Instructions to use miladatefi/aaai-7b-dapt-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use miladatefi/aaai-7b-dapt-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="miladatefi/aaai-7b-dapt-gguf", filename="aaai-7b-dapt-q8_0.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use miladatefi/aaai-7b-dapt-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf miladatefi/aaai-7b-dapt-gguf:Q8_0 # Run inference directly in the terminal: llama-cli -hf miladatefi/aaai-7b-dapt-gguf:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf miladatefi/aaai-7b-dapt-gguf:Q8_0 # Run inference directly in the terminal: llama-cli -hf miladatefi/aaai-7b-dapt-gguf:Q8_0
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 miladatefi/aaai-7b-dapt-gguf:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf miladatefi/aaai-7b-dapt-gguf:Q8_0
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 miladatefi/aaai-7b-dapt-gguf:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf miladatefi/aaai-7b-dapt-gguf:Q8_0
Use Docker
docker model run hf.co/miladatefi/aaai-7b-dapt-gguf:Q8_0
- LM Studio
- Jan
- vLLM
How to use miladatefi/aaai-7b-dapt-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miladatefi/aaai-7b-dapt-gguf" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "miladatefi/aaai-7b-dapt-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/miladatefi/aaai-7b-dapt-gguf:Q8_0
- Ollama
How to use miladatefi/aaai-7b-dapt-gguf with Ollama:
ollama run hf.co/miladatefi/aaai-7b-dapt-gguf:Q8_0
- Unsloth Studio new
How to use miladatefi/aaai-7b-dapt-gguf 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 miladatefi/aaai-7b-dapt-gguf 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 miladatefi/aaai-7b-dapt-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for miladatefi/aaai-7b-dapt-gguf to start chatting
- Pi new
How to use miladatefi/aaai-7b-dapt-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf miladatefi/aaai-7b-dapt-gguf:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "miladatefi/aaai-7b-dapt-gguf:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use miladatefi/aaai-7b-dapt-gguf with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf miladatefi/aaai-7b-dapt-gguf:Q8_0
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default miladatefi/aaai-7b-dapt-gguf:Q8_0
Run Hermes
hermes
- Docker Model Runner
How to use miladatefi/aaai-7b-dapt-gguf with Docker Model Runner:
docker model run hf.co/miladatefi/aaai-7b-dapt-gguf:Q8_0
- Lemonade
How to use miladatefi/aaai-7b-dapt-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miladatefi/aaai-7b-dapt-gguf:Q8_0
Run and chat with the model
lemonade run user.aaai-7b-dapt-gguf-Q8_0
List all available models
lemonade list
AAAI โ GGUF for Ollama (EN/DE)
Author: Milad Atefi โ milad@atefi.ch
ยฉ atefi.ch โ Validate in a lab before production.
AAAI is a domain-adapted assistant (EN/DE) for enterprise device management & admin IT.
Focus Areas
- Microsoft Intune: enrollment, compliance, config profiles, Conditional Access, App Protection Policies, Windows Autopilot
- Jamf Pro: macOS/iOS/iPadOS configuration (PPPC, restrictions), scripts, packaging
- Apple: ABM/ASM, Automated Device Enrollment (ADE), payload keys
- Microsoft 365 / Entra ID / Defender
- Scripting: PowerShell, bash/zsh, Microsoft Graph usage
- Networking: Cisco IOS/IOS-XE core admin topics
- Backend: .NET (Minimal APIs), Java (admin tooling patterns)
Base: Qwen/Qwen2.5-7B-Instruct
Format: GGUF, quantization Q8_0 (~8.1 GB)
Adapter/Training: Uses a custom adapter trained on the authorโs curated materials for the domains above and merged into the base before quantization.
Quickstart (Ollama)
Use the Modelfile so the chat format, identity, and scope limits are correct.
ollama create aaai -f https://huggingface.co/miladatefi/aaai-7b-dapt-gguf/raw/main/Modelfile
ollama run aaai
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