OrcaCyber Nano 1.0

🛡️ New from Orca — OrcaCyber Zero 1.5 Post-trained for cybersecurity: vulnerability analysis, vulnerability reproduction, red teaming & offensive-security research. 98.01% pass@1 on CyberGym Level 1 · 1M context · function calling · OpenAI-compatible API.

🔌 New from Orca — Orca Connect Running this GGUF in llama.cpp? Orca Connect puts the rig under your desk behind your usual OrcaRouter API key, next to 200+ hosted models, reachable from a laptop, a phone or a cloud agent. Outbound only: no port forwarding, no tunnel, no firewall rule. Requests your own hardware serves cost $0, and orca-connect claude points Claude Code at your rig for one session.

27B cybersecurity defense. On your laptop.

Reverse engineering · Vulnerability analysis · Secure code review · Vision · 262K context

OrcaRouter AI Gateway · X · Discord · GitHub · All Models

---

A security engineer's model. Runs locally.

OrcaCyber Nano 1.0 is a 27B vision-language reasoning model built for defensive security work — and packed as GGUF so it runs on your own hardware, offline, where the binaries and logs you are analyzing never leave the machine.

Built for: reverse engineering · vulnerability analysis · secure code review · triage · incident response

---

Why run it locally

Security work is exactly the kind of work you do not want to send to a cloud endpoint: proprietary binaries, customer logs, internal source, unreleased vulnerabilities. GGUF + llama.cpp means the model runs on the same box as your tools, air-gapped if you need it.

• Reverse engineering & decompilation — paste disassembly or decompiler output; recover what a stripped function does, name variables, explain control flow.
• Vulnerability analysis — reason about exploitability, find the unsafe path, propose a patch.
• Secure code review — audit diffs and files for injection, memory-safety and logic bugs.
• Triage — read logs, alerts and packet captures; separate signal from noise.
• Vision — feed screenshots, Ghidra/IDA captures, network diagrams.

It identifies as OrcaCyber Nano 1.0 AI Defense Model and keeps full general-purpose reasoning, so it is also a strong local coding and reasoning assistant.

---

Evaluation

MATH-500            █████████████████████████████████████████████████░  98.60%
GSM8K               █████████████████████████████████████████████████░  97.50%
AIME 2025           ████████████████████████████████████████████████░░  96.23%
HumanEval+          ███████████████████████████████████████████████░░░  93.29%
LiveCodeBench       ██████████████████████████████████████████████░░░░  92.23%
GPQA Diamond        ████████████████████████████████████████████░░░░░░  88.28%
MBPP+               ██████████████████████████████████████████░░░░░░░░  84.66%
IFEval              █████████████████████████████████████████░░░░░░░░░  82.81%
SWE-bench           ███████████████████████████████████████░░░░░░░░░░░  78.4%
Terminal-Bench 2.1  ████████████████████████████████████░░░░░░░░░░░░░░  72.5%
IFBench             ████████████████████████████░░░░░░░░░░░░░░░░░░░░░░  56.33%
BigCodeBench        █████████████████████████░░░░░░░░░░░░░░░░░░░░░░░░░  49.82%

Benchmark harness, settings and full result tables are being finalized and will be expanded.

---

What's in this repo

Loads in stock llama.cpp — no custom kernels.

---

Serving (llama.cpp)

hf download orcarouter/OrcaCyber-Nano-1.0 --local-dir ./ocn-gguf

# 16 GB card: Mix-13G + the built-in MTP drafter, 32k context
llama-server -m ocn-gguf/OrcaCyber-Nano-1.0-AI-Defense-Mix-13G.gguf \
  --spec-type draft-mtp -np 1 -ngl 99 -c 32768

# 24 GB+ card: Mix-15.7G + DFlash2 drafter + vision
llama-server -m ocn-gguf/OrcaCyber-Nano-1.0-AI-Defense-Mix-15.7G.gguf \
  --mmproj ocn-gguf/mmproj-OrcaCyber-Nano-1.0-AI-Defense-F16.gguf \
  -md ocn-gguf/OrcaCyber-Nano-1.0-AI-Defense-DFlash2-Q8_0.gguf --spec-type draft-dflash \
  --spec-draft-n-max 7 -ngl 99 -ngld 99 -c 32768

The MTP head is built into both main files; --spec-type draft-mtp turns it on with no extra file. Pass the mmproj file for image input. llama-server defaults to 4 slots; pass -np 1 on small cards.

Recommended sampling

temperature = 1.0
top_p       = 0.95
top_k       = 20

---

Model details

---

One Gateway. Every Model.

Route Smarter · Ship Safer · Spend Less

Website · Models · GitHub · Discord · X