ethanolivertroy/HackIDLE-NIST-Coder-v1.1-GGUF

HackIDLE-NIST-Coder v1.1 (GGUF)

The most comprehensive NIST cybersecurity model in GGUF format - Compatible with llama.cpp, Ollama, LM Studio, and text-generation-webui.

Model Overview

Fine-tuned on 530,912 examples from 596 NIST publications. Version 1.1 includes:

• +7,206 training examples (530,912 total)
• +28 new documents (596 NIST publications)
• CSWP series: CSF 2.0, Zero Trust Architecture, Post-Quantum Cryptography
• Improved quality: Fixed 6,150 malformed DOI links, 0 broken link markers

Available Quantizations

Quantization	Size	Use Case	Description
F16	~14 GB	Reference Quality	Full precision, best quality
Q8_0	~7.5 GB	High Quality	Minimal quality loss
Q5_K_M	~5.1 GB	Balanced	Good quality/size trade-off
Q4_K_M	~4.4 GB	Recommended	Best speed/quality balance

Recommended: Start with Q4_K_M for best overall performance.

Training Data (v1.1)

Dataset: ethanolivertroy/nist-cybersecurity-training

Coverage:

• FIPS: Cryptographic standards
• SP 800: Security guidelines and controls
• SP 1800: Practice guides
• IR: Technical reports
• CSWP: White Papers (CSF 2.0, Zero Trust, PQC, IoT, Privacy) ✨ NEW

Stats: 530,912 examples • 596 documents • 61,480 working references

Installation

Ollama

# Pull from Ollama registry
ollama pull etgohome/hackidle-nist-coder:v1.1

# Or create from GGUF
ollama create hackidle-nist-coder -f Modelfile

LM Studio

1. Open LM Studio
2. Search for "hackidle-nist-coder"
3. Download Q4_K_M or Q5_K_M quantization
4. Load and chat

llama.cpp

# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make

# Download model (Q4_K_M recommended)
wget https://huggingface.co/ethanolivertroy/HackIDLE-NIST-Coder-v1.1-GGUF/resolve/main/hackidle-nist-coder-v1.1-q4_k_m.gguf

# Run inference
./llama-cli -m hackidle-nist-coder-v1.1-q4_k_m.gguf -p "What is Zero Trust Architecture?"

text-generation-webui

1. Place GGUF file in models/ directory
2. Select model in UI
3. Load and chat

Usage Examples

Ollama

ollama run etgohome/hackidle-nist-coder:v1.1 "Explain the CSF 2.0 GOVERN function"

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    model_path="hackidle-nist-coder-v1.1-q4_k_m.gguf",
    n_ctx=4096,
    n_threads=8
)

response = llm("What are the core principles of Zero Trust Architecture in SP 800-207?",
               max_tokens=500)
print(response['choices'][0]['text'])

Model Capabilities

Trained on comprehensive NIST content:

✅ Security Controls (SP 800-53) ✅ CSF 2.0 with GOVERN function ✅ Zero Trust Architecture (SP 800-207) ✅ Risk Management Framework (RMF) ✅ Cloud Security (SP 800-145, 800-146) ✅ FIPS Cryptography standards ✅ Post-Quantum Cryptography migration ✅ Privacy Engineering ✅ Supply Chain Risk Management ✅ IoT Cybersecurity

What's New in v1.1

Added Content:

• CSF 2.0 (Cybersecurity Framework 2.0)
• Zero Trust Architecture planning guidance
• Post-Quantum Cryptography recommendations
• IoT security and labeling
• Privacy Framework v1.0
• Supply chain risk management case studies

Quality Improvements:

• Fixed 6,150 malformed DOI links
• Removed 202 broken link markers
• Validated 124,946 total links
• Clean training data

System Requirements

Quantization	RAM Required	CPU/GPU
Q4_K_M	6 GB	CPU or GPU
Q5_K_M	7 GB	CPU or GPU
Q8_0	10 GB	CPU or GPU
F16	16 GB	GPU recommended

Other Formats

• MLX: ethanolivertroy/HackIDLE-NIST-Coder-v1.1-MLX-4bit (Apple Silicon)
• Ollama: etgohome/hackidle-nist-coder

Limitations

• Training data current as of October 2025
• May not reflect NIST publications released after training
• 54.2% of references are broken links (cataloged for recovery)
• Optimized for NIST-specific cybersecurity questions

Citation

@misc{hackidle-nist-coder-v1.1-gguf,
  title={HackIDLE-NIST-Coder: NIST Cybersecurity Expert Model},
  author={Troy, Ethan Oliver},
  year={2025},
  version={1.1},
  format={GGUF},
  url={https://huggingface.co/ethanolivertroy/HackIDLE-NIST-Coder-v1.1-GGUF}
}

License

CC0 1.0 Universal (Public Domain) - All NIST publications are in the public domain.

Acknowledgments

• NIST Computer Security Resource Center
• Qwen2.5-Coder base model (Alibaba Cloud)
• llama.cpp quantization (Georgi Gerganov)
• MLX framework (Apple)

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Version: 1.1 Release Date: October 2025 Training Dataset: nist-cybersecurity-training v1.1 Format: GGUF (compatible with llama.cpp ecosystem)