mattdesl

import os
import torch
import time
from diffusers import DiffusionPipeline, AutoencoderTiny
from collections import namedtuple

PredictionResult = namedtuple('PredictionResult', [
    'latents',
    'step',
    'steps',

lewtun

# This is a modified version of TRL's `SFTTrainer` example (https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py), 
# adapted to run with DeepSpeed ZeRO-3 and Mistral-7B-V1.0. The settings below were run on 1 node of 8 x A100 (80GB) GPUs.
#
# Usage:
#   - Install the latest transformers & accelerate versions: `pip install -U transformers accelerate`
#   - Install deepspeed: `pip install deepspeed==0.9.5`
#   - Install TRL from main: pip install git+https://github.com/huggingface/trl.git
#   - Clone the repo: git clone github.com/huggingface/trl.git
#   - Copy this Gist into trl/examples/scripts
#   - Run from root of trl repo with: accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero3.yaml --gradient_accumulation_steps 8 examples/scripts/sft_trainer.py  

sayakpaul

"""
Examples:
    (1) python benchmark_controlnet_sdxl.py --controlnet_id diffusers/controlnet-depth-sdxl-1.0
    (2) python benchmark_controlnet_sdxl.py --controlnet_id diffusers/controlnet-depth-sdxl-1.0-small
    (3) python benchmark_controlnet_sdxl.py --controlnet_id diffusers/controlnet-depth-sdxl-1.0-mid
"""

import argparse
import time

adrienbrault

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp

# Build it
make clean
LLAMA_METAL=1 make

# Download model
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin

sindresorhus

Pure ESM package

The package that linked you here is now pure ESM. It cannot be require()'d from CommonJS.

This means you have the following choices:

1. Use ESM yourself. (preferred)
   Use import foo from 'foo' instead of const foo = require('foo') to import the package. You also need to put "type": "module" in your package.json and more. Follow the below guide.
2. If the package is used in an async context, you could use await import(…) from CommonJS instead of require(…).
3. Stay on the existing version of the package until you can move to ESM.

radames

Connect to your Google Colab Notebook via SSH

Using this amazing project tmate.io you can SSH tunnel to your colab notebook machine. You can with few lines of code download the binary, run an instance in the background and output the generated SSH address.

!wget -nc https://github.com/tmate-io/tmate/releases/download/2.4.0/tmate-2.4.0-static-linux-i386.tar.xz &> /dev/null
!tar --skip-old-files -xvf tmate-2.4.0-static-linux-i386.tar.xz &> /dev/null
!rm -f nohup.out; bash -ic 'nohup ./tmate-2.4.0-static-linux-i386/tmate -S /tmp/tmate.sock new-session -d & disown -a' >/dev/null 2>&1
!./tmate-2.4.0-static-linux-i386/tmate -S /tmp/tmate.sock wait tmate-ready

alexklibisz

from concurrent.futures.thread import ThreadPoolExecutor
from pprint import pprint
from time import time

import boto3
import os

import botocore
import shell as shell

jonathantneal

#!/bin/sh

# Set the macOS installer path as a variable
MACOS_INSTALLER="/Applications/$(ls /Applications | grep "Install macOS")"
MOUNT_POINT="$MACOS_INSTALLER/Contents/SharedSupport"
echo "macOS installer is \"$MACOS_INSTALLER\""

# Set the target disk as a variable
TARGET=$(diskutil info "$(bless --info --getBoot)" | awk -F':' '/Volume Name/ { print $2 }' | sed -e 's/^[[:space:]]*//')
echo "Target disk is \"$TARGET\""

TheNeuralBit

Scrabble Data in an Arrow file

538's data on 45 years of Scrabble games turned into an Arrow file

Usage:

$ python scrabble.py https://media.githubusercontent.com/media/fivethirtyeight/data/master/scrabble-games/scrabble_games.csv scrabble.arrow

AdaRoseCannon

'use strict';

class HTMLElementPlus extends HTMLElement {

	static defaultAttributeValue() {
		/* the name of the attribute is parsed in as a parameter */
		return;
	}

	static parseAttributeValue(name, value) {