* studio: improve onboarding UX, tooltips, and training defaults
- Change splash text to "Train and run LLMs locally"
- Add "Chat Only" card with BubbleChatIcon to skip directly to chat
- Add Skip/Skip to Chat buttons in sidebar and footer
- Back button on step 1 returns to splash screen instead of being disabled
- Change "Watch video guide" to "Get started with our guide" with new URL
- Update intro text to mention all model types + chat
- Make all tooltips clickable (in addition to hover) via React context
- Strip surrounding quotes from pasted HF tokens
- Rename "Eval Split" to "Evaluation Split"
- Add SparklesIcon to "Auto Detect" format option
- Change step 4 heading to "Choose your training parameters"
- Default max_steps to 60
- Learning rate displayed in scientific notation with +/- stepper
- Context length options capped by model's max_position_embeddings (via AutoConfig)
- Fix "QLORA"/"LORA" to "QLoRA"/"LoRA" in summary step
- Backend: add max_position_embeddings to model config endpoint
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* compare for 2 diff models
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* resolving gemini comments
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: disable thinking for Qwen3.5 <9B and always for AI Assist
- Change Qwen3.5 thinking threshold from <=2B to <9B (0.8B, 2B, 4B
all disable thinking by default; 9B+ enables it)
- Always pass enable_thinking=False in AI Assist helper calls
(_run_with_helper and _generate_with_backend) regardless of chat
thinking settings
* studio: address PR review comments
- Extract _get_max_position_embeddings helper to DRY config extraction
- Fix "Skip to Chat" to navigate to /chat on step 1 (was /studio)
* fix: comment out debug print statements
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: skip Shiki highlighting for incomplete SVG code fences
While streaming SVG content, the syntax highlighter (Shiki) re-parses
the entire growing SVG on every token, blocking the main thread and
freezing the code area until the fence closes. Show a plain-text
preview for incomplete SVG fences instead, similar to how Mermaid
diagrams show a placeholder while streaming.
* studio: fix default top_k from 50/40 to 20 for chat inference
Per Qwen3.5 docs (unsloth.ai/docs/models/qwen3.5), top_k should be 20
for both thinking and non-thinking modes. The model-specific config in
inference_defaults.json already had top_k=20 for Qwen3.5, but the
generic fallback defaults were wrong:
- Frontend DEFAULT_INFERENCE_PARAMS.topK: 50 -> 20
- Backend generate_chat_completion top_k: 40 -> 20
- Backend generate_chat_completion_with_tools top_k: 40 -> 20
- Frontend title generation top_k: 40 -> 20
* studio: set universal inference defaults for unknown models
Default params for any model without specific config:
temperature=0.6, top_p=0.95, top_k=20, min_p=0.01,
presence_penalty=0.0, repetition_penalty=1.0
Models with entries in inference_defaults.json (Qwen3.5, Gemma-3,
Llama, etc.) override these with their recommended values.
Updated in: frontend DEFAULT_INFERENCE_PARAMS, backend Pydantic
request models, and backend generate_chat_completion defaults.
* studio: only trust_remote_code for unsloth/ models in AutoConfig
Only set trust_remote_code=True when the model name starts with
"unsloth/". All other models default to False for safety.
* studio: move Generating spinner above the composer
The "Generating" spinner was below the send message bar, causing
the bar to jump up and down. Move it above the composer in both
the regular thread view and the welcome/empty view.
* studio: adjust toast close button position away from edge
Move the X close button on toasts (like "Starting model...") from
top-1.5 to top-3 and add right-3, giving more breathing room from
the top-right corner.
* studio: make Think button smaller with tighter icon-text gap
Reduce gap from 1.5 to 0.5, padding from px-2.5/py-1 to px-2/py-0.5,
and icon from size-3.5 to size-3.
* studio: multiple onboarding and chat UX improvements
- Move Generating spinner above composer (fixes jumping send bar)
- Make Think button smaller with tighter icon-text gap
- Chat card now inside grid (same size as Audio/Embeddings cards)
- Rename "Chat Only" to "Chat"
- Chat card requires Continue to proceed (no auto-advance)
- Continue on Chat selection skips onboarding and goes to /chat
- Tooltip (i) click on Chat card doesn't trigger navigation
- Step 1 footer Back button goes back to splash (label is "Back")
- Splash "Skip Onboarding" renamed to "Skip to Chat", navigates to /chat
- Toast close button moved away from edge
* studio: align Skip to Chat button, add Skip to footer
- Sidebar "Skip to Chat" now uses primary (green) Button style with
arrow icon, full width, aligned like step items. Shows on all steps.
