# Transformer architecture Transformer architecture is a neural network architecture based entirely on attention mechanisms, using stacked self-attention and feed-forward layers for sequence transduction. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## TimesFM-3: A zero-shot foundation model for multivariate forecasting DevFeed: [TimesFM-3: A zero-shot foundation model for multivariate forecasting]() Original publisher: [Read original article]() Published: 2026-08-31T17:19:40Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [Time Series](), [Google](), [datasets](), [Transformer architecture](), [Fine-tuning](), [generalization in machine learning]() Tags: [data-management](), [features](), [fine-tuning](), [foundation-models](), [google](), [machine-intelligence](), [model](), [product](), [time-series](), [transformer-architecture](), [zero-shot]() ### AI overview Google Research introduces TimesFM-3, a 330-million-parameter time-series foundation model designed for accurate multivariate forecasting in a single forward pass. Pre-trained on more than one trillion real-world and synthetic time points, it jointly models coevolving series and external covariates in zero-shot settings without task-specific fine-tuning. ### Source excerpt Data Management ## A guide to Transformer architecture in modern language models DevFeed: [A guide to Transformer architecture in modern language models]() Original publisher: [Read original article]() Author: Ashish Pratap Singh Published: 2026-05-14T04:15:11Z Content type: tutorial Language: en Sources: [AlgoMaster Newsletter]() Topics: [Transformer architecture](), [LLMs](), [Transformer]() Tags: [architecture-pattern](), [better](), [deep-dive](), [layer](), [llms](), [model](), [parallel](), [performance](), [semantics](), [sequence](), [syntax](), [tokens](), [transformer-architecture](), [transformers]() ### AI overview This tutorial explains the Transformer architecture, including its original encoder-decoder design for translation and the decoder-only variant used for modern language generation. It describes decoder components such as masked multi-head self-attention, feed-forward networks, layer normalization, and residual connections, and introduces the Pre-LayerNorm pattern. ### Source excerpt A single 2017 research paper changed the future of AI forever and gave rise to multiple unicorn companies. ## Catalyzing scientific impact through global partnerships and open resources DevFeed: [Catalyzing scientific impact through global partnerships and open resources]() Original publisher: [Read original article]() Published: 2026-05-01T16:37:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Open Source](), [datasets](), [Google](), [Transformer architecture]() Tags: [apis](), [architecture](), [australia](), [collaboration](), [community](), [data-mining-modeling](), [developers](), [general-science](), [global](), [google](), [health-bioscience](), [india](), [japan](), [open](), [open-source](), [open-source-models-datasets](), [partnerships](), [publications](), [research](), [science](), [software](), [technology](), [transformer-architecture]() ### AI overview Google Research describes an open-science approach centered on responsible research, open-source software, open-access datasets, and partnerships with scientific organizations and consortia worldwide. It highlights shared technologies and datasets, including Transformer architecture and specialized models used across fields such as medicine, genomics, neuroscience, climate, and energy. ### Source excerpt Data Mining & Modeling ## Granite 4.1 LLMs: How They're Built DevFeed: [Granite 4.1 LLMs: How They're Built]() Original publisher: [Read original article]() Author: Yousaf Shah Published: 2026-04-29T15:01:48Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [LLMs](), [ibm](), [long-context](), [Fine-tuning](), [grpo](), [Training AI Models](), [Transformer architecture](), [Data Quality]() Tags: [apache](), [fine-tuning](), [grpo](), [ibm](), [learning](), [llms](), [long-context](), [tokens](), [training](), [transformer-architecture]() ### AI overview The article introduces Granite 4.1, IBM's family of dense decoder-only LLMs in 3B, 8B, and 30B sizes. It describes their five-stage training process, which uses about 15 trillion tokens, data-quality refinement, long-context extension up to 512K tokens, supervised fine-tuning, and reinforcement learning with on-policy GRPO and DAPO loss. The models use a dense transformer architecture and are released under the Apache 2.0 license. ### Source excerpt Authors: Granite Team, IBM TL;DR -- Granite 4.1 is a family of dense, decoder-only LLMs (3B, 8B, and 30B) trained on ~15T tokens using a multi-stage pre-training pipeline, including long-context extension of up to 512K tokens. The models are further refined with supervised fine-tuning on ~4.1M high-quality curated samples and reinforcement learning via on-policy GRPO with DAPO loss (Yu et al., 2025). ## AI's capabilities, uncertain trajectory, and potential economic consequences DevFeed: [AI's capabilities, uncertain trajectory, and potential economic consequences]() Original publisher: [Read original article]() Published: 2025-08-13T15:59:56Z Content type: opinion Language: en Sources: [Antirez]() Topics: [Artificial Intelligence](), [Large Language Model](), [Transformer architecture](), [systems]() Tags: [ai](), [future](), [investors](), [llms](), [models](), [science](), [transformer-architecture]() ### AI overview This opinion article examines AI's progress in language understanding, programming, and