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…
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…
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…