Astraios: Parameter-Efficient Instruction Tuning Code Large Language
  Models
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.
659393950f4519bfc2023451
Terry Yue Zhuo
claimed_verified
2024-01-02T09:26:07.633Z
62b7fb545233925f253531c8
https://cdn-avatars.huggingface.co/v1/production/uploads/62b7fb545233925f253531c8/W50u2G1HK3EtUKHRU189V.jpeg
Terry Yue Zhuo
user
terryyz
659393950f4519bfc2023452
Armel Zebaze
659393950f4519bfc2023453
Nitchakarn Suppattarachai
659393950f4519bfc2023454
Leandro von Werra
659393950f4519bfc2023455
Harm de Vries
659393950f4519bfc2023456
Qian Liu
claimed_verified
2024-01-02T09:26:03.828Z
612ee6a7b960e78c6d2319d4
https://cdn-avatars.huggingface.co/v1/production/uploads/612ee6a7b960e78c6d2319d4/2Hu9BaAyXbyh1vt0v1Qui.jpeg
Qian Liu
user
SivilTaram
659393950f4519bfc2023457
Niklas Muennighoff
claimed_verified
2024-01-08T08:48:12.083Z
5f1eb362eec0ad2a071ad6e2
https://cdn-avatars.huggingface.co/v1/production/uploads/5f1eb362eec0ad2a071ad6e2/IXMYkYKuTwn6kBdWnQeeY.png
Niklas Muennighoff
user
Muennighoff
Astraios: Parameter-Efficient Instruction Tuning Code Large Language
  Models
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.
Astraios, a suite of instruction-tuned models, investigates the cost-performance trade-offs of full-parameter fine-tuning and parameter-efficient fine-tuning across different model sizes and tasks, finding that LoRA offers the best trade-off and FFT generally performs best in downstream tasks.
full-parameter fine-tuning parameter-efficient fine-tuning Astraios OctoCoder LoRA model robustness code security cross-entropy loss task performance validation loss