gvanem

/*
 * "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>

pszemraj

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

pszemraj

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

pszemraj

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.

bartowski1182

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

pszemraj

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:

pszemraj

import os

import numpy as np

from datasets import ClassLabel, Dataset, DatasetDict


def split_dataset(
    dataset: Dataset,
    test_size=0.025,

pszemraj

import torch
import logging


def check_ampere_gpu():
    """
    Check if the GPU supports NVIDIA Ampere or later and enable FP32 in PyTorch if it does.
    """
    # Check if CUDA is available
    if not torch.cuda.is_available():

pszemraj

import argparse
import logging
import time
from datetime import datetime
from pathlib import Path
from typing import Optional

from huggingface_hub import upload_folder
from watchdog.events import PatternMatchingEventHandler
from watchdog.observers import Observer

pszemraj

import re
from itertools import chain


def calculate_readability(code_string:str) -> float:
    code = code_string.splitlines()

    # Heuristic 1: Line length
    max_line_length = 80
    long_lines = sum(1 for line in code if len(line) > max_line_length)