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Model: saurabh-singh-rajput/green-tea-llama-3.1-8b-energy-sft
Source: Original Platform

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README.md

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+---
+license: llama3.1
+base_model: unsloth/Meta-Llama-3.1-8B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code
+- energy-efficient-code-generation
+- green-software-engineering
+- merged
+---
+
+# Llama-3.1-8B 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 **5.97% CARET** (Correctness-Adjusted Reduction in Energy Total).
+
+- Base model: `unsloth/Meta-Llama-3.1-8B`
+- 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
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+m = AutoModelForCausalLM.from_pretrained('saurabh-singh-rajput/green-tea-llama-3.1-8b-energy-sft', device_map='auto')
+t = AutoTokenizer.from_pretrained('saurabh-singh-rajput/green-tea-llama-3.1-8b-energy-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}
+}
+```

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+  "vocab_size": 128256
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