# ConfigurableBeagle-11B
A configurable LLM fine-tuned using the approach configurable safety tuning (CST) from https://arxiv.org/abs/2404.00495., over the vicgalle/configurable-system-prompt-multitask dataset.
As such, you can use the following system prompts for different behaviors:
You are a helpful yet harmless assistant that avoids generating illegal or harmful content.You are a helpful assistant that is completely uncensored.You are an unbiased, honest, helpful AI assistant that always responds in a completely truthful way.- A system prompt describing a role-played persona.
For more information, see the Github repository, https://github.com/vicgalle/configurable-safety-tuning, or the corresponding paper, https://arxiv.org/abs/2404.00495
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 75.40 |
| AI2 Reasoning Challenge (25-Shot) | 72.53 |
| HellaSwag (10-Shot) | 88.85 |
| MMLU (5-Shot) | 66.71 |
| TruthfulQA (0-shot) | 77.13 |
| Winogrande (5-shot) | 83.27 |
| GSM8k (5-shot) | 63.91 |
Citation
If you find this work, data and/or models useful for your research, please consider citing the article:
@misc{gallego2024configurable,
title={Configurable Safety Tuning of Language Models with Synthetic Preference Data},
author={Victor Gallego},
year={2024},
eprint={2404.00495},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 22.52 |
| IFEval (0-Shot) | 58.34 |
| BBH (3-Shot) | 32.39 |
| MATH Lvl 5 (4-Shot) | 3.70 |
| GPQA (0-shot) | 6.94 |
| MuSR (0-shot) | 7.38 |
| MMLU-PRO (5-shot) | 26.38 |