updated readme template

app.py

@@ -104,6 +104,67 @@ SUPPORTED_MODEL_TYPES = ['llama',
                          'cohere', 
                          'minicpm']
 
+readme_template = """
+---
+library_name: mlc-llm
+base_model: {base_model}
+tags:
+- mlc-llm
+- web-llm
+---
+
+# {model_name}
+
+This is the [{base_model_name}](https://huggingface.co/{base_model}) model in MLC format `{quant_format}`.
+The conversion was done using the [MLC-Weight-Conversion](https://huggingface.co/spaces/mlc-ai/MLC-Weight-Conversion) space.
+The model can be used for projects [MLC-LLM](https://github.com/mlc-ai/mlc-llm) and [WebLLM](https://github.com/mlc-ai/web-llm).
+
+## Example Usage
+
+Here are some examples of using this model in MLC LLM.
+Before running the examples, please install MLC LLM by following the [installation documentation](https://llm.mlc.ai/docs/install/mlc_llm.html#install-mlc-packages).
+
+### Chat
+
+In command line, run
+```bash
+mlc_llm chat HF://mlc-ai/{model_name}
+```
+
+### REST Server
+
+In command line, run
+```bash
+mlc_llm serve HF://mlc-ai/{model_name}
+```
+
+### Python API
+
+```python
+from mlc_llm import MLCEngine
+
+# Create engine
+model = "HF://mlc-ai/{model_name}"
+engine = MLCEngine(model)
+
+# Run chat completion in OpenAI API.
+for response in engine.chat.completions.create(
+    messages=[{{"role": "user", "content": "What is the meaning of life?"}}],
+    model=model,
+    stream=True,
+):
+    for choice in response.choices:
+        print(choice.delta.content, end="", flush=True)
+print("\\n")
+
+engine.terminate()
+```
+
+## Documentation
+
+For more information on MLC LLM project, please visit our [documentation](https://llm.mlc.ai/docs/) and [GitHub repo](http://github.com/mlc-ai/mlc-llm).
+""".strip()
+
 
 def button_click(hf_model_id, conv_template, quantization, oauth_token: gr.OAuthToken | None, progress=gr.Progress()):
     if oauth_token.token is None:
@@ -180,59 +241,11 @@ def button_click(hf_model_id, conv_template, quantization, oauth_token: gr.OAuth
     card.data.tags.append("MLC-Weight-Conversion")
     card.data.base_model = hf_model_id
 
-    card.text = dedent(
-        f"""
-        # {mlc_model_name}
-
-        This is the [{model_dir_name}]({HF_PATH}{hf_model_id}) model in MLC format `{quantization}`.
-        The conversion was done using the [MLC-Weight-Conversion](https://huggingface.co/spaces/mlc-ai/MLC-Weight-Conversion) space.
-        The model can be used for projects [MLC-LLM](https://github.com/mlc-ai/mlc-llm).
-
-        ## Example Usage
-
-        Here are some examples of using this model in MLC LLM.
-        Before running the examples, please install MLC LLM by following the [installation documentation](https://llm.mlc.ai/docs/install/mlc_llm.html#install-mlc-packages).
-
-        ### Chat
-
-        In command line, run
-        ```bash
-        mlc_llm chat HF://{created_repo_id}
-        ```
-
-        ### REST Server
-
-        In command line, run
-        ```bash
-        mlc_llm serve HF://{created_repo_id}
-        ```
-
-        ### Python API
-
-        ```python
-        from mlc_llm import MLCEngine
-
-        # Create engine
-        model = "HF://{created_repo_id}"
-        engine = MLCEngine(model)
-
-        # Run chat completion in OpenAI API.
-        for response in engine.chat.completions.create(
-            messages=[{"role": "user", "content": "What is the meaning of life?"}],
-            model=model,
-            stream=True,
-        ):
-            for choice in response.choices:
-                print(choice.delta.content, end="", flush=True)
-        print("\n")
-
-        engine.terminate()
-        ```
-
-        ## Documentation
-
-        For more information on MLC LLM project, please visit our [documentation](https://llm.mlc.ai/docs/) and [GitHub repo](http://github.com/mlc-ai/mlc-llm).
-        """
+    card.text = readme_template.format(
+        model_name=f"{user_name}/{mlc_model_name}",
+        base_model=hf_model_id,
+        base_model_name=model_dir_name,
+        quant_format=quantization,
     )
     card.save("./dist/README.md")