- Footer: added "Skip" outline button next to Continue that goes
directly to /studio with progress saved (markOnboardingDone)
* studio: change default max steps from 30 to 60 in toggle hook
The DEFAULT_MAX_STEPS in use-max-steps-epochs-toggle.ts was still 30,
used as fallback when toggling from epochs back to max steps.
* studio: extend context length options to 262K
CONTEXT_LENGTHS now includes 65536, 131072, 262144 in addition to
the existing 512-32768 range. The onboarding step filters these by
the model's max_position_embeddings (e.g. Nemotron-3-Nano-4B has
262144), showing powers of 2 up to the model's maximum.
* studio: auto-select LoRA vs QLoRA based on model size and GPU memory
After selecting a model in onboarding, detect the total model weight
file size from HF Hub (safetensors/bin files). Then estimate memory
needed: model_size_gb * 1.5 * context_scale, where context_scale is:
- <=8192 tokens: 1.0x
- >8192 tokens: 1.7x
- >=16384 tokens: 2.0x
- >=32768 tokens: 4.0x
If the estimate fits in free GPU VRAM, default to LoRA (16-bit).
Otherwise default to QLoRA (4-bit).
Backend changes:
- Add model_size_bytes to ModelDetails (models.py)
- Add _get_model_size_bytes() using HfApi.repo_info (routes/models.py)
- Add vram_free_gb to get_gpu_summary (hardware.py)
Frontend changes:
- Add autoSelectTrainingMethod() in training-config-store.ts
- Called after model defaults are loaded
- Add model_size_bytes to ModelConfigResponse type
- Add vramFreeGb to HardwareInfo hook
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: rename "Importing ML libraries..." to "Importing Unsloth..."
* studio: show model/dataset in training status, fix LoRA/QLoRA casing
- Training status now shows 'Training "model_name"' and 'Dataset = ...'
instead of generic "Starting training..."
- Fix Studio progress section to show QLoRA/LoRA instead of QLORA/LORA
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: rename 'Skip to Chat' to 'Skip Onboarding' on splash screen
* studio: add presence_penalty support for chat inference
Add presence_penalty as a parameter across the full stack:
- Backend: llama_cpp.py generate_chat_completion/with_tools, Pydantic
models (inference.py), routes/inference.py pass-through
- Frontend: InferenceParams type, DEFAULT_INFERENCE_PARAMS (0.0),
chat-adapter.ts payload, chat-settings-sheet.tsx slider (0-2),
model defaults loading from inference_defaults.json
- Set Qwen3.5 default presence_penalty to 1.5 per official docs
- Default for unknown models is 0.0 (off)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fix Chat card deselecting Text and aligning with other cards
* studio: fix presence_penalty not loading from inference defaults
The inference_config.py load_inference_config() was not including
presence_penalty in the returned config dict, so the Qwen3.5
default of 1.5 from inference_defaults.json never reached the
frontend. Added it to the config builder.
* studio: add delete button for cached models in model selector
Add trash icon on each downloaded model row (GGUF and safetensors) with
confirmation dialog. Backend DELETE /api/models/delete-cached endpoint
uses huggingface_hub scan_cache_dir + delete_revisions to cleanly remove
cached repos, refusing if the model is currently loaded.
* studio: restore inference defaults, reasoning, and tools on page refresh
On page refresh with a model already loaded, the frontend was not
re-applying model-specific inference defaults (presence_penalty,
temperature, etc.) or restoring reasoning/tools support flags.
Backend: Add inference config, supports_reasoning, supports_tools,
and context_length to InferenceStatusResponse.
Frontend: In the refresh callback, when an active model is detected,
apply mergeRecommendedInference and restore reasoning/tools flags
with proper Qwen3.5 size-based defaults.
* studio: fix delete dialog closing before async completes
Prevent AlertDialogAction's default close behavior with
e.preventDefault() so the dialog stays open during deletion.
Also block onOpenChange dismiss while deleting is in progress.
* fix: add Dict and Any imports to inference models
* studio: fix Qwen3.5 reasoning threshold in frontend load path
The frontend loadModel handler had the old threshold (<=2) for
disabling reasoning on small Qwen3.5 models. Changed to <9 to
match the backend. This was causing 4B to not properly disable
thinking by default when auto-loaded.
* studio: move GGUF delete to per-variant level
For GGUF repos, the trash icon now appears on each downloaded variant
row inside the quantization expander instead of on the repo-level row.
Backend accepts optional variant param to delete specific GGUF files
(blob + symlink) rather than the entire repo cache.
