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 |