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Today’s Plan of Attack
I’m a strong believer in making lists. It’s something that’s been instilled on me since I was a kid growing up. Both my father and grandfather would regularly remind me of the importance of making a...
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Biennale Internationale Design
Last week I spent time at the Biennale Internationale Design Expo in St. Etienne, France. This was my first time visiting St. Etienne, and the Biennale. It was an engaging and inspiring event that...
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Why are we working on this?
It’s a simple question. One that speaks to a deeper need to understand why you should care and put effort into solving a problem. Knowing the answer to this question — and believing in it — is one of...
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ProductCraft 2019
This past week I attended the ProductCraft conference in San Francisco. It took place at the Palace of Fine Arts, an incredibly unique venue built in 1915 and features Roman and Ancient Greek style...
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Ideas for Zealty
My wife and I are starting to look at houses for sale in our area. There is a wonderful site called Zealty that shares an incredible amount of data, including sales prices, history, and assessed...
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A simple guide to local LLM fine-tuning on a Mac with MLX
Hello there! Are you looking to fine-tune a large language model (LLM) on your Apple silicon Mac? If so, you’re in the right place. Let’s walk through the process of fine-tuning step-by-step. It won’t...
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Part 1: Setting up your environment
This post is the first of five parts of: A simple guide to local LLM fine-tuning on a Mac with MLX. If you’re starting on this journey from scratch, you’ll need to set a few things up on your Mac in...
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Part 2: Building your training data for fine-tuning
This post is the second of four parts of: A simple guide to local LLM fine-tuning on a Mac with MLX. This part of the guide assumes you have your environment with Python and Pip all set up correctly....
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Part 3: Fine-tuning your LLM using the MLX framework
This post is the third of four parts of: A simple guide to local LLM fine-tuning on a Mac with MLX. This part of the guide assumes you have training data available to fine-tune with. The data should...
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Part 4: Testing and interacting with your fine-tuned LLM
This post is the final part of: A simple guide to local LLM fine-tuning on a Mac with MLX. This part of the guide assumes you have completed the fine-tuning process and have an adapters.npz file ready...
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