- 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
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