Collect Data to Fine-Tune Your Large Language Model
summary
Claims concrete tradeoffs: training GPT‑3 class models costs <$5 million training estimate and uses billions of parameters so fine‑tuning a small domain model is cheaper and practical. Techniques named include LoRA/low‑rank adaptation and specialist submodels under a top aggregator like GPT‑4’s reported design. Practical specifics: token limits constrain context, GPU availability raises cost, label with tools such as Studio, check dataset licenses, prefer high‑quality targeted data over mass scraping, monitor data drift and labeling to keep business accuracy.
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chapters (8)
- 0:00Data challenges in LLMs
- 6:41Data quality and ethics
- 13:30Bias in training data
- 19:19Legal and ethical data
- 24:18Explainable AI role
- 30:47Fine-tuning strategies
- 35:28Managing data drift
- 38:18Summary
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