* studio: restore ggufContextLength on page refresh
The Max Tokens slider was capped at 32768 on page refresh because
ggufContextLength was not restored from the status response.
Now set it from statusRes.context_length on reconnect.
* fix: remove <think> from Qwen3.5 response template marker
The train-on-responses-only feature uses template markers to find
where the assistant response starts. The Qwen3.5 response marker
included '<think>\n' which is only present when thinking mode is
enabled. With thinking disabled (default for <9B), the marker
never matched, causing 100% of samples to be dropped.
Changed response marker from '<|im_start|>assistant\n<think>\n'
to '<|im_start|>assistant\n' which works regardless of thinking mode.
* studio: fix sloth ASCII art alignment in training overlay
* fix: correct sloth ASCII art alignment to match Unsloth banner
* studio: add Python and terminal tool calling to chat
Register python and terminal tools alongside web search. Python
executor validates imports (stdlib only) via unsloth_zoo
rl_environments, runs code in a subprocess sandbox with 5-min
timeout and cancel support. Terminal executor blocks dangerous
commands (rm, sudo, etc.) and runs in a temp directory.
Update llama_cpp tool loop to show tool-specific status messages
and pass cancel_event through to executors. Rename composer
toggle from "Search" to "Tools" and show TerminalIcon for
execution status pills.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fix Nemotron/transformers 5.x support, onboarding navigation, port binding
Backend:
- Dynamic transformers 5.x detection via tokenizer_config.json fetch
(checks for TokenizersBackend class, cached per-model)
- Bump transformers 5.x version from 5.2.0 to 5.3.0 across all workers,
setup scripts (setup.sh, setup.ps1)
- Auto-enable trust_remote_code for unsloth/* models needing transformers 5.x
(workaround for NemotronH config parsing bug in transformers)
- Auto-install mamba-ssm/causal-conv1d for SSM models (NemotronH, Falcon-H1)
with --no-build-isolation --no-deps to avoid torch version conflicts
- Add SO_REUSEADDR to port check in run.py (fixes Colab proxy stale connection
falsely reporting port as in-use)
Frontend:
- Fix "Skip to Chat" navigation: use window.location.href instead of React
Router navigate() to bypass useEffect redirect race
- Fix "Skip Onboarding" on splash: navigates to /studio (not /chat)
- Fix onboarding guard: only check isOnboardingDone() on initial mount
- Fix Chat card on step 1: add sr-only spacer for consistent alignment
- Fix Chat+Text both selected: clear RadioGroup value when Chat is selected
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: split tools toggle into Search and Code buttons
Replace the single "Tools" toggle with two independent toggles:
- "Search" (globe icon) enables web search only
- "Code" (terminal icon) enables Python and terminal execution
Add enabled_tools list field to the inference payload so the
backend only registers the tools the user has toggled on. Both
toggles appear in the main composer and the compare composer.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: fix tool calling import validation and error logging
Replace unsloth_zoo-dependent import checker with a standalone
ast-based validator using sys.stdlib_module_names. This properly
blocks non-stdlib imports (numpy, requests, etc.) and returns a
clear error message to the model so it can rewrite using only
stdlib.
Add full traceback to tool streaming error logs for debugging.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: parse gpt-oss harmony channels for clean safetensors chat output
gpt-oss models emit multi-channel output via harmony protocol tokens
(<|channel|>analysis<|message|>... and <|channel|>final<|message|>...).
TextIteratorStreamer with skip_special_tokens=True strips the special
tokens but leaves channel names concatenated with content, producing
garbled output like "analysisWe need to...assistantfinalHello!".
Add HarmonyTextStreamer that decodes with skip_special_tokens=False,
parses harmony markup via regex, and emits <think>analysis</think>
for the analysis channel and plain text for the final channel --
reusing the existing frontend reasoning UI.
Also expose supports_reasoning=True for non-GGUF gpt-oss models in
the /status endpoint so the frontend enables the Think toggle.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: use unsloth_zoo for Python sandbox validation
Set UNSLOTH_IS_PRESENT=1 and import check_python_modules and
check_signal_escape_patterns directly from unsloth_zoo instead
of a standalone fallback. This gives us the full Unsloth
validation including stdlib-only import checks and signal/timeout
escape pattern detection.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* studio: allow all imports in Python tool sandbox
Remove stdlib-only import restriction. Keep signal escape
pattern detection via unsloth_zoo for safety.
* studio: fix ReadTimeout on tool streaming final pass
The 0.5s read timeout used for cancel-checking during streaming
also fires when waiting for the first response from llama-server
(e.g. reasoning model thinking for 15+ seconds). Add
_stream_with_retry() context manager that retries on ReadTimeout
while checking cancel_event, so the model has unlimited time to
think before producing the first token. Applied to both the
regular streaming path and the tool-calling final pass.
