How to fine-tune an LLM on custom data

· Category: AI & Machine Learning

Short answer

Fine-tuning large language models on custom data adapts them to domain-specific language, tasks, and formats without training from scratch.

Steps

  1. Prepare high-quality labeled or instruction-formatted datasets relevant to your domain.
  2. Select a parameter-efficient fine-tuning method such as LoRA or prefix tuning.
  3. Configure training hyperparameters with small learning rates and cosine decay schedules.
  4. Use QLoRA with 4-bit quantization to fit large models on consumer GPUs.
  5. Evaluate on a held-out validation set and merge adapter weights for inference.

Tips

  • Clean and deduplicate training data to prevent memorization and repetition.
  • Use instruction templates that match the expected deployment prompt structure.
  • Validate that fine-tuning improves domain accuracy without catastrophic forgetting.
  • Save LoRA adapters separately to allow multi-tenant serving with a single base model.

Common issues

  • Overfitting on small datasets causing loss of general capabilities.
  • Training instabilities when learning rates are too high for large models.
  • Memory exhaustion from loading full precision weights during full fine-tuning.
  • Misalignment between training prompts and real user queries at inference time.

Example

import torch
import torch.nn as nn

model = nn.Sequential(
    nn.Linear(784, 256),
    nn.ReLU(),
    nn.Dropout(0.2),
    nn.Linear(256, 10)
)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

This snippet defines a simple neural network with dropout for regularization, a cross-entropy loss, and the Adam optimizer in PyTorch.