glombard

# Determine wireless device model (manufacturer 14e4 for Broadcom):
lspci -vvnn | grep 14e4

# Install Broadcom STA driver for BCM43227: 
sudo apt-get update
sudo apt-get install --reinstall linux-headers-generic build-essential dkms bcmwl-kernel-source
sudo modprobe -r b43 ssb wl brcmfmac brcmsmac bcma
sudo modprobe wl

# Connect (press Fn+F3 to enable wifi if necessary first):

boombatower

<?php

const BASE = '';
const COUNT = 1;

$pages = [];
for ($i = 1; $i <= COUNT; $i++) {
  $suffix = $i > 1 ? '/page' . $i : '';
  $pages[] = $page = 'page' . $i . '.pdf';
  $url = BASE . $suffix;

kelunik

<?php

use Amp\Emitter;
use Amp\Iterator;
use Amp\Promise;

class CsvReader implements Iterator {
    /** @var EventEmitter */
    private $eventEmitter;
  

MattPitlyk

ericvenarusso

name: 'Eric Venarusso'
age: '21'
sex: 'male'
sports:
  - name: 'soccer'
    team:
      name: 'corinthians'
  - name: 'basketball'

briansemrau

# This script was adapted from merge.py from the KoboldAI discord server.
# I believe the original author is concedo

import os
import gc
import json
import shutil
import resource

import torch

ChrisHayduk

"""

The code below combines approaches published by both @eugene-yh and @jinyongyoo on Github. 

Thanks for the contributions guys!

"""

import torch
import peft

cgfarmer4

class AudioSessionManager: NSObject, ObservableObject {
    @Published var microphones: [AVCaptureDevice] = []
    var captureSession: AVCaptureSession = .init()
    var audioOutput: AVCaptureAudioDataOutput?
    var configured: Bool = false
    
    private var audioInput: AVCaptureDeviceInput?
    
    let dataOutputQueue = DispatchQueue(label: "audio_queue",
                                        qos: .userInteractive,

abacaj

{"task_id": "HumanEval/0", "prompt": "from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n    \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n    given threshold.\n    >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n    False\n    >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n    True\n    \"\"\"\n", "canonical_solution": "    for idx, elem in enumerate(numbers):\n        for idx2, elem2 in enumerate(numbers):\n            if idx != idx2:\n                distance = abs(elem - elem2)\n                if distance < threshold:\n                    return True\n\n    return False\n", "test": "\n\nMETADATA = {\n    'author': 'jt',\n    'dataset': 'test'\n}\n\n\ndef check(candidate):\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n    assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n    assert candidate([1.0, 2.0, 5.9, 4.0, 5.0], 0.95) == True\n    assert candidate([1.0, 2.0, 

ehartford

import copy
import os
import safetensors.torch
import glob
import json


def transform_st(path: str, out_dir: str):
    data = safetensors.torch.load_file(path)
    old_keys = list(data.keys())