Bring everything together! See the complete Transformer architecture, explore modern LLM variants, and understand how GPT, BERT, and Claude are built.
⏱️ 12 min🎯 Lesson 11 of 11🏆 Final Lesson
Slide 1 of 7
The Complete Transformer
Let's see the full architecture we've been building piece by piece:
Input Tokens
→
Embedding + PE
→
Encoder ×N layers
→
Context Vectors
Output Tokens
→
Embedding + PE
→
Decoder ×N layers
→
Softmax → Next Token
⚡ The encoder and decoder are connected through cross-attention — the decoder's middle layer attends to the encoder's output.
Slide 2 of 7
Full Architecture Diagram
The golden arrow shows the cross-attention connection between encoder and decoder.
Slide 3 of 7
Modern Variants: Encoder-Only
BERT (and its family: RoBERTa, ALBERT) uses only the encoder. It's great for understanding text, not generating it.
📥
Input: Text (both sides of a [MASK])
⚡
Process: Bidirectional encoder — looks at all context
📤
Output: Contextual embeddings / fill-in-the-blank
BERTRoBERTaDeBERTaELECTRA
Best for
✅ Text classification
✅ Named entity recognition
✅ Question answering
✅ Semantic search
❌ Not for generation
Slide 4 of 7
Modern Variants: Decoder-Only
GPT (and Claude, Llama, Gemini) uses only the decoder with causal masking. This is the dominant architecture for modern LLMs.
📥
Input: Prompt / context
⚡
Process: Causal decoder — can only look left
📤
Output: Next token probabilities → generates text
GPT-4ClaudeLlamaGeminiMistral
Best for
✅ Text generation
✅ Chatbots & assistants
✅ Code generation
✅ Creative writing
✅ Reasoning tasks
Slide 5 of 7
How LLMs are Trained
Training a large language model happens in stages:
1
Pre-training (Next Token Prediction) Feed the model trillions of tokens from the internet. Train it to predict the next token. This is self-supervised — no human labels needed!
2
Supervised Fine-Tuning (SFT) Fine-tune on high-quality examples of instructions + good responses. The model learns to follow instructions.
3
RLHF (Reinforcement Learning from Human Feedback) Human raters rank responses. A reward model is trained. The LLM is trained to maximize the reward. Makes models helpful, harmless, and honest.
Slide 6 of 7
The Scale of Modern LLMs
Modern LLMs are mind-bogglingly large:
Model
Parameters
Context
Layers
GPT-2
1.5B
1,024
48
GPT-3
175B
4,096
96
Llama 3 70B
70B
128K
80
Claude 3
~100B+
200K
~90
GPT-4
~1.7T (est.)
128K
~120
💡 GPT-3's 175B parameters weigh about 350 GB of disk space. Training it cost ~$4.6 million in compute!
Slide 7 of 7
🎉 Congratulations! You Made It!
You now understand the complete Transformer architecture! Let's recap everything you've learned:
✓
Tokenization — Text → Token IDs
✓
Embeddings — Token IDs → Meaning vectors
✓
Positional Encoding — Adding order awareness
✓
Self-Attention — Every token attends to every other
✓
Multi-Head Attention — Parallel attention heads
✓
Encoder — Understanding input deeply
✓
Decoder — Auto-regressive generation
🚀 You now understand the technology powering ChatGPT, Claude, Gemini, and every other modern AI language model. Incredible!
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🏛️
The Complete Picture
A Transformer is like a sophisticated translation chamber: input enters the encoder, which builds a deep understanding. The decoder then uses that understanding alongside what it's already written to produce the next word, one at a time — guided by 6 (or 96) layers of multi-head attention, residual connections, and feed-forward networks.
🏗️ Full Architecture Overview
The complete Encoder-Decoder Transformer. Encoder on the left, Decoder on the right, connected by cross-attention.
The Family of Transformers
🔍
Encoder-Only (BERT)
Bidirectional understanding. Best for classification, NER, search. Not for generation.
✍️
Decoder-Only (GPT)
Auto-regressive generation. Powers ChatGPT, Claude, Llama. The dominant architecture today.
🔄
Encoder-Decoder (T5)
Original architecture. Best for seq2seq tasks: translation, summarization. Used by T5, BART.
🚀
Scaling Laws
More parameters + more data = better performance. This discovery drove the LLM revolution.
Final Challenge
GPT-4, Claude, and Llama are all examples of which Transformer variant?
A
Encoder-only (like BERT)
B
Full Encoder-Decoder (like the original Transformer)
C
Decoder-only with causal (masked) self-attention
D
RNN with attention
🎓
Course Complete!
You've completed all 11 lessons and now have a solid understanding of how Transformers work. The technology behind every major LLM.