If you’ve loaded an AWQ-quantized model from Hugging Face (like Qwen2.5-VL-3B-Instruct-AWQ), you might have noticed something confusing: 👉 Hugging Face says the model has ~0.9B parameters, but the architecture is…
Matrix multiplication is one of the most fundamental operations in machine learning. In PyTorch, you’ll often see three different ways to do it: At first glance, they look interchangeable —…
Large language models are huge — billions of parameters, often stored as massive square weight matrices like 4096 × 4096. Fine-tuning all of those parameters for a new task is…
Training today’s deep learning models is resource-hungry. Models have billions of parameters, and every step requires trillions of floating-point operations. To make training feasible, researchers and engineers rely on mixed…
Modern deep learning wouldn’t be possible without floating-point numbers. They’re the backbone of every matrix multiplication, activation, and gradient update. But as models grow larger and GPUs become more specialized,…
If you’ve ever written code in Python, CUDA, or TensorFlow, you’ve probably seen terms like float16, float32, or float64. They map directly to the IEEE-754 floating-point standard: But what do…
Dr. Anjing Wang