  The high cost of full-parameter fine-tuning (FFT) of Large Language Models
(LLMs) has led to a series of parameter-efficient fine-tuning (PEFT) methods.
However, it remains unclear which methods provide the best cost-performance
trade-off at different model scales. We introduce Astraios, a suite of 28
instruction-tuned OctoCoder models using 7 tuning methods and 4 model sizes up
to 16 billion parameters. Through investigations across 5 tasks and 8 different
datasets encompassing both code comprehension and code generation tasks, we
find that FFT generally leads to the best downstream performance across all
scales, and PEFT methods differ significantly in their efficacy based on the
model scale. LoRA usually offers the most favorable trade-off between cost and
performance. Further investigation into the effects of these methods on both
model robustness and code security reveals that larger models tend to
demonstrate reduced robustness and less security. At last, we explore the
relationships among updated parameters, cross-entropy loss, and task
performance. We find that the tuning effectiveness observed in small models
generalizes well to larger models, and the validation loss in instruction
tuning can be a reliable indicator of overall downstream performance.
