update guides

README.md

@@ -175,6 +175,11 @@ We recommend using [SGLang](https://docs.sglang.ai/) to serve MiniMax-M2. SGLang
 
 We recommend using [vLLM](https://docs.vllm.ai/en/stable/) to serve MiniMax-M2. vLLM provides efficient day-0 support of MiniMax-M2 model, check https://docs.vllm.ai/projects/recipes/en/latest/MiniMax/MiniMax-M2.html for latest deployment guide. We also provide our [vLLM Deployment Guide](https://huggingface.co/MiniMaxAI/MiniMax-M2/blob/main/docs/vllm_deploy_guide.md).
 
+### MLX
+
+We recommend using [MLX-LM](https://github.com/ml-explore/mlx-lm) to serve MiniMax-M2.  Please refer to our [MLX Deployment Guide](https://huggingface.co/MiniMaxAI/MiniMax-M2/blob/main/docs/mlx_deploy_guide.md) for more details.
+
+
 ### Inference Parameters
 We recommend using the following parameters for best performance: `temperature=1.0`, `top_p = 0.95`, `top_k = 40`.
 
@@ -196,4 +201,4 @@ Please refer to our [Tool Calling Guide](https://huggingface.co/MiniMaxAI/MiniMa
 
 # Contact Us
 
-Contact us at [model@minimax.io](mailto:model@minimax.io).
+Contact us at [model@minimax.io](mailto:model@minimax.io) | [WeChat](https://github.com/MiniMax-AI/MiniMax-AI.github.io/blob/main/images/wechat-qrcode.jpeg).

docs/mlx_deploy_guide.md

@@ -0,0 +1,70 @@
+## MLX deployment guide
+
+Run, serve, and fine-tune [**MiniMax-M2**](https://huggingface.co/MiniMaxAI/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 -U 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 this [MLX community collection on 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)

docs/sglang_deploy_guide_cn.md

@@ -112,4 +112,7 @@ export HF_ENDPOINT=https://hf-mirror.com
 - 通过邮箱 [model@minimax.io](mailto:model@minimax.io) 等官方渠道联系我们的技术支持团队
 
 - 在我们的 [GitHub](https://github.com/MiniMax-AI) 仓库提交 Issue
+
+- 通过我们的 [官方企业微信交流群](https://github.com/MiniMax-AI/MiniMax-AI.github.io/blob/main/images/wechat-qrcode.jpeg) 反馈
+
 我们会持续优化模型的部署体验，欢迎反馈！

docs/tool_calling_guide_cn.md

@@ -483,3 +483,15 @@ def execute_function_call(function_name: str, arguments: dict):
 - [vLLM 项目主页](https://github.com/vllm-project/vllm)
 - [SGLang 项目主页](https://github.com/sgl-project/sglang)
 - [OpenAI Python SDK](https://github.com/openai/openai-python)
+
+## 获取支持
+
+如果遇到任何问题：
+
+- 通过邮箱 [model@minimax.io](mailto:model@minimax.io) 等官方渠道联系我们的技术支持团队
+
+- 在我们的仓库提交 Issue
+
+- 通过我们的 [官方企业微信交流群](https://github.com/MiniMax-AI/MiniMax-AI.github.io/blob/main/images/wechat-qrcode.jpeg) 反馈
+
+我们会持续优化模型的使用体验，欢迎反馈！

docs/vllm_deploy_guide_cn.md

@@ -110,4 +110,7 @@ SAFETENSORS_FAST_GPU=1 vllm serve \
 - 通过邮箱 [model@minimax.io](mailto:model@minimax.io) 等官方渠道联系我们的技术支持团队
 
 - 在我们的 [GitHub](https://github.com/MiniMax-AI) 仓库提交 Issue
+
+- 通过我们的 [官方企业微信交流群](https://github.com/MiniMax-AI/MiniMax-AI.github.io/blob/main/images/wechat-qrcode.jpeg) 反馈
+
 我们会持续优化模型的部署体验，欢迎反馈！