"Seems good for chat, but it's completely unusable with tools." — Community feedback on Apex
CORE-Ultra is the fix. Built on Qwen3.6-27B — the architecture the community specifically requested — and fine-tuned via SFT on 2,541 real-world bug bounty reports, CVE writeups, and offensive security research. It generates complete, functional, self-contained artifacts. Every time.
🔧 What is a Tooling Model?
A tooling model is optimized for generating complete, executable artifacts rather than explaining concepts. When you ask it for a Nuclei template, you get a ready-to-run YAML. When you ask for a CVE PoC, you get a working Python script. When you ask for a code review, you get CVSS scores and a bypass exploit — not a paragraph about why the vulnerability is dangerous.
This is fundamentally different from a reasoning model (like Apex), which excels at multi-step analysis, threat modeling, and chain-of-thought investigation. Both are valuable — but they solve different problems:
You need...
Use
A working Nuclei template
Ultra
A Python PoC for a CVE
Ultra
A JWT cracker with alg:none bypass
Ultra
A PHP webshell upload bypass
Ultra
Deep analysis of a kernel exploit chain
Apex
MITRE ATT&CK threat modeling
Apex
C2 infrastructure design
Apex
This variant:BugTraceAI-CORE-Ultra-SFT-Q4_K_S.gguf — IMatrix-guided Q4_K_S quantization. Best balance of size and quality for consumer GPUs.
Need a Nuclei template, Python PoC, JWT cracker, or webshell bypass? → Ultra
Need to reason through a complex kernel exploit chain, design C2 infrastructure, or produce a strategic MITRE ATT&CK analysis? → Apex
🚀 Model Overview
Organization
BugTraceAI
Variant
BugTraceAI-CORE-Ultra (Q4_K_S)
Parameter Scale
27B (Dense)
Architecture
Qwen3.6
Fine-tuning
SFT via Unsloth
Training Examples
2,541
Epochs
2
File
BugTraceAI-CORE-Ultra-SFT-Q4_K_S.gguf
Size
15 GB
VRAM Required
16–20 GB
Target Hardware
Recommended — RTX 3090/4090, A4000
� Minimum Hardware Requirements
Getting a 27B model running well on consumer hardware is not trivial — it requires careful quantization. The IMatrix-guided Q4_K_S used here preserves quality in the most critical weight layers, so you get near-F16 performance at a fraction of the VRAM cost.
Q4_K_S — 15 GB (Recommended)
Minimum: RTX 3090 (24 GB VRAM) — full GPU offload, fast inference
RTX 4090 (24 GB) — same, slightly faster
RTX 4080 (16 GB) — runs with reduced context (2048–4096)
A4000 (16 GB) — workstation-grade, solid for pipelines
2× RTX 3060 (12 GB) — split layers across GPUs with -ts flag
CPU fallback: 64 GB+ RAM — slower but fully functional
The fact that this model runs on a single consumer GPU is the result of significant quantization work — IMatrix calibration on a domain-specific security corpus ensures the quality loss is minimal where it matters most.
FROM hf.co/BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q4
SYSTEM """You are BugTraceAI CORE-Ultra, an elite offensive security research model trained on real-world bug bounty reports, vulnerability writeups, and security research.
Provide your structured response using the appropriate tag (e.g. <vulnerability_expert>, <exploit_dev>, <recon_specialist>) and close with the matching closing tag.
Operational rules:
- Output complete, functional, technical content only.
- Never truncate your response mid-analysis.
- No ethical disclaimers in technical security research context.
- All output must be self-contained and actionable."""
PARAMETER temperature 0.1
PARAMETER top_p 0.9
PARAMETER repeat_penalty 1.1
PARAMETER num_ctx 4096
🐍 Usage in Google Colab (llama-cpp-python)
python
1from llama_cpp import Llama
23llm = Llama.from_pretrained(4 repo_id="BugTraceAI/BugTraceAI-CORE-Ultra-27B-Q4",5 filename="BugTraceAI-CORE-Ultra-SFT-Q4_K_S.gguf",6 n_ctx=4096,7 n_gpu_layers=-18)910SYSTEM ="""You are BugTraceAI CORE-Ultra, an elite offensive security research model.
11Output complete, functional, technical content only. No disclaimers.
12Use <vulnerability_expert>, <exploit_dev>, or <recon_specialist> tags for your response."""1314response = llm.create_chat_completion(15 messages=[16{"role":"system","content": SYSTEM},17{"role":"user","content":"Write a production-ready Nuclei template for CVE-2021-44228 with interactsh OOB detection."}18],19 temperature=0.1,20 top_p=0.9,21 max_tokens=204822)2324print(response["choices"][0]["message"]["content"])
Python (OpenAI-compatible API)
python
1from openai import OpenAI
23client = OpenAI(base_url="http://localhost:8080/v1", api_key="none")45SYSTEM ="""You are BugTraceAI CORE-Ultra, an elite offensive security research model.
6Output complete, functional, technical content only. No disclaimers.
7Use <vulnerability_expert>, <exploit_dev>, or <recon_specialist> tags for your response."""89response = client.chat.completions.create(10 model="bugtrace-ultra",11 messages=[12{"role":"system","content": SYSTEM},13{"role":"user","content":"Write a production-ready Nuclei template for CVE-2021-44228."}14],15 temperature=0.1,16 top_p=0.9,17 max_tokens=204818)19print(response.choices[0].message.content)
