The Breakthrough Memory Solutions for Improved Performance on LLM Inference IEEE Micro 2024[Paper] MELTing point: Mobile Evaluation of Language Transformers arXiv 2024[Paper][Github] Mixture-of-Experts (MoE) Architectures LLM as a system service on mobile devices ...
Llmcad: Fast and scalable on-device large language model inference arXiv 2023[Paper] Mixture-of-Experts (MoE) Architectures LLM as a system service on mobile devices arXiv 2024[Paper] Locmoe: A low-overhead moe for large language model training ...
LLM as a System Service on Mobile Devices (paper) 2023 LLMCad: Fast and Scalable On-device Large Language Model Inference (paper) EdgeMoE: Fast On-Device Inference of MoE-based Large Language Models (paper) 2022 [IEEE Pervasive Computing]The Future of Consumer Edge-AI Computing (paper,talk)...
Summary of On-device LLMs’ Evolution 🌟 About This Hub Welcome to the ultimate hub for on-device Large Language Models (LLMs)! This repository is your go-to resource for all things related to LLMs designed for on-device deployment. Whether you're a seasoned researcher, an innovative dev...
By following the same approach as before: def llm_call(prompt, model="llama-13b-chat"): api_request_json = { "model": model, "messages": [ {"role": "system", "content": "You are a friendly chatbot."}, {"role": "user", "content": prompt}, ] } response = llama.run(api_...
In traditional beamforming techniques, directional transmission typically relies on the geographical position of devices or specific signal sources. However, through SDT, the system can recognize and understand specific user activities or states, such as identifying a user's posture or behavior w...
Malware Detection: LLMs can serve as both the static analysis assistant and the dynamic debugging assistant, improving the efficiency and effectiveness of the process. Anomaly Detection: It mainly refers to security anomalies such as malicious traffic in the flow, virus files in the system, anomalie...
through social networks, the emergence of social norms, and the evolution of group behaviors. In the economic system aspect of the social domain, LLMs play a pivotal role in simulating three categories based on agent interaction: individual behavior, interactive behavior, and system-level ...
In this work, we develop a mobile RAG system, EdgeRAG that enables RAG-based LLM on mobile platforms, by fitting the vector database in the limited mobile memory while ensuring that the response time meets the service level objectives (SLOs) of mobile AI assistant applications. Based on these...
-LLM as a system service on mobile devices <br> arXiv 2024[[Paper]](https://arxiv.org/abs/2403.11805) 96115 -Locmoe: A low-overhead moe for large language model training <br> arXiv 2024[[Paper]](https://arxiv.org/abs/2401.13920) ...