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---
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. This model was trained on [this dataset](https://huggingface.co/datasets/AmelieSchreiber/1111K_binding_sites).
Note, this dataset is too small for this model, so overfitting is expected, but overfitting is clearly reduced by including more adapter
layers in the QLoRA.
## 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
``` |