Create app.py

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import gc
+import gradio as gr
+import torch
+from huggingface_hub import snapshot_download, HfApi, notebook_login, create_repo, whoami, login
+
+api = HfApi()
+
+
+def info_fn(text):
+    gr.Info(text)
+
+
+def warning_fn(text):
+    gr.Warning(text)
+
+
+def upload(hf_token, base_model_name_or_path, peft_model_path, output_dir):
+    try:
+      login(hf_token)
+      repo_name = output_dir
+
+      device_arg = {'device_map': "cpu"}
+
+      info_fn(f"Loading base model: {base_model_name_or_path}")
+
+      base_model = AutoModelForCausalLM.from_pretrained(base_model_name_or_path, torch_dtype=torch.float16, **device_arg)
+
+      info_fn(f"Loading PEFT: {peft_model_path}")
+
+      model = PeftModel.from_pretrained(base_model, peft_model_path, **device_arg)
+
+      info_fn(f"Running merge_and_unload")
+
+      model = model.merge_and_unload()
+      tokenizer = AutoTokenizer.from_pretrained(base_model_name_or_path)
+
+      info_fn("Saving model..")
+      model.save_pretrained(output_dir, safe_serialization=True)
+
+      info_fn("Saving tokenizer...")
+      tokenizer.save_pretrained(output_dir)
+
+      info_fn(f"Model saved to {output_dir}")
+
+      del model
+      gc.collect()
+
+      try:
+        info_fn("Creating Repo...")
+        info_fn(api.create_repo(repo_id=repo_name).__dict__['url'])
+      except Exception as e:
+        warning_fn(f"Model already exists: {e}")
+
+      info_fn("Uploading to hub...")
+      uploading = api.upload_folder(
+          folder_path=output_dir,
+          repo_id=output_dir,
+          repo_type="model")
+      
+      return uploading
+
+    except Exception as e:
+      gc.collect()
+      gr.Error(e)
+
+      return e
+
+
+INTRODUCTION_TEXT = f"""
+🎯 The Leaderboard allows you to merge your Lora adapters.
+
+## ❓ What is Lora?
+
+LoRA: Low-Rank Adaptation of Large Language Models allows you to train LLM's with a low cost. Lora freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks.
+You can learn more about LoRa here:
+
+[📝 LoRA: Low-Rank Adaptation of Large Language Models Arxiv](https://arxiv.org/abs/2106.09685)
+
+## 🛠️ How does this space work?
+
+🛠️ The leaderboard's backend mainly runs the transformers and PEFT library.
+
+🤖 The code first loads your original model and then your adapter models.
+
+📚 The code merges your adapter weights using the `merge_and_unload` function from the PEFT library.
+
+📤 The code saves your resulting model temporarily and then pushes the resulting model to the hub.
+
+## 🧮 Required RAM
+
+This space is loading the model to RAM without performing any quantization, so the required RAM is high.
+
+You can merge models up to 13B. (If your adapter weights are too large, it might not work.)
+"""
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("""<h1 align="center" id="space-title">🚀 Lora Merge</h1>""")
+    gr.Markdown(INTRODUCTION_TEXT)
+
+    with gr.Row():
+      with gr.Column(scale=1):
+        hf_token = gr.Textbox(label="Huggingface Write Access Token")
+        base_model_name_or_path = gr.Textbox(label="Base Model")
+        peft_model_path = gr.Textbox(label="Adapter Model")
+        output_dir = gr.Textbox(label="Output Model Name")
+
+      with gr.Column(scale=1):
+        text  = gr.Textbox(label="Output Model Name", lines=14)
+
+
+    submit = gr.Button("Merge lora with adapters")
+    submit.click(fn=upload, inputs=[hf_token, base_model_name_or_path, peft_model_path, output_dir], outputs=text)
+
+
+demo.queue()
+demo.launch(show_error=True)