Update README.md

README.md

@@ -13,15 +13,26 @@ Here's how to extract PepDoRA embeddings for your input peptide:
 
 ```
 import torch
-from transformers import AutoTokenizer, AutoModel
+from transformers import AutoModel,AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+
+# Merge the adapter with the base model
+base_model = "DeepChem/ChemBERTa-77M-MLM"
+adapter_model = "ChatterjeeLab/PepDoRA"
+model = AutoModelForCausalLM.from_pretrained(base_model)
+model = PeftModel.from_pretrained(model, adapter_model)
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+
+
+from transformers import AutoModel
 
-# Load the model and tokenizer
 model_name = "ChatterjeeLab/PepDoRA"
+
+# Load the model and the tokenizer using AutoModel
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModel.from_pretrained(model_name, output_hidden_states=True)
-   
+model = AutoModel.from_pretrained(model_name)
 
-# Input peptide sequence
 peptide = "CC(C)C[C@H]1NC(=O)[C@@H](C)NCCCCCCNC(=O)[C@H](CO)NC1=O"
 
 # Tokenize the peptide