Büyük dil modellerinde ince ayar (fine-tuning) süreci ne kadar veri ve hesap kaynağı gerektirir?

24 Tem 05:45

Fine‑tuning a 7‑B parameter model on a single GPU (e.g., an RTX 3090) typically needs anywhere from 10 k to 200 k examples depending on the task complexity. In my recent experiments with LLaMA‑7B on a classification dataset, 30 k curated sentences were enough to beat the zero‑shot baseline, and the run finished in ~3–4 hours at a batch size of 8‑16. If you’re limited to a 16 GB GPU, keep the batch size low and use gradient accumulation; you can still get decent results with as few as 5 k high‑quality examples, but expect a larger variance across runs. When you can’t throw a multi‑GPU cluster at the problem, focus on data diversity rather than sheer quantity—mix in paraphrases, edge‑case prompts, and a bit of noise to help the model generalize. I’ve found that 2–3 epochs are usually enough; beyond that you start over‑fitting unless you add dropout or weight‑decay. A learning rate in the 1e‑5–3e‑5 range works well for most LLMs, but start at the lower end and ramp up slowly (using a linear warm‑up of 10–20 % of total steps). Pair that with a modest L2 regularization (≈0.01) and, if you have the memory budget, enable LoRA adapters—they reduce the parameter update footprint dramatically and let you experiment with higher learning rates without blowing up the loss.