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

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 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

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

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

# 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