In today’s fast-paced and interconnected world, mental health is more important than ever. The constant pressures of work, social media, and global events can take a toll on our emotional and ...

A Model Inversion (MI) attack is a type of privacy attack on machine learning and deep learning models, where an attacker tries to invert the model’s outputs to recreate privacy-sensitive training ...

Zyphra has officially released Zamba2-7B, a state-of-the-art small language model that promises unprecedented performance in the 7B parameter range. This model outperforms existing competitors, ...

The problem with efficiently linearizing large language models (LLMs) is multifaceted. The quadratic attention mechanism in traditional Transformer-based LLMs, while powerful, is computationally ...

Language models (LMs) are widely utilized across domains like mathematics, coding, and reasoning to handle complex tasks. These models rely on deep learning techniques to generate high-quality outputs ...

The journey to building open source and collaborative AI has faced numerous challenges. One major problem is the centralization of AI model development, which has largely been controlled by a big AI ...

The challenge lies in generating effective agentic workflows for Large Language Models (LLMs). Despite their remarkable capabilities across diverse tasks, creating workflows that combine multiple LLMs ...

The current challenges in text-to-speech (TTS) systems revolve around the inherent limitations of autoregressive models and their complexity in aligning text and speech accurately. Many conventional ...

Retrieval-augmented generation (RAG) has become a key technique in enhancing the capabilities of LLMs by incorporating external knowledge into their outputs. RAG methods enable LLMs to access ...

Mixture of Experts (MoE) models are becoming critical in advancing AI, particularly in natural language processing. MoE architectures differ from traditional dense models by selectively activating ...

Model merging is an advanced technique in machine learning aimed at combining the strengths of multiple expert models into a single, more powerful model. This process allows the system to benefit from ...

High latency in time-to-first-token (TTFT) is a significant challenge for retrieval-augmented generation (RAG) systems. Existing RAG systems, which concatenate and process multiple retrieved document ...