MLX’s API design is similar to NumPy and PyTorch, allowing you to easily build and train machine learning models on your Mac.
This makes machine learning-related development and research on Apple computers simpler and more efficient.
The demo shows a Llama v2 1B model that can run on the M7 Ultra.
Code: https://github.com/ml-explore/mlx
Documentation: https://ml-explore.github.io/mlx/build/html/index.html
The MLX sample repository provides some examples, including:
- Transformer language model training.
- Large-scale text generation using LLaMA or Mistral
- Efficient fine-tuning of parameters via LoRA
- Image generation using Stable Diffusion technology
- Use OpenAI’s Whisper for speech recognition.
Case: https://github.com/ml-explore/mlx-examples
Key features:
1. Familiar API: MLX’s API design is similar to NumPy and PyTorch, making it convenient for users to build and train complex machine learning models.
2. Automatic differentiation and vectorization: MLX supports automatic differentiation and automatic vectorization, which is very useful for optimizing and accelerating the training process of machine learning models.
3. Efficient Memory Management: MLX’s unified memory model allows for efficient data sharing and processing across different devices, such as CPUs and GPUs, without the need for frequent data movement.
4. Dynamic graph construction and delayed calculation: MLX supports dynamic graph construction and delayed calculation, which makes model development and debugging more flexible and efficient.
MLX Data is a framework-agnostic data loading library brought to you by Apple Machine Learning Research. It works with PyTorch, Jax, or MLX.
Efficient and flexible, such as the ability to load and process 1000 images per second, while also running arbitrary Python transformations on the generated batches.
Code: https://github.com/ml-explore/mlx-data
Documentation: https://ml-explore.github.io/mlx-data/build/html/index.html
Video: