Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.
Avoid being a link dump. Try to provide only valuable well tuned information.
Neural network links before starting with transformers.
A list of useful commands for the FFmpeg command line tool.
Download FFmpeg: https://www.ffmpeg.org/download.html
Full documentation: https://www.ffmpeg.org/ffmpeg.html
#!/bin/sh | |
# rename-pictures.sh | |
# Author: Justine Tunney <jtunney@gmail.com> | |
# License: Apache 2.0 | |
# | |
# This shell script can be used to ensure all the images in a folder | |
# have good descriptive filenames that are written in English. It's | |
# based on the Mistral 7b and LLaVA v1.5 models. | |
# | |
# For example, the following command: |
# Clone llama.cpp | |
git clone https://github.com/ggerganov/llama.cpp.git | |
cd llama.cpp | |
# Build it | |
make clean | |
LLAMA_METAL=1 make | |
# Download model | |
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin |
package cache | |
import ( | |
"sync" | |
"time" | |
) | |
// Cache is a basic in-memory key-value cache implementation. | |
type Cache[K comparable, V any] struct { | |
items map[K]V // The map storing key-value pairs. |
// Package main is a sample macOS-app-bundling program to demonstrate how to | |
// automate the process described in this tutorial: | |
// | |
// https://medium.com/@mattholt/packaging-a-go-application-for-macos-f7084b00f6b5 | |
// | |
// Bundling the .app is the first thing it does, and creating the DMG is the | |
// second. Making the DMG is optional, and is only done if you provide | |
// the template DMG file, which you have to create beforehand. | |
// | |
// Example use: |
/* | |
Usage: you'll want to search for the strings <bucket> and <prefix>, and insert the S3 bucket where your access | |
logs are being delivered. Use (or delete) <prefix> to filter to a subset of your logs. | |
*/ | |
/* | |
These commented out configuration settings you can either run yourself in the REPL and source this file using | |
`.read parse_s3_access_logs.sql`, or you can uncomment them and supply values for yourself. |
This repository has a dataset of 184.879 crimes committed in Buenos Aires: https://github.com/ramadis/delitos-caba
Download the raw data like this:
wget 'https://github.com/ramadis/delitos-caba/releases/download/3.0/delitos.json'
Now use Pandas to load that into a dataframe:
#!/bin/sh | |
#set -x | |
# Usage: shibb-cas-get.sh {username} {password} # If you have any errors try removing the redirects to get more information | |
# The service to be called, and a url-encoded version (the url encoding isn't perfect, if you're encoding complex stuff you may wish to replace with a different method) | |
DEST=https://myapp.example.com/ | |
SP=https://myapp.example.com/index.php | |
IDP="https://myidp.example.com/idp/shibboleth&btn_sso=SSOok" |