmstudio source from trainable model filter
- Remove unused import sys
* Scan HF default cache alongside legacy and active caches
When _setup_cache_env overrides HF_HUB_CACHE to the legacy Unsloth
path, the standard HF default cache (~/.cache/huggingface/hub) was
never scanned, hiding models downloaded before Unsloth Studio was
installed.
Add hf_default_cache_dir() and _all_hf_cache_scans() helper that
deduplicates and scans all three HF cache locations (active, legacy,
default). Used in list_local_models, list_cached_gguf,
list_cached_models, and delete_cached_model.
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* feat(db): add SQLite storage layer for training history
* feat(api): add training history endpoints and response models
* feat(training): integrate DB persistence into training event loop
* feat(ui): add training history views and card grid
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* fix(studio): address review issues in training history persistence
- Strip hf_token/wandb_token from config before SQLite storage
- Add UUID suffix to job_id for collision resistance
- Use isfinite() for 0.0 metric handling throughout
- Respect _should_stop in error event finalization
- Run schema DDL once per process, not per connection
- Close connection on schema init failure
- Guard cleanup_orphaned_runs at startup
- Cap _metric_buffer at 500 entries
- Make FLUSH_THRESHOLD a class constant
- Map 'running' to 'training' phase in historical view
- Derive LR/GradNorm from history arrays in historical view
- Fix nested button with div[role=button] in history cards
- Guard String(value) against null/undefined in config popover
- Clear selectedHistoryRunId on auto tab switch
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* fix(studio): address round-2 review findings across training backend and frontend
Backend (training.py):
- Move state mutation after proc.start() so a failed spawn does not wedge
the backend with is_training=True
- Create DB run row eagerly after proc.start() so runs appear in history
during model loading, not after first metric event
- Rewrite _flush_metrics_to_db() with snapshot-before-insert pattern to
preserve metrics arriving during the write and retain buffer on failure
- Guard eval_loss with float() coercion and math.isfinite(), matching the
existing grad_norm guard
- Increase pump thread join timeout from 3s to 8s to cover SQLite's
default 5s lock timeout
Frontend (studio-page.tsx):
- Fix history navigation: check isTrainingRunning instead of
showTrainingView in onSelectRun so completed runs are not misrouted
- Replace activeTab state + auto-switch useEffect with derived tab to
eliminate react-hooks/set-state-in-effect lint violation
Frontend (historical-training-view.tsx):
- Add explicit "running" branch to message ternary so running runs no
longer fall through to "Training errored"
- Derive loading from detail/error state and move cleanup to effect
return to eliminate react-hooks/set-state-in-effect lint violation
Frontend (progress-section.tsx):
- Derive stopRequested from isTrainingRunning && stopRequestedLocal to
eliminate react-hooks/set-state-in-effect lint violation and remove
unused useEffect import
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* fix(studio): resolve 3 remaining bugs from round-2 review
1. Stuck on Current Run tab [12/20]: Only force "current-run" tab when
isTrainingRunning is true, not when stale completed-run data exists.
After training ends, users can freely navigate to Configure.
2. Incomplete metric sanitization [7/20]: Apply float() coercion and
isfinite() guards to loss and learning_rate, matching the existing
pattern used by grad_norm and eval_loss. Prevents TypeError from
string values and NaN leaks into history arrays.
3. Stop button state leak across runs [10/20]: Add key={runtime.jobId}
to ProgressSection so React remounts it when a new run starts,
resetting stopRequestedLocal state.
* fix(studio): deduplicate loss/lr sanitization in training event handler
Reuse _safe_loss/_safe_lr from the progress update block instead of
re-sanitizing the same raw event values for metric history.
* fix(studio): restore loss > 0 guard to prevent eval steps injecting 0.0 into metric histories
Round-2/3 fixes relaxed the history append guard from `loss > 0` to
`loss is not None`, which let eval-only log events (where loss defaults
to 0.0) append fake zeros into loss_history and lr_history. Restore the
`loss > 0` check to match the worker's own has_train_loss gate. The
float() coercion and isfinite() sanitization from round-3 remain intact.
* fix(studio): resolve training history bugs — nullable loss/lr, tab nav, sparkline
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* feat(chat): add server-side timings and context display for GGUF
Extract timings/usage metadata from llama-server SSE stream and forward
through the full stack. Replace client-side estimates with accurate
server-reported metrics (prompt eval, tok/s, token counts, cache hits).
Add context window usage bar to chat top nav.
* feat(chat): source badges with hover cards and 2-row collapse
- Add hover cards to source badges showing favicon, title, URL and
snippet description on hover
- Limit source badges to 2 rows with +X more expand/collapse
- Parse snippet from web search results for hover card descriptions
- Replace individual Source rendering with grouped SourcesGroup component
* fix(chat): add null guards for server timings edge cases
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* fix(chat): reset contextUsage on thread switch, remove unused context-display
* fix(chat): stop double-counting completion tokens in tool-calling path
* fix(chat): skip metadata events in llm_assist consumers
* fix(chat): hide context usage bar in compare mode
* fix(chat): harden timings pipeline and context usage persistence
Accumulate prompt_ms, predicted_ms, and predicted_n from intermediate
tool-detection passes so the final metadata reflects total server work.
