Descriptions:
HRM-Text-1B is a 1 billion parameter pre-trained language model that claims to match or outperform models in the 2–7 billion parameter range—including Llama 3.2, Gemma 3, and Qwen 3.5—while costing approximately $1,500 in compute and training on only 40 billion tokens. The architecture borrows from theories of human cognition, running two modules in a nested loop: a fast “L” module that refines token representations quickly and a slow “H” module that updates higher-level context, iterating multiple times per forward pass to deliver more internal computation than the parameter count implies. Training was performed exclusively on question-answer pairs with loss computed only on answers, pushing every gradient step toward useful output rather than web-text reconstruction.
In this hands-on walkthrough, Fahd Mirza installs and runs the model locally on Ubuntu with an NVIDIA RTX 6000 GPU (48GB VRAM), showing that the model downloads to just 2.37GB and consumes only 2.6–2.7GB of VRAM at inference—making CPU deployment viable as well. Mirza walks through the control token system required to prompt the raw base model, covering chain-of-thought triggers, bidirectional attention flags, and structured output tokens, before running a sample inference that produces coherent step-by-step reasoning.
Released under Apache 2.0, HRM-Text-1B is explicitly a pre-training starting point rather than a production assistant; a follow-up video on custom dataset fine-tuning is promised. For researchers and developers who want to build capable small models without data center infrastructure, the architecture’s efficiency-per-parameter ratio makes it a compelling experiment to run locally.
📺 Source: Fahd Mirza · Published May 29, 2026
🏷️ Format: Tutorial Demo
