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Porting Vision-Language Models to Apple Silicon with MLX: A Tutorial Series
Are you interested in running cutting-edge AI models efficiently on your Mac? We're excited to share a detailed tutorial series on porting Phi-3-Vision to Apple's MLX framework!
This 8-part series covers:
1. Basic Implementation: Translating core components from PyTorch to MLX
2. Su-scaled Rotary Position Embeddings (SuRoPE): Enabling 128K token contexts
3. Batching: Processing multiple inputs simultaneously for improved efficiency
4. Caching: Optimizing inference speed for autoregressive generation
5. Choice Selection: Implementing constrained outputs for multiple-choice scenarios
6. Constrained Decoding: Guiding model outputs with flexible constraints
7. LoRA Training: Fine-tuning models efficiently with Low-Rank Adaptation
8. Agent & Toolchain System: Building flexible AI workflows
Whether you're an AI enthusiast, researcher, or developer looking to leverage Apple Silicon, this series provides a deep dive into optimizing advanced vision-language models. You'll learn hands-on techniques for model porting, performance optimization, and extending model capabilities.
Check out the full series for a comprehensive guide to running state-of-the-art AI on your Mac!
Link to the tutorial series:
https://medium.com/@albersj66
All the code examples and implementations discussed in this tutorial series are available in our GitHub repository:
https://github.com/JosefAlbers/Phi-3-Vision-MLX
This repository contains:
- Full implementation of Phi-3-Vision in MLX
- Step-by-step code for each tutorial part
- Additional utilities and helper functions
We encourage you to explore the code, experiment with it, and contribute to the project. Your feedback and contributions are welcome!
#MachineLearning #AppleSilicon #MLX #VisionLanguageModels #AI #OpenSource #GitHub #AITutorial
Are you interested in running cutting-edge AI models efficiently on your Mac? We're excited to share a detailed tutorial series on porting Phi-3-Vision to Apple's MLX framework!
This 8-part series covers:
1. Basic Implementation: Translating core components from PyTorch to MLX
2. Su-scaled Rotary Position Embeddings (SuRoPE): Enabling 128K token contexts
3. Batching: Processing multiple inputs simultaneously for improved efficiency
4. Caching: Optimizing inference speed for autoregressive generation
5. Choice Selection: Implementing constrained outputs for multiple-choice scenarios
6. Constrained Decoding: Guiding model outputs with flexible constraints
7. LoRA Training: Fine-tuning models efficiently with Low-Rank Adaptation
8. Agent & Toolchain System: Building flexible AI workflows
Whether you're an AI enthusiast, researcher, or developer looking to leverage Apple Silicon, this series provides a deep dive into optimizing advanced vision-language models. You'll learn hands-on techniques for model porting, performance optimization, and extending model capabilities.
Check out the full series for a comprehensive guide to running state-of-the-art AI on your Mac!
Link to the tutorial series:
https://medium.com/@albersj66
All the code examples and implementations discussed in this tutorial series are available in our GitHub repository:
https://github.com/JosefAlbers/Phi-3-Vision-MLX
This repository contains:
- Full implementation of Phi-3-Vision in MLX
- Step-by-step code for each tutorial part
- Additional utilities and helper functions
We encourage you to explore the code, experiment with it, and contribute to the project. Your feedback and contributions are welcome!
#MachineLearning #AppleSilicon #MLX #VisionLanguageModels #AI #OpenSource #GitHub #AITutorial