Persist contextUsage in message metadata (Dexie) and restore on thread
load. Add type guard in gguf_stream_chunks for unexpected dict events.
Clear contextUsage when entering compare mode.
* feat(chat): make GGUF stream metadata OpenAI-compatible
* fix(chat): address PR review feedback
* feat(chat): address PR review feedback
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* fix(recipe-studio): prevent fitView from zooming to wrong location on recipe load
* feat: add pymupdf/python-docx deps and unstructured uploads storage root
* feat: add POST /seed/upload-unstructured-file endpoint
* feat: add multi-file chunking with source_file column
* feat: update frontend types and API layer for multi-file upload
* feat: round-robin preview rows across source files
Ensures every uploaded file is represented in the preview table
by cycling through sources instead of just taking the first N rows.
* fix: disable OCR, fix auto-load timing, fix persistence on reload
- Disable pymupdf4llm OCR with write_images=False, show_progress=False
- Replace onAllUploaded callback with useEffect that detects uploading→done
transition (avoids stale closure reading empty file IDs)
- Fix importer to preserve file IDs from saved recipes instead of clearing
(clearing only happens at share time via sanitizeSeedForShare)
* fix: harden unstructured upload with input validation and state fixes
Validate block_id/file_id with alphanumeric regex to prevent path
traversal, use exact stem match for file deletion, add error handling
for metadata writes and empty files, fix React stale closures and
object mutations in upload loop, and correct validation logic for
unstructured seed resolved_paths.
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* fix: address PR review - legacy path import, share sanitizer, sync effect
Promote legacy source.path into resolved_paths for old unstructured
recipes, clear source.paths in share sanitizer to prevent leaking local
filesystem paths, and gate file sync effect to dialog open transition
so users can actually delete all uploaded files.
* fix: CSV column fix (BOM + whitespace + unnamed index re-save) for #4470
* fix: harden unstructured upload flow and polish dialog UX
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* feat: support full model GGUF export, disable incompatible methods in UI
* fix: resolve base model from config.json for venv_t5 export switching
* feat: detect BNB-quantized models and disable all export methods for quantized non-PEFT checkpoints
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* fix: relocate Ollama Modelfile alongside GGUFs during non-PEFT export cleanup
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* Add elapsed timer to tool status pill in Studio
Show a count-up seconds timer (0s, 1s, 2s, ...) next to the tool status
text in the composer area. Helps users gauge how long a tool call (web
search, code execution) has been running. Timer resets when a new tool
starts and disappears when all tools finish.
* Fix tool call parsing, add tool outputs panel and reasoning copy button
Backend:
- Rewrite tool call XML parser to use balanced-brace JSON extraction
instead of greedy regex, fixing truncation on nested braces in
code/JSON arguments
- Handle optional closing tags (</tool_call>, </function>, </parameter>)
that models frequently omit
- Support bare <function=...> tags without <tool_call> wrapper
- Strip tool call markup from streamed content so raw XML never leaks
into the chat UI
- Use a persistent ~/studio_sandbox/ working directory for tool
execution so files persist across calls within a session
- Emit tool_start/tool_end SSE events so the frontend can display
tool inputs and outputs
Frontend:
- Add collapsible "Tool Outputs" panel below assistant messages showing
each tool call's input and output with copy buttons
- Add copy button to reasoning blocks
- Add elapsed timer to tool status pill
- Update project URLs in pyproject.toml (http -> https, add docs link)
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* Add interactive HTML preview with fullscreen toggle for code blocks
HTML code fences now render an interactive sandboxed iframe preview
below the syntax-highlighted code, similar to how SVG fences show
an image preview. The iframe uses sandbox="allow-scripts" to allow
JavaScript execution while blocking access to the parent page.
Includes a fullscreen toggle (enlarge/minimize button) that expands
the preview into a viewport overlay, dismissible via button, Escape
key, or backdrop click. A streaming placeholder prevents partial
HTML from rendering mid-stream.
* Add tool call settings: auto-heal toggle, max iterations, timeout
Add three user-configurable tool call settings to the Studio Settings panel:
- Auto Heal Tool Calls: toggle to control fallback XML parsing of malformed
tool calls from model output (default: on)
- Max Tool Calls Per Message: slider 0-40 + Max to cap tool call iterations
per message (default: 10)
- Max Tool Call Duration: slider 1-30 minutes + Max to set per-tool-call
execution timeout (default: 5 minutes)
All settings persist to localStorage and flow through the full stack:
frontend store -> API request -> Pydantic model -> route -> llama_cpp -> tools.
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* Fix tool call timeout: respect no-limit and apply to web search
- Use a sentinel to distinguish timeout=None (no limit) from the default
(300s). Previously None was silently replaced with _EXEC_TIMEOUT.
