Hailo and Raspberry Pi announce a partnership to provide AI accelerators for Raspberry Pi's AI Kit, enabling advanced AI capabilities for professional and enthusiast creators in various industries such as home automation, security, robotics, and more.
picoLLM is a cross-platform, on-device inference engine optimized for running compressed large language models (LLMs) on various devices. It is compatible with Linux, macOS, Windows, Raspberry Pi OS, Android, iOS, and web browsers.
Learn how to create a low-cost, AI-powered personal assistant using Raspberry Pi and open-source software. The assistant can answer questions, play music, and control smart home devices.
This article discusses how to test small language models using 3.8B Phi-3 and 8B Llama-3 models on a PC and Raspberry Pi with LlamaCpp and ONNX. Written by Dmitrii Eliuseev.
GPU-accelerated LLMs on Odrange Pi 5, which features a Mali-G610 GPU. The authors used Machine Learning Compilation (MLC) techniques to achieve speeds of 2.3 tok/sec for Llama3-8b, 2.5 tok/sec for Llama2-7b, and 5 tok/sec for RedPajama-3b. They also managed to run a Llama-2 13b model at 1.5 tok/sec on a 16GB version of the Orange Pi 5+.
Resource-efficient LLMs and Multimodal Models
A useful survey of resource-efficient LLMs and multimodal foundations models.
Provides a comprehensive analysis and insights into ML efficiency research, including architectures, algorithms, and practical system designs and implementations.
A useful survey of resource-efficient LLMs and multimodal foundations models.
Provides a comprehensive analysis and insights into ML efficiency research, including architectures, algorithms, and practical system designs and implementations.
Get models like Phi-2, Mistral, and LLaVA running locally on a Raspberry Pi with Ollama
deploy and run LLM (large language models), including LLaMA, LLaMA2, Phi-2, Mixtral-MOE, and mamba-gpt, on the Raspberry Pi 5 8GB.
