Update README.md

README.md

@@ -36,13 +36,28 @@ The model is instruction-tuned to support a text-to-query use case for MongoDB a
 ## 📦 How to Use
 
 ```python
-from peft import PeftModel
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/gemma-2b-it",
+    max_seq_length = 1024,
+    dtype = torch.float16,
+    load_in_4bit = True,
+)
+
+# Load LoRA adapter
+model = FastLanguageModel.get_peft_model(
+    model,
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+    lora_dropout=0.05,
+    bias="none",
+)
+
+# Load parameter
+model.load_adapter("kihyun1998/gemma-2b-it-mongodb-lora", adapter_name="default")
 
-base = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2b-it", load_in_4bit=True, device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained("unsloth/gemma-2b-it")
-
-model = PeftModel.from_pretrained(base, "kihyun1998/gemma-2b-it-mongodb-lora")
 
 prompt = """### Instruction:
 Convert to MongoDB query string.