2d ago · 12 min read · TLDR: This is a practical, notebook-style quantization guide for Google Colab and Hugging Face. You will quantize real models, run inference, compare memory/latency, and learn when to use 4-bit NF4 vs safer INT8 paths. 📖 What You Will Build in Thi...
Join discussion4d ago · 10 min read · AI Research Papers I'm Reading— Issue #01 Paper: Quantum Inspired Self-Attention In A Large Language Model - arXiv:2603.03318 - TL;DR - Researchers have taken an existing language model called GPT-1
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Mar 6 · 13 min read · I highly recommend to have a knowledge of machine learning models or atleast the basics The Core Idea: Transformers Before transformers, the industry relied on RNNs and LSTMs. The paper "Attention
Join discussionFeb 16 · 6 min read · I told Twitter I'd read the most important AI paper ever written. Over the weekend. In public. It took a few days longer than that. But I did it. The paper is "Attention Is All You Need." Published in 2017 by a team at Google. 8 authors. 15 pages. An...
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Feb 16 · 10 min read · Every once in a while, you come across these concepts that are so tricky to grasp, they tickle the insides of your brain. Introducing RoPE, he gave me a hard time last week. Hi future-me, who forgot how RoPE works, this is for you. RoPE stands for Ro...
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Feb 16 · 3 min read · Introduction When I started learning about LLMs, terms like self-attention and Transformers felt almost magical. So instead of only using libraries, I decided to build a language model step by step — starting from a very simple baseline and ending wi...
Join discussionFeb 15 · 20 min read · You send a prompt. The model "thinks" for a few seconds. And generates a response that seems intelligent. What happened in that time? If your way of implementing systems with LLMs is based on using a high-level framework plus OpenAI APIs and derivati...
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