Instructions to use DQN-Labs-Community/dqnScience-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DQN-Labs-Community/dqnScience-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DQN-Labs-Community/dqnScience-v1-GGUF",
	filename="DQN-Science-v1.F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use DQN-Labs-Community/dqnScience-v1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DQN-Labs-Community/dqnScience-v1-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 DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DQN-Labs-Community/dqnScience-v1-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 DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DQN-Labs-Community/dqnScience-v1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DQN-Labs-Community/dqnScience-v1-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": "DQN-Labs-Community/dqnScience-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M

Ollama
How to use DQN-Labs-Community/dqnScience-v1-GGUF with Ollama:
```
ollama run hf.co/DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use DQN-Labs-Community/dqnScience-v1-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 DQN-Labs-Community/dqnScience-v1-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 DQN-Labs-Community/dqnScience-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DQN-Labs-Community/dqnScience-v1-GGUF to start chatting

Pi new

How to use DQN-Labs-Community/dqnScience-v1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DQN-Labs-Community/dqnScience-v1-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": "DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DQN-Labs-Community/dqnScience-v1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DQN-Labs-Community/dqnScience-v1-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 DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use DQN-Labs-Community/dqnScience-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M
```

Lemonade

How to use DQN-Labs-Community/dqnScience-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DQN-Labs-Community/dqnScience-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.dqnScience-v1-GGUF-Q4_K_M

List all available models

lemonade list

dqnScience-v1

dqnScience-v1 is a 4B-parameter flagship reasoning model designed for deep thinking, scientific problem solving, and complex multi-step reasoning.

Unlike lightweight fast-response models, dqnScience-v1 is built to think longer, reason deeper, and solve harder problems—often performing far above its size class.

Model Description

Model type: Causal Language Model
Parameters: 4B
Primary use: Scientific reasoning and advanced problem solving
Style: Deep, structured, step-by-step reasoning

dqnScience-v1 prioritizes reasoning quality over speed, making it ideal for problems that require careful thought, abstraction, and layered logic.

Intended Uses

Direct Use

Solving physics, chemistry, and biology problems
Logical and analytical reasoning tasks
Multi-step problem solving
Conceptual understanding of scientific topics
Competitive exam-style questions (college level to moderate)

Key Characteristics

Strong multi-step reasoning ability
Produces structured and detailed explanations
Excels at breaking down complex problems
Performs above typical 4B models in reasoning capability
Designed for consistency and logical correctness
Handles abstract and conceptual questions effectively

Usage

dqnScience-v1 is available in multiple formats:

GGUF → llama.cpp, LM Studio
MLX → optimized for Apple Silicon (coming soon)
HF Transformers → universal compatibility

Training Details

dqnScience-v1 is fine-tuned with a strong focus on reasoning-heavy datasets, emphasizing:

Deep chain-of-thought reasoning
Scientific and logical problem solving
Conceptual clarity over memorization
Robust multi-step inference

Limitations

Slower than lightweight models due to deeper reasoning
May over-explain simple questions
Not optimized for casual or short-form responses
Performance may vary on highly specialized or research-level topics

Efficiency

Despite its strong reasoning capabilities, dqnScience-v1 is optimized to run moderately efficiently on consumer hardware, with support for quantized formats.

License

Apache 2.0

Author

Developed by DQN Labs.

Special thanks to Ram2 for quantization.

This model card was generated with the help of dqnGPT v1.

Downloads last month: 75

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit