Instructions to use kuper0201/DNA3.0-27B-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 kuper0201/DNA3.0-27B-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 kuper0201/DNA3.0-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kuper0201/DNA3.0-27B-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 kuper0201/DNA3.0-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf kuper0201/DNA3.0-27B-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 kuper0201/DNA3.0-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf kuper0201/DNA3.0-27B-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 kuper0201/DNA3.0-27B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kuper0201/DNA3.0-27B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/kuper0201/DNA3.0-27B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use kuper0201/DNA3.0-27B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kuper0201/DNA3.0-27B-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": "kuper0201/DNA3.0-27B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/kuper0201/DNA3.0-27B-GGUF:Q4_K_M

Ollama
How to use kuper0201/DNA3.0-27B-GGUF with Ollama:
```
ollama run hf.co/kuper0201/DNA3.0-27B-GGUF:Q4_K_M
```

Unsloth Studio

How to use kuper0201/DNA3.0-27B-GGUF 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 kuper0201/DNA3.0-27B-GGUF 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 kuper0201/DNA3.0-27B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kuper0201/DNA3.0-27B-GGUF to start chatting

How to use kuper0201/DNA3.0-27B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kuper0201/DNA3.0-27B-GGUF: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": "kuper0201/DNA3.0-27B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use kuper0201/DNA3.0-27B-GGUF with Docker Model Runner:
```
docker model run hf.co/kuper0201/DNA3.0-27B-GGUF:Q4_K_M
```

Lemonade

How to use kuper0201/DNA3.0-27B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kuper0201/DNA3.0-27B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DNA3.0-27B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use kuper0201/DNA3.0-27B-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 kuper0201/DNA3.0-27B-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 kuper0201/DNA3.0-27B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use kuper0201/DNA3.0-27B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf kuper0201/DNA3.0-27B-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 "kuper0201/DNA3.0-27B-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"

DNA3.0-27B GGUF

Unofficial community GGUF quantization of dnotitia/DNA3.0-27B, intended for local inference with LM Studio and llama.cpp.

The upstream model is a 27B dense vision-language model based on Qwen/Qwen3.6-27B, with additional Korean, enterprise persona, and uncensored post-training by Dnotitia. This repository does not add a separate abliteration pass; it only converts and quantizes the published upstream weights.

Files

File	Purpose	Size
`DNA3.0-27B-Q4_K_M.gguf`	Main language model, Q4_K_M	16,547,399,264 bytes
`mmproj-DNA3.0-27B-Q8_0.gguf`	Vision projector, Q8_0	629,247,424 bytes

For text-only use, download the main model. For image input, also download the mmproj file and select it as the vision projector if your runtime does not detect it automatically.

LM Studio

In LM Studio, search for kuper0201/DNA3.0-27B-GGUF.
Download DNA3.0-27B-Q4_K_M.gguf.
Download mmproj-DNA3.0-27B-Q8_0.gguf as well if you need image input.
Load the model with a modest context size first, then increase it only if memory permits.

The Q4 model is about 15.4 GiB before runtime overhead. A 16 GB unified-memory Mac may use heavy swap and run slowly; 24 GB or more memory is recommended. The upstream model supports very long contexts, but local memory use grows with the selected context length.

Conversion details

Source: dnotitia/DNA3.0-27B
Converter/runtime: llama.cpp commit 6ea215d
Intermediate precision: BF16
Final quantization: Q4_K_M
Vision projector quantization: Q8_0
The conversion used --no-mtp because the published model contains 64 main transformer blocks plus separate MTP tensors.

SHA-256

745c63bdb76bada3f5b525eeab73bad9a53988e7eb98be865ba103e9ee41b084  DNA3.0-27B-Q4_K_M.gguf
28b0e39206f481da17c9ba1aa3d4d41f9df23d203bc51bb24f3f7e5c63e2cfe8  mmproj-DNA3.0-27B-Q8_0.gguf

License and responsible use

The upstream model is published under the Apache License 2.0. Please review the upstream model card for its limitations, biases, and responsible-use guidance. Quantization can also introduce quality differences relative to the original BF16 weights.

Downloads last month: 162

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for kuper0201/DNA3.0-27B-GGUF

Base model

Qwen/Qwen3.6-27B

Finetuned

dnotitia/DNA3.0-27B

Quantized

(3)

this model