Discover gists

This worked on 14/May/23. The instructions will probably require updating in the future.

llama is a text prediction model similar to GPT-2, and the version of GPT-3 that has not been fine tuned yet. It is also possible to run fine tuned versions (like alpaca or vicuna with this. I think. Those versions are more focused on answering questions)

Note: I have been told that this does not support multiple GPUs. It can only use a single GPU.

It is possible to run LLama 13B with a 6GB graphics card now! (e.g. a RTX 2060). Thanks to the amazing work involved in llama.cpp. The latest change is CUDA/cuBLAS which allows you pick an arbitrary number of the transformer layers to be run on the GPU. This is perfect for low VRAM.

Clone llama.cpp from git, I am on commit 08737ef720f0510c7ec2aa84d7f70c691073c35d.

Про токены, JSON Web Tokens (JWT), аутентификацию и авторизацию. Token-Based Authentication

Last major update: 25.08.2020

Что такое авторизация/аутентификация
Где хранить токены
Как ставить куки ?
Процесс логина
Процесс рефреш токенов
Кража токенов/Механизм контроля токенов

How to Install the TP-Link Archer T4U Plus AC1300 USB WiFi Adapter on Linux Mint

❤️ Sponsor This Project

The following steps will install the Linux Mint driver for the TP-Link Archer T4U Plus AC1300 USB Wifi adapter.

Installing the TP-Link Archer T4U Driver

Open a Linux terminal and run the following commands to download and build the driver.

	import { useSyncExternalStore } from "react";
	// For more on the useSyncExternalStore hook, see https://react.dev/reference/react/useSyncExternalStore

	// The code is almost identical to the source code of zustand, without types and some features stripped out.
	// Check the links to see the references in the source code.
	// The links are referencing the v5 of the library. If you plan on reading the source code yourself v5 is the best way to start.
	// The current v4 version contains lot of deprecated code and extra stuff that makes it hard to reason about if you're new to this.

	// https://github.com/pmndrs/zustand/blob/fe47d3e6c6671dbfb9856fda52cb5a3a855d97a6/src/vanilla.ts#L57-L94
	function createStore(createState) {

	--!nocheck


	local RagdollService = {}


	export type ConstraintsInfo = {
	[string]: {
	[string]: boolean \| string \| number
	}

	(* TODO: multicore and/or gpu computation would be fun *)
	(* TODO: mnist sample *)

	module Matrix = struct
	module FA = Float.Array

	type t = { data : FA.t; cols : int }

	let make m n value =
	let data = FA.make (m * n) value in

	import 'package:flutter/material.dart';

	ScaffoldMessenger.of(context)
	..removeCurrentSnackBar()
	..showSnackBar(
	CustomSnackBar(
	content: Text(message),
	behavior: SnackBarBehavior.fixed,
	),
	);

	## Create 3-node environment config
	[tohayash@tohayash-lab tmp]$ cat << EOF > config-3node.yml
	kind: Cluster
	apiVersion: kind.sigs.k8s.io/v1alpha3
	nodes:
	- role: control-plane
	- role: worker
	- role: worker
	# Note: uncomment if you install cni plugin by yourself
	#networking: