license, license_link, base_model, tags, language, quantized_by

license	license_link	base_model	tags	language	quantized_by
apache-2.0	https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE	mlx-community/Qwen2.5-Coder-7B-Instruct-4bit	gguf cybersecurity nist security-controls compliance fine-tuned llama-cpp	en	ethanolivertroy

HackIDLE-NIST-Coder v1.1 (GGUF)

HackIDLE-NIST-Coder is a NIST-focused local model built from Qwen2.5-Coder-7B-Instruct and fine-tuned on a NIST cybersecurity corpus.

This GGUF repo is the portable build for Ollama, llama.cpp, LM Studio, and text-generation-webui.

Use it as a helper. Do not treat it as a source of truth for exact control names, RMF step lists, or reference-architecture component names without checking the source publication.

What went into v1.1

Version 1.1 was trained on 530,912 examples from 596 NIST publications.

Compared with the first release, v1.1 added:

• 7,206 training examples
• 28 additional NIST documents
• CSWP coverage, including CSF 2.0, Zero Trust, and Post-Quantum Cryptography material
• cleanup for 6,150 malformed DOI links
• removal of known broken-link markers in the training corpus

Training dataset:

• ethanolivertroy/nist-cybersecurity-training

Current eval status

I ran a small local smoke eval on April 22, 2026 against etgohome/hackidle-nist-coder:latest. In that local Ollama install, latest matched the v1.1 line.

Result: 1/5 cases passed.

The model stayed in-domain and handled a rough FIPS 140-2 vs. FIPS 140-3 comparison. It still missed exact grounding on:

• SP 800-207 reference-architecture component names
• the full SP 800-37 Rev. 2 RMF sequence
• the exact CM-6 control name and description
• stronger publication selection and logging/audit grounding for a contractor remote-access planning prompt

That is the important limitation. The model can sound close while still being wrong on exact NIST structure.

Good uses

This model is useful for:

• brainstorming where to start in NIST
• drafting first-pass explanations
• surfacing likely document families
• turning NIST-flavored questions into something a human can verify
• local experimentation with domain fine-tuning on Apple Silicon

It is not reliable enough yet for:

• exact control names
• exact framework step ordering
• exact reference-architecture component naming
• answers that need source-level correctness on the first pass

Available quantizations

Quantization	Approx. size	Use case
F16	14 GB	Full precision reference build
Q8_0	7.5 GB	Higher quality local inference
Q5_K_M	5.1 GB	Balanced size and quality
Q4_K_M	4.4 GB	Small local default for most machines

Start with Q4_K_M unless you have a reason to use a larger file.

Run with Ollama

ollama pull etgohome/hackidle-nist-coder:v1.1
ollama run etgohome/hackidle-nist-coder:v1.1 "Which NIST docs would you read before drafting a zero trust migration plan?"

Run with llama.cpp

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

wget https://huggingface.co/ethanolivertroy/HackIDLE-NIST-Coder-v1.1-GGUF/resolve/main/hackidle-nist-coder-v1.1-q4_k_m.gguf

./llama-cli \
  -m hackidle-nist-coder-v1.1-q4_k_m.gguf \
  -p "Which NIST docs would you start with for contractor remote access?" \
  -n 500

Other formats

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

License

The base model is Qwen2.5-Coder-7B-Instruct, released under Apache 2.0. The NIST source publications used for the dataset are public domain U.S. government works. This model card uses Apache 2.0 for the model artifact and documents the NIST data source separately.

Citation

@misc{hackidle_nist_coder_v11_gguf,
  title = {HackIDLE-NIST-Coder v1.1 GGUF},
  author = {Troy, Ethan Oliver},
  year = {2025},
  version = {1.1},
  url = {https://huggingface.co/ethanolivertroy/HackIDLE-NIST-Coder-v1.1-GGUF}
}