Instructions to use theaicmo/MOM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use theaicmo/MOM with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="theaicmo/MOM", filename="qwen3-14b.Q4_K_M.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 theaicmo/MOM with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf theaicmo/MOM:Q4_K_M # Run inference directly in the terminal: llama-cli -hf theaicmo/MOM:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf theaicmo/MOM:Q4_K_M # Run inference directly in the terminal: llama-cli -hf theaicmo/MOM:Q4_K_M
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 theaicmo/MOM:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf theaicmo/MOM:Q4_K_M
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 theaicmo/MOM:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf theaicmo/MOM:Q4_K_M
Use Docker
docker model run hf.co/theaicmo/MOM:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use theaicmo/MOM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "theaicmo/MOM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "theaicmo/MOM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/theaicmo/MOM:Q4_K_M
- Ollama
How to use theaicmo/MOM with Ollama:
ollama run hf.co/theaicmo/MOM:Q4_K_M
- Unsloth Studio new
How to use theaicmo/MOM 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 theaicmo/MOM 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 theaicmo/MOM to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for theaicmo/MOM to start chatting
- Pi new
How to use theaicmo/MOM with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf theaicmo/MOM:Q4_K_M
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": "theaicmo/MOM:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use theaicmo/MOM with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf theaicmo/MOM:Q4_K_M
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 theaicmo/MOM:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use theaicmo/MOM with Docker Model Runner:
docker model run hf.co/theaicmo/MOM:Q4_K_M
- Lemonade
How to use theaicmo/MOM with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull theaicmo/MOM:Q4_K_M
Run and chat with the model
lemonade run user.MOM-Q4_K_M
List all available models
lemonade list
MOM โ Marketing Open Model
The open-source LLM purpose-built for marketing professionals.
MOM is a fine-tuned Qwen3-14B model trained on marketing Q&A pairs covering the full spectrum of modern marketing โ from brand strategy and positioning to paid media, SEO, analytics, and team management. Built by The AI CMO.
Just run: ollama run hf.co/theaicmo/MOM
Highlights
- Base model: Qwen3-14B (4-bit QLoRA via Unsloth)
- Training data: Curated marketing instruction pairs generated from authoritative marketing sources
- 16 marketing domains covered in depth
- Format: GGUF Q4_K_M โ runs locally via Ollama, llama.cpp, LM Studio
- License: MIT โ fully open, commercial use welcome
Quick Start
# Run with Ollama
ollama run hf.co/theaicmo/MOM
Or download the GGUF file and load it in llama.cpp, LM Studio, or any GGUF-compatible runtime.
Example Prompts
What frameworks should I use to position a B2B SaaS product in a crowded market?
How do I structure a marketing budget for a Series A startup with $2M annual spend?
What KPIs should I track for a content marketing program and how do I tie them to revenue?
How should I organize a marketing team of 15 people across brand, growth, and product marketing?
Training Details
- Method: QLoRA 4-bit fine-tuning with Unsloth
- LoRA rank: 32 | Alpha: 32 | Dropout: 0
- Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- Epochs: 3 | Batch size: 8 (2 ร 4 grad accum) | LR: 2e-4 (cosine schedule)
- Quantization: GGUF Q4_K_M via llama.cpp
About The AI CMO
The AI CMO builds AI-powered tools for marketing leaders. MOM is our first open model โ designed to give every marketer access to CMO-level strategic thinking.
License
MIT โ use it however you want, commercially or otherwise.
Citation
@misc{mom2026,
title={MOM: Marketing Open Model},
author={The AI CMO},
year={2026},
url={https://huggingface.co/theaicmo/MOM}
}
- Downloads last month
- 3
4-bit