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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README.md
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@@ -23,7 +23,7 @@ that was fine-tuned without LoRA [here](https://huggingface.co/AmelieSchreiber/e
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This is a Low Rank Adaptation (LoRA) of `esm2_t6_8M_UR50D`,
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trained on `166` protein sequences in the [RNA binding sites dataset](https://huggingface.co/datasets/AmelieSchreiber/data_of_protein-rna_binding_sites)
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using a `75/25` train/test split. It achieves an evaluation loss of `0.
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### Framework versions
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This is a Low Rank Adaptation (LoRA) of `esm2_t6_8M_UR50D`,
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trained on `166` protein sequences in the [RNA binding sites dataset](https://huggingface.co/datasets/AmelieSchreiber/data_of_protein-rna_binding_sites)
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using a `75/25` train/test split. It achieves an evaluation loss of `0.18801096081733704`.
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### Framework versions
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