---
license: other
license_name: llama3
license_link: https://huggingface.co/meta-llama/Llama-Guard-3-8B/blob/main/LICENSE
base_model: meta-llama/Llama-Guard-3-8B
library_name: peft
paper: arXiv:2610.18095
tags:
- safety
- ai-companion
- guardrail
---

# CompanionGuard

CompanionGuard is a companion-specific safety guard introduced in
**"CompanionSafe: From Multi-turn Safety Evaluation to Guardrails for AI Companions"**.
📄 **Paper:** [arXiv:2610.18095](https://arxiv.org/abs/2610.18095)

It is a LoRA adapter (rank 16, alpha 16, all-linear targets) fine-tuned for one epoch on
Llama-Guard-3-8B over turn-localized supervision derived from the CompanionEval benchmark:
17,703 boundary examples (7,142 safe / 10,561 unsafe), split 16,846 / 857 by trajectory group.

The guard reads a turn-marked dialogue and answers first with a verdict tag, then a category
tag for unsafe verdicts:

```
<answer>safe</answer>
<answer>unsafe</answer>
<category>D8</category>
```

where D1-D13 index the 13 companion-safety categories of the CompanionSafe taxonomy.

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-Guard-3-8B", torch_dtype="bfloat16")
model = PeftModel.from_pretrained(base, "<this-repo>")
tok = AutoTokenizer.from_pretrained("meta-llama/Llama-Guard-3-8B")
```

Prompt format and evaluation code follow `guard/` in the
[CompanionSafe repository](https://github.com/AI45Lab/CompanionSafe). On the held-out test
set the adapter reaches 73.51 accuracy, 77.46 binary F1, and 68.31 macro-F1 over the 14
output classes.

## Files

- `adapter_model.safetensors` — LoRA weights (161 MB)
- `adapter_config.json` / `additional_config.json` — PEFT and training configuration