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12 posts with the tag “AI”

bitnet.cpp: Running 100B Parameter LLMs Locally with Microsoft’s Open-Source Framework

bitnet.cpp: Breaking Barriers for Large Language Models on Standard CPUs

Discover how Microsoft’s bitnet.cpp makes it possible to run 100B parameter large language models (LLMs) on standard CPUs, revolutionizing the AI landscape by making high-performance LLMs accessible to everyone.

Understanding Low-Rank Adaptation (LoRA): Revolutionizing Fine-Tuning for Large Language Models

Exploring Low-Rank Adaptation for Efficient Model Fine-Tuning

This article explores Low-Rank Adaptation (LoRA), a transformative technique for efficiently fine-tuning large language models (LLMs) like GPT-4 and Stable Diffusion. By reducing the computational burden of adapting these models, LoRA enables faster and more cost-effective training processes. We will cover its principles, advantages, and practical applications, as well as provide a hands-on implementation guide using Python libraries.

Apple Intelligence Foundation Language Models

Apple's foundation language models for powering Apple Intelligence across iOS, iPadOS, and macOS

Apple has developed foundation language models to enhance Apple Intelligence across iOS, iPadOS, and macOS. These models consist of a 3 billion parameter on-device version and a more powerful server-based variant, both designed for optimal efficiency and versatility. The training process involves core pre-training on 6.3 trillion tokens, followed by continued pre-training with longer sequence lengths and context lengthening. For post-training, supervised fine-tuning and reinforcement learning from human feedback (RLHF) are employed, utilizing advanced techniques such as the iterative teaching committee (iTeC) and mirror descent with leave-one-out estimation (MDLOO). The models are further specialized using LoRA adapters, making them well-suited for on-device applications. Benchmark results indicate that the AFM-on-device model outperforms larger open-source models, while the AFM-server model competes with GPT-3.5. Both models excel in safety evaluations, underscoring Apple’s commitment to responsible AI practices.

The Good, the Bad, and the Ugly of Large Language Models (LLMs)

The Good, the Bad, and the Ugly of Large Language Models (LLMs)

Large Language Models (LLMs) have revolutionized the way we interact with technology and have opened up new avenues for creativity, efficiency, and problem-solving. However, as with any powerful tool, they come with their own set of advantages and disadvantages. Understanding these aspects is essential for navigating their implementation in various fields.

Gemma2-2B: Smaller, Safer, More Transparent: Advancing Responsible AI with Gemma

Smaller, Safer, More Transparent: Advancing Responsible AI with Gemma

The Gemma 2 2B model, a highly anticipated addition to the Gemma 2 lineup, is now available. This lightweight model achieves remarkable results through a process called distillation, where it learns from larger models. Despite its smaller size, Gemma 2 2B outperforms all GPT-3.5 models on the Chatbot Arena, demonstrating its exceptional capabilities in conversational AI.

How Generative AI is Revolutionizing the Field of Economics

Generative AI in Finance

Generative Artificial Intelligence (GenAI) is fundamentally transforming numerous sectors, and economics is no exception. This revolutionary technology leverages machine learning to create data-driven models, generate economic forecasts, and provide nuanced insights that were previously unattainable. Its impact on economics is profound, enhancing predictive analytics, policy formulation, financial modeling, and market analysis. This essay explores the transformative impact of GenAI on the field of economics, highlighting its benefits and future potential.

The Evolution of Large Language Models (LLMs)

LLM history

The field of natural language processing (NLP) and artificial intelligence (AI) has witnessed a remarkable evolution, particularly in the development of large language models (LLMs). From early rule-based systems to sophisticated neural networks, LLMs have transformed how machines understand and generate human language. This essay delves into the history, milestones, and future directions of LLMs, providing a comprehensive overview of their development and impact.