asd

app.py

@@ -3,18 +3,20 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 from huggingface_hub import HfApi
 import gradio as gr
 import os
+from peft import PeftModel, PeftConfig
 
-# Function to merge models
+# Function to merge models using PEFT
 def merge_models():
-    base_model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
+    base_model_name = "meta-llama/Meta-Llama-3-8B"
     finetuned_model_name = "NoaiGPT/autotrain-14mrs-fc44l"
     
     # Load the base model
     base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
     base_tokenizer = AutoTokenizer.from_pretrained(base_model_name)
     
-    # Load the fine-tuned model
-    finetuned_model = AutoModelForCausalLM.from_pretrained(finetuned_model_name)
+    # Load the fine-tuned model using PEFT
+    peft_config = PeftConfig.from_pretrained(finetuned_model_name)
+    finetuned_model = PeftModel.from_pretrained(base_model, peft_config)
     
     # Merge the models (simple weight averaging here for demonstration; adjust as needed)
     for param_base, param_finetuned in zip(base_model.parameters(), finetuned_model.parameters()):
@@ -57,4 +59,4 @@ with gr.Blocks() as demo:
     merge_button.click(merge_button_clicked, outputs=output)
 
 # Launch the Gradio interface
-demo.launch()
+demo.launch()