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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
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