Leo liaocs2008

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 nowhere. 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

海外兔

雅黑PHP探针自用精简版

在原0.4.7版基础上，只保留AJAX部分，显示服务器时时信息。

声明

本版本为自用版。

版本记录

How to recover lost Python source code if it's still resident in-memory

I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6

Attach a shell to the docker container

Install GDB (needed by pyrasite)

apt-get update && apt-get install gdb

	''' Script for downloading all GLUE data.

	Note: for legal reasons, we are unable to host MRPC.
	You can either use the version hosted by the SentEval team, which is already tokenized,
	or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
	For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
	You should then rename and place specific files in a folder (see below for an example).

	mkdir MRPC
	cabextract MSRParaphraseCorpus.msi -d MRPC

	#!/usr/bin/env python
	# coding=utf-8
	# Copyright 2020 The HuggingFace Team All rights reserved.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#

	#!/bin/bash
	#
	# script to fully prepare ImageNet dataset

	## 1. Download the data
	# get ILSVRC2012_img_val.tar (about 6.3 GB). MD5: 29b22e2961454d5413ddabcf34fc5622
	# wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar
	# get ILSVRC2012_img_train.tar (about 138 GB). MD5: 1d675b47d978889d74fa0da5fadfb00e
	# wget https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_train.tar

	import numpy as np
	import numba

	@numba.jit(nopython=True, parallel=True) #parallel speeds up computation only over very large matrices
	# M is a mxn matrix binary matrix
	# all elements in M should be uint8
	def gf2elim(M):

	m,n = M.shape

	''' Script for downloading all GLUE data.

	Note: for legal reasons, we are unable to host MRPC.
	You can either use the version hosted by the SentEval team, which is already tokenized,
	or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
	For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
	You should then rename and place specific files in a folder (see below for an example).

	mkdir MRPC
	cabextract MSRParaphraseCorpus.msi -d MRPC

	# summarize model
	from collections import OrderedDict
	import pandas as pd

	import torch
	from torch import nn
	from torch.autograd import Variable

	class TorchSummarizeDf(object):
	def __init__(self, model, weights=False, input_shape=True, nb_trainable=False, debug=False):