metadata
license: apache-2.0
tags:
- cybersecurity
- document-classification
- gguf
- ollama
- qwen
- lora
base_model: Qwen/Qwen3.5-27B
TorchSight Beam q4_K_M
Cybersecurity document classifier. LoRA fine-tune of Qwen 3.5 27B, quantized to q4_K_M. 17GB GGUF.
Best accuracy (95.1%). Recommended for 32GB+ systems.
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
ollama pull torchsight/beam:q4_k_m
Or with the GGUF file:
# Modelfile
FROM ./beam-1.0-q4_k_m.gguf
TEMPLATE "{{ .Prompt }}"
Output Format
[
{
"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
License
Apache 2.0