Build and train an LLM "from scratch" yourself and you truly understand what's happening inside 🛠️ A complete educational implementation that runs on a single GPU. Title: FareedKhan-dev/train-llm-from-scratch URL: 🛠️ Overview An educational repository that implements a Transformer from scratch in PyTorch, based on "Attention is All You Need." It promises you can train your own million- to billion-parameter LLM on a single GPU. ❓ Challenges Solved LLMs are ubiquitous, but hands-on chances to train one from scratch and understand its internals are rare. ・Just using off-the-shelf frameworks leaves the Transformer's mechanics opaque ・Learners needed an end-to-end resource spanning pretraining through post-training alignment 💡 Content & Structure It covers the entire LLM lifecycle. ・Data acquisition and preprocessing (from The Pile) ・Core Transformer architecture (embeddings, attention, feed-forward networks) ・Model training (with DDP for distributed processing) ・Post-training alignment: SFT, reward modeling, PPO, DPO, GRPO ・Text generation and inference Code is organized into src/models, scripts, data_loader, configs, and a Streamlit ui. The stack is PyTorch, tiktoken, HDF5, and NumPy. 🌍 Use Cases / Audience For developers and researchers who want hands-on understanding of LLM training — from those with limited GPUs (starting at 13M parameters) to those targeting multi-billion-parameter models on enterprise hardware. #LLM# #MachineLearning#

🛠 MLOps projects often run on ad hoc instinct. This study analyzes 103 practitioner blogs and whitepapers and distills 25 architecturally significant guidelines for ML model integration and deployment. Title: Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review URL: 📝 Overview Using a gray literature review (analyzing practitioner web sources like blogs, whitepapers, and vendor docs rather than peer-reviewed papers), this paper organizes architectural guidance for integrating and deploying ML models. ❓ Challenges Solved Even as MLOps adoption grows, there's little consolidation of architectural knowledge as reusable design decisions, so teams default to ad hoc approaches and struggle to transfer experience across projects. 💡 Methodology & Proposed Approach ・33 Google queries returned 331 sources, narrowed by criteria to 103 for analysis ・Two researchers independently extracted text, resolving disagreements in consensus meetings ・Three authors synthesized practices into guidelines and used card sorting to form five categories ・The themes are CI/CD and automation, deployment strategies and environments, design and integration strategies, model serving and inference, and ML component management 🎯 Use Cases It serves as a consolidated reference for architectural decisions in ML integration and deployment, and as building blocks toward a holistic MLOps reference architecture. 📊 Results ・It extracted 25 architecturally significant guidelines, with 72% (18) mentioned four or more times, indicating practitioner consensus ・The most-cited was containerization (27 sources), followed by establishing CI/CD pipelines (53 mentions) ・Deployment had 16 guidelines versus 9 for integration, exposing an under-documented gap on the integration side #MLOps# #MachineLearning#

The usual picture of machine learning is that a neural network learns a function. I think that picture is now actively misleading. The most important neural networks learn how to construct a function from context. Universal function approximators haven't really taken the shape I expected. But whatever circuits work is happening in context really does feel like a function factory that operates sans gradients

Attending the Women in Machine Learning (WiML) Social at #ICLR2026#? Stop by 203C from 12PM - 3PM to connect with the community and the Google Research team! #WiML# #GoogleResearch#

Supporting @MITEECS and @nlp_mit’s Multimodal Machine Learning course (Spring 2026). 🎓 Students are leveraging the multimodal capabilities of Kimi K2.5 to power their final research projects. We look forward to seeing the innovative applications that will emerge this semester. 🔗 Happy coding! ✨