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

sgillies

"""Concurrent read-process-write example"""

import concurrent.futures
from itertools import islice
from time import sleep

import rasterio


CHUNK = 100

alsrgv

# Copyright 2018 Uber Technologies, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

eashish93

vs Questions

---

1. http://stackoverflow.com/questions/804115 (rebase vs merge).
2. https://www.atlassian.com/git/tutorials/merging-vs-rebasing (rebase vs merge)
3. https://www.atlassian.com/git/tutorials/undoing-changes/ (reset vs checkout vs revert)
4. http://stackoverflow.com/questions/2221658 (HEAD^ vs HEAD~) (See git rev-parse)
5. http://stackoverflow.com/questions/292357 (pull vs fetch)
6. http://stackoverflow.com/questions/39651 (stash vs branch)
7. http://stackoverflow.com/questions/8358035 (reset vs checkout vs revert)

kevinzakka

"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.

[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

kmurugulla

#! /bin/bash
#Author : Kiran Murugulla
#Description : Script to create an asset and upload binary from local machine into an S3 bucket

usage="Usage: s3-cp-speed-test.sh bucketname filepath \n"
BUCKET=$1
FILE=$2
if [ ! $# -eq 2 ] ; then
echo -e "$usage"
exit 2

panovr

import argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim

varver

############ REQUIREMENTS ####################
# sudo apt-get install python-pip 
# sudo apt-get install libpq-dev
# sudo pip install psycopg2
# sudo pip install sqlalchemy
# sudo pip install sqlalchemy-redshift
##############################################

import sqlalchemy as sa
from sqlalchemy.orm import sessionmaker

jaron

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import SGD
from keras.regularizers import l2, activity_l2
from keras.utils import np_utils
from sklearn import metrics 

# to run this code, you'll need to load the following data: 

criccomini

import os
import unittest

from airflow.models import DagBag

class TestDags(unittest.TestCase):
    """
    Generic tests that all DAGs in the repository should be able to pass.
    """