* 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>
* fix(llm_assist): disable thinking mode for helper model JSON output
Pass enable_thinking=False to generate_chat_completion() in both
_run_with_helper() and _generate_with_backend() so the Qwen3.5-4B
helper model produces clean JSON instead of wrapping responses in
<think> tags.
* fix(llm_assist): log per-request enable_thinking=False override
Add info-level log lines so the user can see that each helper/advisor
request overrides the server-level thinking default to False.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
- Add SVG preview rendering below code blocks using safe data URI
in <img> tag. Includes sanitization to block script/event handlers.
- Fix GGUF streaming crash: cache response.iter_text() iterator
instead of creating a new one on every loop iteration.
- Fix model selector showing "Select model..." after auto-load by
re-reading store state after setCheckpoint before setParams.
- Remove unused warmupToastShown variable (TS6133 build error).
- Change default suggestion to "Draw an SVG of a cute sloth".
The streaming loop used response.iter_text() with timeout=None, which
blocks until the next chunk arrives from llama-server. On large models
like Qwen3.5-27B where each token takes seconds, pressing Stop in the
UI would not take effect until the next token was produced.
Fix by using a 0.5s read timeout and a new _iter_text_cancellable()
helper that checks cancel_event between timeout windows and explicitly
closes the response when cancelled. Applied to both the regular chat
completion and tool-calling streaming paths.
Creative: temperature=1.5, min_p=0.1, top_p=Off (1.0), top_k=Off (0)
Precise: temperature=0.1, top_p=0.95, top_k=80, min_p=0.01
Also show "Off" in the slider label for top_p=1.0, top_k=0, and
repetition_penalty=1.0 since those values disable their respective
samplers. Changed top_k slider min from -1 to 0.
* studio: switch helper model to Qwen3.5-4B-GGUF
Replace Qwen3-4B-Instruct-2507-GGUF with Qwen3.5-4B-GGUF as the
default helper model for LLM-assisted dataset detection. Same
UD-Q4_K_XL variant.
* studio: fix stale GGUF metadata when switching models (#4347)
Reset _supports_reasoning, _supports_tools, _context_length, and
_chat_template at the start of _read_gguf_metadata() to prevent
stale settings from a previous model leaking into the next load.
Co-authored-by: Daniel Han <daniel@unsloth.ai>
* studio: change login error to "Incorrect password", add reset-password CLI
- Login error now says "Incorrect password" instead of the generic
"Incorrect username or password" since Studio only has one account.
- Add `unsloth studio reset-password` command that deletes the auth
database so a fresh admin account with a new random password is
created on the next server start.
* studio: include reset command in login error message
* studio: change password setup subtitle wording
## 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
* Removing .precommit config
* edited colab comments
* studio: update Unsloth_Studio_Colab.ipynb
* studio: update Unsloth_Studio_Colab.ipynb
* studio: add Colab T4 GPU metadata to force T4 instance
* style: update colab popup to black/white theme with gem icon and play button
* feat: center landscape image in colab notebook
* style: shrink popup to fit content, truncate URL display
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* feat: center landscape image in colab notebook
* feat: use GitHub raw URL for studio landscape image in notebook
* chore: update colab notebook
* feat: add studio landscape colab display image and update notebook
* feat: update notebook with studio landscape image
* style: remove colors, add progress bar, add VERBOSE flag to install output
* docs: add comments explaining VERBOSE flag and progress bar
* chore: update colab notebook
* fix: define VERBOSE, _STEP, _TOTAL at module level to fix NameError
---------
Co-authored-by: LeoBorcherding <LeoBorcherding@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Remove outdated xformers Blackwell version guard
The guard at _utils.py:976-989 blocked xformers 0.0.32.post2 on
Blackwell/RTX 50x/Jetson GPUs (SM 10.0/11.0/12.0) due to a FA3
dispatch bug that caused CUDA errors (issue #1329).
This is no longer needed because:
1. xformers fixed the FA3 dispatch in 0.0.33.post2 by capping it
at SM <= 9.0, so FA3 is never attempted on Blackwell. The FA2
backend works correctly via PTX forward compatibility.
2. The only blocked version (0.0.32.post2) was built for torch 2.8.0
and cannot load on torch 2.9+ due to ABI mismatch, so the guard
never actually triggers for any current user.
