Create README.md

README.md

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+---
+library_name: mlc-llm
+base_model: meta-llama/Llama-3.2-3B-Instruct
+tags:
+- mlc-llm
+- web-llm
+---
+
+# Llama-3.2-3B-Instruct-MLC
+
+This is the [Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) model in MLC format `q4f16_1`.
+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://rohanprichard/Llama-3.2-3B-Instruct-MLC
+```
+
+### REST Server
+
+In command line, run
+```bash
+mlc_llm serve HF://rohanprichard/Llama-3.2-3B-Instruct-MLC
+```
+
+### Python API
+
+```python
+from mlc_llm import MLCEngine
+
+# Create engine
+model = "HF://rohanprichard/Llama-3.2-3B-Instruct-MLC"
+engine = MLCEngine(model)
+
+# Run chat completion in OpenAI API.
+for response in engine.chat.completions.create(
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "How many r's are in the word strawberry?"
+                },
+            ],
+        },
+    ],
+    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 the [documentation](https://llm.mlc.ai/docs/) and [GitHub repo](http://github.com/mlc-ai/mlc-llm).
+Model card based on the template from the MLC team.