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
