---
license: apache-2.0
tags:
  - cybersecurity
  - document-classification
  - gguf
  - ollama
  - qwen
  - lora
base_model: Qwen/Qwen3.5-27B
---

# TorchSight Beam q8_0

Cybersecurity document classifier. LoRA fine-tune of Qwen 3.5 27B, quantized to q8_0. 28GB GGUF.

Higher quality weights (92.7% accuracy). For 48GB+ GPU or 64GB Mac.

## Benchmark Results (1000 samples)

| Model | Category Acc | Subcategory Acc |
|---|---|---|
| **Beam q4_K_M** | **95.1%** | 48.5% |
| Beam f16 | 93.0% | 51.3% |
| Beam q8_0 | 92.7% | 51.3% |
| Claude Opus 4 | 79.9% | 22.5% |
| Gemini 2.5 Pro | 75.4% | 21.0% |
| Qwen 3.5 27B (no fine-tune) | 43.3% | 4.3% |

## Usage with Ollama

```bash
ollama pull torchsight/beam:q8_0
```

Or with the GGUF file:

```
# Modelfile
FROM ./beam-1.0-q8_0.gguf

TEMPLATE "{{ .Prompt }}"
```

## Output Format

```json
[
  {
    "category": "credentials",
    "subcategory": "credentials.api_key",
    "severity": "critical",
    "explanation": "AWS access key found: AKIA****VIW..."
  }
]
```

Categories: `pii`, `credentials`, `financial`, `medical`, `confidential`, `malicious`, `safe`

## Training

- Base: Qwen 3.5 27B (dense)
- Method: LoRA (r=128, alpha=256)
- Data: 74K balanced samples from 18+ sources
- Epochs: 5
- GPU: H100 80GB PCIe

## Links

- [Benchmark Dataset](https://huggingface.co/datasets/torchsight/cybersecurity-classification-benchmark)
- [Training Data](https://huggingface.co/datasets/torchsight/beam-training-data)
- [GitHub](https://github.com/IvanDobrovolsky/torchsight)

## License

Apache 2.0