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ethanolivertroy/HackIDLE-NIST-Coder-GGUF
HackIDLE-NIST-Coder-GGUF is a machine learning model from ethanolivertroy. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
This is the first GGUF build of HackIDLE-NIST-Coder, a NIST-focused local model built from Qwen2.5-Coder-7B-Instruct and fine-tuned on a NIST cybersecurity corpus.
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From the Hugging Face model README
This is the first GGUF build of HackIDLE-NIST-Coder, a NIST-focused local model built from Qwen2.5-Coder-7B-Instruct and fine-tuned on a NIST cybersecurity corpus.
This repo is kept for reproducibility. For new testing, start with the v1.1 GGUF build:
Use this model 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.
This first build used 523,706 examples from 568 NIST cybersecurity documents.
Training dataset:
The dated smoke eval from April 22, 2026 was run against the Ollama latest tag, which matched the v1.1 line in the local install used for that check. I have not rerun that exact eval against this older GGUF build.
The v1.1 result matters for this older build too because it sets the right expectation for the model family: the model can stay in-domain while still missing exact NIST structure.
Be careful with:
| 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 |
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make
wget https://huggingface.co/ethanolivertroy/HackIDLE-NIST-Coder-GGUF/resolve/main/hackidle-nist-coder-q4_k_m.gguf
./llama-cli \
-m hackidle-nist-coder-q4_k_m.gguf \
-p "Which NIST docs would you start with for contractor remote access?" \
-n 500
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.