Transformer Model

Self Attention is the core feature

Transformer is the first thing that actually scales. Before the Transformer, RNN such as LSTEM and stacking them does not get clean scaling.

The Transformer gains a wider perspective and can attend to multiple interaction levels within the input sentence.

CNN

RNN 과 달리 거리가 먼

Long-term dependency 개선 한 게 큰 의의이다. Transformer Model is not just proficient in Language modeling but also versatile token sequence model with broader application across domains.

모든 토큰을 동시에 받아 연산하기 때문에 병렬연산이 가능하고, 논문은 기존의 Attention Mechanism과는 다르게 각 벡터가 모두 가중치 벡터로 사용

이 논문 이후 대세의 변경점은

Layer Normalization의 적용위치나 RMS Normalization로 대체, 그리고 FFN activation 으로 GLU가 사용 등이 있다.

Transformer Model Notion

Attention is all you need

Transformer Model Encoder

Transformer Model Decoder

Transformer Attention

Transformer Model Tool

One Wide Feedforward is All You Need

Transformer Inference

Transformer Models

Transformer-XL

Architecture

arxiv.org

https://arxiv.org/pdf/1706.03762.pdf

hackerllama - The Random Transformer

Understand how transformers work by demystifying all the math behind them

https://osanseviero.github.io/hackerllama/blog/posts/random_transformer/

Transformer’s Encoder-Decoder: Let’s Understand The Model Architecture - KiKaBeN

In 2017, Vaswani et al. published a paper titled “Attention Is All You Need” for the NeurIPS conference. They introduced the original transformer architecture for machine translation, performing better and faster than RNN encoder-decoder models, which were mainstream. The transformer architecture is the basis for recent well-known models like BERT and GPT-3. Researchers have already

https://kikaben.com/transformers-encoder-decoder/

Maxime Labonne - Decoding Strategies in Large Language Models

A Guide to Text Generation From Beam Search to Nucleus Sampling

https://mlabonne.github.io/blog/posts/2022-06-07-Decoding_strategies.html

Transformer Details Not Described in The Paper

Since I’m a complete newbie of the deep learning field, I was looking for something to learn as afirst step. Then, I heard that Transformer-based models are ...

https://tunz.kr/post/4

Pseudo Source code

Transformers for software engineers

Ever since its introduction in the 2017 paper, Attention is All You Need, the Transformer model architecture has taken the deep-learning world by storm. Initially introduced for machine translation, it has become the tool of choice for a wide range of domains, including text, audio, video, and others.

https://blog.nelhage.com/post/transformers-for-software-engineers/

But what is a GPT? Visual intro to Transformers | Deep learning, chapter 5

An introduction to transformers and their prerequisites Early view of the next chapter for patrons: https://3b1b.co/early-attention Special thanks to these supporters: https://3b1b.co/lessons/gpt#thanks To contribute edits to the subtitles, visit https://translate.3blue1brown.com/ Other recommended resources on the topic. Richard Turner's introduction is one of the best starting places: https://arxiv.org/pdf/2304.10557.pdf Coding a GPT with Andrej Karpathy https://youtu.be/kCc8FmEb1nY Introduction to self-attention by John Hewitt https://web.stanford.edu/class/cs224n/readings/cs224n-self-attention-transformers-2023_draft.pdf History of language models by Brit Cruise: https://youtu.be/OFS90-FX6pg ------------------ Timestamps 0:00 - Predict, sample, repeat 3:03 - Inside a transformer 6:36 - Chapter layout 7:20 - The premise of Deep Learning 12:27 - Word embeddings 18:25 - Embeddings beyond words 20:22 - Unembedding 22:22 - Softmax with temperature 26:03 - Up next ------------------ These animations are largely made using a custom Python library, manim. See the FAQ comments here: https://3b1b.co/faq#manim https://github.com/3b1b/manim https://github.com/ManimCommunity/manim/ All code for specific videos is visible here: https://github.com/3b1b/videos/ The music is by Vincent Rubinetti. https://www.vincentrubinetti.com https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown https://open.spotify.com/album/1dVyjwS8FBqXhRunaG5W5u ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly. Mailing list: https://3blue1brown.substack.com Twitter: https://twitter.com/3blue1brown Instagram: https://www.instagram.com/3blue1brown Reddit: https://www.reddit.com/r/3blue1brown Facebook: https://www.facebook.com/3blue1brown Patreon: https://patreon.com/3blue1brown Website: https://www.3blue1brown.com

https://www.youtube.com/watch?v=wjZofJX0v4M

Transformer Explainer

Transformer is a neural network architecture that has fundamentally changed the approach to Artificial Intelligence. Transformer was first introduced in the seminal paper "Attention is All You Need" in 2017 and has since become the go-to architecture for deep learning models, powering text-generative models like OpenAI's GPT, Meta's Llama, and Google's Gemini. Beyond text, Transformer is also applied in audio generation, image recognition, protein structure prediction, and even game playing, demonstrating its versatility across numerous domains.

https://poloclub.github.io/transformer-explainer/

Transformer Model

Self Attention is the core feature

Architecture

Pseudo Source code

Recommendations