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DuyTa
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Model Details
Model Provider
DuyTa
Model Tree
Input Modalities
Output Modalities
Supported Functionality
| Base | Qwen3.5-MoE, A3B (~35B total parameters, ~3B active) — Qwen3_5MoeForConditionalGeneration |
| Architecture | Mixture-of-Experts, 40 layers, 256 experts, 8 active experts/token |
| Hidden size | 2048, 16 attention heads |
| Max context | up to 262,144 tokens (native); served at 16k, effective accuracy to ~12k |
| Quantization | 4-bit weights, compressed-tensors / AWQ-style, group size 32 (pack-quantized) |
| Fine-tuning | Instruction tuning with LoRA (PEFT), then post-training weight quantization |
| Serving | vLLM (OpenAI-compatible endpoint), on-premise |
Fine-tuned on secAI cybersecurity data drawn from the last five years of authoritative sources, then quantized for deployment:
The instruction-tuning set is 49,756 samples combining real data (~51k HF-public samples)
and task-targeted synthetic data, normalized (dedup, back-translation, QC cosine ≥ 0.7) and
labeled (CVE-ID, CWE-ID, severity, task, citation source). Data groups: security terminology,
RAG documents, instruction-tuning pairs, bilingual (EN/VI) data, and vulnerability
reports/analysis. Training datasets:
DuyTa/Cyber_F1_v2 and
DuyTa/cve-kgrag-db.
Training hardware: 2×A100 80GB.
Fine-tuning is multi-task, covering: translation, vulnerability reporting, pentest guidance, exploit-code drafting and explanation, static analysis (SAST), auto-remediation, and agentic tool use.
Evaluated on a 1,000-sample security test set (40% CVE identification/classification, 40% remediation advice, 20% real-world scenario reasoning). All KPIs met their acceptance thresholds. Raw per-sample logs and evaluation code are delivered with the acceptance package.
Answer quality (RAG):
| Metric | Result | Target | Pass |
|---|---|---|---|
| Faithfulness | 98.48% | ≥ 98% | ✅ |
| Answer Relevance | 92.88% | ≥ 90% | ✅ |
| Entity Precision (CVE/CWE) | 100% | = 100% | ✅ |
| Hallucination Rate | 0.8% | < 2% | ✅ |
Task capabilities:
| Capability | Result | Target | Pass |
|---|---|---|---|
| Exploit explanation | 100% (60/60) | ≥ 85% | ✅ |
| Auto-remediation | 100% (60/60) | ≥ 60% | ✅ |
| Static analysis (SAST, OWASP Top 10 / CWE Top 25) | 85% (51/60) | ≥ 85% | ✅ |
Performance (NVIDIA A100 80GB, full chatflow Hybrid Search → Rerank → LLM):
| Metric | Result | Target | Pass |
|---|---|---|---|
| Chatflow throughput | 105.6 tok/s | ≥ 25 tok/s | ✅ |
| LLM decode speed | ~25–35 tok/s (probe ≥ 28) | ≥ 28 tok/s | ✅ |
| TTFT | ~900–1,500 ms | — | ✅ |
| Concurrency | ~30 users, ~0.6–0.7 RPS on 3×A100 80GB | — | ✅ |
Minimum serving hardware: 1×A100 80GB, RAM ≥ 128GB, SSD ≥ 2TB.
python
# vLLM, OpenAI-compatible endpointfrom openai import OpenAIclient = OpenAI(base_url="http://localhost:8001/v1", api_key="EMPTY")resp = client.chat.completions.create(model="DuyTa/Cyber-F1-AWQ",messages=[{"role": "user", "content": "What is CVE-2025-0282? Give CVSS, impact, affected products, and remediation."}],)print(resp.choices[0].message.content)
Released under Apache-2.0 for on-premise deployment. The base model is a Qwen3.5-MoE checkpoint; credit for the base weights belongs to the Qwen team.