Added PEFT instructions

README.md

@@ -73,6 +73,19 @@ The training data consists of 100,000 Python functions and their docstrings extr
 - **Clarity:** Measures readability using simple, unambiguous language. Calculated using the Flesch-Kincaid readability score.
 
 
+## Model Inference
+
+For running inference, PEFT must be used to load the fine-tuned model:
+
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel, PeftConfig
+
+config = PeftConfig.from_pretrained(self.model_id)
+model = AutoModelForCausalLM.from_pretrained("google/codegemma-2b", device_map = self.device)
+fine_tuned_model = PeftModel.from_pretrained(model, "documint/CodeGemma2B-fine-tuned", device_map = self.device)
+```
+
 #### Hardware
 
 Fine-tuning was performed using an Intel 12900K CPU, an Nvidia RTX-3090 GPU, and 64 GB RAM. Total fine-tuning time was 48 GPU hours.