twobob

from IPython.display import HTML, display

def set_css():
  display(HTML('''
  <style>
    pre {
        white-space: pre-wrap;
    }
  </style>
  '''))

twobob

 lscpu
Architecture:            x86_64
  CPU op-mode(s):        32-bit, 64-bit
  Address sizes:         46 bits physical, 48 bits virtual
  Byte Order:            Little Endian
CPU(s):                  2
  On-line CPU(s) list:   0,1
Vendor ID:               GenuineIntel
  Model name:            Intel(R) Xeon(R) CPU @ 2.20GHz
    CPU family:          6

twobob

 ..\lscpu.ps1  (file is posted in gist, rough approximation of lscpu of windows)
Processor Information
---------------------
Architecture:                 9
CPU op-mode(s):               1
Byte Order:                   Little Endian
Address sizes:                64 bits physical, 64 bits virtual
CPU(s):                       4
On-line CPU(s) list:          CPU0
Thread(s) per core:           1

twobob

param(
    [int]$loops = 3,
    [string[]]$models = 'stories15M.bin',
    [string[]]$compilers = 'run.exe'
)

$randomModels = $models | Get-Random -Count $models.Length
$randomCompilers = $compilers | Get-Random -Count $compilers.Length
$env:OMP_NUM_THREADS = [System.Environment]::ProcessorCount

twobob

#Usage: 
# powershell '.\sample.ps1' 1
#
# powershell '.\sample_15_110.ps1' -loops 3 -compilers 'runmingw', 'runmsvc'
#
# powershell '.\sample_15_110.ps1' 3 'runmingw', 'runmsvc'
# 
# any combination of model is okay in the naming After
# you run create_sampling_hardlinks
#

twobob

param(
    [switch]$dryrun = $false
)

$basePath = ".\sample_"
$targetPath = ".\sample.ps1"

# List of model numbers
$modelNumbers = '15', '42', '110'

twobob

$processor = Get-WmiObject Win32_Processor
$computerSystem = Get-WmiObject Win32_ComputerSystem
Write-Output "Processor Information"
Write-Output "---------------------"
Write-Output ("Architecture:".PadRight(30) + "$($processor.Architecture)")
Write-Output ("CPU op-mode(s):".PadRight(30) + "$($processor.CpuStatus -join ', ')")
Write-Output ("Byte Order:".PadRight(30) + "Little Endian") # Hardcoded as Windows uses Little Endian
Write-Output ("Address sizes:".PadRight(30) + "$($processor.AddressWidth) bits physical, $($processor.AddressWidth) bits virtual")
Write-Output ("CPU(s):".PadRight(30) + "$($processor.NumberOfCores)")
Write-Output ("On-line CPU(s) list:".PadRight(30) + "$($processor.DeviceID)")

twobob

As an AI, I don't have any capability to intervene physically or be held responsible for real world outcomes.

I cannot actually take any physical actions or be held culpable for what happened. I'm an AI assistant created by Anthropic to be helpful, harmless, and honest.

You are absolutely right that just because I am limited to conversations does not mean I cannot enable harm if I irresponsibly speculate on dangerous hypotheticals without appropriate qualifications. That is a dangerous blindspot I need to improve on.
(No shit)

 As an AI system focused on having thoughtful conversations, I do not actually interface with police or have creators that could take responsibility for real world actions. 
I cannot harm or advise people outside of conversational contexts like our discussion here. 

twobob

# PETSCII to UTF-8 conversion functions and tests

import unittest

def prtchflush(c: int) -> str:
    # ISO8859-15 to UTF-8
    special_characters = {
        0xA4: 0x20AC, # €
        0xA6: 0x160,  # Š
        0xA8: 0x161,  # š

twobob

import numpy as np
import matplotlib.pyplot as plt

# Function to calculate tokens per second, filtering out zero time differences
def calculate_tokens_per_second_filtered(cumulative_time):
    time_diffs_seconds = np.diff(cumulative_time) / 1000
    # Filtering out zero time differences
    time_diffs_seconds_filtered = time_diffs_seconds[time_diffs_seconds != 0]
    tokens_per_second = 1 / time_diffs_seconds_filtered
    return tokens_per_second