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README.md
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@@ -8,7 +8,9 @@ In this model we added in more QLoRA adapter layers, modifying all of the weight
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train and test metrics, again, are smaller for this model than for the model with fewer adapter layers (only using query, key, and value
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matrices). So, we see that adapting more of the weight matrices in this larger ESM-2 model decreases overfitting and serves as a better
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regularizer. For comparison, see [this model](https://huggingface.co/AmelieSchreiber/esm2_t12_35M_qlora_binding_sites_v0) which only
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has QLoRA adapters on the query, key, and value matrices.
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## Testing for Overfitting
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train and test metrics, again, are smaller for this model than for the model with fewer adapter layers (only using query, key, and value
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matrices). So, we see that adapting more of the weight matrices in this larger ESM-2 model decreases overfitting and serves as a better
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regularizer. For comparison, see [this model](https://huggingface.co/AmelieSchreiber/esm2_t12_35M_qlora_binding_sites_v0) which only
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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).
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Note, this dataset is too small for this model, so overfitting is expected, but overfitting is clearly reduced by including more adapter
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layers in the QLoRA.
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## Testing for Overfitting
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