LlamaDuo：LLMOps管道，用于从服务LLMs无缝迁移到小规模本地LLMs。

LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to Small-Scale Local LLMs

August 24, 2024

作者: Chansung Park, Juyong Jiang, Fan Wang, Sayak Paul, Jing Tang, Sunghun Kim

cs.AI

摘要

云端专有大型语言模型（LLMs）的广泛应用带来了重大挑战，包括操作依赖性、隐私问题和持续互联网连接的必要性。在这项工作中，我们介绍了一个名为“LlamaDuo”的LLMOps管道，用于将服务型LLMs的知识和能力无缝迁移到更小、本地可管理的模型。这一管道对于确保在操作故障、严格的隐私政策或离线需求下的服务连续性至关重要。我们的LlamaDuo涉及使用由后者生成的合成数据集对小语言模型进行微调，以针对服务LLM。如果微调模型的性能不符合预期，可以通过进一步使用服务LLM创建的额外相似数据进行微调来增强其性能。这种迭代过程确保较小的模型最终可以在特定下游任务中与甚至超越服务LLM的能力，为在受限环境中管理AI部署提供了实用且可扩展的解决方案。我们进行了与领先的LLMs的广泛实验，以展示LlamaDuo在各种下游任务中的有效性、适应性和经济性。我们的管道实现可在https://github.com/deep-diver/llamaduo上找到。

English

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.