MLX
GGUF
English
conversational
How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dattaraj/security-attacks-MITRE
# Run inference directly in the terminal:
llama-cli -hf dattaraj/security-attacks-MITRE
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dattaraj/security-attacks-MITRE
# Run inference directly in the terminal:
llama-cli -hf dattaraj/security-attacks-MITRE
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf dattaraj/security-attacks-MITRE
# Run inference directly in the terminal:
./llama-cli -hf dattaraj/security-attacks-MITRE
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf dattaraj/security-attacks-MITRE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dattaraj/security-attacks-MITRE
Use Docker
docker model run hf.co/dattaraj/security-attacks-MITRE
Quick Links

Fine-tuned model for Cybersecurity SOAR usecases.

Base model - microsoft/Phi-3-mini-4k-instruct

Instruction dataset - dattaraj/security-attacks-MITRE

Platform - Apple MLX on Mac M3

Author - Dattaraj Rao

https://in.linkedin.com/in/dattarajrao

Model file - security-attacks-MITRE.gguf

Why fine-tune?

Fine-tuned to act like a Cybersecurity expert. Understand a potential attack scenario. map to MIRE ATT&CK guidelines and provide mitigations. Base model would provide generic recommendations. While the fine-tuned model shows much better domain adoption and MITRE mapping.

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GGUF
Model size
4B params
Architecture
phi3
Hardware compatibility
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Dataset used to train dattaraj/security-attacks-MITRE