Latest news with #transformer
Geeky Gadgets
11-07-2025
- Science
- Geeky Gadgets
Learn the Secrets of Building Your Own GPT-Style AI Large Language Model
What if you could demystify one of the most fantastic technologies of our time—large language models (LLMs)—and build your own from scratch? It might sound like an impossible feat, reserved for elite AI researchers or tech giants. But here's the truth: with the right roadmap, even complex systems like GPT-style models can become accessible to anyone with curiosity and determination. The rise of LLMs has reshaped industries, from content creation to healthcare, and understanding their inner workings isn't just a technical skill—it's a gateway to shaping the future. If you've ever wondered how these models predict text, understand context, or generate human-like responses, this guide will take you from zero to confident practitioner, one step at a time. In this deep dive by Marina Wyss, you'll uncover a structured, five-step approach to mastering LLMs, starting from the mathematical foundations that power them to the advanced techniques that fine-tune their performance. Along the way, you'll explore critical concepts like neural networks, transformer architecture, and alignment strategies, gaining both theoretical knowledge and practical insights. Whether you're an AI enthusiast, a developer aiming to build innovative applications, or simply curious about how these systems work, this roadmap will equip you with the tools to navigate the world of LLMs. By the end, you won't just understand how these models function—you'll see how they can be tailored to solve real-world problems and push the boundaries of what AI can achieve. 5-Step Guide to Building LLMs Step 1: Build a Strong Mathematical Foundation Mathematics forms the backbone of artificial intelligence, and a robust understanding of key mathematical concepts is essential for working with LLMs. Mastering calculus, linear algebra, and probability equips you with the tools to comprehend how these models learn, optimize, and generalize. Calculus: Develop an understanding of gradients and optimization techniques like backpropagation, which enable models to improve during training. Develop an understanding of gradients and optimization techniques like backpropagation, which enable models to improve during training. Linear Algebra: Study tensors, matrix operations, and transformations, which are fundamental to neural network computations. Study tensors, matrix operations, and transformations, which are fundamental to neural network computations. Probability: Explore concepts such as likelihood estimation and uncertainty, which underpin decision-making in AI systems. To strengthen these skills, use resources like 3Blue1Brown's 'Essence of Linear Algebra' and 'Essence of Calculus' series, or Coursera's 'Mathematics for Machine Learning' specialization. These materials provide intuitive explanations and practical examples, making complex mathematical concepts more accessible. Step 2: Understand Neural Networks Neural networks are the foundation of deep learning and serve as the building blocks for LLMs. These computational models, inspired by the human brain, are designed to identify patterns, process data, and make predictions. Learn how neurons, layers, and activation functions work together to process and transform data inputs. Understand backpropagation, the algorithm that adjusts model weights based on errors to improve learning outcomes. Explore optimization techniques such as gradient descent, which fine-tune model performance during training. For practical learning, explore resources like 3Blue1Brown's neural networks playlist, StatQuest's deep learning series, or Andrej Karpathy's tutorials on backpropagation and training. These resources bridge the gap between theoretical knowledge and hands-on application, helping you build a strong foundation in neural networks. Guide to Building Your Own Large Language Model in 2025 Watch this video on YouTube. Master Large Language Models (LLMs) with the help of our in-depth articles and helpful guides. Step 3: Dive Into Transformer Architecture Transformers are at the core of modern LLMs, transforming natural language processing (NLP) by allowing models to process entire sequences of text efficiently. Understanding this architecture is critical for building and scaling LLMs. Attention Mechanisms: Study how self-attention allows models to focus on the most relevant parts of input sequences, improving comprehension and context handling. Study how self-attention allows models to focus on the most relevant parts of input sequences, improving comprehension and context