* Studio: fix recipe dataset preview
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Trim and tighten code comments and docstrings across the repository. Comment-only: every changed file verified code-identical to main via AST/token comparison.
Trim and tighten code comments and docstrings across studio/ Python. Comment-only: every changed file verified code-identical to main via AST/token comparison.
Raise ruff line-length to 100 and extend the local pre-commit format pipeline (def-signature magic-comma normalization, short multi-line assert collapse, kwarg '=' spacing, blank-line-after-short-import removal, adjacent string-literal / f-string+plain merge, redundant-pass pruning). Every transform re-checks the file AST and is dropped if it would differ; the whole-repo reformat is verified AST-identical per file and idempotent.
* feat(studio): add Continued Pretraining (CPT) support
Implements CPT as a first-class training method in Unsloth Studio,
resolving feature request #4565.
Changes:
- frontend/src/types/training.ts: add 'cpt' to TrainingMethod union
- frontend/src/lib/vram.ts: add 'cpt' to VramTrainingMethod (fp16 footprint)
- frontend/src/features/export/constants.ts: add CPT to METHOD_LABELS
- frontend/src/features/training/api/mappers.ts: map 'cpt' -> 'Continued Pretraining',
force packing=true and train_on_completions=false for CPT payloads
- frontend/src/features/studio/sections/model-section.tsx: add 'Continued Pretraining'
option (purple dot) to Method selector; update tooltip
- frontend/src/features/onboarding/.../model-selection-step.tsx: add CPT to
onboarding wizard method dropdown
- backend/models/training.py: update training_type field description
- backend/core/training/worker.py: detect is_cpt flag, force packing=True,
train_on_completions=False, pass is_cpt to _train_worker
- backend/core/training/trainer.py: _train_worker reads is_cpt kwarg, forces
packing on, skips train_on_responses_only for raw-text pretraining
CPT behaviour:
- Full model weights (no LoRA adapters), same as Full Finetuning
- Sequence packing always enabled for GPU efficiency
- Trains on every token (no chat-format masking)
- VRAM estimated at fp16 (2.0 bytes/param)
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Update mappers.ts
* Add CPT raw dataset support and UI fixes
* [pre-commit.ci] auto fixes from pre-commit.com hooks
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* Add missing training methods module
* Handle invalid raw-text rows and expose raw in onboarding
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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: Etherll <61019402+Etherll@users.noreply.github.com>
Co-authored-by: Etherll <mrmrmidessam@gmail.com>
## 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
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.
- Handle dict columns (e.g. squad answers) by extracting text instead
of raw repr()
- Handle list columns by joining or extracting single value
- Catch ValueError in .format() calls (stray { } in column data)
- Add missing json import to dataset_utils.py
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
- Frontend auto-saves suggested_mapping into datasetManualMapping when
check-format returns requires_manual_mapping=false, so the mapping
flows to training via custom_format_mapping (no redundant AI calls)
- Backend returns meaningful warning when column detection fails
(LLM-generated or static fallback) for both text and VLM datasets
- /check-format endpoint merges check_dataset_format warnings with
existing URL-based image detection warnings
- Detect and convert ShareGPT/ChatML conversations with <image> placeholders
- Add file_name/filename as image column keywords
- Detect image paths and URLs by value (string ending in .jpg/.png/etc)
- Add 200-sample parallel probe using ThreadPoolExecutor + safe_num_proc
to estimate download speed and failure rate before full conversion
- Abort with clear error if >=30% of probe images fail to download
- Show estimated download time in the training overlay modal
- Parallel batch conversion for URL-based datasets (vs sequential for local)
- Add warning field to /check-format response for URL-based image datasets
- Display URL warning in dataset preview dialog (amber banner)
- Thread progress_callback from trainer through format_and_template_dataset
to convert_to_vlm_format for real-time status updates
