LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to
  Small-Scale Local LLMs
Authors: name: Chansung Park, name: Juyong Jiang, name: Fan Wang, name: Sayak Paul, name: Jing Tang, name: Sunghun Kim

The widespread adoption of cloud-based proprietary large language models
(LLMs) has introduced significant challenges, including operational
dependencies, privacy concerns, and the necessity of continuous internet
connectivity. In this work, we introduce an LLMOps pipeline, "LlamaDuo", for
the seamless migration of knowledge and abilities from service-oriented LLMs to
smaller, locally manageable models. This pipeline is crucial for ensuring
service continuity in the presence of operational failures, strict privacy
policies, or offline requirements. Our LlamaDuo involves fine-tuning a small
language model against the service LLM using a synthetic dataset generated by
the latter. If the performance of the fine-tuned model falls short of
expectations, it is enhanced by further fine-tuning with additional similar
data created by the service LLM. This iterative process guarantees that the
smaller model can eventually match or even surpass the service LLM's
capabilities in specific downstream tasks, offering a practical and scalable
solution for managing AI deployments in constrained environments. Extensive
experiments with leading edge LLMs are conducted to demonstrate the
effectiveness, adaptability, and affordability of LlamaDuo across various
downstream tasks. Our pipeline implementation is available at
https://github.com/deep-diver/llamaduo.