andrewnc

def soup_two_models(model, second_model):
    souped_model = copy.deepcopy(model)
    for param in souped_model.named_parameters():
        name = param[0]
        param[1].data = (model.state_dict()[name] + second_model.state_dict()[name]) / 2

    return souped_model

andrewnc

import os
import sys
import subprocess
import tempfile
import shutil

def main():
    if len(sys.argv) != 2:
        print("Usage: python script.py <github_repo_url>")
        sys.exit(1)

andrewnc

#!/bin/bash
# ollama run llama3.2:1b
# chmod +x talk.sh
# ./talk.sh "Your question here"

# Check if a question is passed as an argument
if [ -z "$1" ]; then
  echo "Usage: ./talk.sh 'Your question here'"
  exit 1
fi

andrewnc

function theme_precmd {
  local TERMWIDTH=$(( COLUMNS - ${ZLE_RPROMPT_INDENT:-1} ))

  PR_FILLBAR=""
  PR_PWDLEN=""

  local promptsize=${#${(%):---(%n@%m:%l)---()--}}
  local rubypromptsize=${#${(%)$(ruby_prompt_info)}}
  local pwdsize=${#${(%):-%~}}
  local venvpromptsize=$((${#$(virtualenv_prompt_info)}))

andrewnc

import math
import torch
from torch.optim.lr_scheduler import _LRScheduler
from dataclasses import dataclass
from typing import List

@dataclass
class SchedulePhase:
    """Defines a phase in the learning rate schedule"""
    percent: float  # Percentage of total steps this phase covers

andrewnc

def get_GPU_usage():
    cmd = "nvidia-smi --query-gpu=utilization.gpu --format=csv,noheader,nounits"
    result = subprocess.check_output(cmd, shell=True).decode('utf-8')
    usages = list(map(int, result.strip().split('\n')))
    return usages

andrewnc

{
  "cycles": [
    {
      "start_time_min": 0,
      "end_time_min": 3,
      "temperature_fahrenheit": 70.58999633789062
    },
    {
      "start_time_min": 3,
      "end_time_min": 6,