ShawonAshraf

For some reason, it is surprisingly hard to create a bootable Windows USB using macOS. These are my steps for doing so, which have worked for me in macOS Monterey (12.6.1) for Windows 10 and 11. After following these steps, you should have a bootable Windows USB drive.

1. Download a Windows disc image (i.e. ISO file)

You can download Windows 10 or Windows 11 directly from Microsoft.

2. Identify your USB drive

After plugging the drive to your machine, identify the name of the USB device using diskutil list, which should return an output like the one below. In my case, the correct disk name is disk2.

ShawonAshraf

package org.playerchat.core;

import org.playerchat.utils.MessageCounter;
import org.playerchat.utils.MessageLogger;
import org.playerchat.utils.Operations;

import java.io.*;
import java.net.Socket;

public class PlayerMultiProcess  {

ShawonAshraf

from llama_index.vector_stores.qdrant import QdrantVectorStore

def get_forward_nodes(
    node_with_score: NodeWithScore, num_nodes: int, vector_store: QdrantVectorStore
) -> Dict[str, NodeWithScore]:
    """Get forward nodes."""
    node = node_with_score.node
    nodes: Dict[str, NodeWithScore] = {node.node_id: node_with_score}
    cur_count = 0
    # get forward nodes in an iterative manner

ShawonAshraf

<artifacts_info>
The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.

# Good artifacts are...
- Substantial content (>15 lines)
- Content that the user is likely to modify, iterate on, or take ownership of
- Self-contained, complex content that can be understood on its own, without context from the conversation
- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
- Content likely to be referenced or reused multiple times

ShawonAshraf

import os
import shutil

if os.path.exists(".cache/distilabel"):
    shutil.rmtree(".cache/distilabel")


from distilabel.steps.tasks import TextGeneration
from distilabel.steps import LoadHubDataset
from distilabel.pipeline.local import Pipeline

ShawonAshraf

import openai
from pydantic import BaseModel, Field
import json

class ResponseStructure(BaseModel):
    bookname: str = Field(..., title="Book Name")
    author: str = Field(..., title="Author")
    reading_list_name: str = Field(..., title="Reading List Name")
    
    

ShawonAshraf

#include "stm32f4xx.h"
#include "stm32f4xx_hal.h"

ADC_HandleTypeDef hadc1;

void SystemClock_Config(void);
static void MX_GPIO_Init(void);
static void MX_ADC1_Init(void);

int main(void) {

ShawonAshraf

# Import the required libraries
from collections import defaultdict

# Define the Chu-Liu/Edmonds algorithm function
def chu_liu_edmonds(graph):
    # Step 1: Contract the graph to a single node
    contracted_node = max(graph.keys()) + 1
    contracted_graph = defaultdict(list)
    for node in graph:
        for (weight, child) in graph[node]:

ShawonAshraf

import pynvml

pynvml.nvmlInit()

device_count = pynvml.nvmlDeviceGetCount()

for i in range(device_count):
    try:
        handle = pynvml.nvmlDeviceGetHandleByIndex(i)
        util = pynvml.nvmlDeviceGetUtilizationRates(handle)

ShawonAshraf

import torch.nn as nn
from einops.layers.torch import Rearrange
from transformers import AutoModel


class DummyModel(nn.Module):
    def __init__(self, textmodel_weight_name, task_name, batch_size, freeze_text_model=False):
        super(DummyModel, self).__init__()

        self.task_name = task_name