Pham Thanh Lam lampts

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowehre. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture

Chat GPT "DAN" (and other "Jailbreaks")

	# required tensorflow 0.12
	# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word

	from gensim.models import Word2Vec
	import tensorflow as tf
	from tensorflow.contrib.tensorboard.plugins import projector

	# loading your gensim
	model = Word2Vec.load("YOUR-MODEL")

	import pandas as pd
	from collections import Counter
	import tensorflow as tf
	from tffm import TFFMRegressor
	from sklearn.metrics import mean_squared_error
	from sklearn.model_selection import train_test_split
	import numpy as np

	# Loading datasets'

	"""
	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

	# -- coding: utf-8 --

	import os
	import tiktoken
	import time
	import multiprocessing

	working_dir = os.path.dirname(os.path.realpath(__file__))
	dataset = 'data'
	ws = 51210241024 # 128k per chunk

	import os
	os.environ["OPENAI_API_KEY"] = ""

	from flask import Flask, Response
	import threading
	import queue

	from langchain.llms import OpenAI
	from langchain.callbacks.base import CallbackManager
	from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler