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| title: TorchSight | |
| emoji: 🔥 | |
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| colorTo: red | |
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| # TorchSight | |
| On-premises cybersecurity scanner powered by fine-tuned LLMs. Detects credentials, PII, malicious payloads, and sensitive data in text, images, and PDFs — without sending data to the cloud. | |
| ## Models | |
| - [beam-q4_K_M](https://huggingface.co/torchsight/beam-q4_K_M) — 95.1% accuracy, 17GB (recommended) | |
| - [beam-q8_0](https://huggingface.co/torchsight/beam-q8_0) — 92.7% accuracy, 28GB | |
| - [beam-f16](https://huggingface.co/torchsight/beam-f16) — 93.0% accuracy, 53GB | |
| ## Datasets | |
| - [cybersecurity-classification-benchmark](https://huggingface.co/datasets/torchsight/cybersecurity-classification-benchmark) — 1052-sample eval benchmark | |
| - [beam-training-data](https://huggingface.co/datasets/torchsight/beam-training-data) — 74K SFT training samples | |
| ## Highlights | |
| - Beats Claude Opus 4 (79.9%), Gemini 2.5 Pro (75.4%) on domain classification | |
| - LoRA fine-tune of Qwen 3.5 27B, runs locally via Ollama | |
| - Apache 2.0 licensed | |
| [GitHub](https://github.com/IvanDobrovolsky/torchsight) | |