Transformer Architectures and the Future of LLMs
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.
Like Duolingo, but for Transformer Architectures and the Future of LLMs. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
64 levels across 9 sections, about 128 minutes end to end, roughly 26 days at five minutes a day. It moves through The Transformer Core: Advanced Mechanics, Efficiency and the Memory Wall, Beyond Quadratic Complexity, Dynamic Computation and Sparsity, The Long Context Frontier, Reasoning and System 2 Thinking, Multimodality and World Models, Post-Transformer Frontiers, and The Future of AI Development.
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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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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.
Here's what playing it feels like
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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How to learn Transformer Architectures and the Future of LLMs, step by step
Go in order. 64 short levels across 9 sections, about 2 hours 8 minutes in all, five minutes a day.
- 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.
Questions about Transformer Architectures and the Future of LLMs
How do I learn Transformer Architectures and the Future of LLMs?
Go in order: start with The Transformer Core: Advanced Mechanics, then Efficiency and the Memory Wall, then Beyond Quadratic Complexity, then Dynamic Computation and Sparsity, and 5 more sections after that. On Tomo that's 64 short levels, about 2 hours 8 minutes in all, played five minutes a day. Start free and play the first level in under a minute.
Is there a Duolingo-style app for Transformer Architectures and the Future of LLMs?
Yes. Tomo works like Duolingo: short daily lessons, streaks and quick quizzes. Transformer Architectures and the Future of LLMs is one of its free courses, and you can type any other topic to get a course of your own.
Is the Transformer Architectures and the Future of LLMs course free?
Yes. Transformer Architectures and the Future of LLMs is playable on Tomo's free plan, which doesn't expire. No credit card, no waitlist. Download the app and start your first lesson.
How long does it take to learn Transformer Architectures and the Future of LLMs?
64 levels across 9 sections, about 2 hours 8 minutes of play in total. It's built for five minutes a day, so you finish it a little at a time instead of all at once.
Do I need any background to start Transformer Architectures and the Future of LLMs?
No. It's pitched at an advanced level, so it starts where you are and builds up. If you can tap a phone, you can start.
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