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VIP cheatsheet for Stanford's CME 295 Transformers and Large Language Models
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| 2026-07-31 | 4605 |
| 2026-08-02 | 4605 |
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| 2026-08-06 | 4622 |
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# Transformers & LLMs cheatsheet for Stanford's CME 295 Available in [العربية](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/ar) - [Čeština](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/cs) - [English](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/en) - [Español](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/es) - [فارسی](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/fa) - [Français](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/fr) - [Italiano](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/it) - [日本語](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/ja) - [한국어](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/ko) - [Português](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/pt) - [Русский](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/ru) - [Српски](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/sr) - [ไทย](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/th) - [Türkçe](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/tr) - [中文](https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/tree/main/zh) ## Goal This repository aims at summing up in the same place all the important notions that are covered in Stanford's CME 295 Transformers & Large Language Models course. It includes: - **Transformers**: self-attention, architecture, variants, optimization techniques (sparse attention, low-rank attention, flash attention) - **LLMs**: prompting, finetuning (SFT, LoRA), preference tuning, optimization techniques (mixture of experts, distillation, quantization) - **Applications**: LLM-as-a-judge, RAG, agents, reasoning models (train-time and test-time scaling from DeepSeek-R1) ## Content ### VIP Cheatsheet <a href="https://github.com/afshinea/stanford-cme-295-transformers-large-language-models/blob/main/en/cheatsheet-transformers-large-language-models.pdf"><img src="https://cme295.stanford.edu/cheatsheet-en.png" alt="Illustration" width="600px"/></a> ## Class textbook This VIP cheatsheet gives an overview of what is in the "Super Study Guide: Transformers & Large Language Models" book, which contains ~600 illustrations over 250 pages and goes into the following concepts in depth. You can find more details at https://superstudy.guide. ## Class website [cme295.stanford.edu](https://cme295.stanford.edu/) ## Authors [Afshine Amidi](https://www.linkedin.com/in/afshineamidi/) (Ecole Centrale Paris, MIT) and [Shervine Amidi](https://www.linkedin.com/in/shervineamidi/) (Ecole Centrale Paris, Stanford University)
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Afshine Amidi
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Dr. Ayyüce Kızrak · HUX AI · United Kingdom
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Would you bet a product on this? Bounded 0–100 and slow moving.
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