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import os
import sys
with open(sys.argv[0]) as f:
    code = f.read() # read the code of this file ASAP, for logging
import uuid
import glob
import time
import contextlib
from dataclasses import dataclass
from typing import Optional

lapp0

Running pytorch 2.6.0.dev20241126+cu124 compiled for CUDA 12.4
nvidia-smi:
Fri Nov 29 00:54:16 2024       
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.76                 Driver Version: 550.76         CUDA Version: 12.4     |
|-----------------------------------------+------------------------+----------------------+
| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
|                                         |                        |               MIG M. |
|=========================================+========================+======================|

lapp0

Remove evil parts of answer.

Code

import outlines

qwen_model = outlines.models.transformers("Qwen/Qwen2.5-14B-Instruct", model_kwargs=dict(load_in_8bit=True))


def ask_non_evil_question(prompt, pattern, model=qwen_model, max_tokens=100):

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import random
import collections


def trial(first_to=2):
    flips = [random.choice([True, False]) for _ in range(100)]
    alice_flip_checks = flips
    bob_flip_checks = [flips[i] for i in range(1, len(flips), 2)] + [flips[i] for i in range(0, len(flips), 2)]
    # get second_head
    try:

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from datasets import Dataset, load_from_disk
from transformers import TrainingArguments
from transformers.trainer_utils import EvalLoopOutput
from unsloth import FastLanguageModel
import random
from huggingface_hub import create_repo

from scipy.spatial.distance import cosine
from sentence_transformers import SentenceTransformer
import statistics

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# Huge Pattern
`0(0(0([1235679]-(0(2-(0[1-9]|1\\d|2[0-8])|[13578]-(0[1-9]|3[01]|[12]\\d)|[469]-(0[1-9]|30|[12]\\d))|1(1-(0[1-9]|30|[12]\\d)|[02]-(0[1-9]|3[01]|[12]\\d)))|[48]-(0(2-(0[1-9]|[12]\\d)|[13578]-(0[1-9]|3[01]|[12]\\d)|[469]-(0[1-9]|30|[12]\\d))|1(1-(0[1-9]|30|[12]\\d)|[02]-(0[1-9]|3[01]|[12]\\d))))|[13579]([01345789]-(0(2-(0[1-9]|1\\d|2[0-8])|[13578]-(0[1-9]|3[01]|[12]\\d)|[469]-(0[1-9]|30|[12]\\d))|1(1-(0[1-9]|30|[12]\\d)|[02]-(0[1-9]|3[01]|[12]\\d)))|[26]-(0(2-(0[1-9]|[12]\\d)|[13578]-(0[1-9]|3[01]|[12]\\d)|[469]-(0[1-9]|30|[12]\\d))|1(1-(0[1-9]|30|[12]\\d)|[02]-(0[1-9]|3[01]|[12]\\d))))|[2468]([048]-(0(2-(0[1-9]|[12]\\d)|[13578]-(0[1-9]|3[01]|[12]\\d)|[469]-(0[1-9]|30|[12]\\d))|1(1-(0[1-9]|30|[12]\\d)|[02]-(0[1-9]|3[01]|[12]\\d)))|[1235679]-(0(2-(0[1-9]|1\\d|2[0-8])|[13578]-(0[1-9]|3[01]|[12]\\d)|[469]-(0[1-9]|30|[12]\\d))|1(1-(0[1-9]|30|[12]\\d)|[02]-(0[1-9]|3[01]|[12]\\d)))))|[1235679](0([0-35679]-(0(2-(0[1-9]|1\\d|2[0-8])|[13578]-(0[1-9]|3[01]|[12]\\d)|[469]-(0[1-9]|30|[12]\\d))|1(1-(0[1-9]|30

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2024-01-19T13:51:10.557605895-08:00 (RayWorkerVllm pid=3381) (28750, 28725, 28770, 28725, 28782, 28725, 28787, 28725, 28740, 28740, 28725, 28740, 28770, 28725, 28740, 28787, 28725, 28740, 28774, 28725, 28750, 28770, 28725, 28750, 28774, 13, 13, 13, 13)
2024-01-19T13:51:10.557632935-08:00 (RayWorkerVllm pid=3381) (28750, 28725, 28770, 28725, 28782, 28725, 28787, 28725, 28740, 28740, 28725, 28740, 28770, 28725, 28740, 28787, 28725, 28740, 28774, 28725, 28750, 28770, 28725, 28750, 28774, 13, 13, 13)
2024-01-19T13:51:10.557642055-08:00 (RayWorkerVllm pid=3381) (28750, 28725, 28770, 28725, 28782, 28725, 28787, 28725, 28740, 28740, 28725, 28740, 28770, 28725, 28740, 28787, 28725, 28740, 28774, 28725, 28750, 28770, 28725, 28750, 28774, 13, 13)
2024-01-19T13:51:10.557645405-08:00 (RayWorkerVllm pid=3381) (28750, 28725, 28770, 28725, 28782, 28725, 28787, 28725, 28740, 28740, 28725, 28740, 28770, 28725, 28740, 28787, 28725, 28740, 28774, 28725, 28750, 28770, 28725, 28750, 28774, 13)
2024-01-19T13:51:10.557681445-08:00 

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import ray
import torch
import time
import random
import numpy as np
import os

# Initialize Ray
ray.init()

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import urllib.request
import json


def bits_to_gb(bits):
    return bits / (8 * 1024**3)


def calculate_train_vram_requirements(
        batch_size, seq_len, params, precision, num_layers, num_attn_heads, hidden_size, **ignored

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        libfreenect2 = with pkgs; stdenv.mkDerivation rec {
          pname = "freenect2";
          version = "0.2.1";

          src = fetchFromGitHub {
            owner = "OpenKinect";
            repo = "libfreenect2";
            rev = "v${version}";
            sha256 = "sha256-v+NQiR9LTQOwr1kgVpGmFSSemiPw4rmdQE/B6ycoLpU=";
          };