Datasets:

torchsight
/

beam-training-data

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

# 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