Add sample code for loading the adapter

README.md

@@ -23,14 +23,25 @@ pipeline_tag: summarization
 This is a **LoRA fine-tuned adapter** built on [**meta-llama/Llama-3.2-1B-Instruct**](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct). It is designed for scientific paper summarization tasks and leverages **Low-Rank Adaptation (LoRA)** to enhance model performance efficiently while maintaining a low computational overhead.
 
 
-## **Performance Comparison**
+### **Performance comparison**
 | Model                     | ROUGE-1 | ROUGE-2 | ROUGE-3 | ROUGE-L |
 |---------------------------|----------|----------|----------|----------|
 | **Llama-3.2-1B-Instruct** | 36.69    | 7.47     | 1.95     | 19.36    |
 | **Llama-PaperSummarization-LoRA** | **41.56** | **11.31** | **2.67** | **21.86** |
 
-The model was evaluated on a **6K-sample test set** using **ROUGE scores** with the following settings:
-- **Decoding Strategy**: Beam search (beam size = 4)
+The model was evaluated on a **6K-sample test set** using **ROUGE scores** with beam search (beam size = 4).
+
+
+### **How to load**
+```python
+from transformers import LlamaForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+base_model = LlamaForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
+peft_model_id = "gabe-zhang/Llama-PaperSummarization-LoRA"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+model.merge_and_unload()
+```
 
 
 ## **Dataset**