changed model card

app.py

@@ -182,18 +182,56 @@ def button_click(hf_model_id, conv_template, quantization, oauth_token: gr.OAuth
 
     card.text = dedent(
         f"""
-        # {created_repo_id}
-        This model was compiled using MLC-LLM with {quantization} quantization from [{hf_model_id}]({HF_PATH}{hf_model_id}).
+        # {mlc_model_name}
+
+        This is the [{model_dir_name}]({HF_PATH}{hf_model_id}) model in MLC format `e4m3_e4m3_f16` (FP8 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
 
-        To run this model, please first install [MLC-LLM](https://llm.mlc.ai/docs/install/mlc_llm.html#install-mlc-packages).
+        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).
 
-        To chat with the model on your terminal:
+        ### Chat
+
+        In command line, run
         ```bash
         mlc_llm chat HF://{created_repo_id}
         ```
 
-        For more information on how to use MLC-LLM, please visit the MLC-LLM [documentation](https://llm.mlc.ai/docs/index.html).
+        ### 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.save("./dist/README.md")