3. The existing _register_extensions() check plus the except Exception
fallback already handle broken xformers installs gracefully by
falling back to SDPA.
Verified on NVIDIA RTX PRO 6000 Blackwell (SM 12.0) with both
pre-built wheels (0.0.33.post2) and source builds -- all attention
tests pass with exact numerical match vs SDPA.
* Update xformers Blackwell guard with root cause and broader coverage
Changes to the xformers version guard for Blackwell/RTX 50x/Jetson GPUs:
1. Broaden version check from `in (0.0.32.post2,)` to `<= 0.0.32.post2`
to cover all versions with the broken FA3 dispatch, not just one.
2. Add `DEVICE_TYPE == "cuda"` guard to avoid calling
`get_device_capability()` on non-CUDA devices (XPU, etc.).
3. Document the root cause: xformers <= 0.0.32.post2 used
`capability >= (9, 0)` in the FA3 dispatch, which matched
Blackwell SM 12.0 and attempted sm_90a Hopper kernels on it.
Fixed upstream in 0.0.33 with `<= (9, 0)`.
4. Update error message to include the installed version, mention
the fix (upgrade to >= 0.0.33), and keep the build-from-source
fallback. The raise is caught by `except Exception` which shows
the message when UNSLOTH_ENABLE_LOGGING is set and falls back
to SDPA.
Verified on NVIDIA RTX PRO 6000 Blackwell (SM 12.0):
- xformers 0.0.33.post2 pre-built wheel: works (FA2 via PTX)
- xformers source build: works (FA2 native)
- Both have exact numerical match vs SDPA
---------
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
* fix: add Qwen3.5 version gate in loader dispatch (#4188)
Qwen3.5 (model_type qwen3_5) only exists in transformers >= 5.0.0.
Without this gate, loading a Qwen3.5 model on transformers 4.x gives
an unhelpful generic error. This adds a clear version check before the
qwen3 dispatch to prevent substring misrouting and give a useful error
message pointing users to upgrade.
No dedicated FastQwen3_5Model is needed -- the compiler already applies
fused CE automatically via apply_fused_lm_head for both
Qwen3_5ForCausalLM and Qwen3_5ForConditionalGeneration. The generic
FastModel fallback path handles everything.
FORCE_FLOAT32 already has qwen3_5 on main.
Tested on transformers 5.3.0: Qwen3.5-0.8B 4bit, 1.38 GB peak memory.
Backwards compatible: import unsloth works on transformers 4.57.6.
* fix: update FORCE_FLOAT32 comment for qwen3_5
The (1+w) RMSNorm pattern does not overflow float16 since Qwen3_5RMSNorm
computes in float32 internally. The actual reason FORCE_FLOAT32 is needed
is that Qwen3.5 GDN layers produce NaN grad norms during float16 training.
Updated the comment to reflect the real reason.
* fix: move qwen3_5 version check before dispatch chain
The elif block intercepted qwen3_5 on transformers >= 5.0.0 without
setting dispatch_model, causing UnboundLocalError at line 715.
Move the version check before the if/elif dispatch chain so on
transformers >= 5.0.0 the model_type falls through to the generic
FastModel path as intended.
* fix: qwen3_5 requires transformers >= 5.2.0, not 5.0.0
Checked all 5.x releases:
- 5.0.0: no qwen3_5 module
- 5.1.0: no qwen3_5 module
- 5.2.0: qwen3_5 available
* fix: move qwen3_5 version check into AutoConfig error handler
The previous version check at the dispatch chain was unreachable --
AutoConfig.from_pretrained fails first with a generic "does not
recognize this architecture" error on transformers < 5.2.0, so
execution never reached the check.
Move the qwen3_5-specific error message into the AutoConfig exception
handler where "architecture" errors are caught. This intercepts the
error before the generic message and gives users a clear upgrade path.
Also remove the now-redundant check before the dispatch chain.
Both FastLanguageModel and FastModel paths are covered.
Tested: transformers 4.57.6 shows the Qwen3.5-specific error,
transformers 5.3.0 loads and trains normally.
