Making LLMs Smart With Transformers: It’s A Really Big Deal

Here’s how: prior to the transformer, what you had was essentially a set of weighted inputs. You had LSTMs (long short term memory networks) to enhance backpropagation – but there were still some ...
marktechpost

A Coding Implementation to Build a Transformer-Based Regression Language Model to Predict Continuous Values from Text

We will build a Regression Language Model (RLM), a model that predicts continuous numerical values directly from text sequences in this coding implementation. Instead of classifying or generating text ...
marktechpost

Implementing DeepSpeed for Scalable Transformers: Advanced Training with Gradient Checkpointing and Parallelism

In this advanced DeepSpeed tutorial, we provide a hands-on walkthrough of cutting-edge optimization techniques for training large language models efficiently. By combining ZeRO optimization, ...
T&D

Report Warns of Worsening Transformer Shortages Amid Rising U.S. Power Demand

The U.S. power sector is facing mounting strain as demand for transformers outpaces supply, according to a new analysis from Wood Mackenzie. The report projects that by 2025, supply shortages could ...
Reuters

US faces transformer supply shortfall as power demand surges, WoodMac says

Aug 14 (Reuters) - The U.S. is poised to see supply shortages of 30% and 10%, respectively, of power and distribution transformers this year, as surging electricity consumption drives demand for power ...
IEEE

Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization

Abstract: Transformer models have achieved state-of-the-art performance across a wide range of machine learning tasks. There is growing interest in training transformers on resource-constrained edge ...