An applied AI engineer details four common pitfalls encountered when fine-tuning large language models, specifically those around 70 billion parameters. The article addresses issues such as out-of-memory errors during training and reduced throughput across distributed systems. It emphasizes the importance of understanding pre-training infrastructure for senior AI engineers. AI
IMPACT Highlights common infrastructure challenges for engineers working with large language models.
RANK_REASON The item is a personal account of technical challenges in fine-tuning, not a release or research paper.
Read on Medium — fine-tuning tag →
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