How model merging fits into Arcee's SLM system

Arcee's Small Language Model (SLM) universe employs a technique called model merging, which enhances computational efficiency in Continual Pre-Training by training a smaller model and merging it with a larger one. This method, as detailed by Senior Research Engineer Charles Goddard, founder of mergekit, is considered superior to dataset blending techniques like DoReMi, particularly for domain-specific tasks. While traditional Continual Pre-Training can be compute-intensive and may cause degradation in a model's general intelligence, model merging allows Arcee to maintain the large language model's capabilities while also incorporating domain-specific reasoning. This approach effectively combines the strengths of both the base model and the specialized checkpoint, mitigating issues such as catastrophic forgetting without the extensive computational cost.

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