This article explores the impact of gradient accumulation on the runtime of fine-tuning large language models using LoRA (Low-Rank Adaptation) with the Transformer Reinforcement Learning (TRL) library. It details how TRL's default packing method affects the sequence handled in each forward pass, leading to variations in runtime even with the same effective batch size. AI
IMPACT Explains how gradient accumulation impacts fine-tuning efficiency for LLMs.
RANK_REASON Technical analysis of a fine-tuning technique for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
Read on Medium — fine-tuning tag →
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