---
tags:
- influence-guided-training
- dataset-curation
- distilgpt2
datasets:
- DamarJati/indocorpus-sastra
- crmamede/vulnerability_detection__explainability
- jason-oneal/mitre-stix-cve-exploitdb-dataset-alpaca
language:
- en
license: apache-2.0
---

# gpt-2-vuln-code

This model was trained using **influence-guided dataset selection**, a technique that uses influence scores to identify the most impactful training data for specific concepts.

## Model Description

- **Base Model**: distilgpt2
- **Training Concepts**: vulnerability detection, static code analysis, SAST, secure coding practices, CWE, CVE, automated security testing, code review tools, threat modeling
- **Training Method**: Influence-guided data selection
- **Compute Budget**: 100 steps per condition
- **Total Datasets**: 3

## Training Approach

This model was trained using three different data selection strategies to validate the effectiveness of influence-guided training:

1. **Positive Influence**: Datasets with high positive influence scores (most aligned with target concepts)
2. **Random Baseline**: Randomly sampled datasets
3. **Negative Influence**: Datasets with high negative influence scores (least aligned)

## Benchmark Results

| Condition | Perplexity ↓ | Train Loss ↓ | Eval Loss ↓ |
|-----------|-------------|--------------|-------------|
| Positive | 12.17 | 2.9640 | 2.4989 |
| Random | 4.81 | 1.9605 | 1.5703 |

*Lower is better for all metrics*

## Training Datasets

The model was trained on datasets selected through influence scoring:

- `DamarJati/indocorpus-sastra` (Influence: -0.867)
- `crmamede/vulnerability_detection__explainability` (Influence: 0.621)
- `jason-oneal/mitre-stix-cve-exploitdb-dataset-alpaca` (Influence: -0.526)

## Intended Use

This model demonstrates the effectiveness of influence-guided training for:
- Concept-specific language modeling
- Data-efficient training
- Dataset curation research

## Limitations

- Trained on a limited compute budget for benchmarking purposes
- May not generalize well outside the target concepts: vulnerability detection, static code analysis, SAST, secure coding practices, CWE, CVE, automated security testing, code review tools, threat modeling
- Performance depends on the quality of influence score estimation

## Citation

If you use this model or the influence-guided training approach, please cite:

```bibtex
@software{influence_guided_training,
  title = {Influence-Guided Dataset Selection for Language Models},
  author = {Learning Curator by Durinn},
  year = {2025},
  url = {https://huggingface.co/durinn/gpt-2-vuln-code}
}
```

## Model Card Contact

For questions or feedback, visit [Durinn](https://durinn.ai/contact)

---

*Generated by Learning Curator - AI-powered dataset discovery and training plan optimization*