Getting Started with LLM Fine-Tuning on Apple Silicon

Fine-tuning large language models used to require expensive cloud GPUs, but Apple’s MLX framework has made local training on MacBooks fast, efficient, and practical. With LoRA, you can adapt large models using only a small set of trainable parameters, making the entire process lightweight enough for Apple Silicon.

This guide walks you through everything you need to get started.

Why Fine-Tune on Apple Silicon?

Apple’s M-series processors deliver strong performance and unified memory, making them surprisingly capable for LLM workloads. MLX is optimized specifically for these chips.

Advantages:

• No cloud cost: Everything runs locally.
• Privacy: No external servers, your data stays on your machine.
• Convenience: Offline training and rapid iteration.
• Full control: Learn the entire training pipeline hands-on.

Prerequisites

You’ll need:

• A MacBook with M1/M2/M3
• 16GB+ unified memory (32GB is ideal)
• Python 3.9+
• Basic ML knowledge

Setting Up the Environment

Create a new virtual environment and install MLX + utility packages:

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python3 -m venv mlx-env
source mlx-env/bin/activate
pip install mlx mlx-lm

• mlx → Core tensor + ML operations optimized for Apple Silicon
• mlx-lm → Prebuilt helpers for loading, generating, and fine-tuning LLMs

Preparing Your Dataset

MLX expects a JSONL dataset. Each line is an instruction–response pair or conversational example.

Example:

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{"instruction": "Define supervised learning.", "response": "Supervised learning uses labeled data to train models."}
{"instruction": "Write a short welcome message for a travel assistant.", "response": "Welcome! How can I assist with your travel plans today?"}

Clean, domain-specific data will give you far better results.

Fine-Tuning Using LoRA

LoRA dramatically reduces the number of trainable parameters while preserving model quality. A typical MLX LoRA training command looks like this:

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mlx_lm.lora \
  --model mlx-community/Mistral-7B-v0.1 \
  --data your_dataset.jsonl \
  --lora-rank 8 \
  --batch-size 2 \
  --learning-rate 2e-4 \
  --epochs 3 \
  --output-dir ./finetuned-model

Training time varies depending on dataset size and model, but Apple Silicon handles 3B–7B models comfortably for LoRA-based training.

Monitor your loss values, stable downward trends indicate healthy learning.

Testing the Fine-Tuned Model

Run inference using your trained adapter:

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mlx_lm.generate \
  --model ./finetuned-model \
  --prompt "Explain the purpose of LoRA in simple terms."

Evaluate outputs manually and refine your data or hyperparameters if needed.

Important Note: Vision Model Fine-Tuning

MLX does not support vision model fine-tuning at this time.
However, there’s an unofficial project for experimenting with vision fine-tuning:

🔗 Unofficial MLX Vision Fine-Tuning:
https://github.com/Blaizzy/mlx-vlm

Use it cautiously, it's not an official Apple project and may not always build cleanly.

Official MLX Resources

For more details, configuration options, and official examples:

• MLX-LM Repository:
  https://github.com/ml-explore/mlx-lm
• MLX Examples (NLP, training scripts, demos):
  https://github.com/ml-explore/mlx-examples

These repositories are updated frequently and include the latest utilities and reference implementations.

My Implementation & Detailed Walkthrough

If you want a more practical, hands-on example, including scripts, dataset structure, and training experiments, you can check out my repository:

🔗 My MLX Fine-Tuning Project:
https://github.com/muralianand12345/mlx-finetune

This repo documents the full workflow end-to-end.

Best Practices

• Start with 3B–7B models before scaling up.
• Use diverse, high-quality data, garbage in means garbage out.
• Close memory-heavy apps to avoid OOM issues.
• Save checkpoints frequently.
• Experiment with:
  • LoRA rank
  • Learning rate
  • Batch size
  • Dataset style and structure

Conclusion

Thanks to MLX and LoRA, fine-tuning LLMs on Apple Silicon is now practical, cost-effective, and fast. Whether you’re building a custom assistant or adapting a model to a specialized domain, you can now handle the entire process locally on your MacBook.

I’ll cover advanced techniques, dataset engineering, and real-world applications in future posts.