asd

app.py

@@ -1,7 +1,60 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from huggingface_hub import HfApi
 import gradio as gr
+import os
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Function to merge models
+def merge_models():
+    base_model_name = "meta-llama/Meta-Llama-3-8B-Instruct"
+    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)
+    
+    # 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()):
+        param_base.data = (param_base.data + param_finetuned.data) / 2
+    
+    # Save the merged model
+    merged_model_name = "./merged_model"
+    base_model.save_pretrained(merged_model_name)
+    base_tokenizer.save_pretrained(merged_model_name)
+    
+    return merged_model_name
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+# Function to upload the merged model to Hugging Face Hub
+def upload_to_hf(repo_id, merged_model_name):
+    api = HfApi()
+    model_files = [os.path.join(merged_model_name, f) for f in os.listdir(merged_model_name)]
+    
+    for file in model_files:
+        api.upload_file(
+            path_or_fileobj=file,
+            path_in_repo=os.path.basename(file),
+            repo_id=repo_id,
+            repo_type="model"
+        )
+
+    return f"Model uploaded to Hugging Face Hub at {repo_id}."
+
+# Gradio function to handle the merge button click
+def merge_button_clicked():
+    merged_model_name = merge_models()
+    repo_id = "NoaiGPT/autotrain-14mrs-fc44l"
+    return upload_to_hf(repo_id, merged_model_name)
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        merge_button = gr.Button("Merge Models")
+        output = gr.Textbox(label="Output")
+
+    merge_button.click(merge_button_clicked, outputs=output)
+
+# Launch the Gradio interface
 demo.launch()