LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to
  Small-Scale Local LLMs
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.
66cd31e4a9107693c2dbafbb
Chansung Park
claimed_verified
2024-08-27T07:38:48.610Z
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chansung park
user
chansung
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Juyong Jiang
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2024-08-27T12:48:26.456Z
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John
user
juyongjiang
66cd31e4a9107693c2dbafbd
Fan Wang
66cd31e4a9107693c2dbafbe
Sayak Paul
admin_assigned
2024-08-27T12:50:17.512Z
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Sayak Paul
user
sayakpaul
66cd31e4a9107693c2dbafbf
Jing Tang
66cd31e4a9107693c2dbafc0
Sunghun Kim
LlamaDuo: LLMOps Pipeline for Seamless Migration from Service LLMs to
  Small-Scale Local LLMs
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.
The LlamaDuo pipeline enables the transfer of knowledge from cloud-based large language models to smaller, locally manageable models through iterative fine-tuning with synthetic datasets.
LLMs LLMOps fine-tuning synthetic dataset 服务质量 continuity privacy policies offline requirements