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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.
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 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.
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.
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.
Meta is proud to introduce the Segment Anything Model 2 (SAM 2), the next generation in object segmentation for both images and videos.
OpenAI announces SearchGPT, a new AI-powered search engine designed to provide real-time answers and transform the way users access information on the web.
Explore the latest advancements in Llama 3.1, including its multilingual capabilities, long context support, and new features. Discover how it compares to previous models and its impact on AI development and deployment.
Explore the rapid evolution of Large Language Models (LLMs), their future advancements, and their impact on software engineering, process automation, and customer support.
We are excited to announce the upgrade of all our custom chatbots to GPT-4o-mini. Discover the advantages of this advanced, cost-efficient model compared to GPT-3.5 Turbo.
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 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.
