Ecole Centrale Paris, MIT. Twin of @shervinea. New book on Transformers & Large Language Models: https://nitter.cf/t.co/5b89hbceJf
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Joined August 2014
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Our Stanford class "CME 295: Transformers & Large Language Models" is back for a new edition and Lecture 1 is now on YouTube!
We also cover the foundations that make all of this possible, which includes:
• Transformers (tokenization, embeddings, attention mechanism, architecture)
• LLMs (pretraining, MoEs, decoding, scaling laws)
• Systems (distributed training, KV caching, speculative decoding)
All lectures are posted on the "syllabus" page of the course website as they are released: cme295.stanford.edu/syllabus…
Happy learning!
• Training (pre-training, post-training, tuning, distillation, optimizations)
• Common tricks (RoPE-based emebddings, noise schedule, curriculum learning)
• Evaluation (FID, IS, LPIPS and feature-based metrics, Multimodal LLMs)
All lectures are posted on the "syllabus" page of the course website as they are released: cme296.stanford.edu/syllabus…
Happy learning!
Afshine Amidi retweeted
Our Stanford class "CME 295: Transformers & Large Language Models" is now available on YouTube!
Afshine Amidi retweeted
Stanford University released the best cheatsheets you'll ever find to learn LLMs & Transformers!
These cheatsheets cover:
• Self-attention, Flash Attention, LoRA, SFT
• Mixture of Experts, Distillation, Quantization
• RAG, Agents, LLM-as-a-judge
100% Free and Open Source
Afshine Amidi retweeted
Geminiの開発エンジニアが書いたスタンフォード講義資料「Transformer と大規模言語モデル」の日本語版 by @yoshiyukinakai これはがっつり読みたい。書籍紹介ページ superstudy.guide/transformer… と、無償のチートシートもある。github.com/afshinea/stanford…
Announcing the VIP Cheatsheet for Stanford's CME 295 Transformers & Large Language Models class.
Topics include:
- 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)
Link to PDF: github.com/afshinea/stanford…
This Spring, my twin brother @shervinea and I will be teaching a new class at Stanford called "Transformers & Large Language Models" (CME 295).
The goal of this class is to understand where LLMs come from, how they are trained, and where they are most used. We will also explore more recent topics such as reasoning models and agentic workflows.
We will be posting updates on the class website and uploading some relevant material in the coming weeks.
For reference, a large portion of the class is based on our recent book "Super Study Guide: Transformers & Large Language Models".
Stay tuned & happy learning!
「Super Study Guide: Transformer と大規模言語モデル」日本語版が発売されました。250 ページにわたり約 600 点のカラーの図を交えて Transformer と LLM の詳細を解説しています。
Afshine Amidi retweeted
Book Review Video : Super Study Guide: Transformers & Large Language Models by @afshinea & @shervinea 🙂
Video Link : youtube.com/watch?v=VLTj3Se3…
#LLM #GenerativeAI #DataScience #MachineLearning #NLP
