Instructions to use dattaraj/security-attacks-MITRE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dattaraj/security-attacks-MITRE with MLX:

# Download the model from the Hub
pip install huggingface_hub[hf_xet]

huggingface-cli download --local-dir security-attacks-MITRE dattaraj/security-attacks-MITRE

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dattaraj/security-attacks-MITRE with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

LM Studio
Jan
Ollama
How to use dattaraj/security-attacks-MITRE with Ollama:
```
ollama run hf.co/dattaraj/security-attacks-MITRE
```

Unsloth Studio

How to use dattaraj/security-attacks-MITRE with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dattaraj/security-attacks-MITRE to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dattaraj/security-attacks-MITRE to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dattaraj/security-attacks-MITRE to start chatting

Atomic Chat new
Docker Model Runner
How to use dattaraj/security-attacks-MITRE with Docker Model Runner:
```
docker model run hf.co/dattaraj/security-attacks-MITRE
```

Lemonade

How to use dattaraj/security-attacks-MITRE with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dattaraj/security-attacks-MITRE

Run and chat with the model

lemonade run user.security-attacks-MITRE-{{QUANT_TAG}}

List all available models

lemonade list

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.

Downloads last month: 56

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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dattaraj
/

security-attacks-MITRE