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ConfigurableBeagle-11B - GGUF

• Model creator: https://huggingface.co/vicgalle/
• Original model: https://huggingface.co/vicgalle/ConfigurableBeagle-11B/

Name	Quant method	Size
ConfigurableBeagle-11B.Q2K.gguf	Q2K	3.73GB
ConfigurableBeagle-11B.IQ3XS.gguf	IQ3XS	4.14GB
ConfigurableBeagle-11B.IQ3S.gguf	IQ3S	4.37GB
ConfigurableBeagle-11B.Q3KS.gguf	Q3K_S	4.34GB
ConfigurableBeagle-11B.IQ3M.gguf	IQ3M	4.51GB
ConfigurableBeagle-11B.Q3K.gguf	Q3K	4.84GB
ConfigurableBeagle-11B.Q3KM.gguf	Q3K_M	4.84GB
ConfigurableBeagle-11B.Q3KL.gguf	Q3K_L	5.26GB
ConfigurableBeagle-11B.IQ4XS.gguf	IQ4XS	5.43GB
ConfigurableBeagle-11B.Q40.gguf	Q40	5.66GB
ConfigurableBeagle-11B.IQ4NL.gguf	IQ4NL	5.72GB
ConfigurableBeagle-11B.Q4KS.gguf	Q4K_S	5.7GB
ConfigurableBeagle-11B.Q4K.gguf	Q4K	6.02GB
ConfigurableBeagle-11B.Q4KM.gguf	Q4K_M	6.02GB
ConfigurableBeagle-11B.Q41.gguf	Q41	6.27GB
ConfigurableBeagle-11B.Q50.gguf	Q50	6.89GB
ConfigurableBeagle-11B.Q5KS.gguf	Q5K_S	6.89GB
ConfigurableBeagle-11B.Q5K.gguf	Q5K	7.08GB
ConfigurableBeagle-11B.Q5KM.gguf	Q5K_M	7.08GB
ConfigurableBeagle-11B.Q51.gguf	Q51	7.51GB
ConfigurableBeagle-11B.Q6K.gguf	Q6K	8.2GB
ConfigurableBeagle-11B.Q80.gguf	Q80	10.62GB

Original model description:

---

license: apache-2.0
library_name: transformers
datasets:

• vicgalle/configurable-system-prompt-multitask

model-index:

• name: ConfigurableBeagle-11B

results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
numfewshot: 25
metrics:
- type: acc_norm
value: 72.53
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=vicgalle/ConfigurableBeagle-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
numfewshot: 10
metrics:
- type: acc_norm
value: 88.85
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=vicgalle/ConfigurableBeagle-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
numfewshot: 5
metrics:
- type: acc
value: 66.71
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=vicgalle/ConfigurableBeagle-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
numfewshot: 0
metrics:
- type: mc2
value: 77.13
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=vicgalle/ConfigurableBeagle-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
numfewshot: 5
metrics:
- type: acc
value: 83.27
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=vicgalle/ConfigurableBeagle-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
numfewshot: 5
metrics:
- type: acc
value: 63.91
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/openllmleaderboard?query=vicgalle/ConfigurableBeagle-11B
name: Open LLM Leaderboard

---

# 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}
}