Instructions to use dattaraj/security-attacks-MITRE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
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
- llama-cpp-python
How to use dattaraj/security-attacks-MITRE with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="dattaraj/security-attacks-MITRE", filename="security-attacks-MITRE.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use dattaraj/security-attacks-MITRE with 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
- LM Studio
- Jan
- Ollama
How to use dattaraj/security-attacks-MITRE with Ollama:
ollama run hf.co/dattaraj/security-attacks-MITRE
- Unsloth Studio new
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
- 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
| license: apache-2.0 | |
| datasets: | |
| - dattaraj/security-attacks-MITRE | |
| language: | |
| - en | |
| library_name: mlx | |
| ### 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. | |