Create app.py

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+def merge(base_model, trained_adapter, token):
+    base = AutoModelForCausalLM.from_pretrained(
+        base_model, torch_dtype=torch.float16, low_cpu_mem_usage=True, token=token
+    )
+    model = PeftModel.from_pretrained(base, trained_adapter, token=token)
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(base_model, token=token)
+    except RecursionError:
+        tokenizer = AutoTokenizer.from_pretrained(
+            base_model, unk_token="<unk>", token=token
+        )
+
+    model = model.merge_and_unload()
+
+    print("Saving target model")
+    model.push_to_hub(trained_adapter, token=token)
+    tokenizer.push_to_hub(trained_adapter, token=token)
+    return gr.Markdown.update(
+        value="Model successfully merged and pushed! Please shutdown/pause this space"
+    )
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## AutoTrain Merge Adapter")
+    gr.Markdown("Please duplicate this space and attach a GPU in order to use it.")
+    token = gr.Textbox(
+        label="Hugging Face Write Token",
+        value="",
+        lines=1,
+        max_lines=1,
+        interactive=True,
+        type="password",
+    )
+    base_model = gr.Textbox(
+        label="Base Model (e.g. meta-llama/Llama-2-7b-chat-hf)",
+        value="",
+        lines=1,
+        max_lines=1,
+        interactive=True,
+    )
+    trained_adapter = gr.Textbox(
+        label="Trained Adapter Model (e.g. username/autotrain-my-llama)",
+        value="",
+        lines=1,
+        max_lines=1,
+        interactive=True,
+    )
+    submit = gr.Button(value="Merge & Push")
+    op = gr.Markdown()
+    submit.click(merge, inputs=[base_model, trained_adapter, token], outputs=[op])
+
+
+if __name__ == "__main__":
+    demo.launch()