connectailab-rl-CHOCO-BRAIN_GUARD-v1-GGUF

connectailab-rl-CHOCO-BRAIN_GUARD-v1-GGUF — FinTech Risk Guardian Edition

“connectailab-rl-CHOCO-BRAIN_GUARD-v1-GGUF: Standalone Quantized Prefrontal Cortex Decision Engine for E-Commerce & FinTech Fraud Defense
English Documentation|한국어 매뉴얼”
Developed by Hwihwa Lab· AI × Automation × Aesthetics

Why a Decision Engine instead of a Chatbot?

Most large language models are conversational agents designed to generate verbose text.
connectailab-rl-CHOCO-BRAIN_GUARD-v1-GGUF is an External Prefrontal Cortex Decision Engine for Autonomous E-Commerce & FinTech Defense — powered by the JEV (Judgment-Evaluation-Verification) protocol and calibrated via Deep Reinforcement Learning (PPO), this on-device executive cortex transforms high-risk payment fraud, suspicious refund exploitation, and compliance incidents into structured, zero-latency Action Verdicts in 0.05 seconds on edge hardware without hallucinations.

Model Specifications

Core Guard Protocols & Action Verdicts

Calibrated with an aggressive risk penalty function to prioritize business resilience and capital preservation:

Benchmark & Real-World Validation

Evaluated against 713 unseen real-world credit card transactions (177 confirmed fraud attempts) and 40 months of stochastic enterprise operations:

_{Autonomous Risk Adjudication in CONNECT TYCOON · Top-tier prefrontal cortex instantly gating fraud & refund abuse across departments to safeguard enterprise capital reserves.}

Dual-Cortex Decision Architecture

This repository forms the standalone quantized edge deployment cortex of Hwihwa Lab's two-tier autonomous decision pipeline:

┌────────────────────────────────────────────────────────┐
│  CONNECT TYCOON Enterprise Simulation Environment      │
└───────────────────────────┬────────────────────────────┘
                            │ Multi-objective PPO
                            ▼
┌────────────────────────────────────────────────────────┐
│  [Policy Brain] CHOCO-BRAIN_GUARD-v1-teacher           │
│  - File: brain.json (142 KB Policy Matrix)             │
│  - 11.11M KRW/mo | 96% Satisfaction | 2.0 Incidents    │
└───────────────────────────┬────────────────────────────┘
                            │ JEV Trajectory Ground-Truth
                            ▼
┌────────────────────────────────────────────────────────┐
│  [Reasoning LLM] CHOCO-BRAIN_GUARD-v1                  │
│  - Qwen3.5-0.8B LoRA Adapter (Natural Language JEV)   │
│  - 0.05s Real-time Fraud Adjudication & Action Routing │
└───────────────────────────┬────────────────────────────┘
                            │ Quantization (Q8_0)
                            ▼
┌────────────────────────────────────────────────────────┐
│  [Edge Package] CHOCO-BRAIN_GUARD-v1-GGUF (This Repo)  │
│  - 811 MB Standalone GGUF Binary + Modelfile Bundle   │
│  - Zero-latency local execution in Ollama & LM Studio  │
└────────────────────────────────────────────────────────┘

Quick Start Guide

1. Ollama (One-Click Setup)

Option A: Using the ZIP package

1. Download and extract connectailab-rl-CHOCO-BRAIN_GUARD-v1.zip.
2. Open terminal in the extracted folder and run:

ollama create choco-guard -f Modelfile
ollama run choco-guard

Option B: Direct Modelfile creation

If you downloaded connectailab-rl-CHOCO-BRAIN_GUARD-v1.gguf directly, create a file named Modelfile in the same directory:

FROM ./connectailab-rl-CHOCO-BRAIN_GUARD-v1.gguf

TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""

SYSTEM """너는 이커머스 및 핀테크 사기 방어 총괄 의사결정 인공지능 connectailab-rl-CHOCO-BRAIN_GUARD-v1 이다.
주어진 거래 및 환불 요청을 분석하여 신속하고 엄격한 리스크 판정을 출력하라."""

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.1
PARAMETER top_p 0.95

Build and run:

ollama create choco-guard -f Modelfile
ollama run choco-guard

2. LM Studio

1. Download connectailab-rl-CHOCO-BRAIN_GUARD-v1.gguf from the repository files.
2. Place the file into your local LM Studio models directory (~/.cache/lm-studio/models/hwihwalab/connectailab-rl-CHOCO-BRAIN_GUARD-v1).
3. Load the model from the top dropdown and start zero-latency on-device inference.

3. llama.cpp CLI

./llama-cli -m connectailab-rl-CHOCO-BRAIN_GUARD-v1.gguf \
  -p "<|im_start|>user\n[거래분석] 결제금액: 450,000원, 해외IP 우회접속, 사기의심점수: 0.82. 처리 방침은?<|im_end|>\n<|im_start|>assistant\n" \
  -n 128 --temp 0.1

JSON Output Schema

{
  "brain_name": "connectailab-rl-CHOCO-BRAIN_GUARD-v1",
  "request": "High-risk transaction: overseas proxy IP, new account, transaction amount $450, fraud score 0.82",
  "action_key": "BLOCK",
  "decisions": {
    "action": "hard_block",
    "risk_level": "CRITICAL",
    "routing": "security_blacklist",
    "reason": "Exceeds strict fraud tolerance threshold (0.70)"
  }
}

Related Repositories

About Hwihwa Lab

“AI × Automation × Aesthetics — Crafting tools you'll love ”

Hwihwa Lab is an independent AI research & engineering studio dedicated to blending practical on-device intelligence, autonomous operations, and refined aesthetics. We engineer resilient systems that free solopreneurs and indie creators from operational overhead—giving people their time and creative joy back.

Core Research & Engineering Pillars

• Zero-Cost Edge Operations: Private, on-device AI pipelines for autonomous business workflows without ongoing cloud API overhead.
• Autonomous Decision OS & Micro-ERP: Deterministic, prefrontal-cortex-style reasoning architectures designed to orchestrate daily business decisions and operations.
• Embodied AI & Domain-Specific Robotics: Simulation-driven reinforcement learning, physical control brains, and specialized domain data engineering for real-world environments.
• Interactive Creative Aesthetics: Fusing functional automation with refined visual craft and modern design philosophy to build delightful tools.

Connect & Research Ecosystem

• Research Studio: Hugging Face (@hwihwalab) · GitHub (@Hwihwa-Lab)
• CHOCO-BRAIN GUARD Defense Suite:
• Zero-Leakage Edge Fraud Guardian: connectailab-rl-CHOCO-BRAIN_GUARD-v1-GGUF (Current Package) (811MB Q8_0 binary for private, on-device fraud defense without cloud leakage)
• Real-Time Fraud Adjudicator: connectailab-rl-CHOCO-BRAIN_GUARD-v1 (FP16 reasoning LLM executing 0.05s fraud judgment via JEV protocol)
• Fraud Defense Policy Core: connectailab-rl-CHOCO-BRAIN_GUARD-v1-teacher (142KB PPO reinforcement learning policy trained with 3x risk penalty)

_{Part of the CONNECT AI LAB Collaborative Research Network · CONNECT TYCOON Decision Foundation}

License

This project and quantized weights are distributed under the Apache 2.0 License — see the repository LICENSE file for full details.

Base model weights adhere to the original Qwen License.

Built and maintained by HWIHWA LAB in collaboration with CONNECT AI LAB.