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Better & Faster Large Language Models via Multi-Token Prediction

A recent paper titled "Better & Faster Large Language Models via Multi-token Prediction" (arXiv:2404.19737v1) introduces a simple but effective modification to the standard language modeling training loss that significantly improves performance, inference speed, and reasoning capabilities of large language models, especially for code-related tasks.

Key Findings

The authors propose training language models to predict multiple future tokens at once, using a shared model trunk and independent output heads for each future token position. This multi-token prediction approach is compared to the standard next-token prediction loss through comprehensive experiments on both synthetic and natural datasets. The key findings are summarized in the following fact table:

| Fact | Details/Context | Results/Metrics

	#!/usr/bin/env python3
	import torch, gymnasium as gym, numpy as np, time, sys, threading, os, random
	import torch.multiprocessing as mp
	from torch import Tensor

	from bg_record import log_step, bind_logger, log_close

	# torch.set_num_threads(1)

	NUM_PROCS = 16

	#!/usr/bin/env python
	# t - The missing LLM token counting and splitting tool for UNIX

	import argparse
	import sys
	from typing import Optional, List
	import math
	import os

	import tiktoken

	import os
	import asyncio
	import aiohttp
	import json
	import logging
	from threading import Lock

	# Logging setup (for better debugging)
	logging.basicConfig(
	level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"

	#!/bin/bash
	set -e # Exit on any error

	# Check if required arguments are provided
	if [ -z "$REGION" ] \|\| [ -z "$SECURITY_GROUPS" ] \|\| [ -z "$KEY_PAIR" ] \|\| [ -z "$SUBNET" ]; then
	echo "Error: You must provide REGION, SECURITY_GROUPS, KEY_PAIR, and SUBNET as environment variables."
	echo "Example:"
	echo " export REGION=us-east-1"
	echo " export SECURITY_GROUPS=sg-12345678,sg-87654321"
	echo " export KEY_PAIR=my-key-pair"