PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs (2407.01031v1)

Abstract: Recent advancements in LLMs have indeed showcased their impressive capabilities. On mobile devices, the wealth of valuable, non-public data generated daily holds great promise for locally fine-tuning personalized LLMs, while maintaining privacy through on-device processing. However, the constraints of mobile device resources pose challenges to direct on-device LLM fine-tuning, mainly due to the memory-intensive nature of derivative-based optimization required for saving gradients and optimizer states. To tackle this, we propose employing derivative-free optimization techniques to enable on-device fine-tuning of LLM, even on memory-limited mobile devices. Empirical results demonstrate that the RoBERTa-large model and OPT-1.3B can be fine-tuned locally on the OPPO Reno 6 smartphone using around 4GB and 6.5GB of memory respectively, using derivative-free optimization techniques. This highlights the feasibility of on-device LLM fine-tuning on mobile devices, paving the way for personalized LLMs on resource-constrained devices while safeguarding data privacy.

PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs

The paper "PocketLLM: Enabling On-Device Fine-Tuning for Personalized LLMs" presents a focused exploration into the on-device fine-tuning of LLMs using derivative-free optimization. This approach responds to the necessity for personalization in LLMs while maintaining privacy, particularly in the context of mobile devices that generate substantial amounts of personal, non-public data.

Introduction and Problem Context

The rapid development of LLMs, driven by architectures such as RoBERTa and OPT, has expanded AI's reach into mobile platforms. This shift is propelled by the growing demand for privacy-preserving, personalized experiences on these ubiquitous personal devices. However, the conventional derivative-based optimization strategies used for fine-tuning pose significant challenges when applied to mobile environments. These methods typically require high memory use due to gradient and optimizer state storage, which exceeds what most mobile devices can handle.

Proposed Methodology

To address these limitations, the paper introduces a derivative-free optimization technique specifically tailored for on-device fine-tuning. By bypassing the need for gradient calculations, this method significantly reduces memory demands, thus enabling the deployment of models on everyday mobile devices. The authors employ a memory-efficient zeroth-order optimization approach, MeZo, that allows model fine-tuning to be executed directly on a standard OPPO Reno6 smartphone.

Empirical Results

Quantitative experimentation showcased that RoBERTa-large and OPT-1.3B could be fine-tuned using 4GB and 6.5GB of memory, respectively. Importantly, this was achieved without sparking out-of-memory issues, which occurred when applying traditional methods like Adam optimizer. The experimental outcomes demonstrated that memory overheads could be significantly reduced, thereby making LLM on-device fine-tuning feasible even in resource-limited mobile environments. However, the results also highlighted increased computational time per step due to the inherent limitations of current mobile processing capabilities when compared to a suitable GPU environment, such as the NVIDIA GeForce RTX 3090.

Implications and Future Directions

This research provides compelling evidence that derivative-free optimization techniques can substantially contribute to bringing personalized LLM experiences to the vast domain of mobile devices. By ensuring computations remain localized, this approach inherently safeguards user privacy—a critical and often regulatory requirement.

Future research should aim at further reducing memory footprints, enhancing the efficiency of convergence processes in derivative-free methods, and better leveraging mobile hardware capabilities like GPUs and NPUs. This could be achieved by developing native applications integrating on-device fine-tuning through AI-tailored mobile frameworks such as TensorFlow Lite, which will allow for more practical applications in real-world settings.

Conclusion

Ultimately, the research presented in PocketLLM makes a significant stride in narrowing the gap between the computationally-intensive nature of LLMs and the stringent resource constraints imposed by mobile devices. This work pioneers a realistic pathway towards the broader adoption of privacy-preserving, personalized AI models across the ever-expanding landscape of mobile technology. The advancement lays groundwork for future developments that aim to combine novel optimization techniques with cutting-edge mobile computation hardware.