"Almost everyone has heard about large language models, and tens of millions of people have tried out OpenAI ChatGPT and Google Bard. However, the intricate architecture and underlying mathematics driving these remarkable systems remain elusive to many. LLM's are fascinating - so let's grab a drink and find out how these systems are built and dive deep into their inner workings. In the length of time it to enjoy a round of drinks, you'll understand the inner workings of these models. We'll take our first sip of word vectors, enjoy the refreshing taste of the transformer, and drain a glass understanding how these models are trained on phenomenally large quantities of data. Large language models for your streaming application - explained with a little maths and a lot of pub stories"

1. Explaining how GenAI works
( in a noisy pub )
Simon Aubury
@SimonAubury

2. GenAI
You may have heard of it?
2

3. Training
Pre training & fine tuning
Large language models - 3 things …
Data
A bunch of text
Architecture
The transformer
3

4. 1. Data
Let’s compress the internet

5. 5
“Beer”
Image by Republica from Pixabay
WebVectors Online for "beer"

6. Image by Christian_Birkholz from Pixabay

7. 2. Architecture
The transformer

8. Image by delo from Pixabay

9. Understanding language can be challenging
9
The group of friends bought cold beer at the
local pub to celebrate their reunion.
context
context

10. Transformers
10
Source: Attention Is All You Need https://arxiv.org/abs/1706.03762

11. Self-attention
11
Inspired by DeepLearning.AI
The
group
of
friends
bought
cold
beer
The
group
of
friends
bought
cold
beer

12. A transformer + data
= a base model
12
The base model
Data
Transformer
Base model

13. Completion
Base model
13
Completions
Beer
is
served
best
cold
with
friends
Context
Base model + context
= completion

14. 3. Training
Pre training & fine-tuning

15. 15
How to train your LLM
Pretraining (once)
1. PB’s of text.
2. 1000’s of GPUs.
3. Compress the text into
a neural network,
4. Pay 💰 wait 📆
➡ Obtain base model.
Fine-tuning (recurring)
1. 1000’s ideal Q&A
responses (human)
2. Finetune base model
on this data wait 📆
3. Obtain assistant model
4. Evaluate, deploy &
monitor

16. 󰜄 It is faster to pick than to generate.
16
Reinforcement Learning from Human Feedback
(RLHF)
�� ��
��

17. Training
Pre training & fine tuning
Large language models
Data
A bunch of text
Architecture
The transformer
17

18. 18
Retrieval Augmented Generation (RAG)
What drink should
I order?

19. “ The entire history of
software engineering
is one of rising levels
of abstraction.
Grady Booch, IBM chief scientist
19

20. 20
Thanks!
@SimonAubury
❤Presentation template by SlidesCarnival, Icons Flaticon photos Unsplash and Pixabay