uchidama

import keras
from keras.datasets import mnist

import numpy as np
from PIL import Image, ImageOps
import os

def save_image(filename, data_array):
    im = Image.fromarray(data_array.astype('uint8'))
    im_invert = ImageOps.invert(im)

wingrime

import mmh3
import pandas as pd
def get_positive_hash(x):
    s =   " ".join(get_unique_tokens(x))
    return mmh3.hash(s) % 2**31

df['group_id'] = df['query_string'].apply(get_positive_hash )


query_groups = df.groupby("group_id")

aditya-malte

jeethu

import functools

import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.table as table
import numpy as np
import pandas as pd
from scipy.stats import spearmanr

TOURNAMENT_NAME = "kazutsugi"

the-moliver

NaxAlpha

import time
from contextlib import suppress

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cuda as cuda
from torch.utils.data import DataLoader, IterableDataset

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

lucahammer

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// @version      0.7.0
// @description  Delete all your Tweets for free.
// @author       Luca,dbort,pReya,Micolithe,STrRedWolf
// @license      NoHarm-draft
// @match        https://x.com/*
// @icon         https://www.google.com/s2/favicons?domain=twitter.com
// @grant        unsafeWindow