- Pass the configured timeout to DDGS() for web searches so the setting
applies uniformly to all tool types.
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* Add input validation bounds and per-thread sandbox isolation
- Add ge=0 constraint to max_tool_calls_per_message (rejects negative values)
- Add ge=1 constraint to tool_call_timeout (minimum 1 second)
- Thread session_id from frontend through backend to tool execution
- Scope sandbox directories per conversation: ~/studio_sandbox/{thread_id}/
- Backwards compatible: API callers without session_id use ~/studio_sandbox/
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* Fix non-monotonic streaming and Python temp script path
- Split tool markup stripping into closed-only (mid-stream) and full
(final flush) to prevent cumulative text from shrinking mid-stream
- Enforce monotonicity: only emit when cleaned text grows, so the
proxy's delta logic (cumulative[len(prev_text):]) never breaks
- Place Python temp scripts in the sandbox workdir instead of /tmp so
sys.path[0] points to the sandbox and cross-call imports work
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* Sanitize session_id to prevent path traversal in sandbox
Strip path separators and parent-dir references from session_id before
using it as a directory name. Verify the resolved path stays under
~/studio_sandbox/ as a second guard.
* feat(chat): proper assistant-ui tool call UIs with sources
Replace custom metadata-based ToolOutputsGroup with native assistant-ui
tool-call content parts. Backend SSE tool_start/tool_end events now emit
proper { type: "tool-call" } parts from the adapter, enabling per-tool
UIs registered via tools.by_name in MessagePrimitive.Parts.
- Web search: Globe icon, Source badges with favicons, auto-collapse
when LLM starts responding
- Python: Code icon, syntax-highlighted code via Streamdown/shiki,
output block with copy
- Terminal: Terminal icon, command in trigger, output with copy
- ToolGroup wraps consecutive tool calls (skips for single calls)
- Sources component renders URL badges at end of message
- Flattened code block CSS (single border, no nested boxes)
* fix(inference): respect empty enabled_tools allowlist
`if payload.enabled_tools:` is falsy for [], falling through to
ALL_TOOLS. Use `is not None` so an explicit empty list disables
all tools as intended.
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* studio: improve onboarding UX, tooltips, and training defaults
- Change splash text to "Train and run LLMs locally"
- Add "Chat Only" card with BubbleChatIcon to skip directly to chat
- Add Skip/Skip to Chat buttons in sidebar and footer
- Back button on step 1 returns to splash screen instead of being disabled
- Change "Watch video guide" to "Get started with our guide" with new URL
- Update intro text to mention all model types + chat
- Make all tooltips clickable (in addition to hover) via React context
- Strip surrounding quotes from pasted HF tokens
- Rename "Eval Split" to "Evaluation Split"
- Add SparklesIcon to "Auto Detect" format option
- Change step 4 heading to "Choose your training parameters"
- Default max_steps to 60
- Learning rate displayed in scientific notation with +/- stepper
- Context length options capped by model's max_position_embeddings (via AutoConfig)
- Fix "QLORA"/"LORA" to "QLoRA"/"LoRA" in summary step
- Backend: add max_position_embeddings to model config endpoint
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* compare for 2 diff models
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* resolving gemini comments
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* studio: disable thinking for Qwen3.5 <9B and always for AI Assist
- Change Qwen3.5 thinking threshold from <=2B to <9B (0.8B, 2B, 4B
all disable thinking by default; 9B+ enables it)
- Always pass enable_thinking=False in AI Assist helper calls
(_run_with_helper and _generate_with_backend) regardless of chat
thinking settings
* studio: address PR review comments
- Extract _get_max_position_embeddings helper to DRY config extraction
- Fix "Skip to Chat" to navigate to /chat on step 1 (was /studio)
* fix: comment out debug print statements
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* studio: skip Shiki highlighting for incomplete SVG code fences
While streaming SVG content, the syntax highlighter (Shiki) re-parses
the entire growing SVG on every token, blocking the main thread and
freezing the code area until the fence closes. Show a plain-text
preview for incomplete SVG fences instead, similar to how Mermaid
diagrams show a placeholder while streaming.
* studio: fix default top_k from 50/40 to 20 for chat inference
Per Qwen3.5 docs (unsloth.ai/docs/models/qwen3.5), top_k should be 20
for both thinking and non-thinking modes. The model-specific config in
inference_defaults.json already had top_k=20 for Qwen3.5, but the
generic fallback defaults were wrong:
- Frontend DEFAULT_INFERENCE_PARAMS.topK: 50 -> 20
- Backend generate_chat_completion top_k: 40 -> 20
- Backend generate_chat_completion_with_tools top_k: 40 -> 20
- Frontend title generation top_k: 40 -> 20
* studio: set universal inference defaults for unknown models
Default params for any model without specific config:
temperature=0.6, top_p=0.95, top_k=20, min_p=0.01,
presence_penalty=0.0, repetition_penalty=1.0
Models with entries in inference_defaults.json (Qwen3.5, Gemma-3,
Llama, etc.) override these with their recommended values.
