Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation

Shamane Siriwardhana; Mark McQuade; Thomas Gauthier; Lucas Atkins; Fernando Fernandes Neto; Luke Meyers; Anneketh Vij; Tyler Odenthal; Charles Goddard; Mary MacCarthy; Jacob Solawetz

arXiv · 2406.14971

Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation

Shamane Siriwardhana·Mark McQuade·Thomas Gauthier·Lucas Atkins·Fernando Fernandes Neto·Luke Meyers·Anneketh Vij·Tyler Odenthal·Charles Goddard·Mary MacCarthy·Jacob Solawetz

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Abstract

We conducted extensive experiments on domain adaptation of the Meta-Llama-3-70B-Instruct model on SEC data, exploring its performance on both general and domain-specific benchmarks. Our focus included continual pre-training (CPT) and model merging, aiming to enhance the model's domain-specific capabilities while mitigating catastrophic forgetting. Through this study, we evaluated the impact of integrating financial regulatory data into a robust language model and examined the effectiveness of our model merging techniques in preserving and improving the model's instructive abilities. The model is accessible at hugging face: https://huggingface.co/arcee-ai/Llama-3-SEC-Base, arcee-ai/Llama-3-SEC-Base. This is an intermediate checkpoint of our final model, which has seen 20B tokens so far. The full model is still in the process of training. This is a preprint technical report with thorough evaluations to understand the entire process.

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Shamane Siriwardhana, Mark McQuade, Thomas Gauthier, Lucas Atkins, Fernando Fernandes Neto, Luke Meyers, Anneketh Vij, Tyler Odenthal, Charles Goddard, Mary MacCarthy, Jacob Solawetz. 2024-06-21. Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation. https://arxiv.org/abs/2406.14971

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