Prevent negative Train Split Start/End values in the dataset advanced UI and sanitize payload mapping so negative slice values are never sent to the backend.
Made-with: Cursor
Separate pure-audio from audio-VLM logic in runDatasetCheck so pure
audio models are always forced to trainOnCompletions=false regardless
of dataset type, while audio VLMs (gemma3n) only uncheck when the
dataset is audio.
Clear stale isAudioModel in the fallback path when getModelConfig
fails, preventing a previously-selected audio model's flag from
leaking into the next model selection.
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
Pure audio models (orpheus, sparktts, whisper, sesame-csm) now
always have trainOnCompletions auto-unchecked when selected.
Gemma3n (audio_vlm) only unchecks when the dataset is audio.
- Add is_audio to frontend ModelConfigResponse (backend already returns it)
- Add isAudioModel state to training config store
- Auto-set trainOnCompletions=false for pure audio models on model load
- Auto-set trainOnCompletions=false for audio VLMs when dataset is audio
- Respect manual user override via existing _trainOnCompletionsManuallySet flag
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.
- Add "Beta" badge next to AI Assist button text
- When advisor generates a system prompt, show it as a "System (generated)"
column prepended to the data table so user can see it alongside data
- Fix table being squished to near-zero height when advisor notification
banner is present: add min-h-[250px] to table wrapper, change body
from overflow-hidden to overflow-auto
- System prompt is now optional — LLM only generates one when the task
is ambiguous from the data alone (persona, domain, format constraints)
- Sanitize system_prompt extraction (handle literal "null" string)
- Show system prompt, user template, and assistant template in the
advisor notification banner so user can see exactly what was generated
- Templates displayed in monospace with labeled sections
- Reject advisor result when Pass 3 scores < 6 or is_acceptable=false,
falls back to simple column classification instead of using bad output
- Improved Pass 2 prompt: explicit rules for label_mapping completeness,
{column_name} vs {column_name_name} for mapped labels, column_roles
must match which template uses them
- Build suggested_mapping from ALL template-referenced columns (not just
first match per role) — fixes hypothesis being dropped from SNLI mapping
- Guard against LLM returning literal string "null" for revised_system_prompt
- Always show AI Assist button when available, even when mapping looks complete
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