Updated in: frontend DEFAULT_INFERENCE_PARAMS, backend Pydantic
request models, and backend generate_chat_completion defaults.
* studio: only trust_remote_code for unsloth/ models in AutoConfig
Only set trust_remote_code=True when the model name starts with
"unsloth/". All other models default to False for safety.
* studio: move Generating spinner above the composer
The "Generating" spinner was below the send message bar, causing
the bar to jump up and down. Move it above the composer in both
the regular thread view and the welcome/empty view.
* studio: adjust toast close button position away from edge
Move the X close button on toasts (like "Starting model...") from
top-1.5 to top-3 and add right-3, giving more breathing room from
the top-right corner.
* studio: make Think button smaller with tighter icon-text gap
Reduce gap from 1.5 to 0.5, padding from px-2.5/py-1 to px-2/py-0.5,
and icon from size-3.5 to size-3.
* studio: multiple onboarding and chat UX improvements
- Move Generating spinner above composer (fixes jumping send bar)
- Make Think button smaller with tighter icon-text gap
- Chat card now inside grid (same size as Audio/Embeddings cards)
- Rename "Chat Only" to "Chat"
- Chat card requires Continue to proceed (no auto-advance)
- Continue on Chat selection skips onboarding and goes to /chat
- Tooltip (i) click on Chat card doesn't trigger navigation
- Step 1 footer Back button goes back to splash (label is "Back")
- Splash "Skip Onboarding" renamed to "Skip to Chat", navigates to /chat
- Toast close button moved away from edge
* studio: align Skip to Chat button, add Skip to footer
- Sidebar "Skip to Chat" now uses primary (green) Button style with
arrow icon, full width, aligned like step items. Shows on all steps.
- Footer: added "Skip" outline button next to Continue that goes
directly to /studio with progress saved (markOnboardingDone)
* studio: change default max steps from 30 to 60 in toggle hook
The DEFAULT_MAX_STEPS in use-max-steps-epochs-toggle.ts was still 30,
used as fallback when toggling from epochs back to max steps.
* studio: extend context length options to 262K
CONTEXT_LENGTHS now includes 65536, 131072, 262144 in addition to
the existing 512-32768 range. The onboarding step filters these by
the model's max_position_embeddings (e.g. Nemotron-3-Nano-4B has
262144), showing powers of 2 up to the model's maximum.
* studio: auto-select LoRA vs QLoRA based on model size and GPU memory
After selecting a model in onboarding, detect the total model weight
file size from HF Hub (safetensors/bin files). Then estimate memory
needed: model_size_gb * 1.5 * context_scale, where context_scale is:
- <=8192 tokens: 1.0x
- >8192 tokens: 1.7x
- >=16384 tokens: 2.0x
- >=32768 tokens: 4.0x
If the estimate fits in free GPU VRAM, default to LoRA (16-bit).
Otherwise default to QLoRA (4-bit).
Backend changes:
- Add model_size_bytes to ModelDetails (models.py)
- Add _get_model_size_bytes() using HfApi.repo_info (routes/models.py)
- Add vram_free_gb to get_gpu_summary (hardware.py)
Frontend changes:
- Add autoSelectTrainingMethod() in training-config-store.ts
- Called after model defaults are loaded
- Add model_size_bytes to ModelConfigResponse type
- Add vramFreeGb to HardwareInfo hook
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: rename "Importing ML libraries..." to "Importing Unsloth..."
* studio: show model/dataset in training status, fix LoRA/QLoRA casing
- Training status now shows 'Training "model_name"' and 'Dataset = ...'
instead of generic "Starting training..."
- Fix Studio progress section to show QLoRA/LoRA instead of QLORA/LORA
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: rename 'Skip to Chat' to 'Skip Onboarding' on splash screen
* studio: add presence_penalty support for chat inference
Add presence_penalty as a parameter across the full stack:
- Backend: llama_cpp.py generate_chat_completion/with_tools, Pydantic
models (inference.py), routes/inference.py pass-through
- Frontend: InferenceParams type, DEFAULT_INFERENCE_PARAMS (0.0),
chat-adapter.ts payload, chat-settings-sheet.tsx slider (0-2),
model defaults loading from inference_defaults.json
- Set Qwen3.5 default presence_penalty to 1.5 per official docs
- Default for unknown models is 0.0 (off)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: fix Chat card deselecting Text and aligning with other cards
* studio: fix presence_penalty not loading from inference defaults
The inference_config.py load_inference_config() was not including
presence_penalty in the returned config dict, so the Qwen3.5
default of 1.5 from inference_defaults.json never reached the
frontend. Added it to the config builder.
* studio: add delete button for cached models in model selector
Add trash icon on each downloaded model row (GGUF and safetensors) with
confirmation dialog. Backend DELETE /api/models/delete-cached endpoint
uses huggingface_hub scan_cache_dir + delete_revisions to cleanly remove
cached repos, refusing if the model is currently loaded.
