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import random

# Generate random byte data
N = 8  # Number of chunks
byte_data = bytes(random.getrandbits(8) for _ in range(1024))  # Generate 1024 random bytes

# Split into N equal chunks using plain Python
chunk_size = len(byte_data) // N
split_data = [byte_data[i * chunk_size : (i + 1) * chunk_size] for i in range(N)]

secemp9

Got it! I'll find the best resources to get you up to speed on Monte Carlo Tree Search (MCTS) without reinventing the wheel. This will include:

• The most cited and accessible foundational papers.
• High-quality GitHub repositories with clear implementations.
• Tutorials or blog posts that explain MCTS concepts and provide code examples.

I'll get back to you shortly with the best materials!

Best Resources for Learning Monte Carlo Tree Search (MCTS)

Learning MCTS efficiently involves understanding the core algorithm and having practical examples to follow. Below are top resources grouped by foundational readings, tutorials, and code implementations, so you can quickly grasp MCTS concepts and apply them without reinventing the wheel.

secemp9

sudo apt-get update
sudo apt-get install -y build-essential git cuda-libraries-dev-12-8 libopencv-dev gpg wget
wget -O - https://apt.kitware.com/keys/kitware-archive-latest.asc 2>/dev/null | gpg --dearmor - | sudo tee /usr/share/keyrings/kitware-archive-keyring.gpg >/dev/null

echo 'deb [signed-by=/usr/share/keyrings/kitware-archive-keyring.gpg] https://apt.kitware.com/ubuntu/ <distro> main' | sudo tee /etc/apt/sources.list.d/kitware.list >/dev/null

sudo apt-get update
sudo apt-get install gcc-12 g++-12
sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 12

secemp9

import os
import sys
import logging
from git import Repo, GitCommandError

def extract_repo_name(repo_url):
    """
    Extracts the repository name from the given Git URL.
    Supports URLs ending with '.git' and those without it.
    """

secemp9

import argparse
import random
import sys

from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache
import torch
defo = ["\nWait, let's look at this from a system thinking approach:", "\nHmm let's look at this from a step by step approach:"]
# ~ defo = ["\nWait, but", "\nHmm", "\nSo", "\nActually"]
parser = argparse.ArgumentParser()
parser.add_argument("question", type=str)

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import textwrap
from ollama import chat

# ---------------------------
# 1) Setup: Long text & question
# ---------------------------

# ~ LONG_TEXT = """
# ~ Coffee, one of the world’s most beloved beverages, has a storied history that dates back centuries. Legend has it that an Ethiopian goat herder named Kaldi first noticed his goats becoming particularly energetic after nibbling on certain red berries. This discovery eventually led to the cultivation of coffee beans in the Middle East, where Sufi monks used the brew to stay alert during nighttime prayers. Over time, the practice of preparing and drinking coffee spread across the Islamic world and into Europe, sparking a cultural revolution of cafés and intellectual discourse.

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def calculate_model_size(total_parameters, precision="fp32"):
    """
    Calculate the model size in GB given the total number of parameters.

    Parameters:
        total_parameters (int): Total number of parameters in the model.
        precision (str): Precision type ("fp32" or "fp16").

    Returns:
        float: Model size in GB.

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class Agent:
    def __init__(self, name, ports=None):
        self.name = name
        self.ports = ports if ports else []
        # Each element of ports is either None or a tuple (other_agent, other_port_index)

    def __repr__(self):
        return f"{self.name}"

def connect(a, pa, b, pb):

secemp9

;; Process org blocks and continue initialization
(ignore-errors 
  (catch 'init-done
    (let* ((init-file (or load-file-name buffer-file-name))
           (temp-file (concat temporary-file-directory "init-processed.el")))
      
      ;; Extract elisp blocks to temp file
      (with-temp-file temp-file
        (insert-file-contents init-file)
        (goto-char (point-min))

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Direct solution weights: [0.79, 0.85, 0.84]
Forward output: [111, 117, 116]
Loss: 0