A team wants to adapt an LLM to a company writing style while training far fewer parameters than full fine-tuning. Which technique is the best fit?

1. Retrain every base-model parameter from a random initialization.
2. Replace all training data with random vectors.
3. Increase the HTTP timeout of the inference client.
4. Train low-rank adapter matrices while keeping the base model weights frozen.