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README.md

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+---
+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 8.54GB (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`.
+

foundation-sec-8b-q4_k_m.gguf

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