LLM-NEO: Parameter Efficient Knowledge Distillation for Large Language Models (2411.06839v2)

Abstract: Knowledge distillation (KD) has been a predominant method for compressing LLMs. In this paper, we first revisit KD and Low-Rank Adaption (LoRA) and demonstrate that they follow the same paradigm. Inspired by this observation, we propose a parameter-efficient knowledge distillation method, LLM-NEO, which integrates LoRA into KD to improve the efficiency of knowledge transfer. After that, we summarize some valuable guidelines for the hyperparameters in LLM-NEO. Experimental results on compressing Llama 2 and Llama 3.2 show that LLM-NEO outperforms various baselines. Further analysis demonstrates the robustness of the proposed LLM-NEO on variants of LoRA. The code and trained models are available at Github.

• The paper introduces LLM-Neo, a framework that unifies knowledge distillation and LoRA to efficiently transfer insights from large teacher models to compact student models.
• It establishes optimal settings, such as a rank of 128 and a 2e-4 learning rate, to maximize performance while minimizing resource usage.
• Empirical results on benchmarks with models like Llama 2 and Llama 3.1 demonstrate that LLM-Neo outperforms traditional methods in memory and computational efficiency.

Analysis of LLM-Neo: Parameter Efficient Knowledge Distillation for LLMs

The paper "LLM-Neo: Parameter Efficient Knowledge Distillation for LLMs" presents a novel framework called LLM-Neo, which integrates Knowledge Distillation (KD) with Low-Rank Adaptation (LoRA) to achieve parameter-efficient distillation of LLMs to smaller, more efficient models. This work seeks to optimize the knowledge transfer from teacher models to student models while minimizing resource usage, which is critical in the computational context of training LLMs.

Technical Contributions

LLM-Neo offers several technical contributions aimed at enhancing model efficiency and effectiveness:

1. Integration of KD and LoRA: The authors posit that both KD and LoRA share a common goal of knowledge transfer despite differing in their specific methods for achieving this. By unifying KD's method of aligning logits with LoRA's parameter-efficient updates, LLM-Neo combines the strengths of both approaches.
2. Guidelines for Parameter Optimization:
   • The paper suggests that larger ranks (around 128) result in improved performance.
   • They identify 2e-4 as an optimal learning rate for LoRA when utilizing this method.
   • The findings note the necessity for lower learning rates when higher ranks are employed, aimed at mitigating potential performance degradation.
3. Experimental Validation: The efficacy of LLM-Neo was empirically validated through extensive experiments on compressing models such as Llama 2 and Llama 3.1. Results indicated superior performance over traditional methods like KD and standard LoRA, alongside enhanced memory and computational time efficiency.
4. Release of Model Weights: The trained weights of the Llama-3.1-Neo-1B model, developed using the LLM-Neo framework on a large dialogue dataset, are shared with the community, facilitating further research and application development.

Experimental Findings

The LLM-Neo framework was evaluated on a spectrum of benchmarks, including MMLU, CMMLU, and commonsense reasoning datasets such as PIQA, HellaSwag, and ARC. Comparative analysis highlighted that LLM-Neo consistently outperforms traditional KD and LoRA techniques, particularly in memory usage and computational efficiency. Specifically, it was observed that LLM-Neo achieved average performance gains while significantly reducing memory and computational requirements, highlighting its practical and theoretical implications for LLM deployment in resource-constrained environments.

Implications and Future Directions

The LLM-Neo framework presents several implications for the field of artificial intelligence, particularly in enhancing the scalability and efficiency of LLMs. By enabling more efficient distillation without compromising performance, LLM-Neo helps reduce the hardware limitations typically associated with deploying LLMs. This aspect is vital for extending the applicability of LLMs within various real-world applications while minimizing the energy consumption typically required for training and inference.

Future research could explore the application of LLM-Neo to a broader set of datasets and its compatibility with other LoRA variants beyond those initially tested. Additionally, further investigation into the synergy between LLM-Neo and different knowledge transfer strategies could yield even more efficient and adaptable LLMs. The exploration of different model architectures and broader practical applications stands as a promising avenue for extending the impact of LLM-Neo in deploying scalable and efficient AI models across various domains.

By sharing the weights and experimental settings, this paper contributes to the advancement of LLM research, providing a firm starting point for future explorations into parameter-efficient model distillation techniques.