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| Model | Section | Tasks | Variant |
|---|---|---|---|
ESMC-300M-mutafitup-accgrad-all-r4-best-overall | accgrad_lora | disorder, gpsite_atp, gpsite_ca, ... (16 total) | best_overall |
ESMC-300M-mutafitup-align-all-r4-best-overall | align_lora | disorder, gpsite_atp, gpsite_ca, ... (16 total) | best_overall |
model.onnx -- merged ONNX model (LoRA weights folded into backbone)export_metadata.json -- task configuration and preprocessing settingsnormalization_stats.json -- per-task normalization statisticstokenizer/ -- HuggingFace tokenizer fileshistory.json -- training history (per-epoch metrics)best_checkpoints.json -- checkpoint selection metadatacheckpoints/ directory contains minimal trainable-parameter
PyTorch checkpoints for all training runs (45 runs across
4 training sections). These checkpoints contain only the
parameters that were updated during fine-tuning (LoRA adapters and task
heads), not the frozen backbone weights.checkpoints/{section}/{run}/) contains:history.json -- training historybest_checkpoints.json -- checkpoint selection metadatabest_overall_model/model.pt -- best checkpoint by overall metricbest_loss_overall_model/model.pt -- best checkpoint by overall lossbest_task_models/{task}/model.pt -- best checkpoint per task metricbest_loss_task_models/{task}/model.pt -- best checkpoint per task lossMultitaskModel.load_trainable_weights() from
the mutafitup training library.