pheuter

(function(d) { 
  var dl = d.createElement('a');
  dl.innerText = 'Download MP3';
  dl.href = "http://media.soundcloud.com/stream/"+d.querySelector('#main-content-inner img[class=waveform]').src.match(/\.com\/(.+)\_/)[1];
  dl.download = d.querySelector('em').innerText+".mp3";
  d.querySelector('.primary').appendChild(dl);
  dl.style.marginLeft = '10px';
  dl.style.color = 'red';
  dl.style.fontWeight = 700;
})(document);

andyrbell

# use ImageMagick convert
# the order is important. the density argument applies to input.pdf and resize and rotate to output.pdf
convert -density 90 input.pdf -rotate 0.5 -attenuate 0.2 +noise Multiplicative -colorspace Gray output.pdf

tokestermw

import click

import torch
import torch.autograd
import torch.nn.functional as F
from torch.autograd import Variable

import os
import random
import math

jhorsman

/**
 * Blink for Node MCU
 * also see https://arduino.stackexchange.com/questions/38477/does-the-node-mcu-v3-lolin-not-have-a-builtin-led
 *
 * Turns on an LED on for one second,
 * then off for one second, repeatedly.
 */
#include "Arduino.h"

// On a NodeMCU board the built-in led is on GPIO pin 2

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  

willccbb

# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
import re
import torch
from datasets import load_dataset, Dataset
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig
from trl import GRPOConfig, GRPOTrainer