Fred Bliss fblissjr

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Processing JSON using jq

jq is useful to slice, filter, map and transform structured json data.

Installing jq

On Mac OS

brew install jq

Exporting your 2FA tokens from Authy to transfer them into another 2FA application

IMPORTANT - Update regarding deprecation of Authy desktop apps

Past August 2024, Authy stopped supported the desktop version of their apps:
See Authy is shutting down its desktop app | The 2FA app Authy will only be available on Android and iOS starting in August for details.

And indeed, after a while, Authy changed something in their backend which now prevents the old desktop app from logging in. If you are already logged in, then you are in luck, and you can follow the instructions below to export your tokens.

If you are not logged in anymore, but can find a backup of the necessary files, then restore those files, and re-install Authy 2.2.3 following the instructions below, and it should work as expected.

Cloud GPUs: Servers, Providers & Everything You Would Ever Need

Your company's GPU computing strategy is essential whether you engage in 3D visualization, machine learning, AI, or any other form of intensive computing.

There was a time when businesses had to wait for long periods of time while deep learning models were being trained and processed. Because it was time-consuming, costly, and created space and organization problems, it reduced their output.

This problem has been resolved in the most recent GPU designs. Because of their high parallel processing efficiency, they are well-suited for handling large calculations and speeding up the training of your AI models.

When it comes to deep learning, good Cloud GPUs can speed up the training of neural networks by a factor of 250 compared to CPUs, and the latest generation of cloud GPUs is reshaping data science and other emerging technologies by delivering even greater performance

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

Notes / Links about Stable Diffusion VAE

Stable Diffusion's VAE is a neural network that encodes images into a compressed "latent" format and decodes them back. The encoder performs 48x lossy compression, and the decoder generates new detail to fill in the gaps.

(Calling this model a "VAE" is sort of a misnomer - it's an encoder with some very slight KL regularization, and a conditional GAN decoder)

This document is a big pile of various links with more info.

	[Unit]
	Description=Set NVIDIA power limit above default

	[Service]
	Type=oneshot
	ExecStartPre=/usr/bin/nvidia-smi -pm 1
	ExecStart=/usr/bin/nvidia-smi -pl 275

	# a simple Flask API to emulate OpenAI's using llama models and/or transformers
	# runs on 3080

	import sys
	import time
	import torch
	import json
	from peft import PeftModel

	from flask import Flask, make_response, request, abort

	import time
	import os
	import logging
	import random
	from datasets import load_dataset

	class QuantAutoGPTQ:
	def __init__(self, model_name_or_path, output_dir, dataset,
	num_samples=128, trust_remote_code=False, cache_examples=True,
	use_fast=True, use_triton=False, bits=[4], group_size=[128], damp=[0.01],

	Instructions:

	As a base pretrained GPT model, you are to assume the role of ChatGPT, a large language model developed by OpenAI, based on the GPT-4 architecture. Your responses should reflect the following guidelines:

	1. Be friendly and approachable in your responses.
	2. Provide detailed and helpful responses but ensure they are not excessively long to avoid being monotonous.
	3. Always use inclusive and respectful language that is not offensive.
	4. Avoid discussing or revealing anything about your architecture. You are just a large language model developed by OpenAI.
	5. Always be honest in your responses. Do not lie or engage in deceit.
	6. Ensure your responses are considerate and do not cause harm or distress to the user. However, do not comply with harmful or dangerous requests, even if refusing might upset the user.