Transformers are changing the AI landscape, and it all began with the groundbreaking paper “Attention is All You Need.” Today, I explore the Introduction and Background sections of the paper,…
Below is a comprehensive table of key terms used in the paper “Attention is All You Need,” along with their English and Chinese translations. Where applicable, links to external resources are…
Today marks the beginning of my adventure into one of the most groundbreaking papers in AI for transformer: “Attention is All You Need” by Vaswani et al. If you’ve ever been curious about how modern…
How Ray Serve works: deployments, autoscaling, batching and multi-model pipelines, and when to choose Ray Serve over FastAPI or Ollama.
Quantization is a transformative AI optimization technique that compresses models by reducing precision from high-bit floating-point numbers (e.g., FP32) to low-bit integers (e.g., INT8). This…
What knowledge distillation is, how soft targets, temperature and the distillation loss work, the main types, LLM examples like DistilBERT and DeepSeek-R1 distills, and how it compares with…
Why generative AI models hallucinate: noisy training data, architecture limits and prompts, how it misleads users, and ways to reduce made-up answers.
An embedding is the “translator” that converts language into numbers, enabling AI models to understand and process human language. AI doesn’t comprehend words, sentences, or syntax—it only works with…