Keith Hon Keith-Hon

## gist:49594ef8a26379fcc46cf5c11c7cf3f2
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Realtime WebSocket Audio Streaming</title>
    <style>
        body {
            background-color: black;
            color: green;
        }

## preprocessed_brands.json
[
  "at t",
  "molecole",
  "enquiret",
  "fairphone",
  "ラスタバナナ",
  "dcenta",
  "caoy",
  "sonim",
  "jensa",

## distillator.py
## Imports
from typing import Tuple
import torch
from torch import Module, Tensor
from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel, RobertaConfig, RobertaModel, RobertaEncoder
from torch.nn import CrossEntropyLoss, CosineEmbeddingLoss


## Function

## google_taxonomy_parser.py
import requests
en_content = requests.get('https://www.google.com/basepages/producttype/taxonomy-with-ids.en-US.txt')

def load_taxonomy_hierarchy(root, taxonomy_content, is_root):
  lines = taxonomy_content.split('\n')
  filtered_lines = lines[1:]
  filtered_lines = list(filter(lambda line: len(line) > 0, filtered_lines))
  # extract the root taxonomy from content
  root_taxonomy_set = set()


## gist:3e8bcf998d8aeae0f1f41f19a7d1f409
kubeadm reset

## master only
etcdctl rm --recursive registry

rm -rf /var/lib/cni

rm -rf /run/flannel

rm -rf /etc/cni

## gist:ed48526c3d9a249d5d56048df6172762
// pages/api/webhooks/github.js
const { exec } = require("child_process");
const crypto = require("crypto");

// Handle GitHub Webhooks
export default function handler(req, res) {
    try {
        console.log("Incoming Request");
        if (req.method !== 'POST') {
            res.send(404);

## gist:0098bd40a208de2dc9217df457584845
forfiles /S /M *.ext1 /C "cmd /c rename @file @fname.ext2"

## gist:e2c3a1f41ed6afd73f0c85b668e58e1a
apt install wget
wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.tgz
tar xvzf ngrok-stable-linux-amd64.tgz -C /usr/local/bin

## jserv.py
# Near Simplest Language model API, with room to expand!
# runs GPT-J-6B on 3090 and TITAN and servers it using FastAPI
# change "seq" (which is the context size) to adjust footprint
#
# seq   vram usage
# 512   14.7G
# 900   15.3G

# uses FastAPI, so install that
# https://fastapi.tiangolo.com/tutorial/

## j6b_train_hf_ds.py
#  So now you want to finetune that GPT-J-6B on a 3090/TITAN GPU ... okay
#  More exploratory coding. It uses the Huggingface model port, deepspeed and reads all text/md files from a target directory
#  It is a fragment of a larger system with remote editing, but that's another story
#  This is the raw, training tester. Items to look out for:
#  - uses DeepSpeed and has a DS config
#  - to save space uses SGD instead of ADAM
#  - uses gradient checkpointing
#  - freezes 25% of the layers to fit

# Assumes you can already run https://gist.github.com/kinoc/2d636a68876cd3de7b6e9c9452b61089
	<html lang="en">
	<head>
	<meta charset="UTF-8">
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<title>Realtime WebSocket Audio Streaming</title>
	<style>
	body {
	background-color: black;
	color: green;
	}
	[
	"at t",
	"molecole",
	"enquiret",
	"fairphone",
	"ラスタバナナ",
	"dcenta",
	"caoy",
	"sonim",
	"jensa",
	## Imports
	from typing import Tuple
	import torch
	from torch import Module, Tensor
	from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel, RobertaConfig, RobertaModel, RobertaEncoder
	from torch.nn import CrossEntropyLoss, CosineEmbeddingLoss



	## Function
	import requests
	en_content = requests.get('https://www.google.com/basepages/producttype/taxonomy-with-ids.en-US.txt')

	def load_taxonomy_hierarchy(root, taxonomy_content, is_root):
	lines = taxonomy_content.split('\n')
	filtered_lines = lines[1:]
	filtered_lines = list(filter(lambda line: len(line) > 0, filtered_lines))
	# extract the root taxonomy from content
	root_taxonomy_set = set()
	kubeadm reset

	## master only
	etcdctl rm --recursive registry

	rm -rf /var/lib/cni

	rm -rf /run/flannel

	rm -rf /etc/cni
	// pages/api/webhooks/github.js
	const { exec } = require("child_process");
	const crypto = require("crypto");

	// Handle GitHub Webhooks
	export default function handler(req, res) {
	try {
	console.log("Incoming Request");
	if (req.method !== 'POST') {
	res.send(404);
	apt install wget
	wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.tgz
	tar xvzf ngrok-stable-linux-amd64.tgz -C /usr/local/bin
	# Near Simplest Language model API, with room to expand!
	# runs GPT-J-6B on 3090 and TITAN and servers it using FastAPI
	# change "seq" (which is the context size) to adjust footprint
	#
	# seq vram usage
	# 512 14.7G
	# 900 15.3G

	# uses FastAPI, so install that
	# https://fastapi.tiangolo.com/tutorial/
	# So now you want to finetune that GPT-J-6B on a 3090/TITAN GPU ... okay
	# More exploratory coding. It uses the Huggingface model port, deepspeed and reads all text/md files from a target directory
	# It is a fragment of a larger system with remote editing, but that's another story
	# This is the raw, training tester. Items to look out for:
	# - uses DeepSpeed and has a DS config
	# - to save space uses SGD instead of ADAM
	# - uses gradient checkpointing
	# - freezes 25% of the layers to fit

	# Assumes you can already run https://gist.github.com/kinoc/2d636a68876cd3de7b6e9c9452b61089