automaticcat tikikun

QUESTION:

You are a helpful assistant that solves mazes. You will be given a maze represented by a series of tokens.
The tokens represent:

Coordinates: <|row-col|> (e.g., <|0-0|>, <|2-4|>)
Walls: <|no_wall|>, <|up_wall|>, <|down_wall|>, <|left_wall|>, <|right_wall|>, <|up_down_wall|>, etc.
Origin: <|origin|>
Target: <|target|>
Movement: <|up|>, <|down|>, <|left|>, <|right|>, <|blank|>

Your task is to output the sequence of movements (<|up|>, <|down|>, <|left|>, <|right|>) required to navigate from the origin to the target, based on the provided maze representation. Think step by step. At each step, predict only the next movement token. Output only the move tokens, separated by spaces.

Question: let say you need to explain how a matrix multiplication work on cpu level from ram to register l1 l2 l3 cache , can you explain step by step with visualization?

Answer: Here's a step-by-step explanation of how matrix multiplication works at the CPU level, including the memory hierarchy (RAM, L1, L2, L3 cache). I'll include visual elements to aid understanding.

1. High-Level Overview:

Matrices: Let's consider two matrices A (m x n) and B (n x p). The result will be matrix C (m x p).
Operation: The element C[i][j] is calculated as the sum of the products of the elements from the i-th row of A and the j-th column of B: C[i][j] = sum(A[i][k] * B[k][j]) for k = 0 to n-1.

I have taken quite some machine learning courses and have done a few projects already. I think I know the math formula involved in transformers and GPT models. However, I always wondered how they work in reality. The best way for me is to read and understand source codes implementing these models. I am a C/C++ programmer mostly. I am more comfortable to read C/C++ programs. So, recently I started to read, run, and debug ggml's gpt-2 inference example since ggml is entirely written in C and can run many transformer models on a laptop: https://github.com/ggerganov/ggml/tree/master/examples/gpt-2 . The famous llama.cpp is closely connected to this library. My experiment environment is a MacBook Pro laptop+ Visual Studio Code + cmake+ CodeLLDB (gdb does not work with my M2 chip), and GPT-2 117 M model. Here is what I have learned so far:

The high-level main function has the following structure https://github.com/ggerganov/ggml/blob/master/examples/gpt-2/main-backend.cpp

load the model: ggml specific format us

Semantic Commit Messages

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

<scope> is optional

Example

Things to remember when compiling/linking C/C++ software

by Angel Leon. March 17, 2015;

Updated August 29, 2019.

Updated last on February 27, 2023

Include Paths

On the compilation phase, you will usually need to specify the different include paths so that the interfaces (.h, .hpp) which define structs, classes, constants, and functions can be found.

	'use client';
	import { useState, useRef } from 'react';
	import { useChat } from 'ai/react';

	export default function Chat() {
	const [isRecording, setIsRecording] = useState(false);
	const [audioURL, setAudioURL] = useState<string \| null>(null);
	const mediaRecorderRef = useRef<MediaRecorder \| null>(null);
	const audioChunksRef = useRef<Blob[]>([]);

	import requests
	from bs4 import BeautifulSoup
	import pandas as pd

	def fetch_poems(author_name,end_page:int):
	"""
	Fetch poems from isach.info for a given author.

	Args:
	author_name (str): Name of the author.

	import * as zlib from 'zlib';
	import fetch from 'node-fetch';

	// model input params
	let prompt = "mdjrny-v4 style 1girl watch at you";
	let negative_prompt = "NONE"; // replace NONE with actual negative prompt if any
	let samples = 1; // no.of images to generate
	let scheduler = "DPMSolverMultistepScheduler";
	let steps = 20;
	let guidance_scale = 7.5;

	import message from './LLM'

	const streamToComponent = async (
	chatContent: message[],
	setTargetText: React.Dispatch<React.SetStateAction<message[]>>
	) => {
	const url = "https://api.openai.com/v1/chat/completions";

	// set initial value for the chat
	setTargetText([...chatContent, { role: "", content: "" }]);

	import curses
	from random import randint

	# initialize screen
	curses.initscr()
	win = curses.newwin(20, 60, 0, 0)
	win.keypad(1)
	curses.noecho()
	curses.curs_set(0)
	win.border(0)