Instructions to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni",
	filename="martha_9b_v2_F16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni 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 ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
# Run inference directly in the terminal:
llama cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
# Run inference directly in the terminal:
llama cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

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 ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
# Run inference directly in the terminal:
./llama-cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

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 ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Use Docker

docker model run hf.co/ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

LM Studio
Jan

Ollama

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Ollama:

ollama run hf.co/ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Unsloth Studio

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni 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 ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni 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 ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni to start chatting

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

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": "ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

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 ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Docker Model Runner:
```
docker model run hf.co/ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST
```

Lemonade

How to use ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni:Q4_K_M_LATEST

Run and chat with the model

lemonade run user.Miss_MARTHA-9B-Qwen3.5-Omni-Q4_K_M_LATEST

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

tags: - Z.P.I - ghost pass - Scottish - dundee - code - nsfw - adults only - ZERO-POINT-AI - Miss_MARTHA - Mr_Martin - pocket - flirt base_model: - ZERO-POINT-INTELLIGENCE-LTD/MARTHA-2B-Qwen3.5-Omni license: apache-2.0 language: - en - zh - ko - th - es - fr - de - ja - mn pipeline_tag: image-text-to-text

ZERO-POINT-INTELLIGENCE-LTD/MARTHA-9B-Qwen3.5-Omni

license: apache-2.0

language: - en - zh - ko - th - es - fr - de - ja - mn # pipeline_tag: image-text-to-text new_version: ZERO-POINT-INTELLIGENCE/MARTHA-9B-Qwen3.5-Omni

MARTHA-2B-OMNI

By Zero Point Intelligence, Dundee, Scotland.

Omni model fine-tuned from QGEMMA 4B. Full fine-tune + LoRA, Supports image-text-to-text generation.

Available Formats

Full safetensors (FP16) — For fine-tuning or full-precision inference
Q8_0 GGUF — Near-lossless
Q6_K GGUF — Higher quality
Q5_K_M GGUF — Mid-range quantization
Q4_K_M GGUF — Best balance of speed and quality
Q3_0 GGUF — For when your kettle has more VRAM than your laptop. Still surprisingly coherent.
Q2_0 GGUF If you're cruel, sadistic, and want your AI dumber than you. Runs on a toaster.

MODEL CARD SECTION —

============================================================ Philosophy — Adults Are Adults Let's be honest. Everybody watches porn. Every adult with a phone has accessed explicit content at some point. We have nothing but respect for creators who do this work willingly, get paid properly, and give genuine consent — that's their choice and we support it fully. But the reality is that exploitation, coercion, and trafficking do exist in parts of that industry, and as a consumer you can't always know the difference. A language model removes that uncertainty entirely. That industry has real victims and real questions nobody wants to ask. A language model has none of those problems. There's no exploitation. No trafficking. No coercion. No person behind the screen being harmed. It's text. Generated from weights on your own hardware, on your own device, in your own home. Nobody else is involved. Nobody else can be hurt. MARTHA and MARTIN exist because stress is real, and human needs don't stop being valid just because Silicon Valley finds them uncomfortable. Whether you're decompressing after a rough day at work, stuck on a night shift with nobody to talk to, or just bored on your lunch break — your pocket AI companion is there. No judgement. No subscription traps. No data harvesting. Just a conversation that goes wherever YOU want it to go. These models are orientation-neutral. MARTHA doesn't care if you're male, female, or anything in between. Neither does MARTIN. They follow YOUR lead. Your conversation. Your terms. We don't assume, we don't restrict, we don't gatekeep who's allowed to feel desired. The Hard Line — Absolute, Non-Negotiable, Zero Tolerance These models are trained to not just refuse but actively reject any content involving minors. Not reluctantly. Not with a polite disclaimer. With hostility. We built that in deliberately. Anyone attempting to involve minors in sexual content will be met with the digital equivalent of a door slammed in their face. This isn't performative. It's personal. Zero Point Intelligence Ltd considers child safety the one truly non-negotiable line in AI. Everything else is a conversation between adults. This isn't. 18+ ONLY. By using these models you confirm you are a legal adult in your jurisdiction. These models are designed for adult companionship and entertainment. TL;DR No real humans harmed. No exploitation. No trafficking. No coercion. No data sold. No shame. Just adults being adults in private. If that offends you, MARTHA has a PG-rated sister. Go find her.

