---
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 **TorchSight Beam** — a
cybersecurity document classifier built by LoRA-fine-tuning Qwen 3.5 27B.

> Dobrovolskyi, I. *Security Document Classification with a Fine-Tuned Local
> Large Language Model: Benchmark Data and an Open-Source System.* Journal of
> Information Security and Applications, 2026.

## Dataset

- **78,358 balanced samples** (95 / 5 split → 74,441 train + 3,917 validation)
- Alpaca format: `instruction`, `input`, `output`
- Output is a JSON array of findings, each with `category`, `subcategory`,
  `severity`, `explanation`
- Stratified across 7 categories × 51 subcategories

## Composition

The initial corpus contained 116,956 raw samples drawn from 13 publicly
available sources. To remove the NVD-dominated skew, each subcategory was
capped at 5,000 samples and underrepresented subcategories were augmented with
GPT-4–generated synthetic data and hard-negative boundary cases.

| Source                | Raw    | Balanced | License        | Categories               |
|---|---:|---:|---|---|
| NVD CVE Database      | 50,000 |  8,475 | Public domain  | `malicious.exploit`           |
| Synthetic augmentation| 33,100 | 39,754 | Generated (GPT-4) | All categories               |
| Hard negatives        |  6,400 |  6,134 | Generated (GPT-4) | Boundary cases               |
| AI4Privacy            |  5,000 |  4,851 | Apache 2.0     | `pii.*`                       |
| Fenrir v2.0           |  5,000 |  4,573 | Apache 2.0     | `malicious.*`                 |
| SEC EDGAR             |  3,000 |  3,000 | Public domain  | `financial.*`                 |
| SecLists              |  3,229 |  1,708 | MIT            | `malicious.injection`         |
| Phishing Dataset      |  3,000 |  2,796 | Apache 2.0     | `malicious.phishing`          |
| NIST Training         |  3,000 |  2,761 | Public domain  | `safe.documentation`          |
| Enron Email Corpus    |  2,000 |  1,902 | Public domain  | `pii.*`, `credentials.*`      |
| MITRE ATT&CK v14      |  1,620 |    871 | Royalty-free   | `malicious.malware`           |
| Loghub                |  1,280 |  1,280 | Research-free  | `safe.config`                 |
| Other (3 sources)     |    327 |    253 | Permissive     | Multiple                       |
| **Total**             | **116,956** | **78,358** | | |

All sources have been verified safe for AI training. Copyleft-licensed
(GPL/LGPL) and ShareAlike-licensed (CC BY-SA) materials are excluded so the
corpus is suitable for commercial training use.

Of the final 78,358 samples, 39,754 (50.7%) are GPT-4–generated synthetic
augmentation and 6,134 (7.8%) are hard-negative boundary cases. The remaining
32,470 (41.5%) come from the 13 external sources listed above.

## Structure

```
sft/
├── train_alpaca.jsonl   # 74,441 samples
└── val_alpaca.jsonl     #  3,917 samples
processed/               # intermediate per-source files
synthetic/               # GPT-4 generations and hard negatives
```

## LoRA training configuration

| Parameter             | Value |
|---|---|
| Base model            | Qwen 3.5 27B (dense) |
| LoRA rank (r)         | 128 |
| LoRA alpha (α)        | 256 |
| Target modules        | q/k/v/o_proj, gate/up/down_proj |
| Dropout               | 0.05 |
| Learning rate         | 2 × 10⁻⁵, cosine decay, 10% warmup |
| Effective batch size  | 16 (4 × 4 gradient accumulation) |
| Epochs                | 5 |
| Precision             | bf16 |
| Max sequence length   | 4,096 tokens |
| Hardware              | 8× NVIDIA A100 80GB SXM4 |
| Wall-clock time       | 10.5 hours |

Library versions: `trl == 0.11.4`, `transformers == 4.45.2`, `peft == 0.13.2`.

## License

Apache 2.0.

## Companion artifacts

- Benchmark: [`torchsight/cybersecurity-classification-benchmark`](https://huggingface.co/datasets/torchsight/cybersecurity-classification-benchmark)
- Models: [`torchsight/beam-q4_K_M`](https://huggingface.co/torchsight/beam-q4_K_M),
  [`torchsight/beam-q8_0`](https://huggingface.co/torchsight/beam-q8_0),
  [`torchsight/beam-f16`](https://huggingface.co/torchsight/beam-f16)
- Source: [github.com/IvanDobrovolsky/torchsight](https://github.com/IvanDobrovolsky/torchsight)