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
January 13, 2023 Hello! I’ve been thinking about writing a zine about how things are represented on computers in bytes, so I was thinking about floating point. I’ve heard a million times about the dangers of floating point arithmetic, like: addition isn’t associative (x + (y + z) is different from (x + y) + z) if you add very big values to very small values, you can get inaccurate results (the sma
Solving Quantitative Reasoning Problems With Language Models
Solving Quantitative Reasoning Problems with Language Models Aitor Lewkowycz∗, Anders Andreassen†, David Dohan†, Ethan Dyer†, Henryk Michalewski†, Vinay Ramasesh†, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur∗, Guy Gur-Ari∗, and Vedant Misra∗ Google Research Abstract Language models have achieved remarkable performance on a wide range of tasks that require
Accelerate Python code 100x by import taichi as ti | Taichi Docs
Python has become the most popular language in many rapidly evolving sectors, such as deep learning and data sciences. Yet its easy readability comes at the cost of performance. Of course, we all complain about program performance from time to time, and Python should certainly not take all the blame. Still, it's fair to say that Python's nature as an interpreted language does not help, especially
Pandas is a very popular tool for data analysis. It comes built-in with many useful features, it's battle tested and widely accepted. However, pandas is not always the best tool for the job. SQL databases have been around since the 1970s. Some of the smartest people in the world worked on making it easy to slice, dice, fetch and manipulate data quickly and efficiently. SQL databases have come such
富の分布
拙著『Rで楽しむ統計』の53〜55ページに書いた「富の分布」のシミュレーションをPythonでしてみましょう。 神様が100人に500枚の金貨を投げ与えました。全員がちょうど5枚ずつ金貨を手にすれば貧富の差がないのですが、現実には、たまたま多くの金貨を手にする人もいれば、そうでない人もいます。どのような分布になるでしょうか？ 1人の人に着目します。神様が金貨を1枚投げたとき、この人が金貨を手にする確率は0.01です。これが500回繰り返されたとすると、この人が $k$ 個の金貨を手にする確率は \[ p_k = {}_{500}C_k 0.01^k (1-0.01)^{500-k} \] という2項分布になります。 これくらいなら簡単ですが、人数 $m$ が100よりずっと多かったらどうでしょうか。神様は1人あたり平均5個の金貨を与えたいので、$5m$ 個の金貨を用意しなければなりません。
The 'eu' in eucatastrophe – Why SciPy builds for Python 3.12 on Windows are a minor miracle
This matrix would be a lot larger if it included historical OSes and less common architectures, where support with the respective compiler was often in a 1:1 relationship (i.e. that combination would cover a single cell in the matrix). The matrix also does not cover which programming languages a given compiler is able to process, but for simplicity, you can picture C/C++ here. Of course, GCC remai
Sean Moriarity and I are glad to announce that the project we have been working on for the last 3 months, Nx, is finally publicly available on GitHub. Our goal with Nx is to provide the foundation for Numerical Elixir. In this blog post, I am going to outline the work we have done so far, some of the design decisions, and what we are planning to explore next. If you are looking for other resources
NumPy library on Python is an essential tool for data scientists to work on numerical data, especially when they deal with data arrays, especially multi-dimensional, and need a memory-efficient fast indexing of arrays, However, knowing about other useful packages when solving data science problems is essential. So, let’s see which packages are available in Python programming language and are used
GitHub - ComfyUI-Workflow/awesome-comfyui: A collection of awesome custom nodes for ComfyUI
ComfyUI-Gemini_Flash_2.0_Exp (⭐+172): A ComfyUI custom node that integrates Google's Gemini Flash 2.0 Experimental model, enabling multimodal analysis of text, images, video frames, and audio directly within ComfyUI workflows. ComfyUI-ACE_Plus (⭐+115): Custom nodes for various visual generation and editing tasks using ACE_Plus FFT Model. ComfyUI-Manager (⭐+113): ComfyUI-Manager itself is also a cu
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GPT in 60 Lines of NumPy | Jay Mody
Examples of floating point problems
Practical SQL for Data Analysis
Nx (Numerical Elixir) is now publicly available - Dashbit Blog
NumPy for Data Science Beginners in Python