ESMC-300M-mutafitup / README.md
Moomboh's picture
Upload 2 ONNX model(s) + 45 checkpoint run(s): ESMC-300M-mutafitup-accgrad-all-r4-best-overall, ESMC-300M-mutafitup-align-all-r4-best-overall
c823ad4 verified
metadata
license: other
license_name: cambrian-open
license_link: >-
  https://huggingface.co/Moomboh/ESMC-300M-mutafitup/blob/main/CAMBRIAN_OPEN_LICENSE.md
tags:
  - protein-language-model
  - onnx
  - fine-tuning
  - multi-task
  - esm

Moomboh/ESMC-300M-mutafitup

Multi-task LoRA fine-tuned ONNX models derived from ESM-C 300M by EvolutionaryScale.

Built with ESM.

ONNX Models

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

Each ONNX model directory contains:

  • model.onnx -- merged ONNX model (LoRA weights folded into backbone)
  • export_metadata.json -- task configuration and preprocessing settings
  • normalization_stats.json -- per-task normalization statistics
  • tokenizer/ -- HuggingFace tokenizer files
  • history.json -- training history (per-epoch metrics)
  • best_checkpoints.json -- checkpoint selection metadata

PyTorch Checkpoints

The checkpoints/ 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.

Each run directory (checkpoints/{section}/{run}/) contains:

  • history.json -- training history
  • best_checkpoints.json -- checkpoint selection metadata
  • best_overall_model/model.pt -- best checkpoint by overall metric
  • best_loss_overall_model/model.pt -- best checkpoint by overall loss
  • best_task_models/{task}/model.pt -- best checkpoint per task metric
  • best_loss_task_models/{task}/model.pt -- best checkpoint per task loss

To load a checkpoint, use MultitaskModel.load_trainable_weights() from the mutafitup training library.

License

The ESMC 300M base model is licensed under the EvolutionaryScale Cambrian Open License Agreement.

Fine-tuning code and pipeline are licensed under the MIT License.

See NOTICE for full attribution details.