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Nathan-Maine/cmmc-expert-gemma4-31b-poc
cmmc-expert-gemma4-31b-poc is a machine learning model from Nathan-Maine. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for gguf. The card lists the license as gemma.
A proof-of-concept compliance-specialized large language model fine-tuned by Nathan Maine on Gemma 4 31B for U.S. defense and regulated-industry cybersecurity compliance: CMMC 2.0, NIST SP 800-171 / 172 / 53, HIPAA, a…
Downloads · 30 days
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.gguf21.8 GB · 100%
From the Hugging Face model README
A proof-of-concept compliance-specialized large language model fine-tuned by Nathan Maine on Gemma 4 31B for U.S. defense and regulated-industry cybersecurity compliance: CMMC 2.0, NIST SP 800-171 / 172 / 53, HIPAA, and DFARS.
Fine-tuned with QLoRA on an NVIDIA DGX Spark (GB10) over a curated, provenance-tracked compliance instruction dataset, and shipped as a quantized GGUF for fully local, air-gapped inference via Ollama or llama.cpp, with no cloud dependency.
q5_k_m (~21.9 GB)ollama run hf.co/Nathan-Maine/cmmc-expert-gemma4-31b-poc
Or download the GGUF and run it with llama.cpp.
A research and drafting aid for compliance practitioners, and a proof of concept for larger-model CMMC specialization. It can be wrong; verify any control interpretation against the authoritative source documents before relying on it. Not legal advice.
This is a fine-tune of Google Gemma 4; use is subject to the Gemma Terms of Use.
Built by Nathan Maine. Training data and evaluation benchmarks: https://huggingface.co/Nathan-Maine . Pipeline and training code: https://github.com/NathanMaine/cmmc-compliance-ai-model