Birch-san

from transformers import (
  AutoConfig,
  AutoTokenizer,
  BitsAndBytesConfig,
  GenerationConfig,
  AutoModelForCausalLM,
  LlamaTokenizerFast,
  PreTrainedModel,
  TextIteratorStreamer,
  StoppingCriteria,

mara004

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# SPDX-FileCopyrightText: 2025 geisserml <geisserml@gmail.com>
# SPDX-License-Identifier: Apache-2.0 OR MPL-2.0

# See also https://github.com/extremeheat/JSPyBridge/blob/master/examples/python/pdfjs.py

codekansas

import argparse
import contextlib
import logging
import math
import random
import time
from dataclasses import dataclass
from pathlib import Path
from typing import Callable

Chillee

import torch
import torch._inductor.config
import time

torch._inductor.config.triton.cudagraphs = False
torch.set_float32_matmul_precision('high')

def bench(f, name=None, iters=100, warmup=5, display=True, profile=False):
    for _ in range(warmup):
        f()

sayakpaul

Work	Details
Augmenting convnets with aggregated attention	Tutorial by Aritra
Train a Vision Transformer on small datasets	Tutorial by Aritra
MobileViT	Tutorial by Sayak
Compact Convolutional Transformers	Tutorial by Sayak
Data efficient image transformers	TF implementation, TF pre-trained models, tutorial by Sayak
Class attention image transformers	TF implementation, TF pre-trained models by Sayak
Masked Autoencoders	TF implementation, tutorial by Aritra and Sayak, Contribution to Hugging Face Transformers by Aritra and Sayak
Probing the representation of ViTs

simonster

# Copyright 2022 Google LLC.
# SPDX-License-Identifier: Apache-2.0
# Author: Maithra Raghu <maithra@google.com>


def compute_distance_matrix(patch_size, num_patches, length):
  """Helper function to compute distance matrix."""

  distance_matrix = np.zeros((num_patches, num_patches))

madlag

#!/usr/bin/env python

# Any copyright is dedicated to the Public Domain.
# https://creativecommons.org/publicdomain/zero/1.0/

# Written by Francois Fleuret <francois@fleuret.org>
# Modified by François Lagunas <francois.lagunas@m4x.org>

import time, torch

jstheoriginal

struct SearchBar : View {
    @Binding var searchText: String
    
    var body: some View {
        HStack {
            Image(systemName: "magnifyingglass").foregroundColor(.secondary)
            TextField(
                $searchText,
                placeholder: Text("Search")) {
                    UIApplication.shared.keyWindow?.endEditing(true)

dannguyen

Simple scraper of the ASPX search form for U.S. Congress House financial disclosure results

The following script, given someone's last name, prints a CSV of financial disclosure PDFs (the first 20, for simplicity's sake) as found on the House Financial Disclosure Reports. It's meant to be a proof-of-concept of how to scrape ASPX (and other "stateful" websites) with using plain old requests -- without too much inconvenience -- rather than resorting to something heavy like the selenium websdriver

The search page can be found here: http://clerk.house.gov/public_disc/financial-search.aspx

Here's a screenshot of what it does when you search via web browser:

maks-ivanov

import numpy as np
from numba import jit
from numba import float64
from numba import int64

@jit((float64[:], int64), nopython=True, nogil=True)
def _ewma(arr_in, window):
    r"""Exponentialy weighted moving average specified by a decay ``window``
    to provide better adjustments for small windows via: