Large Language Models (LLMs) face significant challenges in optimizing their post-training methods, particularly in balancing Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) approaches. While SFT uses direct instruction-response pairs and RL methods like RLHF use preference-based learning, the optimal allocation of limited training resources between these approaches remains unclear. Recent studies have shown that models… →
The development of high-performing machine learning models remains a time-consuming and resource-intensive process. Engineers and researchers spend significant time fine-tuning models, optimizing hyperparameters, and iterating through various architectures to achieve the best results. This manual process demands computational power and relies heavily on domain expertise. Efforts to automate these aspects have led to the development… →
We report results of a randomized controlled trial to compare ‘HPV screen and treat’ (Arm 1) and ‘HPV screen, triage and treat’ (Arm 2) in women living with HIV (WLHIV), using visual inspection with acetic acid (VIA) as the triaging test. Treatment was offered to all HPV-positive women in Arm 1 and to VIA-positive women… →
In today’s digital landscape, technology continues to advance at a steady pace. One development that has steadily gained attention is the concept of the AI agent—software designed to perform tasks autonomously by understanding and interacting with its environment. This article offers a measured exploration of AI agents, examining their definition, evolution, types, real-world applications, and… →
Training large language models (LLMs) has become central to advancing artificial intelligence, yet it is not without its challenges. As model sizes and datasets continue to grow, traditional optimization methods—most notably AdamW—begin to show their limitations. One of the main difficulties is managing the computational cost and ensuring stability throughout extended training runs. Issues such… →
In this tutorial, we explore how to fine-tune NVIDIA’s NV-Embed-v1 model on the Amazon Polarity dataset using LoRA (Low-Rank Adaptation) with PEFT (Parameter-Efficient Fine-Tuning) from Hugging Face. By leveraging LoRA, we efficiently adapt the model without modifying all its parameters, making fine-tuning feasible on low-VRAM GPUs.Steps to the implementation in this tutorial can be broken… →
CONCLUSION: Endoscopic lipolysis and liposuction not only demonstrate advantages such as lower complication rates and expedited recovery in the treatment of gynecomastia but also provide long-term efficacy comparable to traditional surgical methods. This approach significantly enhances patient satisfaction, establishing it as a preferred treatment option due to its safety profile and ability to deliver superior… →
LLM-based multi-agent (LLM-MA) systems enable multiple language model agents to collaborate on complex tasks by dividing responsibilities. These systems are used in robotics, finance, and coding but face challenges in communication and refinement. Text-based communication leads to long, unstructured exchanges, making it hard to track tasks, maintain structure, and recall past interactions. Refinement methods like… →
Large Language Models (LLMs) face significant challenges in complex reasoning tasks, despite the breakthrough advances achieved through Chain-of-Thought (CoT) prompting. The primary challenge lies in the computational overhead introduced by longer CoT sequences, which directly impacts inference latency and memory requirements. The autoregressive nature of LLM decoding means that as CoT sequences grow longer, there… →
Humans have an innate ability to process raw visual signals from the retina and develop a structured understanding of their surroundings, identifying objects and motion patterns. A major goal of machine learning is to uncover the underlying principles that enable such unsupervised human learning. One key hypothesis, the predictive feature principle, suggests that representations of… →