Prerequisites for LLaMA and Open Weight Models

Direct prerequisites (7)

1. Transformer Architecturelayer 4, tier 2
2. Token Prediction and Language Modelinglayer 3, tier 2
3. Scaling Lawslayer 4, tier 1
4. Mixture of Expertslayer 4, tier 2
5. Post-Training Overviewlayer 5, tier 2
6. Tokenization and Information Theorylayer 4, tier 3
7. Fine-Tuning and Adaptationlayer 3, tier 1

Reachable through the chain (25)

These topics are not directly cited as prerequisites but are reached transitively by following the chain upward. Working through the direct prerequisites pulls these in.

• Attention Mechanism Theorylayer 4, tier 2
• Matrix Operations and Propertieslayer 0A, tier 1
• Sets, Functions, and Relationslayer 0A, tier 1
• Basic Logic and Proof Techniqueslayer 0A, tier 2
• Softmax and Numerical Stabilitylayer 1, tier 1
• Feedforward Networks and Backpropagationlayer 2, tier 1
• Differentiation in Rnlayer 0A, tier 1
• Vectors, Matrices, and Linear Mapslayer 0A, tier 1
• Continuity in Rⁿlayer 0A, tier 1
• Metric Spaces, Convergence, and Completenesslayer 0A, tier 1
• Matrix Calculuslayer 1, tier 1
• The Jacobian Matrixlayer 0A, tier 1
• The Hessian Matrixlayer 0A, tier 1
• Eigenvalues and Eigenvectorslayer 0A, tier 1
• Activation Functionslayer 1, tier 1
• Convex Optimization Basicslayer 1, tier 1
• Information Theory Foundationslayer 0B, tier 2
• Common Probability Distributionslayer 0A, tier 1
• RLHF and Alignmentlayer 4, tier 2
• Policy Gradient Theoremlayer 3, tier 1
• Markov Decision Processeslayer 2, tier 1
• Concentration Inequalitieslayer 1, tier 1
• Expectation, Variance, Covariance, and Momentslayer 0A, tier 1
• Random Variableslayer 0A, tier 1
• Kolmogorov Probability Axiomslayer 0A, tier 1