* studio: restore inference defaults, reasoning, and tools on page refresh
On page refresh with a model already loaded, the frontend was not
re-applying model-specific inference defaults (presence_penalty,
temperature, etc.) or restoring reasoning/tools support flags.
Backend: Add inference config, supports_reasoning, supports_tools,
and context_length to InferenceStatusResponse.
Frontend: In the refresh callback, when an active model is detected,
apply mergeRecommendedInference and restore reasoning/tools flags
with proper Qwen3.5 size-based defaults.
* studio: fix delete dialog closing before async completes
Prevent AlertDialogAction's default close behavior with
e.preventDefault() so the dialog stays open during deletion.
Also block onOpenChange dismiss while deleting is in progress.
* fix: add Dict and Any imports to inference models
* studio: fix Qwen3.5 reasoning threshold in frontend load path
The frontend loadModel handler had the old threshold (<=2) for
disabling reasoning on small Qwen3.5 models. Changed to <9 to
match the backend. This was causing 4B to not properly disable
thinking by default when auto-loaded.
* studio: move GGUF delete to per-variant level
For GGUF repos, the trash icon now appears on each downloaded variant
row inside the quantization expander instead of on the repo-level row.
Backend accepts optional variant param to delete specific GGUF files
(blob + symlink) rather than the entire repo cache.
* studio: restore ggufContextLength on page refresh
The Max Tokens slider was capped at 32768 on page refresh because
ggufContextLength was not restored from the status response.
Now set it from statusRes.context_length on reconnect.
* fix: remove <think> from Qwen3.5 response template marker
The train-on-responses-only feature uses template markers to find
where the assistant response starts. The Qwen3.5 response marker
included '<think>\n' which is only present when thinking mode is
enabled. With thinking disabled (default for <9B), the marker
never matched, causing 100% of samples to be dropped.
Changed response marker from '<|im_start|>assistant\n<think>\n'
to '<|im_start|>assistant\n' which works regardless of thinking mode.
* studio: fix sloth ASCII art alignment in training overlay
* fix: correct sloth ASCII art alignment to match Unsloth banner
* studio: add Python and terminal tool calling to chat
Register python and terminal tools alongside web search. Python
executor validates imports (stdlib only) via unsloth_zoo
rl_environments, runs code in a subprocess sandbox with 5-min
timeout and cancel support. Terminal executor blocks dangerous
commands (rm, sudo, etc.) and runs in a temp directory.
Update llama_cpp tool loop to show tool-specific status messages
and pass cancel_event through to executors. Rename composer
toggle from "Search" to "Tools" and show TerminalIcon for
execution status pills.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fix Nemotron/transformers 5.x support, onboarding navigation, port binding
Backend:
- Dynamic transformers 5.x detection via tokenizer_config.json fetch
(checks for TokenizersBackend class, cached per-model)
- Bump transformers 5.x version from 5.2.0 to 5.3.0 across all workers,
setup scripts (setup.sh, setup.ps1)
- Auto-enable trust_remote_code for unsloth/* models needing transformers 5.x
(workaround for NemotronH config parsing bug in transformers)
- Auto-install mamba-ssm/causal-conv1d for SSM models (NemotronH, Falcon-H1)
with --no-build-isolation --no-deps to avoid torch version conflicts
- Add SO_REUSEADDR to port check in run.py (fixes Colab proxy stale connection
falsely reporting port as in-use)
Frontend:
- Fix "Skip to Chat" navigation: use window.location.href instead of React
Router navigate() to bypass useEffect redirect race
- Fix "Skip Onboarding" on splash: navigates to /studio (not /chat)
- Fix onboarding guard: only check isOnboardingDone() on initial mount
- Fix Chat card on step 1: add sr-only spacer for consistent alignment
- Fix Chat+Text both selected: clear RadioGroup value when Chat is selected
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: split tools toggle into Search and Code buttons
Replace the single "Tools" toggle with two independent toggles:
- "Search" (globe icon) enables web search only
- "Code" (terminal icon) enables Python and terminal execution
Add enabled_tools list field to the inference payload so the
backend only registers the tools the user has toggled on. Both
toggles appear in the main composer and the compare composer.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fix tool calling import validation and error logging
Replace unsloth_zoo-dependent import checker with a standalone
ast-based validator using sys.stdlib_module_names. This properly
blocks non-stdlib imports (numpy, requests, etc.) and returns a
clear error message to the model so it can rewrite using only
stdlib.
Add full traceback to tool streaming error logs for debugging.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: parse gpt-oss harmony channels for clean safetensors chat output
gpt-oss models emit multi-channel output via harmony protocol tokens
(<|channel|>analysis<|message|>... and <|channel|>final<|message|>...).
TextIteratorStreamer with skip_special_tokens=True strips the special
tokens but leaves channel names concatenated with content, producing
garbled output like "analysisWe need to...assistantfinalHello!".
Add HarmonyTextStreamer that decodes with skip_special_tokens=False,
parses harmony markup via regex, and emits <think>analysis</think>
for the analysis channel and plain text for the final channel --
reusing the existing frontend reasoning UI.
