kalomaze

What the original function actually looked like:

f32 atan2(f32 startX, f32 startZ, f32 endX, f32 endZ) {
    f32 xDiff = endX - startX;
    f32 zDiff = endZ - startZ;
    f32 absXDiff = fabsf(xDiff);
    f32 absZDiff = fabsf(zDiff);
    f32 ret;

Original Text ("Reading a Sign 43 Times Heals Your Axe Durability" by Hunter R.)

We all know that the axe in Animal Crossing will usually break after using it too much. Of course, the axe is intentionally designed to break like this in order to make the unbreakable Golden Axe an appealing item to unlock. And yet what if I told you that by simply reading a sign over and over you can actually prevent your standard axe from ever breaking? And no, I'm not joking—you can actually sit here and read this sign over and over to heal the durability on your axe, making it theoretically invincible. I'm sure a lot of you are wondering how or why this even works, so let's take a closer look.

Creating an unbreakable axe is a really funny glitch that was recently discovered by Animal Crossing spreadsheet owner Phil. To understand how interacting with a sign heals your axe, let's discuss how axe durability works.

Normally an axe can withstand 72 hits on normal trees before breaking. Since trees take three hits to cut

	class RescaleDescentTrainer(Trainer):
	def __init__(self, args, *kwargs):
	super().__init__(args, *kwargs)
	# Initialize all buffers
	self.tokens_buffer = [] # for raw token loss
	self.weighted_tokens_buffer = [] # for entropy weighted token loss
	self.unigram_rate_buffer = []
	self.bigram_rate_buffer = []
	self.trigram_rate_buffer = []
	self.weighted_unigram_buffer = []

	from transformers import AutoTokenizer
	from huggingface_hub import snapshot_download
	import os

	def add_token_boundaries(tokenizer, tokens):
	"""Add brackets around token boundaries"""
	text = ""
	for token in tokens:
	decoded = tokenizer.decode([token])
	text += f"[{decoded}] "

	import sys
	import random
	import numpy as np
	import string
	from datetime import datetime
	from PIL import Image, ImageEnhance, ImageOps
	from PyQt5.QtWidgets import (QApplication, QMainWindow, QWidget, QVBoxLayout,
	QHBoxLayout, QTextEdit, QPushButton, QCheckBox,
	QLabel, QSpinBox, QComboBox, QSlider, QFileDialog,
	QFrame)

	import React, { useState } from 'react';
	import { Settings, Bookmark, Download, Library, HelpCircle, RefreshCw, ArrowLeft } from 'lucide-react';

	const STORY_BRANCHES = {
	root: {
	text: `The darkness grew absolute, not that the hyperstitioner could see in the first place. His ears pricked up, however; he could hear the skittering, the mechanical hum as the machine followed him invisibly...`,
	continuations: [
	{
	id: 'a1',
	text: " The mechanical tendrils wrapped tighter around his shoulder, its grip a cold reminder of their symbiosis...",

	datasets:
	- path: anthracite-core/c2_logs_8k_llama3_v1.2
	# contents of this dataset were filtered for quality, but not safety or safe for work-ness. be advised
	type: sharegpt
	conversation: llama3
	- path: anthracite-org/kalo-opus-instruct-22k-no-refusal
	type: sharegpt
	conversation: llama3
	- path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered
	type: sharegpt

	== Results torch.int8 meta-llama/Llama-2-7b-hf-TP1 ====
	[--------------------------------------- scaled-torch.int8-gemm --------------------------------------]
	\| pytorch_bf16_bf16_bf16_matmul-no-scales \| cutlass_i8_i8_bf16_scaled_mm
	1 threads: --------------------------------------------------------------------------------------------
	MKN=(1x4096x12288) \| 195.3 \| 142.4
	MKN=(1x4096x4096) \| 64.5 \| 47.5
	MKN=(1x4096x22016) \| 322.9 \| 235.6
	MKN=(1x11008x4096) \| 162.6 \| 112.9
	MKN=(16x4096x12288) \| 187.5 \| 142.6
	MKN=(16x4096x4096) \| 66.2 \| 47.6

	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
	import random
	import os
	import shutil

	# Set a seed for reproducibility
	random.seed(42)

	# Load the model, tokenizer, and configuration

	# coding=utf-8
	# Copyright 2023 Mixtral AI and the HuggingFace Inc. team. All rights reserved.
	#
	# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
	# and OPT implementations in this library. It has been modified from its
	# original forms to accommodate minor architectural differences compared
	# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.