#pytorch articles

Building a PyTorch Container Image

PyTorch is a flexible machine learning library for building deep learning models that can perform a wide range of tasks such as image recognition and language processing. This guide walks through clon

vultr.hashnode.dev

ZyVOP5d ago · 12 min read

I Built the Smallest LLM I Could to Understand How They Work

I use LLMs every day and had no real idea what happens inside one. So I built the smallest one I could: a transformer with roughly 108,000 parameters that learns to write Shakespeare one character at

blog.zyvop.com

Subhojeet ChandaOct 23 · 3 min read

Why Supervised Fine-Tuning Fails Against Prompt Injection: Co-Evolving LLM Defenses with Multi-Agent RL (MAPPO)

The Problem: Static Defenses Lose to Adaptive Attackers Most teams defend Large Language Models (LLMs) against prompt-injection and jailbreak attacks using Supervised Fine-Tuning (SFT) on a static dat

subhojeetchanda.hashnode.dev

Nitesh Kumar TuduOct 23 · 6 min read

AMD and Intel Just Ended the x86 AI Inference War — Here's What Python Devs Need to Know

Two CPU giants who have been at each other's throats for decades quietly announced something last week that will change how most of the world's servers run AI workloads. No acquisition, no drama — jus

blog.thecodingant.in

Mene-Ejegi OgbemiOct 21 · 19 min read

What Is Model Distillation? A Guide for Developers

Over time, knowledge has been passed down in various forms. Before writing was invented in ancient Mesopotamia (modern-day Iraq), laws, history, myths, songs, and other information were all transferre

freecodecamp.org

Sakshi TyagiOct 17 · 11 min read

Yesterday, It Fit: Dividing a 70B Model Across 64 GPUs

Yesterday, it fit. A smaller model, shorter examples, and a cluster that handled the job comfortably. Then the workload grew: 70 billion parameters, 32K-token sequences, the same 64 GPUs. The next tra

sakshityagi.hashnode.dev

Mahesh MoholkarOct 13 · 43 min read

Building a Modern LLM from Scratch: RMSNorm, RoPE, GQA and SwiGLU

This is the fourth part of a series on how language models work, built from the ground up. Part 1 built a neural network as an office building and trained it. Part 2 put text through the building and

maheshmoholkar.hashnode.dev

DineshOct 13 · 19 min read

I Thought RUL Prediction Was Just Regression. Then the Data Fought Back.

Read the full code and architecture here 001 — The High-Stakes Reality and the “Blind Model” Trap I initially took on this project because of a long-standing personal interest in aerospace engineering

colt007.hashnode.dev

Sandra BambooOct 12 · 7 min read

I Trained a Language Model From Scratch on a 4 GB GPU

A 4 GB GPU, a small corpus and a lot of things that went wrong in instructive ways. I trained a 22-million-parameter language model from scratch on a laptop with 4 GB of VRAM. It learned to imitate te

sandrabamboo.hashnode.dev

tensorVizOct 11 · 4 min read

Read a large PyTorch model one block at a time

A graph with thousands of nodes is hard to read when every operation is open at once. Start with a smaller question: what does this block take in, and what does it pass to the next block? This walkthr

1 tensorviz.hashnode.dev

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