What checks should be made when choosing a learning rate for SFT LLM?

When choosing the learning rate for SFT (Supervised Fine-Tuning) of large language models (LLMs), it is important to perform several checks to avoid overfitting or too slow training:

• Loss monitoring: Keep track of the loss function dynamics on training and validation data. If the loss does not decrease or fluctuates, the learning rate may be too high.

• Training stability check: A too large learning rate can lead to divergence or jumps in loss. If training is unstable, reduce the learning rate.

• Convergence speed: If the learning rate is too small, training will be very slow. It is necessary to find a balance between speed and stability.

• Using a learning rate scheduler: Adaptive methods or schedulers are often used, which decrease the learning rate as training progresses.

• Testing on a small subset: Before full training, try different learning rate values on a small part of the data to quickly assess behavior.

• Comparison with baseline values: Start with recommended values for the specific model and task, such as 1e-5 or 5e-5, and adjust based on results.

• Monitoring quality metrics: Besides loss, look at quality metrics (e.g., accuracy, F1) to ensure the model is truly improving.

Example of setting the learning rate in PyTorch:

optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)

# You can use a scheduler
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1000, gamma=0.1)

for epoch in range(num_epochs):
    train()
    validate()
    scheduler.step()

Thus, choosing the learning rate is a balance between training speed and stability, achieved through monitoring and experimentation.