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  • Timeline
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  • Modules
    • 00 Introduction
    • 01 Tensors
    • 02 Autograd

    • 03 Tokenization
    • 04 Embeddings

    • 05 Attention
    • 06 Transformer

    • 07 Training
    • 08 Generation

    • 09 Efficient Attention
    • 10 Long Context
    • 11 Mixture of Experts

    • 12 Alignment
    • 13 Reasoning & Test-Time Compute

    • 14 Quantization
    • 15 Retrieval-Augmented Generation
    • 16 Speculative Decoding

    • 17 Evaluation
    • 18 Tool Use & Agents

    • 19 State-Space Models (Mamba)
    • 20 Multimodal
    • 21 Interpretability
    • 22 Linear & Recurrent Attention
    • 23 Distributed Training
    • 24 Pretraining Data

    • 25 Parameter-Efficient Fine-Tuning
    • 26 Diffusion Language Models
    • 27 Model Merging
    • 28 Knowledge Distillation

The Evolution of Large Language Models

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