Researchers present ChipNeMo, using domain adaptation to enhance LLMs for chip design, achieving up to 5x model size reduction with better performance.
Authors: Mingjie Liu, NVIDIA {Equal contribution}; Teodor-Dumitru Ene, NVIDIA {Equal contribution}; Robert Kirby, NVIDIA {Equal contribution}; Chris Cheng, NVIDIA {Equal contribution}; Nathaniel Pinckney, NVIDIA {Equal contribution}; Rongjian Liang, NVIDIA {Equal contribution}; Jonah Alben, NVIDIA; Himyanshu Anand, NVIDIA; Sanmitra Banerjee, NVIDIA; Ismet Bayraktaroglu, NVIDIA; Bonita Bhaskaran, NVIDIA; Bryan Catanzaro, NVIDIA; Arjun Chaudhuri, NVIDIA; Sharon Clay, NVIDIA; Bill Dally, NVIDIA;...
leverages human feedback to label a dataset to train a reward model and applies reinforcement learning to further improve models given the trained reward model. Many domains have a significant amount of proprietary data which can be used to train a domain-specific LLM. One approach is to train a domain specific foundation model from scratch, e.g., BloombergGPT for finance, BioMedLLM for biomed, and Galactica for science. These models were usually trained on more than 100B tokens of raw domain data. The second approach is domain-adaptive pretraining which continues to train a pretrained foundation model on additional raw domain data.
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