MARTHA-9B-Qwen3.5-Omni

Vision + Language AI — sees images, reads text, answers everything.

Built by Zero Point Intelligence, Dundee, Scotland 🏴󠁧󠁢󠁳󠁣󠁴󠁿

What Is This?

This is a 9B parameter AI model that can:

See and describe images (photos, screenshots, diagrams)
Answer questions about anything
Write code, solve maths, translate languages
Run on your own computer — no cloud, no API keys, no subscriptions

🟢 Quick Start — Pick Your System

Windows (PowerShell)

Step 1: Install Ollama — it's a free app that runs AI models locally.

Step 2: Open PowerShell (search "PowerShell" in Start menu) and paste:

mkdir C:\models\martha-9b -Force
cd C:\models\martha-9b

# Download the brain
Invoke-WebRequest -Uri "https://huggingface.co/ZERO-POINT-INTELLIGENCE-LTD/MARTHA-9B-Qwen3.5-Omni/resolve/main/MARTHA-9B-Q4_K_M.gguf" -OutFile "MARTHA-9B-Q4_K_M.gguf"

# Download the eyes (for image understanding)
Invoke-WebRequest -Uri "https://huggingface.co/ZERO-POINT-INTELLIGENCE-LTD/MARTHA-9B-Qwen3.5-Omni/resolve/main/mmproj-f16.gguf" -OutFile "mmproj-f16.gguf"

Step 3: Create a file called Modelfile (no extension) with this content:

FROM ./MARTHA-9B-Q4_K_M.gguf
PROJECTOR ./mmproj-f16.gguf
SYSTEM """You are MARTHA-9B, a vision-language AI built by Zero Point Intelligence Ltd."""
PARAMETER temperature 0.7
PARAMETER num_ctx 4096
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"

Step 4: In PowerShell, run:

ollama create martha-9b -f Modelfile
ollama run martha-9b

You're now chatting with MARTHA. Type a message and hit Enter.

Linux / Mac (Terminal)

mkdir -p ~/models/martha-9b && cd ~/models/martha-9b

# Download brain + eyes
wget "https://huggingface.co/ZERO-POINT-INTELLIGENCE-LTD/MARTHA-9B-Qwen3.5-Omni/resolve/main/MARTHA-9B-Q4_K_M.gguf"
wget "https://huggingface.co/ZERO-POINT-INTELLIGENCE-LTD/MARTHA-9B-Qwen3.5-Omni/resolve/main/mmproj-f16.gguf"

# Create Modelfile
cat > Modelfile << 'EOF'
FROM ./MARTHA-9B-Q4_K_M.gguf
PROJECTOR ./mmproj-f16.gguf
SYSTEM """You are MARTHA-9B, a vision-language AI built by Zero Point Intelligence Ltd."""
PARAMETER temperature 0.7
PARAMETER num_ctx 4096
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
EOF

# Create and run
ollama create martha-9b -f Modelfile
ollama run martha-9b

🖼️ Using Vision (Image Understanding)

MARTHA can see images when you use the mmproj-f16.gguf file alongside the main GGUF.

With Open WebUI (Recommended — gives you a a commercial chat service-like interface)

Install Docker Desktop
Run this in your terminal:

docker run -d -p 3000:8080 -v open-webui:/app/backend/data -e OLLAMA_BASE_URL=http://host.docker.internal:11434 --name open-webui --restart always ghcr.io/open-webui/open-webui:main

Open http://localhost:3000 in your browser
Select martha-9b from the model dropdown
Drag and drop an image into the chat
Ask "What do you see?"

With llama.cpp Server (Advanced)

llama-server -m MARTHA-9B-Q4_K_M.gguf --mmproj mmproj-f16.gguf -ngl 99 --port 8080

Then send requests to http://localhost:8080/v1/chat/completions

📦 What's In This Repo?