---------
Co-authored-by: Daniel Han <danielhanchen@users.noreply.github.com>
* Removing .precommit config
* edited colab comments
* studio: update Unsloth_Studio_Colab.ipynb
* studio: update Unsloth_Studio_Colab.ipynb
* studio: add Colab T4 GPU metadata to force T4 instance
* style: update colab popup to black/white theme with gem icon and play button
* feat: center landscape image in colab notebook
* style: shrink popup to fit content, truncate URL display
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* feat: center landscape image in colab notebook
* feat: use GitHub raw URL for studio landscape image in notebook
* chore: update colab notebook
---------
Co-authored-by: LeoBorcherding <LeoBorcherding@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* fix: prefer existing CUDA_PATH toolkit to avoid version mismatch on multi-CUDA systems
* fix: validate GPU arch support before accepting CUDA toolkit (sm_120 + CUDA 12.4 fallback)
* debug: add temporary CUDA compatibility check print
* fix: auto-copy CUDA VS integration files when missing (No CUDA toolset found)
* fix: return false when nvcc --list-gpu-arch unavailable (reject old toolkit, scan for newer)
* fix: re-sanitize CUDA env vars before cmake build (survives Refresh-Environment)
* fix: use --list-gpu-code (sm_*) instead of --list-gpu-arch (compute_*) for arch probing
* 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>
* Strip <think> blocks from LLM assist model output
* Add debug logging for raw LLM assist output
* Quiet llama-server logs, use structlog in llm_assist
* Fix think-tag stripping when response is inside tags
* Remove debug logging of raw model output
* Clarify GGUF download logs: show cache hit vs actual download
* Clarify heuristic-detected mapping in UI text
* Default helper model to Qwen3-4B-Instruct-2507 UD-Q4_K_XL
* Remove package-lock.json from tracking, add to .gitignore
* Auto-open mapping dialog on Start Training for custom_heuristic format
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Use last think block when extracting inner content (review feedback)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
* Fix VLM GRPO matmul shape mismatch in _get_per_token_logps_and_entropies
VLM models (e.g. Qwen2.5-VL) can return logits [B*T, vocab_size] instead
of hidden states [B*T, hidden_dim] from their forward pass. When this
happens, chunked_hidden_states_selective_log_softmax tries to compute
logits @ lm_head.t() which fails with a shape mismatch.
Add a shape guard in the VLM branch of _get_per_token_logps_and_entropies:
check output.shape[-1] against lm_head.shape[1] (hidden_dim). When hidden
states are returned, the existing path is taken. When logits are returned,
scaling/softcapping/temperature are applied manually and
chunked_selective_log_softmax is used instead.
Also add chunked_selective_log_softmax to the import from unsloth_zoo.
The text-only branch (pixel_values is None) is unchanged.
Companion PR to unslothai/unsloth-zoo for grpo_accumulated_loss.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Remove redundant scaling in logits fallback path
When COMPILE_DISABLE=1 and the model returns logits directly, scaling
and softcapping are already applied by the model forward. Only
temperature (a GRPO training parameter) needs to be applied.
* Pass temperature to chunked_selective_log_softmax instead of manual cast
Use the new temperature parameter in chunked_selective_log_softmax
(added in companion zoo PR) to avoid casting the entire logits tensor
to float32 before the function call.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
The existing fix that removes use_reentrant=False from
gradient_checkpointing_kwargs was gated behind RLConfig_name ==
"GRPOConfig", so only GRPOConfig was protected. SFTConfig, DPOConfig,
KTOConfig, CPOConfig, ORPOConfig etc. were all still affected.
Remove the GRPOConfig guard so the fix applies to all compiled trainer
configs when TRL >= 0.27.0.
This is defense-in-depth alongside the unsloth_zoo fix that forces
use_reentrant=True in unsloth_checkpoint() itself.
