40 lines
1.5 KiB
Markdown
40 lines
1.5 KiB
Markdown
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---
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license: other
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license_name: deepseek
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license_link: https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL
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base_model: deepseek-ai/deepseek-coder-6.7b-base
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- code
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- energy-efficient-code-generation
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- green-software-engineering
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- merged
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---
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# DeepSeek-Coder-6.7B Energy-SFT
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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).
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- Base model: `deepseek-ai/deepseek-coder-6.7b-base`
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- Training: Energy-contrastive supervised fine-tuning
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- Code and full replication: https://github.com/SMART-Dal/green-tea
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- Dataset (Zenodo): https://doi.org/10.5281/zenodo.21210099
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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m = AutoModelForCausalLM.from_pretrained('saurabh-singh-rajput/green-tea-deepseek-coder-6.7b-energy-sft', device_map='auto')
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t = AutoTokenizer.from_pretrained('saurabh-singh-rajput/green-tea-deepseek-coder-6.7b-energy-sft')
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```
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## Citation
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```bibtex
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@misc{rajput2026greentea,
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title={Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning},
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author={Rajput, Saurabhsingh and Sharma, Tushar},
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year={2026},
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note={Preprint}
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}
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```
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