Update app.py

app.py

@@ -207,6 +207,36 @@ def quantize(model_path, repo_id, quant_method=None):
     
     return final_path
 
+def create_readme(repo_name, base_model_name, lora_model_name, quant_methods):
+    readme_path = os.path.join("output", repo_name, "README.md")
+    readme_template = f"""
+---
+tags:
+- autotrain
+- text-generation-inference
+- text-generation
+- peft{"\n- quantization" if len(quant_methods) > 0 else ""}
+library_name: transformers
+base_model: {base_model_name}
+widget:
+- messages:
+    - role: user
+        content: What is your favorite condiment?
+license: other
+datasets:
+- {lora_model_name}
+---
+# Model
+{repo_name}
+base_model: {base_model_name}
+lora_model: {lora_model_name}
+quant_methods: {quant_methods}
+created_at: {time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())}
+created_by: [Steven10429/apply_lora_and_quantize](https://github.com/Steven10429/apply_lora_and_quantize)"""
+    
+    with open(readme_path, "w") as f:
+        f.write(readme_template)
+
 @timeit
 def process_model(base_model_name, lora_model_name, repo_name, quant_methods, hf_token):
     """
@@ -243,6 +273,8 @@ def process_model(base_model_name, lora_model_name, repo_name, quant_methods, hf
         # 量化模型
         for quant_method in quant_methods:
             quantize(output_dir, repo_name, quant_method=quant_method)
+            
+        create_readme(repo_name, base_model_name, lora_model_name, quant_methods)
         
         # 上传合并后的模型和量化模型
         api.upload_large_folder(