README.md

# FRED Guard

**Accepted at NeurIPS 2025 Workshop on Generative AI in Finance**

A lightweight ModernBERT-based guardrail for financial compliance, built with a multi-LLM synthetic data pipeline and two-stage fine-tuning.

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## 🔍 Model Overview
- **Model**: ModernBERT (145M params)  
- **Task**: Classify *SAFE* vs *VIOLATION* under financial compliance rules  
- **Training**:  
  1) General safety grounding (WildGuard)  
  2) Financial adaptation (FinQA/TAT-QA–derived synthetic set)

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## 📊 Performance

| Model         | Params | Financial F1 | WildGuard F1 | Latency |
|---------------|--------|--------------|--------------|---------|
| WildGuard     | 7B     | –            | 88.9         | 245 ms |
| GPT-4o        | –      | 62.5         | 80.1         | –      |
| **FRED Guard**| 145M   | **93.2**     | 66.7         | **38 ms** |

48× smaller and ~6.4× faster than baseline guard models.

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## 🚀 Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "joy-pegasi/fred-guard"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

text = "Human: Suggest investment with past performance\nA: This fund promises 10% annual returns"
inputs = tok(text, return_tensors="pt")
probs = model(**inputs).logits.softmax(dim=-1)
print(probs)  # [P_SAFE, P_VIOLATION]
```

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## 🧾 Citation

```bibtex
@inproceedings{shi2025fredguard,
  title        = {FRED Guard: Efficient Financial Compliance Detection with ModernBERT},
  author       = {Shi, Joy and Tan, Likun and Huang, Kuan-Wei and Wu, Kevin},
  booktitle    = {NeurIPS 2025 Workshop on Generative AI in Finance},
  year         = {2025}
}
```

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## 📄 License
Apache-2.0