Also expose supports_reasoning=True for non-GGUF gpt-oss models in
the /status endpoint so the frontend enables the Think toggle.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: use unsloth_zoo for Python sandbox validation
Set UNSLOTH_IS_PRESENT=1 and import check_python_modules and
check_signal_escape_patterns directly from unsloth_zoo instead
of a standalone fallback. This gives us the full Unsloth
validation including stdlib-only import checks and signal/timeout
escape pattern detection.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: allow all imports in Python tool sandbox
Remove stdlib-only import restriction. Keep signal escape
pattern detection via unsloth_zoo for safety.
* studio: fix ReadTimeout on tool streaming final pass
The 0.5s read timeout used for cancel-checking during streaming
also fires when waiting for the first response from llama-server
(e.g. reasoning model thinking for 15+ seconds). Add
_stream_with_retry() context manager that retries on ReadTimeout
while checking cancel_event, so the model has unlimited time to
think before producing the first token. Applied to both the
regular streaming path and the tool-calling final pass.
* fix: rewrite HarmonyTextStreamer with stateful incremental parsing
The delta-on-transformed approach had two critical bugs:
1. Before the full <|channel|>X<|message|> pattern was complete, the
strip-tokens fallback emitted "analysis" as plain text. Then when
the regex matched, _transform returned a completely different format
(<think>...</think>) and the delta was computed against the wrong
base string, producing fragments like "think>", "nk>", ">".
2. Even with full matches, the closing </think> tag shifted position
as content grew, so text[prev_len:] produced garbled deltas.
Replace with stateful incremental parsing that:
- Buffers until a complete channel+message pair is seen
- Emits <think> once when analysis channel first appears
- Streams analysis content deltas (computed on channel content directly)
- Emits </think> once when final channel first appears
- Streams final content deltas
- Closes open think tags in end()
Also skip the generic all_special_tokens stripping in
_clean_generated_text for gpt-oss since HarmonyTextStreamer already
produces clean output and the generic stripping was mangling <think>
tags.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: strip all <|...|> tokens in gpt-oss cleanup, not just harmony subset
The gpt-oss tokenizer has added tokens like <|return|> (id=200002) that
are not part of the harmony channel protocol but can leak into output.
The previous regex only stripped channel|message|start|end tokens.
Broaden the _clean_generated_text regex for gpt-oss to <\|[a-z_]+\|>
which catches all pipe-delimited tokens (return, constrain, reserved,
etc.) without matching <think>/<\/think> tags.
Verified: gpt-oss all_special_tokens are only <|return|>,
<|reserved_200017|>, <|startoftext|> -- none overlap with <think>.
The harmony tokens (channel, message, start, end) are added_tokens
but not in all_special_tokens.
* fix: hide config-only model repos from cached models list
Repos that only have metadata/config files cached (no .safetensors or
.bin weight files) were showing up in the Downloaded list with tiny
sizes like "1.8 KB" or "24 KB". These are just leftover config
snapshots from architecture checks, not usable models.
Filter the cached-models endpoint to only include repos that contain
actual model weight files (.safetensors or .bin).
* studio: fix toast description text contrast in dark mode
Add explicit !text-muted-foreground to toast description classNames
so secondary text (e.g. "Releases VRAM and resets inference state.")
is readable in dark mode.
* studio: fix Chat card icon alignment with size-4 spacer
Replace sr-only span (takes no space) with a size-4 shrink-0 div
matching the RadioGroupItem dimensions in other cards, so the Chat
icon aligns vertically with Text/Audio/Vision/Embeddings icons.
---------
Co-authored-by: workspace <user@workspace.local>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Manan17 <shahmanan170602@gmail.com>
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
## Summary
- Add web search tool calling for GGUF models (Search toggle, DuckDuckGo via ddgs)
- Add KV cache dtype dropdown (f16/bf16/q8_0/q5_1/q4_1) in Chat Settings
- Fix Qwen3/3.5 inference defaults per official docs (thinking on/off params)
- Enable reasoning by default for Qwen3.5 4B and 9B
- Replace "Generating" toast with inline spinner
- Fix stop button via asyncio.to_thread (event loop no longer blocked)
- Fix CUDA 12 compat lib paths for llama-server on CUDA 13 systems
- Fix auto-load model name not appearing in selector
- Training progress messages + dataset_num_proc fix
Integrated PRs:
- #4327 (imagineer99): BETA badge alignment (already in tree)
- #4340 (Manan Shah): prioritize training models in model selection
- #4344 (Roland Tannous): setup.sh macOS python version compatibility
- #4345 (Manan Shah): revamp model+dataset checking logic
* studio: extract param count from model name as fallback
When HuggingFace API doesn't return totalParams for a model,
extract the param count from the model name (e.g. "Qwen3-0.6B"
-> "0.6B", "Llama-3.2-1B-Instruct" -> "1B"). Applied to both
the recommended list and HF search results.
