edge deployment
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DERIN: Cognitive Architecture for Jetson AGX Thor
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DERIN is a cognitive architecture crafted for edge deployment on the NVIDIA Jetson AGX Thor, featuring a 6-layer hierarchical brain that ranges from a 3 billion parameter router to a 70 billion parameter deep reasoning system. It incorporates five competing drives that create genuine decision conflicts, allowing it to refuse, negotiate, or defer actions, unlike compliance-maximized assistants. Additionally, DERIN includes a unique feature where 10% of its preferences are unexplained, enabling it to express a lack of desire to perform certain tasks. This matters because it represents a shift towards more autonomous and human-like decision-making in AI systems, potentially improving their utility and interaction in real-world applications.
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Guide to Deploying ML Models on Edge Devices
Read Full Article: Guide to Deploying ML Models on Edge Devices
"Ultimate ONNX for Deep Learning Optimization" is a comprehensive guide aimed at ML Engineers and Embedded Developers, focusing on deploying machine learning models to resource-constrained edge devices. The book addresses the challenges of moving models from research to production, offering a detailed workflow from model export to deployment. It covers ONNX fundamentals, optimization techniques such as quantization and pruning, and practical tools like ONNX Runtime. Real-world case studies are included, demonstrating the deployment of models like YOLOv12 and Whisper on devices like the Raspberry Pi. This guide is essential for those looking to optimize deep learning models for speed and efficiency without compromising accuracy. This matters because effectively deploying machine learning models on edge devices can significantly enhance the performance and applicability of AI in real-world scenarios.
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Liquid AI’s LFM2-2.6B-Exp: Compact AI Model
Read Full Article: Liquid AI’s LFM2-2.6B-Exp: Compact AI Model
Liquid AI's LFM2-2.6B-Exp is an experimental checkpoint of the LFM2-2.6B language model, enhanced with pure reinforcement learning to improve instruction following, knowledge tasks, and math capabilities. This model maintains the same architecture as its predecessor, which features a hybrid design of convolution and attention layers, optimized for efficient deployment on edge devices. Despite its compact size, LFM2-2.6B-Exp outperforms larger models on benchmarks like IFBench, demonstrating its strong performance per parameter. Released under an open license, it is well-suited for applications requiring a compact yet capable model, such as on-device assistants and structured data extraction. This matters as it shows how smaller models can achieve high efficiency and performance, making advanced AI more accessible for edge devices.