barron9

verified on Tplink Vc220-g3u

Dns cache disable from your os
Dhcp ip rental set 1 or 5 or 8 min not like 60
Ağ geçidi sil
qos [ssid] = (max download) - 1000~
Reset 
;)

dns cache i windowstan disable edin

barron9

#include <iostream>
#include <queue>
#include <thread>
#include <mutex>
#include <condition_variable>

std::queue<int> dataQueue;
std::mutex mtx;
std::condition_variable cv;
const int maxQueueSize = 10; // Maximum queue size before backpressure is applied

barron9

#include <iostream>
#include <vector>
#include <cmath>
#include <string>

int main(){
    
    std::vector<std::pair<int, int>> vec;
    std::vector<std::pair<int, int>> vecquen;
    

barron9

#include <iostream>
#include <vector>
#include <cmath>
#include "attention.h"
// Function to compute the attention weights
std::vector<double> computeAttentionWeights(const std::vector<double>& query, const std::vector< std::vector<double> >& keys) {
    int numKeys = keys.size();
    std::vector<double> attentionWeights(numKeys, 0.0);
    double totalWeight = 0.0;

barron9

import Foundation

class ThreadInterruptionSimulator {
    var threads: [Thread] = []
    var interrupterThread: Thread?
    
    // Function to simulate work done by threads
    func worker(threadNum: Int) {
        // for _ in 0..<5 {
        

barron9

/* This is the original elf.h file from the GNU C Library; I only removed
   the inclusion of feature.h and added definitions of __BEGIN_DECLS and
   __END_DECLS as documented in
   https://cmd.inp.nsk.su/old/cmd2/manuals/gnudocs/gnudocs/libtool/libtool_36.html

   On macOS, simply copy the file to /usr/local/include/. 

   Mathias Lafeldt <mathias.lafeldt@gmail.com> */

/* This file defines standard ELF types, structures, and macros.

barron9

4 Steps in Running LLaMA-7B on a M1 MacBook

The large language models usability

The problem with large language models is that you can’t run these locally on your laptop. Thanks to Georgi Gerganov and his llama.cpp project, it is now possible to run Meta’s LLaMA on a single computer without a dedicated GPU.

Running LLaMA

There are multiple steps involved in running LLaMA locally on a M1 Mac after downloading the model weights.

barron9

[Unit]
Description=Run script after screen unlock/wake
After=sleep.target
After=suspend.target

[Service]
User=root
Type=oneshot
ExecStart=/home/berkin/wakefix.sh

barron9

from keras.models import Model
from keras.layers import Input, Dense
from keras.optimizers import Adam

# Define the input layer
inputs = Input(shape=(784,))

# Define the hidden layers
hidden1 = Dense(128, activation='relu')(inputs)
hidden2 = Dense(64, activation='relu')(hidden1)

barron9

#include <stdio.h>
#include <pthread/pthread.h>
void* someprocess(void* arg);
void *anotherprocess(void* arg);

pthread_mutex_t lock;
pthread_t tid[2];
int counter;
int main(int argc, const char * argv[]) {