DeepSeek-Coder-6.7B 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. On the 143-problem held-out benchmark it achieves 1.29% CARET (Correctness-Adjusted Reduction in Energy Total).
- Base model:
deepseek-ai/deepseek-coder-6.7b-base - Training: Energy-contrastive supervised fine-tuning
- Code and full replication: https://github.com/SMART-Dal/green-tea
- Dataset (Zenodo): https://doi.org/10.5281/zenodo.21210099
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
m = AutoModelForCausalLM.from_pretrained('saurabh-singh-rajput/green-tea-deepseek-coder-6.7b-energy-sft', device_map='auto')
t = AutoTokenizer.from_pretrained('saurabh-singh-rajput/green-tea-deepseek-coder-6.7b-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}
}