---
license: apache-2.0
language:
  - en
base_model: fdtn-ai/Foundation-Sec-8B
pipeline_tag: text-generation
library_name: transformers
tags:
  - security
  - llama
  - gguf
  - quantization
---

# Foundation-Sec-8B-Q4_K_M-GGUF Model Card

**This model was quantized from [fdtn-ai/Foundation-Sec-8B](https://huggingface.co/fdtn-ai/Foundation-Sec-8B) to an 4-bit (Q4_K_M) 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 4.92GB (Q4_K_M) for inference.** 

## Model Description

`fdtn-ai/Foundation-Sec-8B-Q4_K_M-GGUF` is an 4-bit quantized variant of **Foundation-Sec-8B** — an 8B-parameter LLaMA 3.1–based model that was continued-pretrained on a curated corpus of cybersecurity-specific text (e.g., CVEs, threat intel reports, exploit write-ups, compliance guides). The base model was originally released on April 28, 2025 under Apache 2.0, and excels at tasks such as:

- **Threat intelligence summarization** (e.g., summarizing CVE details)  
- **Vulnerability classification** (mapping CVEs/CWEs to MITRE ATT&CK)  
- **Incident triage assistance** (extracting IoCs, summarizing log data)  
- **Red-team simulation prompts** and **security-workflow generation**

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:** 4-bit, GPTQ-inspired “Q4_K_M” (vector-wise quantization with per-group scales)  
- **Toolchain:** Converted via [llama.cpp’s export utilities](https://github.com/ggml-org/llama.cpp) (commit `v0.1.81` or newer) to GGUF format.   
- **Resulting File Size:** ~ 4.92 GB on disk (raw GGUF blob)   
- **Runtime Footprint:** 
  - Memory: ≈ 4.94 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 4-bit quantized form  
  - Compliant with LlamaCpp Python wrapper (`llama_cpp`) and C++ CLI (`llama.cpp`) inference engines  

## How to Use

### Install llama.cpp on Mac

Use Homebrew:
```bash
brew install llama-cpp
```

or install from scratch:

```bash
# 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

```bash
llama-cli -m foundation-sec-8b-q4_k_m.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](https://huggingface.co/fdtn-ai/Foundation-Sec-8B) (April 28, 2025) – continued pretraining of LLaMA 3.1-8B on cybersecurity data.

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 `paulkass@cisco.com`.