Pratiksha Pai pratikshappai

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

	"""
	stable diffusion dreaming
	creates hypnotic moving videos by smoothly walking randomly through the sample space

	example way to run this script:

	$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

	to stitch together the images, e.g.:
	$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4

	import logging

	import cv2
	import PySpin

	logger = logging.getLogger(__name__)


	class Camera(object):
	def __init__(self, cam):

	# Automated AMI Backups
	#
	# @author Bobby Kozora
	#
	# This script will search for all instances having a tag with the name "backup"
	# and value "Backup" on it. As soon as we have the instances list, we loop
	# through each instance
	# and create an AMI of it. Also, it will look for a "Retention" tag key which
	# will be used as a retention policy number in days. If there is no tag with
	# that name, it will use a 7 days default value for each AMI.

	"""
	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)

Pratiksha Pai pratikshappai

Anti-hype LLM reading list

Foundational Concepts

Pre-Transformer Models