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@veekaybee
veekaybee / normcore-llm.md
Last active May 12, 2024 12:10
Normcore LLM Reads

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

Screenshot 2023-12-18 at 10 40 27 PM

Pre-Transformer Models

@karpathy
karpathy / stablediffusionwalk.py
Last active May 11, 2024 05:26
hacky stablediffusion code for generating videos
"""
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
@unkn-one
unkn-one / live_view.py
Last active February 8, 2024 22:14
Live view from FLIR Blackfly camera using OpenCV
import logging
import cv2
import PySpin
logger = logging.getLogger(__name__)
class Camera(object):
def __init__(self, cam):
@bkozora
bkozora / lambdaAMIBackups.py
Last active October 22, 2023 20:49
AWS Lambda AMI Backups
# 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.
@karpathy
karpathy / min-char-rnn.py
Last active May 11, 2024 20:19
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
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)