* fix: rewrite HarmonyTextStreamer with stateful incremental parsing
The delta-on-transformed approach had two critical bugs:
1. Before the full <|channel|>X<|message|> pattern was complete, the
strip-tokens fallback emitted "analysis" as plain text. Then when
the regex matched, _transform returned a completely different format
(<think>...</think>) and the delta was computed against the wrong
base string, producing fragments like "think>", "nk>", ">".
2. Even with full matches, the closing </think> tag shifted position
as content grew, so text[prev_len:] produced garbled deltas.
Replace with stateful incremental parsing that:
- Buffers until a complete channel+message pair is seen
- Emits <think> once when analysis channel first appears
- Streams analysis content deltas (computed on channel content directly)
- Emits </think> once when final channel first appears
- Streams final content deltas
- Closes open think tags in end()
Also skip the generic all_special_tokens stripping in
_clean_generated_text for gpt-oss since HarmonyTextStreamer already
produces clean output and the generic stripping was mangling <think>
tags.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* fix: strip all <|...|> tokens in gpt-oss cleanup, not just harmony subset
The gpt-oss tokenizer has added tokens like <|return|> (id=200002) that
are not part of the harmony channel protocol but can leak into output.
The previous regex only stripped channel|message|start|end tokens.
Broaden the _clean_generated_text regex for gpt-oss to <\|[a-z_]+\|>
which catches all pipe-delimited tokens (return, constrain, reserved,
etc.) without matching <think>/<\/think> tags.
Verified: gpt-oss all_special_tokens are only <|return|>,
<|reserved_200017|>, <|startoftext|> -- none overlap with <think>.
The harmony tokens (channel, message, start, end) are added_tokens
but not in all_special_tokens.
* fix: hide config-only model repos from cached models list
Repos that only have metadata/config files cached (no .safetensors or
.bin weight files) were showing up in the Downloaded list with tiny
sizes like "1.8 KB" or "24 KB". These are just leftover config
snapshots from architecture checks, not usable models.
Filter the cached-models endpoint to only include repos that contain
actual model weight files (.safetensors or .bin).
* studio: fix toast description text contrast in dark mode
Add explicit !text-muted-foreground to toast description classNames
so secondary text (e.g. "Releases VRAM and resets inference state.")
is readable in dark mode.
* studio: fix Chat card icon alignment with size-4 spacer
Replace sr-only span (takes no space) with a size-4 shrink-0 div
matching the RadioGroupItem dimensions in other cards, so the Chat
icon aligns vertically with Text/Audio/Vision/Embeddings icons.
---------
Co-authored-by: workspace <user@workspace.local>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Manan17 <shahmanan170602@gmail.com>
Co-authored-by: Roland Tannous <rolandtannous@gravityq.ai>
## Summary
- Add web search tool calling for GGUF models (Search toggle, DuckDuckGo via ddgs)
- Add KV cache dtype dropdown (f16/bf16/q8_0/q5_1/q4_1) in Chat Settings
- Fix Qwen3/3.5 inference defaults per official docs (thinking on/off params)
- Enable reasoning by default for Qwen3.5 4B and 9B
- Replace "Generating" toast with inline spinner
- Fix stop button via asyncio.to_thread (event loop no longer blocked)
- Fix CUDA 12 compat lib paths for llama-server on CUDA 13 systems
- Fix auto-load model name not appearing in selector
- Training progress messages + dataset_num_proc fix
Integrated PRs:
- #4327 (imagineer99): BETA badge alignment (already in tree)
- #4340 (Manan Shah): prioritize training models in model selection
- #4344 (Roland Tannous): setup.sh macOS python version compatibility
- #4345 (Manan Shah): revamp model+dataset checking logic
* user can upload eval dataset, removed bugs
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* resolving merge conflicts
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* resolving gpt comments
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Roland Tannous <115670425+rolandtannous@users.noreply.github.com>
- Workers now compute backend_path and venv_t5 locally via Path(__file__)
- Moved .venv_t5 to ~/.unsloth/studio/.venv_t5
- Added ensure_studio_directories() call on server startup
- Expanded CLI studio command into sub-app with setup subcommand
Instead of downloading the full dataset and then slicing, use
streaming mode to only fetch the rows needed (up to slice_end + 1)
when a manual dataset slice is configured.
start_training() cherry-picks kwargs into a config dict but was missing
is_embedding, so config.get("is_embedding", False) in worker.py always
returned False and embedding training never ran.
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
