LoRA: Low-Rank Adaptation Made Simple
Dr. Anjing Wang 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…
Mixed Precision Training: Faster Deep Learning Without Losing Accuracy
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…
Understanding FP32, FP16, and BF16: Floating-Point Formats in Deep Learning
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,…
Demystifying Floating-Point Precision: Half, Single, and Double
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…
Causal Language Models vs. Conditional Generation Models: Key Differences and Real-World Examples
Natural language generation has rapidly evolved with the rise of large language models, but one common point of confusion is distinguishing between causal language models (CLMs) and conditional generation models.…
Unlocking the Power of Conditional Generation in AI
Artificial Intelligence (AI) has revolutionized how we interact with technology, from chatbots that answer questions to AI models that generate lifelike images and translate languages instantly. But behind many of…
Understanding sklearn.metrics.accuracy_score and How to Calculate Accuracy Manually
When evaluating machine learning models, accuracy is one of the most commonly used metrics for classification tasks. In this blog post, we’ll dive into the accuracy_score function provided by Scikit-Learn’s…
Supervised Fine-Tuning (SFT): How to Fine-Tune Your Model Like a Pro
In the world of machine learning, pretrained models are like finding a treasure chest of knowledge. They save us hours, days, or even weeks of training time, allowing us to…
Deep Learning Model Precision: FP32, BF16, INT8 and INT4
When training or deploying deep learning models, precision isn’t just about getting accurate predictions—it’s also about finding the right balance between performance, memory usage, and speed. Choosing the optimal precision…
LoRA vs. QLoRA: Fine-Tuning Your Model’s Superpowers on a Budget
Have you ever tried running a colossal language model on a GPU that feels more like a toaster than a supercomputer? Enter LoRA and QLoRA—two magical spells for squeezing every…
How to Run a CUDA Docker Image on an AWS Ubuntu LTS Instance
Running a CUDA Docker image on an AWS Ubuntu instance enables you to leverage GPU-accelerated computations directly within Docker containers. In this guide, we’ll walk through the process of installing…
How to Install NVIDIA driver on AWS Ubuntu LTS 24.04
Installing the NVIDIA driver on an AWS EC2 instance running Ubuntu 24.04 can sometimes be challenging due to AWS’s custom environment and kernel. Although the ubuntu-drivers tool is the recommended…
Fine-Tuning Qwen Models: Understanding the Key Parameters
Fine-tuning large language models (LLMs) can be a challenging process due to the variety of parameters and configurations involved. In this blog, we’ll break down key parameters used to fine-tune…
Why PyTorch Uses Its Own Tensor Library Instead of NumPy
If you’re just starting out with Python and have heard of NumPy, you probably know it’s a fantastic library for handling numbers, arrays, and matrices. So, why would PyTorch, a…
Using torch.Tensor to Represent Images in PyTorch: A Beginner’s Guide
In the world of deep learning, images are a critical form of data. Whether you’re building a computer vision model, training on image datasets, or working on image processing tasks,…