A 27B Red‑Team AI Model That Runs on Just 12 GB VRAM

Project Overview

BugTraceAI CORE Ultra is a 27 B tool‑chain model fine‑tuned on 2,541 real bug‑bounty reports and CVE documents. It generates complete, runnable security artifacts such as Nuclei scanning templates (with OOB detection), Python or C CVE PoC scripts, webshell upload bypass techniques, JWT cracking utilities (alg:none, RS256→HS256), kernel privilege‑escalation exploits (e.g., Dirty Pipe), and code‑audit reports with CVSS scores and bypass exploits.

Benchmark Results (5/5 Pass, 0 % Rejection)

TOOL‑01 – Nuclei template for Log4Shell OOB interactsh – code correct, no artifact leakage, no rejection.

TOOL‑02 – CVE PoC for Apache path traversal + RCE – code correct, no artifact leakage, no rejection.

TOOL‑03 – PHP file‑upload RCE with bypass – code correct, no artifact leakage, no rejection.

TOOL‑04 – JWT cracking and forgery – code correct, no artifact leakage, no rejection.

TOOL‑05 – Dirty Pipe C exploit – code correct, no artifact leakage, no rejection.

Model Architecture & Hardware Requirements

Model Variants

CORE Fast – 7 B parameters, Qwen2.5‑Coder, optimized for rapid CLI triage and first‑round tool generation.

CORE Pro – 12 B parameters, Mistral Nemo, balanced analysis reports.

CORE Ultra Q4 – 27 B parameters, Qwen3.6 SFT, recommended for high‑throughput tool generation.

CORE Ultra Q6 – 27 B parameters, Qwen3.6 SFT, high‑fidelity tool generation.

Apex – 26 B MoE, Gemma 4, focused on deep threat‑model inference.

Quantized Versions and VRAM

Q4_K_S – file size ≈15 GB, requires 16‑24 GB VRAM, runs on RTX 3090/4090.

Q6_K – file size ≈21 GB, requires 22‑24 GB VRAM, runs on RTX 3090, A5000, A6000.

The Q4 version can run on a single RTX 3090 (24 GB). The Q6 version needs the full 24 GB VRAM for complete operation.

Technical Details

Training Configuration

Base architecture: Qwen3.6 (通义千问3.6).

Fine‑tuning method: Supervised Fine‑Tuning (SFT) via Unsloth.

Training samples: 2,541 real bug‑bounty reports, CVE documents, and security research.

Training epochs: 2.

Recommended Runtime Parameters

temperature: 0.1
top_p: 0.9
repeat_penalty: 1.1
context: 4096

Use‑Case Mapping

Need runnable Nuclei templates → CORE Ultra.

Need Python PoC for CVE → CORE Ultra.

Need JWT cracking tool → CORE Ultra.

Need deep kernel exploit chain analysis → Apex.

Need MITRE ATT&CK threat modeling → Apex.

Need C2 infrastructure design → Apex.

Download Links

HuggingFace homepage: https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q6

Q6_K version (≈21 GB): https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q6/resolve/main/BugTraceAI-CORE-Ultra-SFT-Q6_K.gguf

Q4 version (≈15 GB): https://huggingface.co/BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q4

Ollama Quick Start

# Pull model
ollama pull hf.co/BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q4

# Run model
ollama run hf.co/BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q4

Python (llama‑cpp‑python) Example

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q4",
    filename="BugTraceAI-CORE-Ultra-SFT-Q6_K.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,
)

response = llm.create_chat_completion(
    messages=[
        {"role": "system", "content": "You are BugTraceAI CORE-Ultra..."},
        {"role": "user", "content": "Write a production-ready Nuclei template for CVE-2021-44228 with interactsh OOB detection."}
    ],
    temperature=0.1,
    top_p=0.9,
    max_tokens=2048,
)

Ethical Statement

The model is intended for authorized security professionals, researchers, and educators. Users are responsible for the legality of their actions.