rafszul

Putting the “Web” into Webcomics ('-' * 32) Webcomics are enormous, inaccessible bitmaps that don't fit into an increasingly mobile and pixel-dense world. But they don't have to be.

A Pen by Taylor Hunt on CodePen.

License.

sskylar

{% for post in site.posts %}
  {% if post.categories contains 'Foo' %}
  <li>{{ post.title }}</li>
  {% endif %}
{% endfor %}

jeromecoupe

"use strict";

// Load plugins
const autoprefixer = require("autoprefixer");
const browsersync = require("browser-sync").create();
const cp = require("child_process");
const cssnano = require("cssnano");
const del = require("del");
const eslint = require("gulp-eslint");
const gulp = require("gulp");

tadamcz

# Jekyll plugin to replace Markdown image syntax with {% picture %} tag for crafting responsive images
# Adapted from https://gist.github.com/mmistakes/77c68fbb07731a456805a7b473f47841
# Use as a gem: https://github.com/tadamcz/jekyll-markdown-responsive-image


Jekyll::Hooks.register [:pages, :documents], :pre_render do |post, payload|
  file_ext = post.extname.tr('.', '')

  # This regex will match all of the following correctly:
  #

rain-1

How large are large language models? (2025)

This aims to be factual information about the size of large language models. None of this document was written by AI. I do not include any information from leaks or rumors. The focus of this document is on base models (the raw text continuation engines, not 'helpful chatbot/assistants'). This is a view from a few years ago to today of one very tiny fraction of the larger LLM story that's happening.

History

• GPT-2,-medium,-large,-xl (2019): 137M, 380M, 812M, 1.61B. Source: openai-community/gpt2. Trained on the unreleased WebText dataset said to 40GB of Internet text - I estimate that to be roughly 10B tokens. You can see a list of the websites that went into that data set here domains.txt.
• GPT-3 aka davinci, davinci-002 (2020): 175B parameters. There is a good breakdown of how those parameters are 'spent' here [How d