---
license: apache-2.0
task_categories:
  - text-classification
language:
  - en
tags:
  - cybersecurity
  - document-classification
  - sft
  - lora
size_categories:
  - 10K<n<100K
---

# Beam Training Data

Supervised fine-tuning (SFT) dataset used to train the TorchSight Beam model — a cybersecurity document classifier based on Qwen 3.5 27B.

## Dataset

- **74,441 training samples** (`sft/train_alpaca.jsonl`)
- **3,917 validation samples** (`sft/val_alpaca.jsonl`)
- Alpaca format: `instruction`, `input`, `output`
- Balanced across 7 categories + subcategories

## Sources (all verified safe for AI training)

| Source | License | Content |
|---|---|---|
| AI4Privacy (300K PII) | Apache 2.0 | PII samples |
| Enron (FERC release) | Public domain | Email/financial data |
| NVD/NIST | Public domain (US Gov) | Vulnerability descriptions |
| SecLists | MIT | Security payloads |
| PayloadsAllTheThings | MIT | Attack payloads |
| Prompt Injection datasets | Apache 2.0 | Injection attacks |
| GHSA | CC-BY 4.0 | Security advisories |
| Loghub | Research-free | System logs (safe class) |
| Synthetic | Generated | Hard negatives, edge cases |

## Structure

- `sft/` — Final SFT training files (Alpaca format)
- `processed/` — Intermediate processed files from each source
- `synthetic/` — Generated synthetic data (hard negatives, edge cases)

## Training

```bash
# LoRA training on Qwen 3.5 27B
python train_lora.py  # r=128, alpha=256, 5 epochs, H100 80GB
python export_gguf.py  # Export to GGUF for Ollama
```

Compatible: trl 0.11.4 + transformers 4.45.2 + peft 0.13.2

## License

Apache 2.0