OOM debugging is a waste of time. If a model is on the Hugging Face Hub in Safetensors, you can estimate most of the VRAM it will need before downloading weights — by reading only the metadata hea...
Everyone wants to throw Transformers at every computer vision problem. The research papers show impressive gains. The hype is real. But I wanted to know: What happens when you actually run the sam...
The cloud isn’t always the answer. Sometimes the best deployment strategy is no deployment at all—let the user’s hardware do the work. Train in PyTorch, export to ONNX, run anywhere: browser, deskt...
You can train almost anything in PyTorch with seven lines of code. The problem? Those seven lines are correct but incomplete for real-world engineering. You need mini-batches, validation, device ha...
Extracting structured data from unstructured financial documents is a mess. Traditional NER (Named Entity Recognition) fails on domain-specific terminology. Regex breaks on minor format changes. Cu...
I keep seeing posts claiming: “AI image detection completely fails in the real world. JPEG compression and resizing destroy all detection signals. These methods only work in labs with pristine imag...
If the loss function tells your model what to minimize, the optimizer tells it how to get there. Pick the wrong optimizer and your model might converge slowly, get stuck in bad minima, or blow up ...
Choosing the right loss function is one of those decisions that can make or break your model. Get it wrong, and your network might struggle to learn anything useful. Get it right, and training beco...
Running a 70B parameter model on a single GPU? Not happening. Even the beefiest H100 with 80GB of VRAM can’t hold Llama-2-70B in full precision. This is where Tensor Parallelism (TP) comes in — it...
Understanding why modern AI is so fast requires understanding the hardware that powers it. This is a companion post to What Is a Tensor? A Practical Guide for AI Engineers, focusing specifically...