toby petty toby-p

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

Create a new Telegram bot:
1. start a chat with BotFather;
2. type /newbot, select a name (to be shown in chats) and handle for your bot;
3. note the bot token to access HTTP API - a long string with a colon in the middle (later referred to as <token>);
4. optionally /setdescription and /setuserpic.
Add the bot to the chat/channel and note its id:
1. add the bot to a new or existing chat or group - this is where your bot will send notifications to;
2. go to https://api.telegram.org/bot<token>/getUpdates (replace <token> with your token);
3. within the chat part, find and note the id field - a positive or negative number (later referred to as <chat_id>).
Programmatically send notifications:

tmux Cheat Sheet

Table of Contents

General Usage
Shortcuts
Commands
Scripting tmux
Configuring tmux

Overview: http://matplotlib.org/faq/usage_faq.html#usage

Multiple, manual Axes: https://python4mpia.github.io/plotting/advanced.html

Another intro article http://pbpython.com/effective-matplotlib.html

Great Foo

From - https://github.com/Kunena/Kunena-Forum/wiki/Create-a-new-branch-with-git-and-manage-branches

git checkout -b [name_of_your_new_branch]

Push the branch on github :

git push origin [name_of_your_new_branch]

When you want to commit something in your branch, be sure to be in your branch.

	#!/bin/bash
	#
	# script to extract ImageNet dataset
	# ILSVRC2012_img_train.tar (about 138 GB)
	# ILSVRC2012_img_val.tar (about 6.3 GB)
	# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
	#
	# https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
	#
	# train/

	<!DOCTYPE html>
	<html lang="en">
	<head>
	<meta charset="utf-8">
	{{ bokeh_css }}
	{{ bokeh_js }}
	<link rel="stylesheet" href="stocktext/static/css/styles.css"/>
	</head>
	<body>
	{{ plot_div\|indent(8) }}

	import argparse
	import os

	import boto3


	class S3MultipartUpload(object):
	# AWS throws EntityTooSmall error for parts smaller than 5 MB
	PART_MINIMUM = int(5e6)

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	import pymssql
	import pandas as pd

	## instance a python db connection object- same form as psycopg2/python-mysql drivers also
	conn = pymssql.connect(server="172.0.0.1", user="howens",password="some_fake_password", port=63642) # You can lookup the port number inside SQL server.

	## Hey Look, college data
	stmt = "SELECT * FROM AlumniMirror..someTable"
	# Excute Query here
	df = pd.read_sql(stmt,conn)