Instructions to use ceselder/lol-loracle-v2-oneq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ceselder/lol-loracle-v2-oneq with PEFT:
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- Notebooks
- Google Colab
- Kaggle
LoL LoRAcle v2 (oneq)
Fine-tuned from ceselder/loracle-pretrain-v7-sweep-A-oneq-final-step3120 on Sonnet-4.6-generated Q/A about the 1,170 Lots-of-LoRAs Qwen3-14B tasks. 1 random Q/A per task, mirroring the v7 oneq regime.
Training
- Q/A: ceselder/lol-loracle-qa-v1 (8 question types per task, 1 random sampled with seed=42 per task)
- 1129 train tasks (40 holdout), 141 steps, lr=1e-5 linear, grad_accum=8
- val_loss: 2.4669 (step 0, v7 baseline) -> 1.5108 (step 140 final)
- Cross-LoRA: matched=1.5107, crossed=1.7976 (gap=0.2869) -- loracle IS conditioning on direction tokens
- wandb: https://wandb.ai/adamkarvonen/lora-oracles/runs/pk6jmmne
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