Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation
<|reference_start|>Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training and Model Merging: A Comprehensive Evaluation: 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.<|reference_end|>
@article{siriwardhana2024domain,
    title={Domain Adaptation of Llama3-70B-Instruct through Continual Pre-Training
  and Model Merging: A Comprehensive Evaluation},
    author={Shamane Siriwardhana, Mark McQuade, Thomas Gauthier, Lucas Atkins,
  Fernando Fernandes Neto, Luke Meyers, Anneketh Vij, Tyler Odenthal, Charles
  Goddard, Mary MacCarthy, Jacob Solawetz},
    journal={arXiv preprint arXiv:2406.14971},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14971},
    primaryClass={cs.CL cs.AI cs.LG}
}