bug finding while emphasizing uncertainty about future advances. It also considers how greater AI independence could challenge labor markets, service businesses, and the concentration of intelligence providers. ### Source excerpt Regardless of their flaws, AI systems continue to impress with their ability to replicate certain human skills. Even if imperfect, such systems were a few years ago science fiction. It was not even clear that we were so near to create machines that could understand the human language, write programs, and find bugs in a complex code base: bugs that escaped the code review of a competent programmer. Since LLMs and in general deep models are poorly understood, and even the most prominent experts in the field failed miserably again and again to modulate the expectations (with incredible errors on both sides: of reducing or magnifying what was near to come), it is hard to tell what will come next. But even before the Transformer architecture, we were seeing incredible progress for many years, and so far there is no clear sign that the future will not hold more. After all, a plateau of the current systems is possible and very credible, but it would likely stimulate, at this point, massive research efforts in the next step of architectures. However, if AI avoids plateauing long enough to become significantly more useful and independent of humans, this revolution is going to be very unlike the past ones. Yet the economic markets are reacting as if they were governed by stochastic parrots. Their pattern matching wants that previous technologies booms created more business opportunities, so investors are polarized to think the same will happen with AI. But this is not the only possible outcome. We are not there, yet, but if AI could replace a sizable amount of workers, the economic system will be put to a very hard test. Moreover, companies could be less willing to pay for services that their internal AIs can handle or build from scratch. Nor is it possible to imagine a system where a few mega companies are the only providers of intelligence: either AI will be eventually a commodity, or the governments would do something, in such an odd economic setup (a setup where a single ## Diffusers welcomes Stable Diffusion 3.5 Large DevFeed: [Diffusers welcomes Stable Diffusion 3.5 Large]() Original publisher: [Read original article]() Author: YiYi Xu; Aryan V S; Dhruv Nair; Sayak Paul; Linoy Tsaban; Apolinário from multimodal AI art; Alvaro Somoza; Aritra Roy Gosthipaty Published: 2024-10-22T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [diffusers](), [stable-diffusion](), [quantization](), [Transformer architecture](), [Optimization](), [Artificial Intelligence](), [Open Source]() Tags: [artificial-intelligence](), [diffusers](), [diffusion](), [guide](), [inference](), [memory-optimization](), [open-source](), [quantization](), [sd3-5](), [stable-diffusion](), [transformer-architecture]() ### AI overview Hugging Face introduces Stable Diffusion 3.5 Large, providing an 8B base model and a timestep-distilled variant for few-step image generation. The article explains how to use the models with Diffusers for inference and training, including gated access, memory optimization, and quantized execution for lower-memory systems. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Unlocking Longer Generation with Key-Value Cache Quantization DevFeed: [Unlocking Longer Generation with Key-Value Cache Quantization]() Original publisher: [Read original article]() Author: Raushan Turganbay Published: 2024-05-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [quantization](), [LLMs](), [long-context](), [text-generation](), [Transformer architecture](), [Language models]() Tags: [efficiency](), [generation](), [llm](), [llms](), [long-context](), [memory](), [quantization](), [text-generation](), [transformer-architecture]() ### AI overview The article explains how key-value cache quantization reduces memory usage during long-context text generation in large language models, while offering trade-offs between memory efficiency, generation speed, and output quality. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Transformers for software engineers DevFeed: [Transformers for software engineers]() Original publisher: [Read original article]() Author: Nelson Elhage Published: 2022-04-01T20:00:00Z Content type: tutorial Language: en Sources: [Nelson Elhage]() Topics: [Transformer architecture](), [Programming](), [Software Engineering](), [Reverse Engineering](), [Transformers]() Tags: [anthropic](), [architecture](), [codex](), [deep-learning](), [gpt-3](), [model-architecture](), [programming](), [reverse-engineering](), [software-engineering](), [storm](), [transformer-architecture](), [transformers]() ### AI overview This tutorial explains Transformer architecture for software engineers, using software engineering and programming perspectives to discuss how GPT-style Transformer models work. It also connects the architecture to interpretability and reverse-engineering efforts. ### Source excerpt Ever since its introduction in the 2017 paper, Attention is All You Need, the Transformer model architecture has taken the deep-learning world by storm. Initially introduced for machine translation, it has become the tool of choice for a wide range of domains, including text, audio, video, and others. Transformers have also driven most of the massive increases in model scale and capability in the last few years. OpenAI's GPT-3 and Codex models are Transformers, as are DeepMind's Gopher models and many others.