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.
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.
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
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.
