---
license: mit
---

# ESM-2 QLoRA for Binding Sites Prediction

In this model we added in more QLoRA adapter layers, modifying all of the weight matrices with QLoRA. The differences between the 
train and test metrics, again, are smaller for this model than for the model with fewer adapter layers (only using query, key, and value
matrices). So, we see that adapting more of the weight matrices in this larger ESM-2 model decreases overfitting and serves as a better 
regularizer. For comparison, see [this model](https://huggingface.co/AmelieSchreiber/esm2_t12_35M_qlora_binding_sites_v0) which only
has QLoRA adapters on the query, key, and value matrices.

## Testing for Overfitting

```python
Train metrics:
{'eval_loss': 0.17861589789390564,
'eval_accuracy': 0.9336392007583741,
'eval_precision': 0.24007189695313816,
'eval_recall': 0.9234520216135872,
'eval_f1': 0.38107489676203077,
'eval_auc': 0.9286608447868842,
'eval_mcc': 0.4519203165484902}

Test metrics:
{'eval_loss': 0.2265990674495697,
'eval_accuracy': 0.913988661430497,
'eval_precision': 0.1725452162312655,
'eval_recall': 0.8272126203209694,
'eval_f1': 0.28553230637278637,
'eval_auc': 0.8715212375759034,
'eval_mcc': 0.3539008454498742
```