handling. Positional Encoding: Learn how transformers capture the order of words in a sequence, a crucial feature for language understanding. Learn how transformers capture the order of words in a sequence, a crucial feature for language understanding. Scalability: Discover why transformers outperform traditional recurrent neural networks (RNNs) when handling large datasets and complex tasks. Resources such as 'The Illustrated Transformer' blog and Andrej Karpathy's GPT tutorials provide accessible explanations and practical insights into transformer architecture. These materials will help you understand how transformers power LLMs and their role in pre-training large-scale models. Step 4: Master Fine-Tuning Techniques Fine-tuning is a vital step in adapting pre-trained LLMs to specific tasks or domains. This process involves training a model on a smaller, task-specific dataset to enhance its performance in targeted applications. Learn traditional fine-tuning methods, such as adjusting weights on pre-trained models to improve task-specific accuracy. Explore advanced techniques like Low-Rank Adaptation (LoRA) and Quantized LoRA (QLoRA), which reduce computational costs while maintaining high performance. Understand the importance of domain-specific data in achieving precise and reliable results for specialized applications. Books like 'Natural Language Processing with Transformers' and courses such as 'Fine-Tuning LLMs' offer in-depth guidance on these techniques. By mastering fine-tuning, you can customize models for a wide range of applications, from chatbots to domain-specific NLP tools. Step 5: Focus on Alignment Techniques Alignment ensures that LLMs generate outputs that are helpful, ethical, and safe. This step is essential for building responsible AI systems that align with human values and expectations. Reinforcement Learning with Human Feedback (RLHF) is a widely used approach for achieving alignment. Understand how RLHF combines reinforcement learning with curated human feedback to refine model behavior and outputs. Study case studies like OpenAI's InstructGPT, which demonstrate the practical application of alignment techniques in real-world scenarios. Learn about the challenges of balancing utility, safety, and fairness in AI systems, and explore strategies to address these issues. Recommended resources include StatQuest's RLHF overview, OpenAI's 'Spinning Up in Deep RL,' and the 'InstructGPT' paper. These materials provide a comprehensive understanding of alignment strategies and their importance in responsible AI development. By following this roadmap, you can build a strong foundation in LLM development. Start with mathematical principles, progress through neural networks and transformers, and master fine-tuning and alignment techniques. With dedication and curiosity, you will be well-equipped to prototype GPT-style models and contribute to advancements in AI. Staying informed and continuously learning will ensure you remain at the forefront of this rapidly evolving field. Media Credit: Marina Wyss Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
BBC News
06-07-2025
- Automotive
- BBC News
Abnormal load under escort to close North Yorkshire roads
A 200-tonne "supergrid transformer" will be escorted through North Yorkshire later, with several rolling road closures planned to accommodate abnormal load's route will follow the A19 from Middlesbrough through Birdforth, Thormanby and Shipton by Beningbrough before turning onto Overton 80-metre specialist vehicle will transport the transformer, which will travel at a reduced speed accompanied by a police of its size, in some areas, benches, litter bins and road signs will be temporarily removed, and parking restrictions may be put in place. When the road is single carriageway or "too narrow", the convoy will travel under a rolling road closure, meaning other vehicles may be stopped at points along the supergrid transformer is one of eight National Grid transformers to be delivered to Overton and Monk Fryston electrical substations by October 2025, as part of the ongoing Yorkshire Green McGready, National Grid project director, said: "Supergrid transformers are essential to our project to upgrade and reinforce the high-voltage energy network in Yorkshire and further afield."We are working closely with other organisations to limit as much of the potential disruption as possible, and we'd like to thank local communities for their support and understanding while we undertake this vital work."Letters have been sent to local residents directly affected by the load. Listen to highlights from North Yorkshire on BBC Sounds, catch up with the latest episode of Look North.
