Phate334

services:
  llm:
    image: ghcr.io/ggml-org/llama.cpp:server-cuda-b5391
    ports:
      - "7071:8080"
    volumes:
      - ./models:/models
    env_file:
      - llm.env
    deploy:

Phate334

#!/bin/bash
set -e
set -u

if [ $# -lt 1 ]; then
    echo "Usage: $0 BASE_MODEL [DRAFT_MODEL] [--skip-base] [--skip-draft] [--skip-sd]"
    exit 1
fi

BASE_MODEL="$1"

Phate334

#!/bin/bash
set -euo pipefail

# Function to get Docker API version
get_docker_api_version() {
  docker version --format '{{.Server.APIVersion}}'
}

# Function to get volume paths from docker compose and backup
backup_volumes() {

Phate334

import os
import sys
import uuid
import time
import math
import multiprocessing
from abc import ABC, abstractmethod
from typing import (
    List,
    Optional,

Phate334

Install

!pip install faster-whisper
!pip install googletrans==3.1.0a0
!pip install pysubs2

Phate334

{
  "$schema": "https://raw.githubusercontent.com/JanDeDobbeleer/oh-my-posh/main/themes/schema.json",
  "blocks": [
    {
      "alignment": "left",
      "segments": [
        {
          "background": "#2f2f2f",
          "foreground": "#26C6DA",
          "leading_diamond": "\ue0c7\u2593",

Phate334

version: "3"

services:
  janusgraph:
    image: janusgraph/janusgraph:0.6.2
    container_name: jce-janusgraph
    env_file:
      - janusgraph.env
    ports:
      - "8182:8182"

Phate334

javascript: (() => {
    const blocked = new Set(['fbclid', 'utm_source', 'utm_medium', 'utm_campaign', 'ref_src', 'ref_url']);
    let url = new URL(document.URL);
    let params = new URLSearchParams(url.searchParams);
    for (let key of params.keys()) {
        if (blocked.has(key)) {
            url.searchParams.delete(key);
        }
    }
    let result = `${url.origin}${url.pathname}${url.searchParams.toString() ? '?' + url.searchParams.toString() : ''}${url.hash}`;

Phate334

javascript: (() => {
    const vid = new URL(document.URL).searchParams.get('v');
    navigator.clipboard.writeText("https://youtu.be/" + vid);
})();

Phate334

from pydantic import BaseModel


def func_helper(parameter_type, return_type=None):
    def decorator(function):
        def wrapper(*args, **kwargs):
            result = function(parameter_type(**kwargs))
            return return_type(**result) if return_type else result

        return wrapper