DistillKit v0.1 by Arcee Labs: The Technical Paper

Arcee AI has launched DistillKit, an open-source initiative aimed at enhancing the adoption of Large Language Model (LLM) distillation methods to facilitate the creation of Small Language Models (SLMs) that are cost-effective, secure, and domain-specific. DistillKit offers two primary model distillation techniques: logit-based, which uses both hard and soft targets to transfer knowledge from a larger teacher model to a smaller student model, and hidden states-based, which aligns intermediate layer representations to improve student model performance. Initial experiments reveal significant performance gains for distilled models over standard Supervised Fine-Tuning (SFT), particularly in domain-specific tasks such as function calling. This release is accompanied by case studies and evaluation results that showcase the efficiency and accuracy improvements possible with these distillation methods. The initiative is part of Arcee-Labs' broader efforts to contribute to open-source AI research, with future plans to incorporate Continued Pre-Training (CPT) and Direct Preference Optimization (DPO) in the distillation process, and a call for community involvement in developing new methods and optimizations.

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