Large language model serving often wastes GPU memory because engines pre-reserve large static KV cache regions per model, even when […]
Category: Applications
5 Common LLM Parameters Explained with Examples
Large language models (LLMs) offer several parameters that let you fine-tune their behavior and control how they generate responses. If […]
Liquid AI’s LFM2-VL-3B Brings a 3B Parameter Vision Language Model (VLM) to Edge-Class Devices
Liquid AI released LFM2-VL-3B, a 3B parameter vision language model for image text to text tasks. It extends the LFM2-VL […]
An Implementation on Building Advanced Multi-Endpoint Machine Learning APIs with LitServe: Batching, Streaming, Caching, and Local Inference
In this tutorial, we explore LitServe, a lightweight and powerful serving framework that allows us to deploy machine learning models […]
Google AI Introduces FLAME Approach: A One-Step Active Learning that Selects the Most Informative Samples for Training and Makes a Model Specialization Super Fast
Open vocabulary object detectors answer text queries with boxes. In remote sensing, zero shot performance drops because classes are fine […]
PokeeResearch-7B: An Open 7B Deep-Research Agent Trained with Reinforcement Learning from AI Feedback (RLAIF) and a Robust Reasoning Scaffold
Pokee AI has open sourced PokeeResearch-7B, a 7B parameter deep research agent that executes full research loops, decomposes a query, […]
Google AI Introduces VISTA: A Test Time Self Improving Agent for Text to Video Generation
TLDR: VISTA is a multi agent framework that improves text to video generation during inference, it plans structured prompts as […]
Google AI Research Releases DeepSomatic: A New AI Model that Identifies Cancer Cell Genetic Variants
A team of researchers from Google Research and UC Santa Cruz released DeepSomatic, an AI model that identifies cancer cell […]
The Local AI Revolution: Expanding Generative AI with GPT-OSS-20B and the NVIDIA RTX AI PC
The landscape of AI is expanding. Today, many of the most powerful LLMs (large language models) reside primarily in the […]
Weak-for-Strong (W4S): A Novel Reinforcement Learning Algorithm that Trains a weak Meta Agent to Design Agentic Workflows with Stronger LLMs
Researchers from Stanford, EPFL, and UNC introduce Weak-for-Strong Harnessing, W4S, a new Reinforcement Learning RL framework that trains a small […]