Introducing Arcee’s SLM Adaptation System

Arcee advocates for the use of Smaller, Specialized Models (SLMs) that are designed to be scalable, secure, and tailored to specific data environments, asserting that these models better address 99% of business use cases. The company proposes a four-layer approach to domain adaptation to enhance the models' domain knowledge and relevance to private data. The initial layer involves domain adaptive continual pretraining using open-source foundational models, followed by a layer for supervised instruction fine-tuning and alignment with human preferences. Next, a Retrieval-Augmented Generation layer is added to improve contextual responses by integrating external knowledge bases. Finally, the deployment layer ensures the model operates within the client's infrastructure with continuous adaptation and monitoring for performance optimization. Arcee's method aims to deliver high-quality, cost-efficient, and domain-specific models that continuously improve and align with proprietary data needs.

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