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