- GGUF: use -c 0 for model's native context size (no 4096 cap)
- GGUF: hide Max Seq Length slider (irrelevant), set Max Tokens to Max
- Non-GGUF: default Max Tokens to 4096
- Max Tokens slider shows "Max" label when at ceiling for GGUFs
- Run non-GGUF load_model in asyncio.to_thread for progress polling
- Auto-load smallest downloaded model when chatting without selection
- Wait for in-progress model load before inference (modelLoading store flag)
- Recommended list: 4 GGUFs + 4 hub models after case-insensitive dedup
- Model selector waits for cached data before rendering
- Toast close button repositioned, Sampling section open by default
- Add logging to _get_repo_size_cached exception handler
- Use -c 0 for llama-server (model's native context size, no 4096 cap)
- Run non-GGUF backend.load_model in asyncio.to_thread for progress polling
- Auto-load smallest downloaded model when user chats without selecting one
- Wait for in-progress model load before inference (no "No model loaded" error)
- Add modelLoading flag to zustand store for cross-component coordination
- Dynamic top models: send 8 GGUFs + 8 hub models, frontend caps 4+4 after dedup
- Case-insensitive dedup: downloaded models correctly hide from recommended list
- Prevent duplicate toasts: guard against double selectModel calls
- Model selector waits for cached data before rendering (no empty flash)
- Toast close button positioned at top-right with proper spacing
- Sampling section expanded by default in chat settings
- Global toast close button styling fix
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)
- maxTokens: 2048 -> 8192. The old 2048 limit caused generation to
stop mid-output for longer responses (e.g. reasoning/thinking models
that produce long chain-of-thought before the answer).
- repetitionPenalty: 1.1 -> 1.0 (disabled). Most models handle
repetition well on their own. A penalty of 1.1 can hurt quality
for creative tasks like code generation and ASCII art.
- Change welcome message from "Run LLMs or test your fine-tune" to
"Chat with your model".
Merge the toast UX refactor from PR #4304 (by @Shine1i):
- Toast duration 5s default with close button (X) for manual dismiss
- Inline progress bar component (ModelLoadInlineStatus) shown in the
header after toast is dismissed
- Model switch warning only for image compatibility (not generic)
- activeThreadId tracked in store via ActiveThreadSync
- Loading state cleanup via resetLoadingUi helper
- Toast uses Infinity duration during loading with onDismiss handler
Re-applied non-GGUF download progress additions on top:
- getDownloadProgress for all models (not just GGUF)
- hasShownProgress flag, loadingModelRef race condition checks
- First poll at 500ms, bytes-only fallback when expected size unknown
Don't show "Model changed for this chat" toast when the thread has
no messages. On a fresh page load with a stale thread from a previous
session, this warning is confusing. The warning is only useful
mid-conversation to alert about image compatibility with the new model.
When messages.length === 0, silently update the thread's modelId and
proceed with loading.
The _VISION_CHECK_SCRIPT subprocess used logger.info() but logger was
never defined in the subprocess context. This caused a NameError on
every vision check, making all transformers 5.x models (Qwen3.5,
GLM, etc.) fall back to text-only mode even when they support vision.
Replace logger.info() with print() since the parent process reads
the subprocess stdout via result.stdout.
- Add sloth emoji prefix to "Downloaded" and "Recommended" section
labels in the Hub model picker so they are visually distinct.
- Replace browser network errors ("NetworkError when attempting to
fetch resource" / "Failed to fetch") with a clearer message:
"Studio isn't running -- please relaunch it."
llama-server uses stb_image internally which does not support WebP,
TIFF, AVIF, and other formats that browsers accept for upload.
Uploading a WebP image to a vision GGUF model caused a 400 error:
"Failed to load image or audio file" / "failed to decode image bytes".
Convert all uploaded images to PNG via PIL before base64-encoding and
forwarding to llama-server. This handles WebP, TIFF, BMP, GIF, AVIF,
and any other format PIL supports. RGBA images are converted to RGB
first since PNG with alpha can cause issues in some vision pipelines.
GGUF repos with mmproj files (e.g. Qwen3.5-0.8B-GGUF) are already
detected as vision-capable by list_gguf_variants(), and is_vision is
set correctly in ModelConfig. However, the HF download path only
downloaded the main GGUF file without the mmproj projection file,
so llama-server started without --mmproj and rejected image uploads
with "text-only model" errors.
Add _download_mmproj() to LlamaCppBackend that:
- Lists repo files for mmproj*.gguf matches
- Prefers mmproj-F16.gguf (best quality), falls back to any mmproj
- Downloads via hf_hub_download (uses the same HF cache)
In load_model(), when is_vision=True and no explicit mmproj_path was
provided (HF mode), auto-download the mmproj after the main GGUF.
The downloaded path is passed to llama-server via --mmproj.
