data2json

# Step 1: Install necessary libraries
!pip install dask dask_ml

# Step 2: Import required libraries
import dask.dataframe as dd
import dask.array as da
from dask_ml.impute import SimpleImputer
from dask_ml.preprocessing import StandardScaler
from dask_ml.decomposition import PCA
from dask_ml.feature_selection import SelectKBest, f_classif

data2json

version: "3"
services:
    # configuration manager for NiFi
    zookeeper:
        hostname: myzookeeper
        container_name: zookeeper_container_persistent
        image: 'bitnami/zookeeper:3.7.0'  # latest image as of 2021-11-09.
        restart: on-failure
        environment:
            - ALLOW_ANONYMOUS_LOGIN=yes

data2json

version: "3"
services:
    # configuration manager for NiFi
    zookeeper:
        hostname: myzookeeper
        container_name: zookeeper_container_persistent
        image: 'bitnami/zookeeper:3.7.0'  # latest image as of 2021-11-09.
        restart: on-failure
        environment:
            - ALLOW_ANONYMOUS_LOGIN=yes

data2json

import torch

def test_gpu(gpu_id):
    try:
        # Set the device to the specified GPU
        device = torch.device(f"cuda:{gpu_id}")
        
        # Create a random tensor on the GPU
        tensor = torch.randn(1024, 1024, device=device)
        

data2json

import os
import asyncio
import aiohttp
import json
import logging
from threading import Lock

# Logging setup (for better debugging)
logging.basicConfig(
    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"

data2json

Better & Faster Large Language Models via Multi-Token Prediction

A recent paper titled "Better & Faster Large Language Models via Multi-token Prediction" (arXiv:2404.19737v1) introduces a simple but effective modification to the standard language modeling training loss that significantly improves performance, inference speed, and reasoning capabilities of large language models, especially for code-related tasks.

Key Findings

The authors propose training language models to predict multiple future tokens at once, using a shared model trunk and independent output heads for each future token position. This multi-token prediction approach is compared to the standard next-token prediction loss through comprehensive experiments on both synthetic and natural datasets. The key findings are summarized in the following fact table:

| Fact | Details/Context | Results/Metrics

data2json

import anthropic

client = anthropic.Anthropic(
    # defaults to os.environ.get("ANTHROPIC_API_KEY")
    api_key="sk-nope-nope-nope",
)


TASK_DESCRIPTION="Act as a writer that supplies the missing context for the given text."
# Replace placeholders like {{TASK_DESCRIPTION}} with real values,

data2json

#!/usr/bin/env python
#  t - The missing LLM token counting and splitting tool for UNIX

import argparse
import sys
from typing import Optional, List
import math
import os

import tiktoken

data2json

curl -s -X POST 'http://0.0.0.0:8000/v1/chat/completions' -H "Content-Type: application/json" -d '{
  "model": "gpt-3.5-turbo",
  "messages": [
    {
      "role": "system",
      "content": "Environment: ipython\nTools: brave_search, wolfram_alpha\n\nCutting Knowledge Date: December 2023\nToday Date: 23 Jul 2024\n\nYou are a helpful Assistant."
    },
    {
      "role": "user",
      "content": "Can you help me solve this equation: x^3 - 4x^2 + 6x - 24 = 0"

data2json

curl -X POST http://192.168.50.146:8000/v1/chat/completions \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
  "messages": [
    {
      "content": "You are a helpful assistant that can check the weather. Use the get_weather tool when asked about weather conditions. If you choose to call a function ONLY reply in the following format: <{sta
rt_tag}={function_name}>{parameters}{end_tag} where start_tag => <function parameters => a JSON dict with the function argument name as key and function argument value as value. end_tag => </function>",
      "role": "system"