Delete docs/mlx_deploy_guide.md with huggingface_hub

docs/mlx_deploy_guide.md

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-## MLX deployment guide
-
-Run, serve, and fine-tune [**MiniMax-M2.1**](https://huggingface.co/MiniMaxAI/MiniMax-M2.1) 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-M2.1-4bit \
-  --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-M2.1-4bit")
-
-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-m2.1) for `MiniMax-M2.1-4bit`, `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)