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