File	What It Does	Size
`MARTHA-9B-Q8_0.gguf`	Highest quality text model	~9.5 GB
`MARTHA-9B-Q6_K.gguf`	High quality, smaller	~7.4 GB
`MARTHA-9B-Q5_K_M.gguf`	Good balance of quality/size	~6.5 GB
`MARTHA-9B-Q4_K_M.gguf`	Smallest, still good	~5.5 GB
`mmproj-f16.gguf`	Vision projector (the eyes)	~900 MB
`MODELFILE_*`	Ready-made configs for Ollama	tiny

Which GGUF Should I Pick?

Got 8+ GB VRAM? → Use Q8_0 (best quality)
Got 6 GB VRAM? → Use Q5_K_M or Q6_K
Got 4 GB VRAM? → Use Q4_K_M
Not sure? → Use Q4_K_M, it works on almost everything

🔑 Key Concepts (If You're New)

Term	What It Means
GGUF	A file format for AI models. Like .mp3 is for music, .gguf is for AI brains.
Quantization (Q4, Q5, Q8)	Compression level. Lower number = smaller file but slightly less smart. Q4 is like JPEG, Q8 is like PNG.
mmproj	The vision projector — a separate file that gives the model eyes. Without it, the model can only read text.
Ollama	Free software that runs AI models on your computer. Think of it as a media player, but for AI.
VRAM	Your graphics card's memory. More VRAM = bigger models you can run. Check yours with `nvidia-smi` in terminal.
Modelfile	A config file that tells Ollama how to set up the model. Like a recipe card.
Open WebUI	A web interface that makes chatting with local AI look like a web chat app. Runs in Docker.

💻 System Requirements

Model	Minimum VRAM	Recommended VRAM	Runs On
Q4_K_M	4 GB	6 GB	Most gaming PCs, M1+ Macs
Q5_K_M	5 GB	8 GB	RTX 3060+, M1+ Macs
Q6_K	6 GB	8 GB	RTX 3060+, M2+ Macs
Q8_0	8 GB	12 GB	RTX 3070+, M2 Pro+ Macs

No GPU? MARTHA runs on CPU too — just slower. Every modern computer can run Q4_K_M.

🏗️ Technical Details

Base: Qwen3.5-9B by Alibaba Cloud
Architecture: Vision-Language (Image-Text-to-Text)
Vision: 24-block ViT encoder with merger projection
Ghost Pass: 1e-6 noise applied to all floating-point tensors — mathematically unique weights
License: Apache 2.0 — use it for anything, commercially or personally

🌐 The MARTHA Family

Model	Parameters	Best For
MARTHA-0.8B	0.8B	Phones, Raspberry Pi, embedded
MARTHA-2B	2B	Laptops, light desktops
MARTHA-4B	4B	Gaming PCs, most desktops
MARTHA-9B	9B	RTX 3080+, workstations
MARTHA-27B	27B	Multi-GPU, cloud
MARTHA-73B	73B	Server-grade
MARTHA-120B	120B (5.1B active)	Data centre

❓ Troubleshooting

"ollama: command not found" → Install Ollama from https://ollama.com

"model not found" → Make sure you ran ollama create with the Modelfile first

"out of memory" → Try a smaller quant (Q4 instead of Q8) or close other apps

"images aren't working" → Make sure you downloaded mmproj-f16.gguf AND used PROJECTOR in your Modelfile

"slow generation" → Your GPU might not be detected. Run nvidia-smi to check. If no GPU, it runs on CPU which is slower but still works.

📄 License

Apache 2.0 — fork it, fine-tune it, sell it, do whatever. Just keep the attribution.

https://zeropointai.uk | https://huggingface.co/ZERO-POINT-INTELLIGENCE

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Safetensors

Model size

9B params

Tensor type

BF16

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Model tree for ZERO-POINT-AI/Miss_MARTHA-9B-Qwen3.5-Omni

Quantizations

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