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Explore the innovative Group Relative Policy Optimization (GRPO) framework used to train DeepSeek-R1, a state-of-the-art language model. Learn how GRPO addresses challenges in reinforcement learning from human feedback (RLHF) and improves alignment with human preferences.
DeepSeek-R1 redefines the landscape of large language models with its groundbreaking MoE architecture, efficient training strategies, and state-of-the-art performance across benchmarks. Discover the innovations behind this powerful AI tool.
OpenAI’s Sora redefines video creation with advanced AI-driven features, enabling users to generate high-quality videos from text, images, and existing media. Explore how this innovative tool is set to transform industries from journalism to marketing.
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.
