Peter pszemraj

Install essential build tools

sudo apt update sudo apt install -y build-essential gcc g++

Verify installation

gcc --version which gcc

Set the CC environment variable

export CC=/usr/bin/gcc

usage

Credit/source: here

how to use unsloth grad checkpointing

steps

To integrate the provided monkey patch for offloading gradient checkpointing into the Hugging Face transformers library, you need to follow these steps:

	"""
	UL2 Data Collator for PyTorch + Transformers
	==============================================

	Standalone implementation of UL2 (Unified Language Learner) denoising objectives
	for encoder-decoder models (T5, UL2, Flan-T5, etc.).

	Based on: "Unifying Language Learning Paradigms" (Tay et al., 2022)
	https://arxiv.org/abs/2205.05131

	# Copy file contents or stdin to clipboard
	# Usage: cz [file]
	# cz file.txt - copy file to clipboard
	# cmd \| cz - copy stdin to clipboard
	# Fails on: binary files, files >10MB, non-existent files
	cz() {
	if [ -z "$1" ]; then
	xclip -selection clipboard
	elif [ -f "$1" ]; then
	# Check if it's a text file

	/*
	* "Bleh" -- a "potato-friendly" cmatrix clone.
	*
	* Screenshot: https://i.imgur.com/dt6RmU7.png
	*
	* Adapted to Windows from:
	* https://www.reddit.com/r/commandline/comments/1jcnyht/bleh_a_potatofriendly_cmatrix_clone/
	*/
	#include <stdio.h>
	#include <stdlib.h>

	import logging
	import os

	import fire
	import torch
	from datasets import load_dataset
	from huggingface_hub import PyTorchModelHubMixin
	from torch import nn
	from transformers import AutoConfig, AutoModel, AutoTokenizer

	import logging
	import os

	import fire
	import torch
	from datasets import load_dataset
	from huggingface_hub import PyTorchModelHubMixin
	from torch import nn
	from transformers import AutoConfig, AutoModel, AutoTokenizer

	from typing import List, Tuple, Optional, Set
	import torch.nn as nn
	from transformers import PreTrainedModel


	def model_summary(
	model: PreTrainedModel, max_depth: int = 4, show_input_size: bool = False
	) -> None:
	"""
	Prints an accurate summary of the model, avoiding double-counting of parameters.

	In addition to a significant decrease in hepatic lipid accumulation in the IOE group, which inhibited energy intake by propionate enrichment, hepatic lipids were also significantly reduced in the mice in the IOP group, which was largely enriched with butyrate. Compared with the IOE group, IOP had a stronger regulatory effect on hepatic metabolism and triglyceride metabolism and higher levels of TCA cycle in the host. In addition, butyrate has the ability to promote browning of white adipose tissue (WAT) to brown adipose tissue (BAT).^[@ref39],[@ref40]^ WAT stores energy, whereas BAT uses energy for heating and consequently host energy expenditure increases.^[@ref41],[@ref42]^ However, adipose tissue weight does not change after WAT browning.^[@ref43]^ Therefore, the weight of adipose tissue of mice in the IOP group dominated by butyrate was greater than that of the mice in the IOE group dominated by propionate.

	In conclusion ([Figure [7](#fig7){ref-type="fig"}](#fig7){ref-type="fig"}C), the improvement of ob

	import os

	import numpy as np

	from datasets import ClassLabel, Dataset, DatasetDict


	def split_dataset(
	dataset: Dataset,
	test_size=0.025,