silphendio

# to use this, first install python and exllamav2 (https://github.com/turboderp/exllamav2)
# load a model, rearrange the layers as you like, set generation parameters, and run it
# duplicate layers share tensors, but still need extra memory for the cache
# thanks to  @dnhkng for showing that the cache needs to be re-created
# licensed under WTFPL (http://www.wtfpl.net/about/) - Silphendio


from exllamav2 import *
from exllamav2.generator import *
import sys, torch

younesbelkada

# pip install -U bitsandbytes
# pip install -U git+https://github.com/huggingface/transformers.git
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
path_to_hub = XXX
tokenizer = AutoTokenizer.from_pretrained(model_id)

model = AutoModelForCausalLM.from_pretrained(model_id, load_in_4bit=True)
model.push_to_hub(path_to_hub)

thistleknot

import torch
import torch.nn.functional as F
from transformers import GPTNeoForCausalLM, AutoTokenizer
from datasets import load_dataset
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
import random

# Parameters

sekstini

thistleknot

#!/usr/bin/env python
# coding: utf-8

import lade
from transformers import AutoTokenizer, AutoModel
import torch
lade.augment_all()
lade.config_lade(LEVEL=5, WINDOW_SIZE=7, GUESS_SET_SIZE=7, DEBUG=0)
from datasets import load_dataset
from torch.utils.tensorboard import SummaryWriter

pszemraj

# pip install transformers
from fnmatch import fnmatch
from pathlib import Path
from typing import Dict, List, Optional, Union
from packaging import version
import os

from huggingface_hub import HfApi, HfFolder, cached_download, hf_hub_url
import huggingface_hub

slowpeek

#!/usr/bin/env bash

# MIT license (c) 2022-2024 https://github.com/slowpeek
# Homepage: https://gist.github.com/slowpeek/006680bb06e671bc489d4a3274ff3d19

: <<'README'

Applicable nvidia driver versions: 525, 530, 535, 545, 550

ABOUT --

melsov

# ##### BEGIN GPL LICENSE BLOCK #####
#
#  This program is free software; you can redistribute it and/or
#  modify it under the terms of the GNU General Public License
#  as published by the Free Software Foundation; either version 2
#  of the License, or (at your option) any later version.
#
#  This program is distributed in the hope that it will be useful,
#  but WITHOUT ANY WARRANTY; without even the implied warranty of
#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the

benfoxall

# Extract frames from GEOS Image Viewer to generate a video
# https://www.star.nesdis.noaa.gov/goes/fulldisk_band.php?sat=G16&band=GEOCOLOR&length=12&dim=1

mkdir -p images output

BASE=https://cdn.star.nesdis.noaa.gov/GOES16/ABI/FD/GEOCOLOR/
DIM=1808x1808

curl $BASE \
    | grep -o -e "\".*GEOCOLOR-$DIM.jpg\"" \

Nerixyz

function wrapObject(fn, that, args, name) {
	const instance = fn.apply(that, args);
	instance.__base = instance;
	return new Proxy(instance, {
		get: (target, key) => {
			if(!['__base'].includes(key)) console.log(`[GET] ${name}.${key}`);
			const value = target[key];
			return value;
		},
		set: (target, key, value) => {