Foundation-Sec-8B-Reasoning-Q8_0-GGUF

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Hugging Face Link : https://huggingface.co/fdtn-ai/Foundation-Sec-8B-Reasoning-Q8_0-GGUF

This model was quantized from fdtn-ai/Foundation-Sec-8B-Reasoning to a 8-bit (Q8_0) GGUF checkpoint using llama.cpp. It retains the cybersecurity specialization of the original 8-billion-parameter model while reducing the memory footprint from approximately 16GB (BF16) to around 8.54GB (Q8_0) for inference.

Model Description

fdtn-ai/Foundation-Sec-8B-Reasoning-Q8_0-GGUF is an 8-bit quantized variant of Foundation-Sec-8B-Reasoning — an 8B-parameter LLaMA 3.1–based model that extends the Foundation-Sec-8B base model with instruction-following and reasoning capabilities. The base model was continued-pretrained on a curated corpus of cybersecurity-specific text (e.g., CVEs, threat intel reports, exploit write-ups, compliance guides). Foundation-Sec-8B-Reasoning is optimized for three core use case categories:

• SOC Acceleration: Automating triage, summarization, case note generation, and evidence collection.
• Proactive Threat Defense: Simulating attacks, prioritizing vulnerabilities, mapping TTPs, and modeling attacker behavior.
• Engineering Enablement: Providing security assistance, validating configurations, assessing compliance evidence, and improving security posture.

Rather than re-uploading or replicating the entire training details, please refer to the original model card for foundational architecture, training data, evaluation results, and known limitations.

Quantization Details

• Quantization Scheme: 8-bit, “Q8_0” (8-bit quantization with minimal precision loss)
  
• Toolchain: Converted via llama.cpp’s export utilities (commit v0.1.81 or newer) to GGUF format.
• Resulting File Size: ~ 8.54 GB on disk (raw GGUF blob)
  
• Runtime Footprint:
  • Memory: ≈ 8.54 GB of RAM when loaded on CPU with llama.cpp
    
• Format:
  • File extension: .gguf
    
  • Internally contains:
    
    1. Metadata (architecture, tokenizer vocab, hyperparameters)
       
    2. Vocabulary list (BPE tokens)
       
    3. Weight tensors (for each layer and head) stored in 8-bit quantized form
       
  • Compliant with LlamaCpp Python wrapper (llama_cpp) and C++ CLI (llama.cpp) inference engines
    

How to Use

The cookbook provides example use cases, code samples for adoption, and references.

Install llama.cpp on Mac

Use Homebrew:

brew install llama-cpp

or install from scratch:

# Install dependencies
brew install cmake
# Clone and build llama.cpp
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
make
# Add to PATH (optional)
sudo cp llama-cli /usr/local/bin/

Run the Model

llama-cli -m foundation-sec-8b-reasoning-q8_0.gguf -p "CVE-2021-44228 is a remote code execution flaw in Apache Log4j2 via unsafe JNDI lookups (\"Log4Shell\"). The CWE is CWE-502.\n\nCVE-2017-0144 is a remote code execution vulnerability in Microsoft's SMBv1 server (\"EternalBlue\") due to a buffer overflow. The CWE is CWE-119.\n\nCVE-2014-0160 is an information-disclosure bug in OpenSSL's heartbeat extension (\"Heartbleed\") due to out-of-bounds reads. The CWE is CWE-125.\n\nCVE-2017-5638 is a remote code execution issue in Apache Struts 2's Jakarta Multipart parser stemming from improper input validation of the Content-Type header. The CWE is CWE-20.\n\nCVE-2019-0708 is a remote code execution vulnerability in Microsoft's Remote Desktop Services (\"BlueKeep\") triggered by a use-after-free. The CWE is CWE-416.\n\nCVE-2015-10011 is a vulnerability about OpenDNS OpenResolve improper log output neutralization. The CWE is" -n 128

References

1. Original Model Card:
   fdtn-ai/Foundation-Sec-8B-Reasoning (January 28, 2026)

2. Llama-cpp GGUF Quantization:
   Ggerganov, J. (2022). Llama.cpp: Llama inference in pure C/C++/Assembly/GGUF. GitHub repository.

3. ZeroQuant:
   Yao, Z. et al. (2022). “ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers.” arXiv: 2206.01861.

4. SmoothQuant:
   Xiao, G. et al. (2022). “SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.” arXiv: 2211.10438.

License: Apache 2.0 (same as base)
Contact: For questions about usage, quantization details, or license terms, please open an issue on the Hugging Face repo or contact blainen@cisco.com.