Instructions to use aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF 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 aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF: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 aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF: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 aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aj9o9/K2-Horizon-MoVA-36B-A4B-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": "aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M

Ollama
How to use aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF with Ollama:
```
ollama run hf.co/aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF with Docker Model Runner:
```
docker model run hf.co/aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M
```

Lemonade

How to use aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.K2-Horizon-MoVA-36B-A4B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF: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 aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF: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 "aj9o9/K2-Horizon-MoVA-36B-A4B-GGUF: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"

K2-Horizon-MoVA-36B-A4B GGUF quants

GGUF quants of IFM/K2-Horizon-MoVA-36B-A4B. The base model has 36B total parameters and approximately 4B active parameters per token.

These are straight llama-quantize conversions from the original BF16 GGUF published by IFM. No importance matrix or calibration data was used.

Files

File	Quantization	Size
`K2-Horizon-MoVA-36B-A4B-Q4_K_M.gguf`	Q4_K_M	22.37 GB
`K2-Horizon-MoVA-36B-A4B-Q5_K_M.gguf`	Q5_K_M	26.44 GB
`K2-Horizon-MoVA-36B-A4B-Q6_K.gguf`	Q6_K	30.77 GB

Required llama.cpp build

K2 Horizon architecture support is required. Use the IFM fork/branch until support is available in the upstream version you are using:

git clone --depth 1 --single-branch --branch model/K2Horizon \
  https://github.com/MBZUAI-IFM/llama.cpp.git llama.cpp-k2horizon
cd llama.cpp-k2horizon
cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release -j"$(nproc)"

Run a quant with all GPU layers and 36 CPU MoE layers:

./build/bin/llama-cli \
  -m K2-Horizon-MoVA-36B-A4B-Q5_K_M.gguf \
  -ngl 999 -ncmoe 36

Benchmark

Measured locally on an NVIDIA GeForce RTX 3090 (24,103 MiB) and AMD Ryzen 9 9900X. The benchmark used -p 512 -n 128 -b 512 -ub 512 -r 3 -ngl 999 -ncmoe 36.

Quant	Prompt processing (512 tokens)	Generation (128 tokens)
Q4_K_M	832.14 ± 6.21 tok/s	41.00 ± 0.07 tok/s
Q5_K_M	750.78 ± 13.13 tok/s	36.85 ± 0.08 tok/s
Q6_K	672.93 ± 7.53 tok/s	32.68 ± 0.22 tok/s

The benchmark results and short inference checks were run with the K2Horizon branch and are included alongside this README in the local build logs.

Source

Base model: IFM/K2-Horizon-MoVA-36B-A4B
GGUF conversion source: IFM/K2-Horizon-MoVA-36B-A4B-GGUF
Required llama.cpp fork: MBZUAI-IFM/llama.cpp, model/K2Horizon

Downloads last month: 1,145

GGUF

Model size

37B params

Architecture

k2-horizon

Hardware compatibility

4-bit

5-bit

6-bit