* studio: read GGUF context_length via fast header parser, set max tokens
- Fast GGUF metadata reader (~30-55ms) parses only KV header, skips
tensor data and large arrays (tokenizer vocab etc)
- Extracts context_length and chat_template from GGUF metadata
- Returns context_length in LoadResponse for frontend to use
- Frontend sets maxTokens to actual context_length for GGUFs (e.g.
262144 for Qwen3.5-9B, 131072 for Qwen2.5-7B)
- Max Tokens slider shows "Max" and is locked for GGUFs
- Auto-load path also uses actual context_length from load response
- Toast auto-dismiss (5s) and close button for auto-load toast
* studio: GGUF TTS audio support (from PR #4318)
Add GGUF TTS audio generation via llama-server. When a GGUF model
loads, the backend probes its vocabulary to detect audio codecs
(SNAC/BiCodec/DAC/CSM/Whisper). If detected, the codec is pre-loaded
and the model is reported as audio to the frontend.
During chat, TTS models route to the audio generation path which sends
a per-codec prompt to llama-server's /completion endpoint, extracts
generated tokens/text, and decodes to WAV using AudioCodecManager.
Also strips base64 audio data from prior assistant messages to prevent
context overflow.
Co-authored-by: Manan Shah <mananshah511@gmail.com>
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Remove package-lock.json from tracking
* studio: per-model inference defaults, GGUF max tokens fix, reasoning toggle
- Add inference_defaults.json with per-model-family sampling parameters
for ~50 families (Qwen3.5, Qwen3, Gemma-3, Llama-3, DeepSeek, etc.).
Values sourced from unslothai/docs and Ollama params blobs.
- Family-based lookup in inference_config.py: extracts model family from
identifier, matches against patterns (longest match first), merges with
priority: model-specific YAML > family JSON > default.yaml.
- Fix GGUF Max Tokens slider locked at "Max": store ggufContextLength
separately from maxTokens so the slider is adjustable (step=64).
- Fix Ministral YAML: top_p was literal string "default", now 0.95.
- Add reasoning toggle for thinking models (Qwen3.5, Qwen3, DeepSeek-R1,
DeepSeek-V3.1, etc.): detect enable_thinking support from GGUF chat
template metadata, pass --jinja to llama-server, send
chat_template_kwargs per-request. Frontend shows "Reasoning is ON/OFF"
pill button next to attachment button in composer.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: remove default system prompt injection
Backend was injecting "You are a helpful AI assistant." when no system
prompt was provided. Neither unslothai/docs nor Ollama specify a default
system prompt for most models. Now defaults to empty string, letting the
model's own chat template handle system behavior.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: use lightbulb icons and "Think" label for reasoning toggle
Lightbulb on when thinking enabled, lightbulb-off when disabled.
Label is just "Think" in both states; grayed out styling when off.
* studio: fix HTML file upload breaking chat
Replace SimpleTextAttachmentAdapter with custom TextAttachmentAdapter
(excludes text/html) and HtmlAttachmentAdapter that strips tags via
DOMParser, removing scripts/styles and extracting readable text content
instead of dumping raw HTML markup into the conversation.
* studio: show chat template in Configuration panel
Display the model's Jinja2 chat template in a new "Chat Template"
section under Settings (now open by default). For GGUFs, reads from
GGUF metadata; for safetensors, reads from tokenizer.chat_template.
Template is editable with a "Restore default chat template" button
that appears when modified. Section only shows when a model with a
chat template is loaded.
* studio: editable chat template with Apply & Reload
Chat template section now functional:
- Editing the template shows "Apply & Reload" (reloads model with
custom template) and "Revert changes" buttons
- For GGUFs: writes template to temp .jinja file, passes
--chat-template-file to llama-server on reload
- For non-GGUF: passes chat_template_override in load request
- Settings section now open by default
- selectModel supports forceReload to reload same model
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* studio: fix DeepSeek reasoning detection and auto-load metadata
- Set _model_identifier before _read_gguf_metadata so DeepSeek
"thinking" template detection works (was always None before)
- Populate ggufContextLength, supportsReasoning, reasoningEnabled,
defaultChatTemplate in autoLoadSmallestModel GGUF path
* studio: add spacing before BETA badge in navbar
Add gap-1.5 on the logo Link container to space the BETA label
from the wordmark.
Co-authored-by: Imagineer99 <Imagineer99@users.noreply.github.com>
* studio: vertically center BETA badge with logo
---------
Co-authored-by: Manan Shah <mananshah511@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Imagineer99 <Imagineer99@users.noreply.github.com>
Change all repetition_penalty defaults from 1.1 (or 1.05/1.2 in
presets) to 1.0 across the entire backend and frontend. Most models
handle repetition well on their own and a non-1.0 penalty can degrade
output quality, especially for code, structured output, and creative
tasks.
