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New course: Transformers in Practice. You'll get a practical view of how transformer-based LLMs work, so you can reason about their behavior, diagnose problems like slow inference, and make smarter decisions about deployment. This course is built in partnership with @AMD and taught by @realSharonZhou. You'll see how transformers generate text one token at a time, how the model decides which earlier words matter most when predicting the next one, and how techniques like quantization speed up inference on GPUs. This is not a video-only course; interactive visualizations throughout let you play with these concepts and build intuition that sticks. Skills you'll gain: - Understand why LLMs hallucinate, and RAG and chain-of-thought shape what they generate - Look inside the model to see how attention and layers combine to predict the next token - Diagnose inference bottlenecks and learn the techniques that speed up transformers on GPUs Join and understand what's really happening inside your LLMs:

Meet China’s real-life Transformers. These powerful rescue platforms deployed to SW China’s Guangxi provide vital lifelines in raging floods and carry people out of danger.

要理解 LLM，必须读的论文顺序： 1. Attention Is All You Need（Transformers） 2. GPT-2（Scaling + Zero-shot） 3. Scaling Laws（Kaplan, 2020） 4. GPT-3（Few-shot） 5. Chinchilla（Data Needed） 6. InstructGPT（RLHF） 7. LoRA（Fine-Tuning w/o Broken） 8. FlashAttention（Fast） 9. Chain-of-Thought（Reasoning） 10. DPO（RLHF w/o Pain）