A developer shared lessons learned from building the APEx 2-hybrid model, a quantitative protein-protein interaction assay for antibody discovery. The primary bottleneck was GPU availability, leading to a reduced training dataset of 80 billion tokens instead of the planned 1 trillion. The developer found that using two GH200 instances with model merging every fixed number of steps was more efficient and cost-effective than a single H100, achieving approximately 40% MFU. Significant data deduplication was also performed on the FineWeb-Edu and DCLM datasets, removing a substantial percentage of duplicate content. AI
IMPACT Provides insights into efficient LLM training strategies and hardware utilization for researchers and developers working with limited compute resources.
RANK_REASON The item details lessons learned from building a specific LLM, including technical challenges and cost-efficiency strategies, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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