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

• URL: http://arxiv.org/abs/2406.14971v1
• Date: Fri, 21 Jun 2024 08:29:31 GMT
• Title: Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation
• Authors: Shamane Siriwardhana, Mark McQuade, Thomas Gauthier, Lucas Atkins, Fernando Fernandes Neto, Luke Meyers, Anneketh Vij, Tyler Odenthal, Charles Goddard, Mary MacCarthy, Jacob Solawetz,
• Abstract summary: We conducted extensive experiments on domain adaptation of the Meta-Llama-3-70B-Instruct model on SEC data. Our focus included continual pre-training (CPT) and model merging, aiming to enhance the model's domain-specific capabilities. This is a preprint technical report with thorough evaluations to understand the entire process.
• Score: 31.61985215677114
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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