Emily Ekdahl emekdahl

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

	from abc import ABC
	from typing import List, Optional, Any

	import chromadb
	from langchain.docstore.document import Document
	from langchain.embeddings.base import Embeddings
	from langchain.vectorstores import Chroma


	class CachedChroma(Chroma, ABC):

	from metaflow import FlowSpec, step, IncludeFile

	def dataset_wine():
	from sklearn import datasets
	return datasets.load_wine(return_X_y=True)

	def model_knn(train_data, train_labels):
	from sklearn.neighbors import KNeighborsClassifier
	model = KNeighborsClassifier()
	model.fit(train_data, train_labels)

	.responsive-nav-icon::before,
	.responsive-nav-close::before {
	color: #93a748;
	content: "\f0c9";
	font-family: FontAwesome;
	font-size: 22px;
	position: relative;
	}

	.responsive-nav-close::before {

	# principal amount

	p = 10_000

	# annual rate of interest
	r = 0.05

	# number of years
	t = 5

Emily Ekdahl emekdahl

Anti-hype LLM reading list

Foundational Concepts

Pre-Transformer Models