Instructions to use AmelieSchreiber/esm2_t12_35M_LoRA_RNA_binding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AmelieSchreiber/esm2_t12_35M_LoRA_RNA_binding with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("facebook/esm2_t12_35M_UR50D") model = PeftModel.from_pretrained(base_model, "AmelieSchreiber/esm2_t12_35M_LoRA_RNA_binding") - Transformers
How to use AmelieSchreiber/esm2_t12_35M_LoRA_RNA_binding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AmelieSchreiber/esm2_t12_35M_LoRA_RNA_binding", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# ESM-2 RNA Binding Site LoRA
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This is a Parameter Efficient Fine Tuning (PEFT) Low Rank Adaptation (LoRA) of
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the [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) model for the (binary) token classification task of
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predicting RNA binding sites of proteins. The Github with the training script and conda env YAML can be
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[found here](https://github.com/Amelie-Schreiber/esm2_LoRA_binding_sites/tree/main). You can also find a version of this model
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---
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# ESM-2 RNA Binding Site LoRA
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This is a Parameter Efficient Fine Tuning (PEFT) Low Rank Adaptation ([LoRA](https://huggingface.co/docs/peft/task_guides/token-classification-lora)) of
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the [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) model for the (binary) token classification task of
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predicting RNA binding sites of proteins. The Github with the training script and conda env YAML can be
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[found here](https://github.com/Amelie-Schreiber/esm2_LoRA_binding_sites/tree/main). You can also find a version of this model
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