Sky News
02-07-2025
- General
- Sky News
National Grid's maintenance the damning failure identified by report into fire that sparked Heathrow chaos
The reliability of our grid is, it turns out, paper thin. A fire at the National Grid substation at North Hyde, which supplies Heathrow Airport, was caused by the "catastrophic failure" of a bushing in a high-voltage transformer at the site. A bushing is made of paper and foil, soaked in oil. The resulting fire, which knocked out the substation and in turn, power to more than 70,000 customers, including the west London airport, led to 1,300 flight cancellations affecting nearly 300,000 travellers. 0:51 The bushing on which the blame falls is the insulated wire that at North Hyde was one of three carrying a whopping 275kV of electricity into the transformer. In many transformers - especially older ones like this installed in 1968 - bushings are insulated by several layers of paper wrapped around alternating layers of metal foil, all soaked in a special insulating "bushing oil". But if air bubbles or moisture get into the absorbent paper, it loses its insulating power. As the report found, the most likely cause of the fire at North Hyde was "moisture entering the bushing causing a short circuit." That short circuit caused a spark that ignited the oil in the bushing, and in turn, the 150 litres of oil that insulates and cools the transformer, resulting in an inferno. 0:49 The result was a fire so catastrophic, it took out not just one transformer at the site but also its sister transformer next to it. Oil and paper insulators date back to the turn of the last century, and they work perfectly well if properly maintained. And it is National Grid's maintenance that is the damning failure identified by this report. The report finds that the highest "category 1" moisture reading was identified during inspection of the doomed bushing in 2018. According to National Grid's procedures, that reading should have resulted in its immediate replacement. Yet, for some reason, no action was taken. In fact, the report finds, routine maintenance of the failing transformer was deferred in 2022 - potentially another missed chance to rectify the fault. National Grid said in response to the report that it has already carried out an "end-to-end review" of its relevant inspection and maintenance processes. The review also found that no other maintenance red flags have been missed. It's nonetheless a serious failing and explains why Ofgem, the energy regulator, has now ordered an enforcement investigation into our largest grid operator. It raises questions, too, about the level of investment made by privatised network companies in maintaining or replacing ageing infrastructure. Recent analysis by Common Wealth found that all our electricity network operators have underspent their budgets for replacing grid hardware. This is money they are allowed by Ofgem to add to customers' bills as part of "network charges". Those charges are set to rise again in Ofgem's latest review. Other infrastructure companies, our water firms in particular, stand accused of "sweating" ageing assets to increase their profits. A key question for Ofgem's investigation into National Grid is whether money for maintenance and replacement was instead going toward their profits, at the expense of customers and ultimately the resilience of our electricity supply.
CTV News
30-06-2025
- Climate
- CTV News
Transformer catches fire in Cambridge
Video of a transformer on fire in Cambridge on Sunday. Video of a transformer on fire in Cambridge on Sunday. A transformer caught fire in Cambridge on Sunday. According to a witness, the fire happened in the area of Cedar and Glenmorris at around 1:45 p.m. Video of the fire showed the transformer blazing and letting off plumes of smoke. The same witness said residents in the area also lost power for a number of hours. Power allegedly was restored by 9 p.m. Cambridge Fire attended the scene and told CTV News they used CO2 extinguishers to put out the fire. CTV News reached out to GrandBridge Energy, who operates the hydro in the area.
Yahoo
13-06-2025
- Automotive
- Yahoo
Road closures expected as almost 600K pound ‘superload' moves through Miami Valley
Drivers will need to look for an alternative route as a 'superload' moves through part of the Miami Valley today. [DOWNLOAD: Free WHIO-TV News app for alerts as news breaks] We explain how long crews expect this trip to last, and the changes drivers should expect today on News Center 7 Daybreak from 4:25 a.m. until 7 a.m. TRENDING STORIES: Have you seen her? Police searching for missing 40-year-old woman Ohio lawmakers react to Israel attacking Iran's nuclear, missile sites Motorcyclist injured trying to avoid hitting deer in Darke County As previously reported by News Center 7, Piqua Steel Company will move an electric transformer from the Fairborn Railroad site to an AES substation on Dayton Xenia Road, according to the Greene County Engineer's Office. The transformer weighs over 369,000 pounds. The total weight of the truck will be almost 600,000 pounds with the transformer loaded up. The transformer will be escorted by law enforcement on the following route: West on E Xenia Dr. to E. Dayton Dr. Southwest on E. Dayton Dr. to OH-444S (S. Central Ave./ Kauffman Ave.) Southeast on OH-444S to W. Dayton Yellow Springs Rd. South on W. Dayton Yellow Springs Rd. to Trebein Rd. South Trebein Rd. to Dayton Xenia Rd. Dayton Xenia Rd to the substation News Center 7's Mason Fletcher says there will be a moving road closure starting at 9 a.m. Updates will be posted on the Greene County Engineer's social media. [SIGN UP: WHIO-TV Daily Headlines Newsletter]
