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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| .. | ||
| backend | ||
| frontend | ||
| __init__.py | ||
| install_llama_prebuilt.py | ||
| install_python_stack.py | ||
| LICENSE.AGPL-3.0 | ||
| setup.bat | ||
| setup.ps1 | ||
| setup.sh | ||
| Unsloth_Studio_Colab.ipynb | ||