Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,59 @@
+---
+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