Yachint

import csv
import matplotlib.pyplot as plt

def read_csv(path="sumprod_bench.csv"):
    n = []
    zip_s = []
    sumprod_s = []
    speedup = []

    with open(path, newline="") as f:

Yachint

import csv
import math
import random
import time
from statistics import median

def dot_zip(p, q):
    return sum(a * b for a, b in zip(p, q))

def dot_sumprod(p, q):

Yachint

import logging
logger = logging.getLogger('MathApp')


def divide_numbers(a, b):
    try:
        logger.debug(f"Params provided a: {a}, b: {b}")
        result = a / b
        logger.info(f"Divide of {a}/{b} success => {result}")

Yachint

Mechanistic Interpretability Explanation

Created: 18/8/2025, 10:54:29 AM Last Updated: 18/8/2025, 10:54:31 AM

User

Explain mechanistic interpretability in LLM's in a simple way with easy to understand examples

Yachint

Mechanistic Interpretability Explanation

Created: 18/8/2025, 10:54:50 AM Last Updated: 18/8/2025, 10:54:52 AM

User

Explain mechanistic interpretability in LLM's in a simple way with easy to understand examples