SlideShare a Scribd company logo
2023
10-26
ALLAN C. TAN
Agenda
04
Now, lets go through actual
programming of extending GPT
using Streamlit and Langchain.
02
TransformerModel
How does GPT work and what
makes iit work?
Hands-OnCoding
03
What are the different
approaches to extend GPT for
different use cases.
01
ABriefHistory
Let’s talk about how it all
started and GPT evolves.
GPTArchitectures
ABriefHistory
Origins date back in 60s
Evolution of neural network
POS-Tagger (2011, C.Manning, etc.)
Transformer Model (2017, Viswani, etc)
ChatGPT (Nov 2022, OpenAI)
PREDICTIVE
SYSTEMS
page - 3
WordPredictions
DataDriven
PREDICTIVE
SYSTEMS
The bank interest increase this year.
I have no interest in buying your car.
Butlanguageiscomplex
Happy, not sad.
Sad, not happy.
I have no interest in buying a new car because
the interest rate is high.
Transformer
Model
Positional Encoding
The bank interest increase this year.
interest
I have no interest in buying your car.
interest
interest
Attention
Tokens
Embeddings
Raw Data Pre-training Model
(Embeddings)
Inference Response
User Query
TrainingvsInference
Approaches
03
SupervisedFineTuning
Updating the base model to include
proprietary corpus and examples.
02
RetrievalAugmented
Generation
Saving embeddings of large
documents and searching them for
retrieval and generation.
01
PromptEngineering
Issuing different prompts (text) to
GPT to generate the desired result.
04
ReinforcedLearning
fromHumanFeedback
Optimization of weights by giving
rewards based on human feedback
and updating the model.
Winrate
https://www.arxiv-vanity.com/papers/2203.02155/
GPT-3 + fine-tuning
GPT-3 + prompting
GPT-3
GPT-3 + fine-tuning + RLHF
RetrievalAugmentedGeneration
Vector DB
Tokens & Embeddings
Process Private / Large
Documents
User Query Similarity Search Retrieved Context
Inference
Response
Letsgetinto
Action!
Enough Talk...

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Extending GPT