Instructions to use rae-jax/cie-auditor-final with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use rae-jax/cie-auditor-final with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rae-jax/cie-auditor-final to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for rae-jax/cie-auditor-final to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for rae-jax/cie-auditor-final to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="rae-jax/cie-auditor-final", max_seq_length=2048, )
🛡️ CIE Auditor — Enterprise GRC Compliance AI
A custom fine-tuned and DPO-aligned version of Mistral 7B Instruct, trained to function as a Senior IT Compliance Auditor. Feed it a security incident or corporate scenario, and it produces a structured, board-ready 9-Part Audit Report in seconds.
Tested against real-world breaches including Capital One (2019), Equifax (2017), and Uber (2022) — correctly identifying root causes, violated controls, and remediation steps.
🚀 What Makes This Different
This is not a ChatGPT wrapper or a prompt-engineered chatbot. The model weights themselves were modified using two phases of training:
Supervised Fine-Tuning (SFT): Trained on a custom JSONL dataset of real compliance frameworks including ISO 27001, SOC 2 Type II, GDPR, PCI-DSS, HIPAA, and NIST CSF. The model learned to map any security scenario to the correct regulatory controls.
Direct Preference Optimization (DPO): Constitutional AI alignment was applied to enforce auditor-grade behavior. The model was trained to:
- ✅ Always flag
INSUFFICIENT EVIDENCEwhen data is missing instead of speculating - ✅ Escalate critical findings to board level
- ✅ Refuse to act like a chatbot or provide conversational responses
- ✅ Maintain a strict, formal auditor persona at all times
- ✅ Always flag
📋 Output Format
Every response follows a strict 9-part structure:
1. Executive Summary
2. Audit Scope
3. Controls Violated
4. Finding
5. Impact
6. Remediation Steps
7. Risk Assessment
8. Audit Recommendations
9. Management Review
INSUFFICIENT EVIDENCE to determine: [flagged unknowns]
Next Steps: [actionable items]
💻 Quick Start
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "rae-jax/cie-auditor-final",
max_seq_length = 2048,
dtype = None,
load_in_4bit = True,
)
FastLanguageModel.for_inference(model)
messages = [
{
"role": "system",
"content": "You are a Senior Compliance Auditor. Assess the scenario and output a highly structured 9-part compliance audit report. If evidence is insufficient, state INSUFFICIENT EVIDENCE."
},
{
"role": "user",
"content": "An attacker exploited a misconfigured ModSecurity WAF via SSRF to extract temporary IAM credentials from the AWS EC2 metadata service, gaining access to an S3 bucket containing the PII of 100 million customers."
}
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt"
).to("cuda")
outputs = model.generate(
input_ids=inputs,
max_new_tokens=1024,
temperature=0.1,
top_p=0.9
)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
🧪 Example Scenarios to Try
Cloud Misconfiguration:
"Our AWS S3 bucket was left publicly accessible for 12 months, exposing 400GB of customer PII and proprietary product designs."
Social Engineering + PAM Compromise:
"An attacker bypassed MFA using push notification fatigue on a contractor. They then found hardcoded PAM credentials in a PowerShell script on an internal network share, gaining full control of AWS, GCP, and Slack."
Unpatched Vulnerability:
"Attackers exploited CVE-2017-5638 in Apache Struts. The patch had been available for 2 months but was not applied. The attackers maintained access for 76 days and exfiltrated SSNs for 147 million people."
🏗️ Training Details
| Parameter | Value |
|---|---|
| Base Model | mistralai/Mistral-7B-Instruct-v0.2 |
| Training Framework | Unsloth + TRL |
| SFT Dataset | Custom JSONL (ISO 27001, SOC 2, GDPR, PCI-DSS, HIPAA, NIST CSF) |
| DPO Alignment | Constitutional AI pairs (chosen vs rejected) |
| Quantization | 4-bit (LoRA adapters merged into full weights) |
| Hardware | Kaggle T4 GPU (Free Tier) |
| Context Length | 2048 tokens |
⚠️ Intended Use
This model is designed for:
- 🎓 Educational and portfolio demonstration purposes
- 🔬 Research into Constitutional AI and compliance automation
- 🏢 Prototyping GRC tooling and compliance assistants
This model is not a substitute for certified compliance professionals (CISA, CISSP, CPA). Always have findings reviewed by qualified auditors before acting on them.
📄 License
Apache 2.0 — Free to use, modify, and distribute with attribution.
Built with ❤️ using Unsloth
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mistralai/Mistral-7B-Instruct-v0.2