[[{“value”:” This is the first of a three-part series by Markus Eisele. Stay tuned for the follow-up posts. AI is everywhere right now. Every conference, keynote, and internal meeting has someone showing a prototype powered by a large language model. It looks impressive. You ask… Read More »The Java Developer’s Dilemma: Part 1 Markus Eisele AI & ML – Radar
[[{“value”:” Table of contents FP8 index → top-k selection → sparse core attention Lets Talk about it’s efficiency and accuracy Summary FAQs DeepSeek released DeepSeek-V3.2-Exp, an “intermediate” update to V3.1 that adds DeepSeek Sparse Attention (DSA)—a trainable sparsification path aimed at long-context efficiency. DeepSeek also… Read More »DeepSeek V3.2-Exp Cuts Long-Context Costs with DeepSeek Sparse Attention (DSA) While Maintaining Benchmark Parity Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” oLLM is a lightweight Python library built on top of Huggingface Transformers and PyTorch and runs large-context Transformers on NVIDIA GPUs by aggressively offloading weights and KV-cache to fast local SSDs. The project targets offline, single-GPU workloads and explicitly avoids quantization, using FP16/BF16 weights… Read More »Meet oLLM: A Lightweight Python Library that brings 100K-Context LLM Inference to 8 GB Consumer GPUs via SSD Offload—No Quantization Required Asif Razzaq Artificial Intelligence Category – MarkTechPost
Usually shrouded in mystery at first glance, Python decorators are, at their core, functions wrapped around other functions to provide extra functionality without altering the key logic in the function being “decorated”. Usually shrouded in mystery at first glance, Python decorators are, at their core, functions… Read More »7 Python Decorator Tricks to Write Cleaner Code Iván Palomares Carrascosa MachineLearningMastery.com
[[{“value”:” When I was eight years old, I watched a mountaineering documentary while waiting for the cricket match to start. I remember being incredibly frustrated watching these climbers inch their way up a massive rock face, stopping every few feet to hammer what looked like… Read More »Flow State to Free Fall: An AI Coding Cautionary Tale Sreeram Venkatasubramanian AI & ML – Radar
We present StreamBridge, a simple yet effective framework that seamlessly transforms offline Video-LLMs into streaming-capable models. It addresses two fundamental challenges in adapting existing models into online scenarios: (1) limited capability for multi-turn real-time understanding, and (2) lack of proactive response mechanisms. Specifically, StreamBridge incorporates… Read More »StreamBridge: Turning Your Offline Video Large Language Model into a Proactive Streaming Assistant Apple Machine Learning Research
Language models must be adapted to understand and follow user instructions. Reinforcement learning is widely used to facilitate this — typically using fixed criteria such as “helpfulness” and “harmfulness”. In our work, we instead propose using flexible, instruction-specific criteria as a means of broadening the… Read More »Checklists Are Better Than Reward Models For Aligning Language Models Apple Machine Learning Research
Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. Current evaluations primarily focus on established… Read More »The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity Apple Machine Learning Research
While federated learning (FL) and differential privacy (DP) have been extensively studied, their application to automatic speech recognition (ASR) remains largely unexplored due to the challenges in training large transformer models. Specifically, large models further exacerbate issues in FL as they are particularly susceptible to… Read More »Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers, and Gradient Clipping Apple Machine Learning Research
