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 |
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| .. | ||
| assets | ||
| auth | ||
| core | ||
| loggers | ||
| models | ||
| plugins | ||
| requirements | ||
| routes | ||
| state | ||
| storage | ||
| tests | ||
| utils | ||
| __init__.py | ||
| _platform_compat.py | ||
| colab.py | ||
| main.py | ||
| run.py | ||
| startup_banner.py | ||