Instructions to use sayeed105236/CuttyMOA-1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sayeed105236/CuttyMOA-1.2 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 sayeed105236/CuttyMOA-1.2:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf sayeed105236/CuttyMOA-1.2:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sayeed105236/CuttyMOA-1.2:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf sayeed105236/CuttyMOA-1.2:Q4_K_S

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 sayeed105236/CuttyMOA-1.2:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf sayeed105236/CuttyMOA-1.2:Q4_K_S

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 sayeed105236/CuttyMOA-1.2:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sayeed105236/CuttyMOA-1.2:Q4_K_S

Use Docker

docker model run hf.co/sayeed105236/CuttyMOA-1.2:Q4_K_S

LM Studio
Jan

vLLM

How to use sayeed105236/CuttyMOA-1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sayeed105236/CuttyMOA-1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sayeed105236/CuttyMOA-1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sayeed105236/CuttyMOA-1.2:Q4_K_S

Ollama
How to use sayeed105236/CuttyMOA-1.2 with Ollama:
```
ollama run hf.co/sayeed105236/CuttyMOA-1.2:Q4_K_S
```

Unsloth Studio

How to use sayeed105236/CuttyMOA-1.2 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 sayeed105236/CuttyMOA-1.2 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 sayeed105236/CuttyMOA-1.2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sayeed105236/CuttyMOA-1.2 to start chatting

How to use sayeed105236/CuttyMOA-1.2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sayeed105236/CuttyMOA-1.2:Q4_K_S

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": "sayeed105236/CuttyMOA-1.2:Q4_K_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use sayeed105236/CuttyMOA-1.2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sayeed105236/CuttyMOA-1.2:Q4_K_S

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 "sayeed105236/CuttyMOA-1.2:Q4_K_S" \
  --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 sayeed105236/CuttyMOA-1.2 with Docker Model Runner:
```
docker model run hf.co/sayeed105236/CuttyMOA-1.2:Q4_K_S
```

Lemonade

How to use sayeed105236/CuttyMOA-1.2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sayeed105236/CuttyMOA-1.2:Q4_K_S

Run and chat with the model

lemonade run user.CuttyMOA-1.2-Q4_K_S

List all available models

lemonade list

Hermes Agent

How to use sayeed105236/CuttyMOA-1.2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sayeed105236/CuttyMOA-1.2:Q4_K_S

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 sayeed105236/CuttyMOA-1.2:Q4_K_S

Run Hermes

hermes

Atomic Chat

CuttyMOA-1.2

The flagship Mixture-of-Experts AI model of the CUTEADMOA ecosystem.


👤 Developed by	MD ABU SAYEED
⚡ Powered by	DOTPROGRAMMERS
🚀 Platform	CUTEADMOA (Mixture-of-Agents)
🧠 Base Architecture	Qwen 3.6 35B A3B (MoE)
📦 Format	GGUF (Q4_K_S — CPU-optimized)
🎯 Active Params	~3B per token (35B total, 128 experts)
📊 Context Window	8,192 tokens (extensible to 32K)
🔒 License	Apache 2.0

Overview

CuttyMOA-1.2 is a CPU-first Mixture-of-Experts language model built on the Qwen 3.6 35B A3B architecture. With only ~3B parameters active per token from a 35B expert pool, it achieves strong reasoning performance while running efficiently on CPU hardware — no GPU required.

It is the direct successor to CuttyMOA-1.0, upgraded from a 15B dense model to 35B MoE with 2.3× more total knowledge capacity.

Why CuttyMOA-1.2?

Feature	CuttyMOA-1.0	CuttyMOA-1.2
Architecture	Dense 15B	MoE 35B (3B active)
Context	8K	8K (extendable to 32K)
GGUF Size	8.4 GB (Q4_K_M)	17 GB (Q4_K_S)
Inference Speed (CPU)	8-12 t/s	2-4 t/s
Knowledge Breadth	Moderate	2.3× larger expert pool
Training Data	Base only	Base + 857K curated pairs

Quick Start

Option 1: llama.cpp (Recommended for VPS)

# Download the GGUF
huggingface-cli download sayeed105236/CuttyMOA-1.2 \
  CuttyMOA-1.2-Q4_K_S.gguf --local-dir ./

# Run with llama-server
./llama-server \
  -m CuttyMOA-1.2-Q4_K_S.gguf \
  -c 8192 -n 1024 \
  --port 5401 --host 127.0.0.1

# Test
curl http://127.0.0.1:5401/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"Who are you?"}],"max_tokens":50}'

Option 2: Ollama

# Download GGUF + Modelfile from this repo, then:
ollama create cuttymoa-1.2 -f Modelfile
ollama run cuttymoa-1.2

Option 3: CUTEADMOA Integration

CuttyMOA-1.2 plugs directly into the CUTEADMOA Mixture-of-Agents platform as a local inference engine. See cuteadmoa.site for platform documentation.

Hardware Requirements

Setup	RAM	Disk	Notes
Minimum	22 GB	18 GB	Q4_K_S, 4K context
Recommended	28 GB	20 GB	Q4_K_S, 8K context
Optimal	32 GB	25 GB	Q4_K_M, 8K context

Runs on any x86_64 CPU. Tested on AMD EPYC, Intel Xeon, Apple M-series.

Training Data

The model ships with the CUTEADMOA Knowledge Corpus:

857,223 instruction-response pairs across 24 domains
Domains: code, security, news, medical, legal, finance, science, multilingual, RAG, agentic-tools, reasoning, dialogue, and more
Full dataset available at sayeed105236/cuttymoa-training-corpus

Future Fine-Tuning

QLoRA fine-tuning recipes will be published for users who want to adapt CuttyMOA-1.2 to their own domains. Budget ~$2-4 on RunPod A40 for a full fine-tuning run.

CUTEADMOA Ecosystem

CuttyMOA-1.2 is the local inference engine for CUTEADMOA, a Mixture-of-Agents platform combining:

23+ text models across 6 providers
24 security intents with HTML/DOCX reports
Enterprise web scraping (ScrapePower)
Native macOS & iOS apps
Smart routing with multi-model aggregation

Version History

Version	Date	Base	Params	Notes
CuttyMOA-1.0	2026-08	Custom	15B dense	First release
CuttyMOA-1.2	2026-08	Qwen 3.6	35B MoE	Current

Downloads last month: 183

GGUF

Model size

35B params

Architecture

qwen35moe

Hardware compatibility

4-bit

Model tree for sayeed105236/CuttyMOA-1.2

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

(745)

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