Which Open-Source LLM Should You Choose in 2024?

Author(s): Dr. Leon Eversberg

Originally published on Towards AI.

LLMs are evolving at a rapid speed. Photo by Johannes Plenio on Unsplash

Since the 2017 paper “Attention Is All You Need” invented the Transformer architecture, natural language processing (NLP) has seen tremendous growth. And with the release of ChatGPT in November 2022, large language models (LLMs) has captured everyone’s interest.

Do you want to use LLMs for your own use case but not pay for every prompt? This article will help you understand the current state of LLMs in 2024. It will also help you decide which open-source model to choose for your own use case.

Without going into too much detail, the original Transformer architecture is divided into two interconnected parts: an encoder on the left and a decoder on the right.

The encoder’s job is to encode an input word into a deep vector representation. The decoder’s job is to generate new words.

The originally published Transformer architecture by Vaswani et al. [1]

First, an input sentence must be tokenized; that is, words (strings) must be mapped to tokens (numbers). For example, the word “the” can be mapped to the token 342.

The tokens are then converted into high-dimensional embedding vectors. Similar word embeddings are close to each other in this high-dimensional vector space…. Read the full blog for free on Medium.

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Published via Towards AI