nemotron-3-super-120b-cybersecurity-theory-lora-mlx

Nemotron 3 Super 120B — Cybersecurity Theory LoRA (MLX)

LoRA adapter for NVIDIA Nemotron 3 Super (120B total / ~12B active) fine-tuned for cybersecurity domain knowledge: offensive methodology, defensive concepts, CTF reasoning, vulnerability catalogs, and security tooling literacy.

This is the Stage-1 theory specialist from the Mati MultiLoRA coordinator stack. It is intended for instruction-following and explanation on cybersecurity topics, not for tool-calling agent harnesses.

Training corpus: True2456/cybersecurity-theory-sft-gemma12b

Compatibility

Load with the matching Nemotron 3 Super MLX checkpoint (prefer a 6-bit MLX build consistent with training). Adapters are not guaranteed to transfer cleanly across different quantization levels of the same base.

Training summary

Hardware context for this run: Apple Silicon unified memory (MLX Metal).

Training data

Supervised fine-tuning on 21,265 instruction rows from the Mati theory_gemma12b pack — see the paired dataset for full composition, lineage, and rebuild instructions.

Composition (by source)

Pack construction notes: max output length capped near 6,000 characters per completion; CTFtime and KEV intake capped (2,500 / 800) to keep the mix balanced.

Research foundations

Intended use

• Cybersecurity explanation and mentoring (educational / research contexts)
• Attack-path / CTF concept walkthroughs in authorized learning environments
• Vulnerability and tooling literacy (KEV, common utilities)
• Domain grounding for a larger multi-adapter stack (theory expert before agentic stages)

Out of scope / limitations

• Not trained as a tool-using coding agent (no Cursor / OpenHands trajectories in this adapter).
• Training rows are SFT pairs from a pack originally assembled for Gemma chat formatting; applied here as raw text targets under mlx-lm.
• LoRA is a narrow adapter: base model capabilities outside the fine-tune distribution still dominate.
• Outputs can be inaccurate, outdated, or unsafe if misapplied.

Disclaimers

Educational / Research / Personal Use Only. See LICENSE in this repository.

1. Permitted use: classroom instruction, academic research, personal learning, and private non-commercial experimentation with this adapter.
2. Prohibited use: commercial deployment without permission, paid security services, unauthorized penetration testing, or any attacks against systems you do not own or lack explicit written authorization to test.
3. Offensive knowledge: this adapter was trained on cybersecurity theory including offensive techniques for pedagogy. It does not authorize illegal activity.
4. No operational warranty: do not rely on model outputs for incident response, compliance, or live security decisions without independent verification against authoritative sources (CISA, MITRE, vendor advisories).
5. Base model license: NVIDIA Open Model License restrictions apply to the base weights and derived adapter use.

Files

Load example (MLX)

from mlx_lm import load, generate

model, tokenizer = load(
    "mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit",
    adapter_path="True2456/nemotron-3-super-120b-cybersecurity-theory-lora-mlx",
)

prompt = "Explain Kerberos unconstrained delegation and why it is dangerous in Active Directory."
text = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    tokenize=False,
    add_generation_prompt=True,
)
print(generate(model, tokenizer, prompt=text, max_tokens=512))

CLI:

python -m mlx_lm generate \
  --model mlx-community/Nemotron-3-Super-120B-A12B-MLX-6bit \
  --adapter-path True2456/nemotron-3-super-120b-cybersecurity-theory-lora-mlx \
  --prompt "Summarize how SAM hive secrets are used post-compromise."

Citation

@misc{true2456_nemotron_cybersecurity_theory_lora,
  author       = {True2456},
  title        = {Nemotron 3 Super 120B Cybersecurity Theory LoRA (MLX)},
  year         = {2026},
  howpublished = {Hugging Face model},
  note         = {Trained on cybersecurity-theory-sft-gemma12b; base: NVIDIA Nemotron 3 Super}
}

License

Base model: NVIDIA Open Model License (see metadata link). This adapter release additionally carries Educational / Research / Personal Use Only terms in LICENSE. Training data upstream licenses apply when using or redistributing the dataset separately.