GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
DuyTa
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
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 OpenAI client = 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.
# vLLM, OpenAI-compatible endpointfrom openai import OpenAI client = 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)