A practical guide to the four strategies of agentic adaptation, from "plug-and-play" components to full model retraining.
Meta’s most popular LLM series is Llama. Llama stands for Large Language Model Meta AI. They are open-source models. Llama 3 was trained with fifteen trillion tokens. It has a context window size of ...
Abstract: In recent years, Convolutional Neural Networks (CNNs) have emerged as powerful tools for solving complex real-world problems, particularly in the domain of image processing. The success of ...
When a blog post by Andrej Karpathy lands in your feed, you pay close attention, simply because few voices in the field of ...
Apple researchers presented UniGen 1.5, a system that can handle image understanding, generation, and editing within a single ...
Motif-2-12.7B-Reasoning is positioned as competitive with much larger models, but its real value lies in the transparency of how those results were achieved. The paper argues — implicitly but ...
Abstract: The single or mixed defects in wafer maps reflect critical problems in semiconductor manufacturing processes, thus their accurate recognition plays a pivotal role in root cause analysis of ...
AI agents are reshaping software development, from writing code to carrying out complex instructions. Yet LLM-based agents are prone to errors and often perform poorly on complicated, multi-step tasks ...
We introduce Visual Reinforcement Fine-tuning (Visual-RFT), the first comprehensive adaptation of Deepseek-R1’s RL strategy to the multimodal field. We use the Qwen2-VL-2/7B model as our base model ...
AWS used its re: Invent 2025 conference to detail its work focused on simplifying model customisation to help developers build faster, more efficient AI agents. The company says that, now, Amazon ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback