Transformer Architectures and the Future of LLMs
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Key ideas in Transformer Architectures and the Future of LLMs
- The residual stream is an additive vector space
- Early layer features remain accessible via identity path
- The stream acts as an asynchronous bulletin board
- Attention heads move existing features
- QK is decoupled from Value
- Information movement handles sequence-level context
- The residual stream's role as a persistent, additive communication channel
- The specific mechanism of information transport handled by attention heads
- MLPs operate on tokens in isolation (position-wise), meaning they cannot move information across the sequence
- Additive residual updates cause the variance of the stream to grow with depth, potentially leading to activation saturation
- MLPs function as key-value memories where the first linear layer 'keys' into specific patterns and the second 'values' provides the update
- The residual stream's role as a persistent, additive communication channel rather than a sequential processor
- LayerNorm acts as a gain control that re-scales the 'volume' of the signal to a fixed range for the next layer's circuitry
- The MLP's role is to refine or expand the internal representation of a token based on its current features
- Normalization ensures that the model can remain sensitive to small updates even after hundreds of previous additions
- The Logit Lens applies the final unembedding matrix to intermediate residual states
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You watched. You nodded. By Sunday it was gone.
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Eight weeks. You meant to finish. You didn't.
Tomo gives Transformer Architectures and the Future of LLMs the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
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A real question from this course. Take your best guess.
How do features from the very first layer manage to survive all the way to the end of a deep model?
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Where Transformer Architectures and the Future of LLMs takes you
Master the intricate mechanics of modern large language models and explore the frontier of post-transformer architectures, from State Space Models to neuro-symbolic reasoning.
- 1
The Transformer Core: Advanced Mechanics
- The Residual Stream as a Communication Channel
- Scaling Laws and Chinchilla Optimality
- 2
Efficiency and the Memory Wall
- KV Cache Management and PagedAttention
- IO-Awareness with FlashAttention
- Model Distillation and Quantization
- 3
Beyond Quadratic Complexity
- Linear Transformers and Kernel Tricks
- State Space Models (SSMs) and Mamba
- RWKV and Receptance-Weighted RNNs
- 4
Dynamic Computation and Sparsity
- Mixture of Experts (MoE) Architectures
- Conditional Computation and Early Exiting
- 5
The Long Context Frontier
- Positional Encoding Evolution
- Retrieval-Augmented Generation (RAG) at Scale
- 6
Reasoning and System 2 Thinking
- Chain of Thought and Self-Correction
- Search-Based Inference (Q* and Beyond)
- 7
Multimodality and World Models
- Native Multimodality vs. Adapters
- JEPA and Predictive World Models
- 8
Post-Transformer Frontiers
- Liquid Neural Networks
- Neuro-symbolic Integration
- Energy-Based Models and Spiking Neural Networks
- 9
The Future of AI Development
- Hardware-Software Co-design
- The Path to AGI: Agentic Workflows
9 sections · 21 units · 64 levels. Built to play, not to enroll.
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