Instructions to use sayeed105236/CuttyMOA-1.0 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-1.0 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-1.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sayeed105236/CuttyMOA-1.0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sayeed105236/CuttyMOA-1.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sayeed105236/CuttyMOA-1.0: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 sayeed105236/CuttyMOA-1.0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sayeed105236/CuttyMOA-1.0: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 sayeed105236/CuttyMOA-1.0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sayeed105236/CuttyMOA-1.0:Q4_K_M

Use Docker

docker model run hf.co/sayeed105236/CuttyMOA-1.0:Q4_K_M

LM Studio
Jan

vLLM

How to use sayeed105236/CuttyMOA-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sayeed105236/CuttyMOA-1.0"
# 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-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sayeed105236/CuttyMOA-1.0:Q4_K_M

Ollama
How to use sayeed105236/CuttyMOA-1.0 with Ollama:
```
ollama run hf.co/sayeed105236/CuttyMOA-1.0:Q4_K_M
```

Unsloth Studio

How to use sayeed105236/CuttyMOA-1.0 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-1.0 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-1.0 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-1.0 to start chatting

How to use sayeed105236/CuttyMOA-1.0 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-1.0: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": "sayeed105236/CuttyMOA-1.0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use sayeed105236/CuttyMOA-1.0 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-1.0:Q4_K_M

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-1.0:Q4_K_M" \
  --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-1.0 with Docker Model Runner:
```
docker model run hf.co/sayeed105236/CuttyMOA-1.0:Q4_K_M
```

Lemonade

How to use sayeed105236/CuttyMOA-1.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sayeed105236/CuttyMOA-1.0:Q4_K_M

Run and chat with the model

lemonade run user.CuttyMOA-1.0-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use sayeed105236/CuttyMOA-1.0 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-1.0: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 sayeed105236/CuttyMOA-1.0:Q4_K_M

Run Hermes

hermes

Atomic Chat

🚀 CuttyMOA-1.0 Pro (24K)

Self-trained language model built on Qwen2.5-14B-Instruct, combining two independent LoRA training phases (4K + 20K) via TIES delta merge into a single 24K-step model.

What's New — 20K Merge Update

This release merges both training adapters into one coherent model:

Phase	Platform	Steps	Method	Status
Phase 1	HuggingFace Spaces (L40S 48GB)	~4,000	QLoRA 4-bit nf4, r=128, α=256	✅ Completed
Phase 2	RunPod A40 (48GB VRAM)	~20,000	QLoRA 4-bit nf4, r=128, α=256	✅ Completed
TIES Merge	VPS (12 cores, 47GB RAM)	—	Sign-resolve + magnitude delta combine	✅ Completed
GGUF Q4_K_M	VPS (llama.cpp)	—	4.87 BPW	✅ Completed
Deploy + HF Upload	VPS + HuggingFace	—	llama-server :5401 + this repo	✅ Live

Why TIES Merge?

The 20K RunPod phase was a fresh training (not a continuation of 4K)
Both adapters trained independent learning trajectories on the same base
TIES (Trim, Elect Sign, Merge) resolves cases where the two adapters modified weights in opposite directions, keeping the larger-magnitude delta — preventing destructive interference
Result: a combined model that captures knowledge from both phases

Training Data

204 verified datasets (24 categories) → 4.5M training pairs
Categories: core LLM, code, security/cyber, Q&A, multilingual (10 lang), reasoning/math, agentic-tools, finance, medical, legal, RAG, enterprise, image, video, audio, presentation, science, sentiment, dialogue, docs/PDF, news, SQL, dialogue systems, translation
RAG corpus: 857K domain pairs captured from live CUTEADMOA platform usage (MOA fleet, OpenAI-compatible API, security scanning)
Historical sessions: 2,323 logged interactions

Files

File	Purpose
`cuttymoa-1.0-pro-Q4_K_M.gguf`	Pro model (24K TIES-merged) — recommended
`cuttymoa-1.0-Q4_K_M.gguf`	Original 4K Phase-1 model (legacy)

Deployment

Self-hosted: llama-server on port 5401 (Q4_K_M, 8.99 GB, 32K context)
API: via CUTEADMOA-5.6 MOA fleet (cuttymoa_pro model id)
Inference: ~30-50 tokens/sec on CPU (12-core VPS)

Capabilities

General chat & instruction following
Code generation (Python, JS, SQL, shell)
Cybersecurity (17 security intents): vulnerability analysis, code audit, threat intel
Multilingual (10 languages)
Agentic / tool use
RAG-augmented domain knowledge

Limitations

14B parameters, domain-specialized (not frontier-scale)
Trained on ~50M tokens (far less than commercial models' trillions)
Best used within the CUTEADMOA Mixture-of-Agents fleet as a specialized engine

Citation

@misc{cuttymoa2026,
  title={CuttyMOA-1.0: Self-Trained 14B Model with TIES Multi-Adapter Merge},
  author={Sayeed},
  year={2026},
  url={https://huggingface.co/sayeed105236/CuttyMOA-1.0}
}

Downloads last month: -

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

4-bit