veekaybee

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

rcy8

云

IaaS指提供系统（可以自己选）或者储存空间之类的硬件，软件要自己手动装；PaaS提供软件/框架（可以自己选）；SaaS只能使用开发好的软件（卖软件本身）；BaaS一般类似于非关系数据库，但各家不通用，有时还有一些其它东西。

其他人的集合

• https://github.com/ripienaar/free-for-dev
• https://github.com/AchoArnold/discount-for-student-dev
• https://github.com/qinghuaiorg/free-for-dev-zh 很久没更新了
• https://github.com/vincenthou/free-for-china-dev 很久没更新了

sebjai

mementum

#!/usr/bin/env python
# -*- coding: utf-8; py-indent-offset:4 -*-
###############################################################################
# Copyright (C) 2019 Daniel Rodriguez - MIT License
#  - https://opensource.org/licenses/MIT
#  - https://en.wikipedia.org/wiki/MIT_License
###############################################################################
import argparse
import random

BarclayII

import pandas as pd
import dgl
import os
import torch

class MovieLens(object):
    def __init__(self, directory):
        '''
        directory: path to movielens directory which should have the three
                   files:

bgweber

import featuretools as ft
from pyspark.sql.functions import pandas_udf, PandasUDFType

@pandas_udf(schema, PandasUDFType.GROUPED_MAP)
def apply_feature_generation(pandasInputDF):
    
    # create Entity Set representation 
    es = ft.EntitySet(id="events")
    es = es.entity_from_dataframe(entity_id="events", dataframe=pandasInputDF)
    es = es.normalize_entity(base_entity_id="events", new_entity_id="users", index="user_id")

gabrieltseng

import torch
import torch.nn as nn
from torch.autograd import Variable, Function
import random

class WARP(Function): 
    '''
    autograd function of WARP loss
    '''
    @staticmethod

harv

#! /bin/sh

# update glibc to 2.17 for CentOS 6

wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-2.17-55.el6.x86_64.rpm
wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-common-2.17-55.el6.x86_64.rpm
wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-devel-2.17-55.el6.x86_64.rpm
wget http://copr-be.cloud.fedoraproject.org/results/mosquito/myrepo-el6/epel-6-x86_64/glibc-2.17-55.fc20/glibc-headers-2.17-55.el6.x86_64.rpm

sudo rpm -Uvh glibc-2.17-55.el6.x86_64.rpm \

cbecker

#include <LightGBM/config.h>
#include <LightGBM/dataset_loader.h>
#include <LightGBM/boosting.h>
#include <LightGBM/objective_function.h>
#include <LightGBM/metric.h>
#include <LightGBM/utils/common.h>

#include <iostream>
#include <random>
#include <algorithm>

j-min

from __future__ import print_function
'''
Basic Multi GPU computation example using TensorFlow library.
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''

'''
This tutorial requires your machine to have 2 GPUs
"/cpu:0": The CPU of your machine.