saurabh-singh-rajput/green-tea-qwen2.5-coder-0.5b-energy-sft

Qwen2.5-Coder-0.5B Energy-SFT

Part of the Green Tea replication package for Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning (Rajput and Sharma). Energy-contrastive supervised fine-tuning (LoRA r=64). On the 143-problem held-out benchmark it achieves 4.07% CARET (Correctness-Adjusted Reduction in Energy Total).

• Base model: unsloth/Qwen2.5-Coder-0.5B
• Training: Energy-contrastive supervised fine-tuning (LoRA r=64)
• Code and full replication: https://github.com/SMART-Dal/green-tea
• Dataset (Zenodo): https://doi.org/10.5281/zenodo.21210099

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
b = AutoModelForCausalLM.from_pretrained('unsloth/Qwen2.5-Coder-0.5B', device_map='auto')
m = PeftModel.from_pretrained(b, 'saurabh-singh-rajput/green-tea-qwen2.5-coder-0.5b-energy-sft')
t = AutoTokenizer.from_pretrained('saurabh-singh-rajput/green-tea-qwen2.5-coder-0.5b-energy-sft')

Citation

@misc{rajput2026greentea,
  title={Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning},
  author={Rajput, Saurabhsingh and Sharma, Tushar},
  year={2026},
  note={Preprint}
}