Vinicius viniciusarruda

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

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	#
	# this is adapted from https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L213
	# the tokenizer is replaced with ord() to make it easier to see whats actually happening

	from typing_extensions import TypedDict, Literal
	from typing import List, Optional

	Role = Literal["system", "user", "assistant"]

	class Message(TypedDict):

	import baker
	import json
	import os
	from cytoolz import merge, join, groupby
	from cytoolz.compatibility import iteritems
	from cytoolz.curried import update_in
	from itertools import starmap
	from collections import deque
	from lxml import etree, objectify
	from scipy.io import savemat

	"""
	this code is not mine! i shamelessly copied it from http://stackoverflow.com/questions/19790188/expanding-english-language-contractions-in-python
	all credits go to alko and arturomp @ stack overflow.

	basically, it's a big find/replace.
	"""

	import re
	cList = {
	"ain't": "am not",