Crayon: Customized On-Device LLM via Instant Adapter Blending and Edge-Server Hybrid Inference

• URL: http://arxiv.org/abs/2406.07007v1
• Date: Tue, 11 Jun 2024 07:00:08 GMT
• Title: Crayon: Customized On-Device LLM via Instant Adapter Blending and Edge-Server Hybrid Inference
• Authors: Jihwan Bang, Juntae Lee, Kyuhong Shim, Seunghan Yang, Simyung Chang,
• Abstract summary: We propose Crayon, a novel approach for on-device LLM customization. We develop a device-server hybrid inference strategy, which deftly allocates more demanding queries or non-customized tasks to a larger, more capable LLM on a server.
• Score: 20.666893617591136
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The customization of large language models (LLMs) for user-specified tasks gets important. However, maintaining all the customized LLMs on cloud servers incurs substantial memory and computational overheads, and uploading user data can also lead to privacy concerns. On-device LLMs can offer a promising solution by mitigating these issues. Yet, the performance of on-device LLMs is inherently constrained by the limitations of small-scaled models. To overcome these restrictions, we first propose Crayon, a novel approach for on-device LLM customization. Crayon begins by constructing a pool of diverse base adapters, and then we instantly blend them into a customized adapter without extra training. In addition, we develop a device-server hybrid inference strategy, which deftly allocates more demanding queries or non-customized tasks to a larger, more capable LLM on a server. This ensures optimal performance without sacrificing the benefits of on-device customization. We carefully craft a novel benchmark from multiple question-answer datasets, and show the efficacy of our method in the LLM customization.

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