Instructions to use ohora23/Kanana-2-30B-A3B-Instruct-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 ohora23/Kanana-2-30B-A3B-Instruct-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 ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ohora23/Kanana-2-30B-A3B-Instruct-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 ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ohora23/Kanana-2-30B-A3B-Instruct-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 ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ohora23/Kanana-2-30B-A3B-Instruct-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 ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ohora23/Kanana-2-30B-A3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ohora23/Kanana-2-30B-A3B-Instruct-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": "ohora23/Kanana-2-30B-A3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M

Ollama
How to use ohora23/Kanana-2-30B-A3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ohora23/Kanana-2-30B-A3B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ohora23/Kanana-2-30B-A3B-Instruct-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": "ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ohora23/Kanana-2-30B-A3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use ohora23/Kanana-2-30B-A3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Kanana-2-30B-A3B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ohora23/Kanana-2-30B-A3B-Instruct-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 ohora23/Kanana-2-30B-A3B-Instruct-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 ohora23/Kanana-2-30B-A3B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ohora23/Kanana-2-30B-A3B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ohora23/Kanana-2-30B-A3B-Instruct-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 "ohora23/Kanana-2-30B-A3B-Instruct-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"

Kanana-2-30B-A3B-Instruct — GGUF (Q4_K_M)

A GGUF quantization (Q4_K_M) of kakaocorp/kanana-2-30b-a3b-instruct-2601, so it runs in llama.cpp — e.g. as the Korean endpoint of local-llm-toolkit on a 16 GB GPU.

Why GGUF

Kanana-2 uses a DeepSeek-V3-style MLA + MoE architecture that ExLlamaV3 (EXL3) does not support, so GGUF via llama.cpp is the practical path to run it locally on consumer hardware. Only the file was quantized — no weights were retrained or altered.

Details


Base model	`kakaocorp/kanana-2-30b-a3b-instruct-2601` (30B, ~3B active MoE)
Format	GGUF, Q4_K_M (~18.6 GB)
Tooling	`llama.cpp` `convert_hf_to_gguf.py` (BF16 → F16) → `llama-quantize` (Q4_K_M)

Usage

# llama.cpp server (fits 16 GB with some MoE layers offloaded to CPU)
llama-server -m kanana-2-30b-a3b-instruct-2601-Q4_K_M.gguf \
  -ngl 99 --n-cpu-moe 18 -c 16384 --jinja -a kanana-2

# or via local-llm-toolkit
./llm up ko && ./llm chat --ko

License & attribution

This is a Derivative Work of Kanana-2, redistributed under the KANANA LICENSE AGREEMENT (see license_link). Per the agreement's redistribution terms: this notice and the KANANA license are included, the base model is attributed above, and this is identified as a modified (quantized) version. Use must follow Kakao's Guidelines for Responsible AI. All rights in the underlying model remain with Kakao Corp.

Downloads last month: 147

GGUF

Model size

31B params

Architecture

deepseek2

Hardware compatibility

4-bit

Model tree for ohora23/Kanana-2-30B-A3B-Instruct-GGUF

Base model

kakaocorp/kanana-2-30b-a3b-base-2601

Finetuned

kakaocorp/kanana-2-30b-a3b-mid-2601

Finetuned

kakaocorp/kanana-2-30b-a3b-instruct-2601

Quantized

(3)

this model