Create merge_and_save_model.py

merge_and_save_model.py

@@ -0,0 +1,25 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+# Use GPU if available
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Base model and adapter paths
+base_model_name = "microsoft/phi-2"  # Pull from HF Hub directly
+adapter_path = "Shriti09/Microsoft-Phi-QLora"  # Update with your Hugging Face repo path
+
+# Load the base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32
+)
+
+# Load the LoRA adapter
+adapter_model = PeftModel.from_pretrained(base_model, adapter_path)
+
+# Merge the LoRA adapter into the base model
+merged_model = adapter_model.merge_and_unload()
+
+# Save the merged model to Hugging Face (space storage)
+merged_model.save_pretrained("./merged_model")