djinn

#include <iostream>
#include <chrono>
#include <vector>
#include <random>
#include <iomanip>
#include <cmath>
#include <cassert>

// E4M3 FP8 Format (NVIDIA standard)
// 1 bit sign, 4 bits exponent, 3 bits mantissa

djinn

import time
from vllm import LLM, SamplingParams
import lmdeploy

# Test prompts for real-world language use cases
PROMPTS = [
    "Explain quantum computing in simple terms.",
    "What is the future of renewable energy?",
    "Describe the Great Wall of China.",
    "Why is the sky blue?",

djinn

#!/usr/bin/env python3
import base64, time, sys

encoded_resume = b"""
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
"""

banner = r"""
   ____                              _   
  / ___|___  _ __ ___  _ __ ___   ___| |_ 

djinn

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

model_name_or_path = "IntervitensInc/pangu-pro-moe-model"

generation_config = GenerationConfig(
    do_sample=True,
    top_k=50,
    top_p=0.95,
    temperature=0.6

djinn

const {
  Worker,
  isMainThread,
  parentPort,
  workerData,
  MessageChannel,
} = require("node:worker_threads");
const http = require("node:http");

if (isMainThread) {

djinn

import matplotlib.pyplot as plt
import pandas as pd
import re

def parse_gc_log(gc_log):
    # Split the input log by lines
    lines = gc_log.split("\n")

    # Prepare lists for each graph's data
    gc_timing = []

djinn

#!/bin/sh

lsb_release -a
add-apt-repository ppa:deadsnakes/ppa --yes
apt install python3.11 python3.11-venv python3.11-dev
python3.11 -m venv env
source env/bin/activate
pip install packaging wheel
pip install https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl#sha256=d4c3e9a8d31a7c0fcbb9da17c31a1917e1fac26c566a4cfbd8c9568ad7cade79
pip install  --no-build-isolation 'instructlab[cuda]' -C cmake.args="-DLLAMA_CUDA=on" -C cmake.args="-DLLAMA_NATIVE=off"

djinn

const users = [
  { id: 'user_111', email: '[email protected]' },
  { id: 'user_222', email: '[email protected]' },
  { id: 'user_333', email: '[email protected]' },
]
// Wait for all the get operations to complete and store the results.
const getResults = await Promise.all(
  users.map((user) => {
    console.log(`Getting document: ${user.id}`)
    return usersCollection.get(user.id, user)

djinn

import time
import random
from pymongo import MongoClient
from couchbase.cluster import Cluster
from couchbase.management.buckets import BucketManager
from couchbase.options import ClusterOptions
from couchbase.auth import PasswordAuthenticator
import os
import string
from tqdm import tqdm

djinn

#include <iostream>
#include <vector>
#include <random>
#include <algorithm>
#include <numeric>
#include <thread>
#include <mutex>

const int num_simulations = 10000; // Number of Monte Carlo simulations
const int num_days = 252;          // Number of trading days in a year