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…
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…
Your quantized model got worse, and nothing told you
Getting a model onto a phone means exporting it, and almost always quantizing it. Both of those change the numbers. Everyone knows that part. What g…
Running Qwen3 Through the ExecuTorch MLX Delegate: Up to 4.52x Faster on M1 Max
Hello, everyone. There are now many ways to run an LLM on a Mac, but exporting a PyTorch model for Apple Silicon and executing it in a lightweight r…
Debugging a Python "Memory Leak" That Was Actually a Measurement Bug (ru_maxrss vs VmRSS)
I was running a pipeline for building a RAG knowledge base: crawl web articles, split them into chunks, create embeddings, and push them into Qdrant…
Classifier-free guidance above 7.5 oversaturated our product renders
TL;DR: Classifier-free guidance above a scale of ~7.5 pushed our SDXL product renders into oversaturation and clipped highlights. Adding CFG rescale…
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…
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…
Our event-camera detector lost 6 mAP to a badly chosen accumulation window
TL;DR: We spent three weeks chasing a 6 mAP regression in an event-camera object detector. The model was fine. The bug was the accumulation window w…
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…
LLM-as-judge variance broke our DPO training signal for 3 weeks
TL;DR: Our DPO pipeline used a single LLM as the preference judge. Training reward climbed every run. Production accuracy fell 4 points. The judge w…
The bf16 grad accumulator that killed our SDXL LoRA training
TL;DR: Our SDXL LoRA fine-tune for a Photoroom product photography model trained for six days while silently corrupting its adapter weights. The cau…
Prefix caching in vLLM under multi-tenant agent traffic
TL;DR: We turned on vLLM's prefix cache for our agent workloads at Nexus Labs and watched TTFT drop from 480ms to 110ms on one tenant and stay exact…
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 PyTorch Training Crawls on a Beefy GPU (And How to Fix It)
Last month I was helping a friend debug a training loop that was running at maybe 15% GPU utilization on an A100. Fifteen percent. On a card that co…
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…
Нейросети, генетика и десктоп: как я построил микрофреймворк для обучения AI-агентов с неблокирующим GUI
Микрофреймворк для параллельного обучения AI-агентов в средах Gymnasium с графическим интерфейсом на wxPython. Решает классическую проблему «зависше…