jthomasmock

library(tidyverse)
library(jsonlite)
library(shiny)

# get_json_data(date)
# score_letters(letters)
# read_keywords() on mac
# split_char(word) into words
# limit_words(words, str_length) to specific length and lower case
# top_words(word, words_in, top_n = 50) - combo of split_char, limit_words

jthomasmock

library(tidyverse)
library(stringr)
library(jsonlite)

# create a function to solve the Keyword game
# this game uses a 6 letter horizontal word at the
# intersection of 6 other words, where a missing letter from each of the vertical words
# accounts for one letter of the mystery 6 letter length horizontal word

# how to play

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

knbknb

#!/usr/bin/env bash
set -Eeu

trap 'STATUS=${?}; echo "${0}: Error on line ${LINENO}: ${BASH_COMMAND}"; exit ${STATUS}' ERR
trap 'rm -rf ${tempDir}' EXIT

readonly supportedUbuntuVersion="22.04"
readonly tempDir="/tmp/setup"
readonly devDir="${HOME}/dev"
readonly scriptsDir="${devDir}/scripts"

georgestagg

Rcpp::sourceCpp("mandeldrop.cpp")

xlims=c(-0.65,0.65)
ylims=c(-0.6,2.2)
m <- mandeldrop(xlims[[1]], xlims[[2]], ylims[[1]], ylims[[2]], 1560, 2160, 512)

# Colour palette from https://stackoverflow.com/q/48069990
rainbow=c(rgb(0.47,0.11,0.53),rgb(0.27,0.18,0.73),rgb(0.25,0.39,0.81),rgb(0.30,0.57,0.75),rgb(0.39,0.67,0.60),rgb(0.51,0.73,0.44),rgb(0.67,0.74,0.32),rgb(0.81,0.71,0.26),rgb(0.89,0.60,0.22),rgb(0.89,0.39,0.18),rgb(0.86,0.13,0.13))
cols=c(colorRampPalette(rainbow)(100),rev(colorRampPalette(rainbow)(100)),"black")

wch

# @drob's version from https://twitter.com/drob/status/1501747414780239879
pluck_recursive <- function(lst, name) {
  if (is.list(lst)) {
    if (!is.null(lst[[name]])) {
      return(lst[[name]])
    }
    return(unname(unlist(purrr::map(lst, pluck_recursive, name))))
  }
}

hadley

# What's the most natural way to express this code in base R?
library(dplyr, warn.conflicts = FALSE)
mtcars %>%
  group_by(cyl) %>%
  summarise(mean = mean(disp), n = n())
#> # A tibble: 3 x 3
#>     cyl  mean     n
#>   <dbl> <dbl> <int>
#> 1     4  105.    11
#> 2     6  183.     7

chrishoerl

#!/bin/bash
# Purpose: Bulk-delete GitLab pipelines older than a given date
# Author: github.com/chrishoerl
# GitLab API: v4
# Requirements: jq must be instaled ($ sudo apt install jq)
# API example: https://gitlab.example.com/api/v4/projects
# API example: https://gitlab.example.com/api/v4/projects/<projectid>/pipelines
#
# NOTE: Script is just a dryrun. To really delete pipelines, simply uncomment line 49 to activate
#

isteves

Trying to grok tidyselect

Key takeaways:

• everything inside vars(...) is exactly the same as the stuff inside select(...)!!!
• vars() is used for all scoped variants of dplyr verbs (I assume bc the variables need to “fit” into a single argument, .vars. In select(...), the ellipses take everything)
• vars_select() is probably more of a developer-facing function (seen in select_helpers documentation)

Some "gotchas":

jdhitsolutions

#requires -version 5.1

Function Get-DockerDiskUsage {
    [cmdletbinding()]
    [alias("gddu")]
    [OutputType("Get-DockerDiskUsage.DockerDiskUsage")]
    Param(
        [Parameter(Position = 0, HelpMessage = "Get specific usage types. The default is All")]
        [ValidateSet("Images","Containers","Volumes","Cache")]
        [string[]]$Type