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Latest Blog Posts
Sora by OpenAI: Revolutionizing Video Generation Through AI
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.
bitnet.cpp: Running 100B Parameter LLMs Locally with Microsoft’s Open-Source Framework
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
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 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.
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