Files changed:
- Backend: inference.py, llama_cpp.py, orchestrator.py, worker.py,
models/inference.py (Field defaults)
- Frontend: chat-settings-sheet.tsx (Creative/Precise presets),
runtime-provider.tsx (auto-title generation)
* user can upload eval dataset, removed bugs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* resolving merge conflicts
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* resolving gpt comments
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
- Backend: /gguf-variants now checks HF cache for each variant's file
and returns a downloaded flag per variant
- Frontend: downloaded variants sort before non-downloaded (after
recommended), and show a green "downloaded" badge
- Sort order: recommended -> downloaded+fits -> downloaded+tight ->
fits -> tight -> OOM
Remove the hard max_tokens=2048 default and le=4096 cap for GGUF
chat completions. When max_tokens is not set (None), the field is
omitted from the llama-server payload entirely, letting the model
generate until it produces an EOS token or hits the context limit.
This is critical for thinking/reasoning models (Qwen3.5, DeepSeek-R1,
etc.) where the thinking phase alone can consume 1000+ tokens before
the actual answer. With the previous 2048 default, simple questions
like "What is 2+2?" used all tokens on thinking and produced empty
visible responses.
Changes:
- llama_cpp.py: max_tokens default None, only include in payload
when explicitly set
- models/inference.py: default None, remove le=4096 cap
- routes/inference.py: pass max_tokens directly, no "or 2048" fallback
llama-server handles omitted max_tokens gracefully (generates until
EOS or context limit). The context size (-c flag, default 4096) acts
as the hard upper bound.
* miscallenous studio
* chore: upload dataset misc
* chore: redudancy studio cleanup
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: adress the pr comments
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* fix: adress comments about recipes
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix: quotation marks
* diceware passphrase generation
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
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 end-to-end embedding/sentence-transformer training pipeline using
FastSentenceTransformer, SentenceTransformerTrainer, and
MultipleNegativesRankingLoss with BatchSamplers.NO_DUPLICATES.
Backend:
- Add is_embedding_model() detection via HF tags + pipeline_tag
- Add /check-embedding/ API route and EmbeddingCheckResponse
- Extend derive_model_type() to return "embeddings"
- Add _run_embedding_training() in worker.py with progress callbacks,
stop handling, LoRA (task_type=FEATURE_EXTRACTION), and model saving
- Add is_embedding field to TrainingStartRequest and ModelDetails
- Add YAML configs for 5 models: all-MiniLM-L6-v2, bge-m3,
embeddinggemma-300m, gte-modernbert-base, Qwen3-Embedding-0.6B
Frontend:
- Wire isEmbeddingModel flag through store, API types, and mappers
- Force packing=false, train_on_completions=false, warmup_ratio=0.03
- Hide packing and train_on_completions checkboxes for embedding models
- Auto-set modelType to "embeddings" from backend model_type response
The advisor now only assigns columns to user/assistant roles and
generates a system prompt. Templates (user_template, assistant_template)
are removed entirely — the LLM was frequently putting all columns in
user or copying actual data values into templates.
Column values are now used directly as message content, grouped and
concatenated by role. This is simpler, more robust, and prevents the
class of bugs where the advisor generates bad template content.
Derive a single model_type string ("text" | "vision" | "audio" | "embeddings")
from existing is_vision and audio_type detection, so the frontend doesn't have
to infer modality from scattered boolean flags.
Non-conversational HF datasets (e.g. stanfordnlp/snli) were naively mapped
column→role, producing poor training results. The AI Assist button now runs
a 3-pass advisor using Qwen 7B that:
1. Fetches the HF dataset card/README to understand the dataset purpose
2. Classifies the dataset type and determines if conversion is needed
3. Generates a system prompt, user/assistant templates with {column}
placeholders, and label mappings (e.g. 0→entailment)
4. Validates the conversion quality (score ≥7/10 required)
Architecture: advisor metadata flows as __-prefixed keys in
custom_format_mapping (e.g. __system_prompt, __user_template,
__assistant_template, __label_mapping). The existing _apply_user_mapping()
detects these keys and routes to template-based conversation construction.
No __ keys = existing simple mode (backwards compatible).
Backend: upgraded llm_assist.py (7B default, multi-pass advisor,
HF card fetching), extended API models, added _apply_template_mapping()
to dataset_utils.py.
Frontend: extended store with advisor state fields, wired AI Assist
to store templates/system prompt, inject __ metadata in training request,
show advisor notification banner in mapping card.
Move LLM-assisted column mapping from silent /check-format automation
to an explicit "AI Assist" button in the dataset mapping dialog. This
makes the feature transparent and user-controlled.
- Remove llm_classify_columns() from check_dataset_format() (heuristic-only)
- Remove auto-save suggested_mapping from use-training-actions.ts
- Add POST /api/datasets/ai-assist-mapping endpoint (receives preview
samples from frontend, no dataset re-loading needed)
- Add AiAssistMappingRequest/Response models
- Add aiAssistMapping() frontend API function
- Add Sparkles AI Assist button to DatasetMappingCard with loading state
- Wire up handleAiAssist handler in dataset-preview-dialog.tsx
Daniel Han