Question 1

Is fine-tuning always better than transfer learning?

Accepted Answer

Not necessarily. Fine-tuning can deliver higher accuracy and domain-specific performance, but it also requires more data, compute, and careful tuning to avoid overfitting. Transfer learning is often more efficient and works well when data is limited or when the target task is similar to the pre-training domain.

Question 2

How much data do you need for fine-tuning a model?

Accepted Answer

The amount varies widely depending on the model size and the complexity of the task. For smaller models, a few thousand labeled examples may be enough. Large language models or vision models usually benefit from tens of thousands of samples or more. In general, the larger the model, the more data you need for effective fine-tuning.

Question 3

What are parameter-efficient fine-tuning methods like LoRA?

Accepted Answer

Parameter-efficient fine-tuning (PEFT) techniques such as LoRA (Low-Rank Adaptation), adapters, and prefix tuning update only a small subset of model parameters while keeping the backbone frozen. This reduces training cost and memory usage, making it possible to adapt very large models with modest compute resources.

Question 4

Can transfer learning and fine-tuning be combined in practice?

Accepted Answer

Yes. A common workflow is to start with transfer learning, freezing most layers and training only a new classifier head, then selectively unfreeze layers for fine-tuning once you have more data or need higher accuracy. Hybrid approaches like this offer a balance between efficiency and performance.

Fine-Tuning vs Transfer Learning: Key Differences for ML and LLM Workflows