AmelieSchreiber/esm2_t12_35M_qlora_binding_2600K_cp1

ESM-2 QLoRA for Predicting Binding Sites

This model is the ESM-2 model esm2_t12_35M_UR50D finetuned with QLoRA on this dataset of 2.6M protein sequences with binding and active site annotations from UniProt. The model and dataset size were scaled in a one-to-one way (following the Chinchilla paper) up from the smaller QLoRA adaptations of the esm2_t6_8M_UR50D models which were trained on 600K proteins. Since this model is 4.375 times larger, a dataset approximately 4.375 times larger is needed if Chinchilla scaling laws hold for QLoRA finetuning of protein language models. Determining if such scaling laws also hold is part of this project, so checking for improvements in performance metrics over a period of 3 epochs, as well as checking for signs of overfitting for each epoch are underway.

QLoRA Info

trainable params: 71046 || all params: 17246053 || trainable%: 0.41195512967517844

'eval_loss': 0.6011912822723389,
'eval_accuracy': 0.9297529150299436,
'eval_precision': 0.22835223718675476,
'eval_recall': 0.697386656717114,
'eval_f1': 0.3440490710592986,
'eval_auc': 0.8167222019799886,
'eval_mcc': 0.3730152153022164

To use this model, run:

!pip install transformers -q
!pip install peft -q

Then run:

from transformers import AutoModelForTokenClassification, AutoTokenizer
from peft import PeftModel
import torch

# Path to the saved LoRA model
model_path = "AmelieSchreiber/esm2_t12_35M_qlora_binding_2600K_cp1"
# ESM2 base model
base_model_path = "facebook/esm2_t12_35M_UR50D"

# Load the model
base_model = AutoModelForTokenClassification.from_pretrained(base_model_path)
loaded_model = PeftModel.from_pretrained(base_model, model_path)

# Ensure the model is in evaluation mode
loaded_model.eval()

# Load the tokenizer
loaded_tokenizer = AutoTokenizer.from_pretrained(base_model_path)

# Protein sequence for inference
protein_sequence = "MAVPETRPNHTIYINNLNEKIKKDELKKSLHAIFSRFGQILDILVSRSLKMRGQAFVIFKEVSSATNALRSMQGFPFYDKPMRIQYAKTDSDIIAKMKGT"  # Replace with your actual sequence

# Tokenize the sequence
inputs = loaded_tokenizer(protein_sequence, return_tensors="pt", truncation=True, max_length=1024, padding='max_length')

# Run the model
with torch.no_grad():
    logits = loaded_model(**inputs).logits

# Get predictions
tokens = loaded_tokenizer.convert_ids_to_tokens(inputs["input_ids"][0])  # Convert input ids back to tokens
predictions = torch.argmax(logits, dim=2)

# Define labels
id2label = {
    0: "No binding site",
    1: "Binding site"
}

# Print the predicted labels for each token
for token, prediction in zip(tokens, predictions[0].numpy()):
    if token not in ['<pad>', '<cls>', '<eos>']:
        print((token, id2label[prediction]))