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LLMs from Scratch
🚀 Course Highlights
01. Tokenization
02. Building a Tiny LLM
03. Advancing our LLM
04. The Art and Science of Data for LLMs
05. Scaling Laws
06. Pre-training
07. Supervised Fine-Tuning
08. Reinforcement Learning from Human Feedback
09. Reasoning and Adapters
10. Pruning, Distillation, and Speculative Decoding
11 (Appendix). Advanced Positional Embeddings for Long-Context Generalization
12 (Appendix). Quantisation and Quantisation-Aware Training
13 (Appendix). Parameter-Efficient Fine-Tuning beyond LoRA
14 (Bonus). Energy-Based Models and Diffusion LLMs
15 (Bonus). State Space Models
16. What’s Next?
Index