Add MLX example

README.md

@@ -167,6 +167,80 @@ We look forward to your feedback and to collaborating with developers and resear
 
 Download the model from HuggingFace repository: https://huggingface.co/MiniMaxAI/MiniMax-M2. We recommend using the following inference frameworks (listed alphabetically) to serve the model:
 
+Here's an improved, polished, and professional version of your documentation with better structure, clarity, grammar, accuracy, and usability:
+
+
+### MLX
+
+Run, serve, and fine-tune **MiniMax-M2** locally on your Mac using the **MLX** framework. This guide gets you up and running quickly.
+
+> **Requirements**  
+> - Apple Silicon Mac (M3 Ultra or later)  
+> - **At least 256GB of unified memory (RAM)**  
+
+
+**Installation**
+
+Install the `mlx-lm` package via pip:
+
+```bash
+pip install mlx-lm
+```
+
+**CLI**
+
+Generate text directly from the terminal:
+
+```bash
+mlx_lm.generate \
+  --model mlx-community/MiniMax-M2-4bit \
+  --prompt "How tall is Mount Everest?"
+```
+
+> Add `--max-tokens 256` to control response length, or `--temp 0.7` for creativity.
+
+**Python Script Example**
+
+Use `mlx-lm` in your own Python scripts:
+
+```python
+from mlx_lm import load, generate
+
+# Load the quantized model
+model, tokenizer = load("mlx-community/MiniMax-M2-4bit")
+
+prompt = "Hello, how are you?"
+
+# Apply chat template if available (recommended for chat models)
+if tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+
+# Generate response
+response = generate(
+    model,
+    tokenizer,
+    prompt=prompt,
+    max_tokens=256,
+    temp=0.7,
+    verbose=True
+)
+
+print(response)
+```
+
+**Tips**
+- **Model variants**: Check [Hugging Face](https://huggingface.co/collections/mlx-community/minimax-m2) for `MiniMax-M2-4bit`, `6bit`, `8bit`, or `bfloat16` versions.
+- **Fine-tuning**: Use `mlx-lm.lora` for efficient parameter-efficient fine-tuning (PEFT).
+
+**Resources**  
+- GitHub: [https://github.com/ml-explore/mlx-lm](https://github.com/ml-explore/mlx-lm)  
+- Models: [https://huggingface.co/mlx-community](https://huggingface.co/mlx-community)
+
 ### SGLang
 
 We recommend using [SGLang](https://docs.sglang.ai/) to serve MiniMax-M2. SGLang provides solid day-0 support for MiniMax-M2 model. Please refer to our [SGLang Deployment Guide](https://huggingface.co/MiniMaxAI/MiniMax-M2/blob/main/docs/sglang_deploy_guide.md) for more details, and thanks so much for our collaboration with the SGLang team.