diff --git a/TTS/demos/xtts_ft_demo/xtts_demo.py b/TTS/demos/xtts_ft_demo/xtts_demo.py
index 7ac38ed6ee..07585cf006 100644
--- a/TTS/demos/xtts_ft_demo/xtts_demo.py
+++ b/TTS/demos/xtts_ft_demo/xtts_demo.py
@@ -14,6 +14,38 @@
 from TTS.tts.configs.xtts_config import XttsConfig
 from TTS.tts.models.xtts import Xtts
 
+def export_model(output_path):
+    try:
+        output_folder = os.path.join(output_path, "Finished_model_files")
+        os.makedirs(output_folder, exist_ok=True)
+
+        with zipfile.ZipFile(os.path.join(output_folder, "dataset.zip"), "w", zipfile.ZIP_DEFLATED) as zipf:
+            dataset_path = os.path.join(output_path, "dataset")
+            for root, dirs, files in os.walk(dataset_path):
+                for file in files:
+                    zipf.write(os.path.join(root, file),
+                               os.path.relpath(os.path.join(root, file), os.path.join(dataset_path, "..")))
+
+        search_path = os.path.join(output_path, "run", "training", "**", "best_model.pth")
+        model_path = max(glob.glob(search_path, recursive=True), key=os.path.getctime)
+
+        checkpoint = torch.load(model_path, map_location=torch.device("cpu"))
+        del checkpoint["optimizer"]
+        for key in list(checkpoint["model"].keys()):
+            if "dvae" in key:
+                del checkpoint["model"][key]
+
+        torch.save(checkpoint, os.path.join(output_folder, "model.pth"))
+
+        model_dir = os.path.dirname(model_path)
+        shutil.copy2(os.path.join(model_dir, "config.json"), output_folder)
+        shutil.copy2(os.path.join(model_dir, "vocab.json"), output_folder)
+
+        return f"Model exported successfully to {output_folder}"
+    except Exception as e:
+        return f"Export failed: {str(e)}"
+
+
 
 def clear_gpu_cache():
     # clear the GPU cache
@@ -384,6 +416,10 @@ def train_model(
                     progress_gen = gr.Label(label="Progress:")
                     tts_output_audio = gr.Audio(label="Generated Audio.")
                     reference_audio = gr.Audio(label="Reference audio used.")
+                # Button to export the fine-tuned model with optimizers removed
+                with gr.Column() as col4:
+                    export_btn = gr.Button(value="Export Fine-tuned Model")
+                    export_progress = gr.Label(label="Export Progress:")
 
             prompt_compute_btn.click(
                 fn=preprocess_dataset,
@@ -429,5 +465,11 @@ def train_model(
                 ],
                 outputs=[progress_gen, tts_output_audio, reference_audio],
             )
+            
+            export_btn.click(
+                fn=export_model,
+                inputs=[out_path],
+                outputs=[export_progress],
+            )
 
     demo.launch(share=True, debug=False, server_port=args.port, server_name="0.0.0.0")