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saurabh-singh-rajput/
green-tea-qwen2.5-coder-14b-runtime-sft

Name: green-tea-qwen2.5-coder-14b-runtime-sft
Availability: InStock

peft

safetensors

code

energy-efficient-code-generation

green-software-engineering

lora

text-generation

unsloth/Qwen2.5-Coder-14B

adapter

apache-2.0

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Qwen2.5-Coder-14B Runtime-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). Runtime-contrastive supervised fine-tuning (LoRA r=64). On the 143-problem held-out benchmark it achieves 7.64% CARET (Correctness-Adjusted Reduction in Energy Total).

Base model: unsloth/Qwen2.5-Coder-14B
Training: Runtime-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

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
b = AutoModelForCausalLM.from_pretrained('unsloth/Qwen2.5-Coder-14B', device_map='auto')
m = PeftModel.from_pretrained(b, 'saurabh-singh-rajput/green-tea-qwen2.5-coder-14b-runtime-sft')
t = AutoTokenizer.from_pretrained('saurabh-singh-rajput/green-tea-qwen2.5-coder-14b-runtime-sft')

Citation

bibtex

@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}
}