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README.md

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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