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.
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
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.
When multiple image columns are found, probes them (HEAD for URLs,
os.path.exists for paths) and picks the first that works.
Skips probing when top candidate is PIL/dict (score >= 75).
find_image_column now scores candidates by resolvability (PIL > dict > URL > path)
and has a Pass 2 value-based fallback for columns not matching image keywords.
Fixes phiyodr/coco2017 picking file_name (unresolvable) over coco_url (resolvable).
- 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)
Datasets like VQAonline store image filenames (e.g. "img.png") without
the directory prefix. Build a basename→repo_path lookup using
list_repo_files, then resolve each file via hf_hub_download.
Tier 1 check-format was picking images.zip over testmini.parquet,
causing wrong columns (image/label) and broken VLM mapping.
Also log first VLM conversion failure instead of swallowing silently.
