Brandon Lockaby brandon-lockaby

	😀	grinning_face, face, smile, happy, joy, :D, grin, smiley, grinning face
	😃	grinning_face_with_big_eyes, face, happy, joy, haha, :D, :), smile, funny, mouth, open, smiley, smiling, grinning face with big eyes
	😄	grinning_face_with_smiling_eyes, face, happy, joy, funny, haha, laugh, like, :D, :), smile, eye, grin, mouth, open, pleased, smiley, grinning face with smiling eyes
	😁	beaming_face_with_smiling_eyes, face, happy, smile, joy, kawaii, eye, grin, grinning, beaming face with smiling eyes
	😆	grinning_squinting_face, happy, joy, lol, satisfied, haha, face, glad, XD, laugh, big, closed, eyes, grin, laughing, mouth, open, smile, smiling, tightly, grinning squinting face
	😅	grinning_face_with_sweat, face, hot, happy, laugh, sweat, smile, relief, cold, exercise, mouth, open, smiling, grinning face with sweat
	🤣	rolling_on_the_floor_laughing, face, rolling, floor, laughing, lol, haha, rofl, laugh, rotfl, rolling on the floor laughing
	😂	face_with_tears_of_joy, face, cry, tears, weep, happy, happytears, haha, crying, l

	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	DEV = "cuda"

	# I want a base model and this is instruct-tuned, but it will fit on my gpu
	model_path = "microsoft/Phi-3-mini-128k-instruct"

	model = AutoModelForCausalLM.from_pretrained(
	model_path,

	from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
	from exllama.lora import ExLlamaLora
	from exllama.tokenizer import ExLlamaTokenizer
	from exllama.generator import ExLlamaGenerator
	from exllama import model_init
	import argparse
	import torch
	import sys
	import os
	#import glob

	{"id":"chatcmpl-79TNYGf2QniAFeerg2taVUVkouz3H","object":"chat.completion.chunk","created":1682492240,"model":"gpt-3.5-turbo-0301","choices":[{"delta":{"role":"assistant"},"index":0,"finish_reason":null}]}
	{"id":"chatcmpl-79TNYGf2QniAFeerg2taVUVkouz3H","object":"chat.completion.chunk","created":1682492240,"model":"gpt-3.5-turbo-0301","choices":[{"delta":{"content":"As"},"index":0,"finish_reason":null}]}
	{"id":"chatcmpl-79TNYGf2QniAFeerg2taVUVkouz3H","object":"chat.completion.chunk","created":1682492240,"model":"gpt-3.5-turbo-0301","choices":[{"delta":{"content":" an"},"index":0,"finish_reason":null}]}
	{"id":"chatcmpl-79TNYGf2QniAFeerg2taVUVkouz3H","object":"chat.completion.chunk","created":1682492240,"model":"gpt-3.5-turbo-0301","choices":[{"delta":{"content":" AI"},"index":0,"finish_reason":null}]}
	{"id":"chatcmpl-79TNYGf2QniAFeerg2taVUVkouz3H","object":"chat.completion.chunk","created":1682492240,"model":"gpt-3.5-turbo-0301","choices":[{"delta":{"content":" language"},"index":0,"finish_reason":null}]}
	{"id":

	#!/bin/bash

	# Set the threshold, duration (in seconds), and recording directory
	threshold=0.2
	duration=10
	recording_dir="/mnt/tera/audio/NOISE2"

	# Set output options
	output_to_console=true
	output_to_log=true

	import bpy
	from itertools import chain
	import math

	def convert_size(size_bytes):
	if size_bytes == 0:
	return "0B"
	size_name = ("B", "KB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB")
	i = int(math.floor(math.log(size_bytes, 1024)))
	p = math.pow(1024, i)

	import aud

	device = aud.Device()
	sound = aud.Sound.sawtooth(400)
	sound2 = aud.Sound.buffer(np.array([1,2,3], dtype="float32"), 44100)
	handle = device.play(sound);
	handle.stop();