How Hugging Face's Transformers Added UltraBERT with Layer-Specific Rotary Embeddings
Why UltraBERT Integration Matters The Hugging Face Transformers library is a cornerstone for NLP research and applications, providing a unified inte…
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Demystifying LLMs: Building a 124M-Parameter Decoder-Only Transformer in PyTorch
If you want to truly understand how ChatGPT, Llama, or Claude works, reading about them isn't enough. You have to build one from the ground up. In 2…
Discriminative Fine-Tuning: Why Your Backbone and Your Head Shouldn't Learn at the Same Speed
I was recently fine-tuning DEIMv2 — a DETR-style detector with a DINOv3 Vision Transformer backbone — starting from a COCO-pretrained checkpoint, to…
Undefined type Float8_e4m3fn on Apple Silicon: BF16 and GGUF Workarounds for FP8 Models
Two FP8 image models crashed with Undefined type Float8_e4m3fn on an Apple Silicon Mac, and each needed a different fix — a BF16 swap for one, a GGU…
GPU-Accelerating MSCRED with CUDA, im2col, GEMM, and a Custom PyTorch Extension
Introduction During my M.Tech in Data Science and Artificial Intelligence at IIT Bhilai (2021–2023), I conducted thesis research on multivariate tim…
I Trained a 6.4M-Parameter Transformer From Scratch to Talk About Recipes
Every LLM-powered app I'd built up to this point followed the same recipe (pun intended): call an API, write a good prompt, wrap it in a nice UI. Th…
Channels-last memory format cut our conv backbone latency 22%
TL;DR: Switching our convolutional segmentation backbone to PyTorch's channels-last memory format cut inference latency by about 22% on A100s, with…
The SDXL VAE overflow that decoded black images in fp16
TL;DR: The SDXL VAE decoder pushes activations past 65504, the max value fp16 can hold, so the last decode step overflows to inf and you get a fully…
Why JAX Is a Much Better Backend for Quantum Circuit Simulation Than PyTorch
Modern quantum circuit simulation is not just “machine learning with complex tensors.” It involves irregular tensor contractions, sparse operators,…
AI Coding Tools for Machine Learning Engineers in 2026: Jupyter, PyTorch, and the CUDA Trap
This article was originally published on aicoderscope.com ML engineers aren't software engineers who happen to write some Python. They live in noteb…
From Bayesian to deep knowledge tracing — upgrading NumPath's student model with a PyTorch LSTM
BKT told us how well a student knows subtraction-with-borrowing. It had no idea that a student who reverses digits on subtraction problems probably…
QAT vs PTQ on our edge vision model: 6 months of A/B data
TL;DR: We ran post-training quantisation (PTQ) and quantisation-aware training (QAT) side by side on the same defect-classification model deployed o…
I Built a Diagnostic Toolkit for PyTorch Because I Was Tired of Guessing Why Models Fail
Every time a PyTorch model refuses to learn, the debugging process looks the same: Stare at the loss curve Wonder if gradients are flowing Add print…
Why your diffusion model is slow at batch size 1 (and what actually helps)
TL;DR: Single-image diffusion inference is bottlenecked by kernel launch overhead and attention memory traffic, not raw FLOPs. torch.compile with mo…
Использование машинного обучения в работе с SolidWorks (2 часть): тестирование и практическое применение
В прошлой статье я поделился своими наработками в области работы ИИ с CAD-программами (в моём и нашем случае, SolidWorks). В этот раз поделюсь…
Использование машинного обучения в работе с SolidWorks (1 часть)
Работа инженера-конструктора, помимо творчества, порой заставляет заниматься рутинными задачами, которые отнимают время, силы. Столкнувшись с постоя…