January 30, 2023 In this post, we'll implement a GPT from scratch in just 60 lines of numpy. We'll then load the trained GPT-2 model weights released by OpenAI into our implementation and generate some text. Note: This post assumes familiarity with Python, NumPy, and some basic experience with neural networks. This implementation is for educational purposes, so it's missing lots of features/improv
MAI-Thinking-1: Building a Hill-Climbing Machine The Microsoft AI Team 1 Abstract Progress in AI is driven not by a single model, but by the ability to continually improve upon the current state of models. Achieving this requires treating model development as a system-level optimization problem, for which the solution is building a hill-climbing machine for rapid improvement. Our process includes
Faiss Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning. Faiss is written in C++ with complete wrappers for Python. Some of the most useful algorithms are implemented on the GPU. It is
The Pitchfork Story
A bit more than two years ago, as part of my work in Shopify’s Ruby and Rails Infrastructure team, I released a new Ruby HTTP server called Pitchfork. It has a bit of an unusual design and makes hard tradeoffs, so I’d like to explain the thought process behind these decisions and how I see the future of that project. Unicorn’s Design Is Fine Ever since I joined Shopify over 11 years ago, the main
Accelerating Generative AI with PyTorch: Segment Anything, Fast – PyTorch
Blog Accelerating Generative AI with PyTorch: Segment Anything, Fast This post is the first part of a multi-series blog focused on how to accelerate generative AI models with pure, native PyTorch. We are excited to share a breadth of newly released PyTorch performance features alongside practical examples of how these features can be combined to see how far we can push PyTorch native performance.
Version 1.0
Version 1.0# For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.0. Legend for changelogs Major Feature something big that you couldn’t do before. Feature something that you couldn’t do before. Efficiency an existing feature now may not require as much computation or memory. Enhancement a miscellaneous minor improvement. Fix somethin
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GPT in 60 Lines of NumPy | Jay Mody
MAI-Thinking-1: Building a Hill-Climbing Machine
Welcome to Faiss Documentation — Faiss documentation