| --- |
| 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 |
|
|
