d96e3a7096
- Replace loading-only VRAM formula with full training estimate (weights + LoRA adapters + optimizer states + gradients + activations + overhead) for all three methods: QLoRA, LoRA, full fine-tuning - Expose architecture-based VRAM estimates from backend /api/models/config, reusing already-loaded AutoConfig to avoid extra HF round-trip - Store per-method estimates in training config state; selected model badge uses authoritative backend estimate (handles MoE like gpt-oss-20b correctly) - Replace file-size heuristic in autoSelectTrainingMethod with backend estimates - Use total VRAM (not free) since chat models are offloaded before training |
||
|---|---|---|
| .. | ||
| .gitkeep | ||
| __init__.py | ||
| auth.py | ||
| data_recipe.py | ||
| datasets.py | ||
| export.py | ||
| inference.py | ||
| models.py | ||
| responses.py | ||
| training.py | ||
| users.py | ||