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
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.
startswith(prefix) could match unrelated split variants whose names
extend the selected file's prefix (e.g. model-Q8_0-v2-00001-of-...).
Now builds an exact regex from the chosen file's base prefix and shard
total so only true siblings are downloaded.
Substring matching (e.g. "Q8_0" in filename) could match superset
variants like "IQ8_0", causing wrong quantizations to be downloaded.
Now uses word-boundary regex for variant matching and discovers split
shards by shared filename prefix rather than treating all variant
matches as shards.
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.
LlamaCppBackend.load_model() only downloaded the first matching GGUF
file. For split models (e.g. 7B Q8_0 with 3 shards), llama-server
needs all shards present. Now collects and downloads all matching files.
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
- Pass 1: clearer definition of "conversational" vs non-conversational,
constrained dataset_type to specific enum values
- Pass 2: much more explicit worked examples with step-by-step reasoning,
added "skip" role for metadata columns, stronger reminder at end that
all-user is wrong
- Pass 3: returns raw text instead of JSON for cleaner system prompts,
removed system message to give model more freedom
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.
Derive a single model_type string ("text" | "vision" | "audio" | "embeddings")
from existing is_vision and audio_type detection, so the frontend doesn't have
to infer modality from scattered boolean flags.
The LLM was putting all columns in user_template (e.g. summarization
dataset had both document AND summary as user input). Fixed by:
- Reframed system message: explicitly states user=INPUT, assistant=OUTPUT
- Added 4 concrete correct examples (summarization, NLI, translation, QA)
showing exactly how to split columns
- Added "NEVER put the output/target column in the user template" rule
- Added sanity check: if assistant_template has no column placeholders,
reject the result and fall back to simple classification
Pass 3 now sees the label mapping from Pass 2 (e.g. "0 = does not follow,
1 = follows, 2 = entailed") so the generated system prompt can explain
what each label value means. Also bumped to 2-4 sentences to give room
for the label descriptions.
