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

Libraries

How to use DQN-Labs-Community/dqnGPT-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/dqnGPT-v1-GGUF",
	filename="dqnGPT-v1.IQ4_XS.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DQN-Labs-Community/dqnGPT-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/dqnGPT-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DQN-Labs-Community/dqnGPT-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/dqnGPT-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DQN-Labs-Community/dqnGPT-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/dqnGPT-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DQN-Labs-Community/dqnGPT-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/dqnGPT-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DQN-Labs-Community/dqnGPT-v1-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

How to use DQN-Labs-Community/dqnGPT-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/dqnGPT-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/dqnGPT-v1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.dqnGPT-v1-GGUF-Q4_K_M

List all available models

lemonade list

dqnGPT-v1

dqnGPT-v1 is a general-purpose, multimodal AI assistant designed to act as the central interface of the DQN Labs model ecosystem.

It combines strong reasoning, natural conversation, and expressive personality to deliver an engaging and capable AI experience, while working alongside specialized models such as dqnCode, dqnMath, and dqnScience.

🧠 Overview

dqnGPT-v1 is built on top of the Gemma 4 E4B architecture, a compact mixture-of-experts model that balances performance and efficiency.

It is designed not just to answer questions, but to:

Communicate naturally
Explain clearly
React intelligently
Delegate when appropriate

Unlike specialist models, dqnGPT-v1 acts as a controller and personality layer, making it ideal as a primary AI assistant.

🎯 Positioning

dqnGPT-v1 is not optimized for a single domain.

Instead, it is designed to:

Handle a wide variety of everyday tasks
Provide clear and engaging explanations
Act as the front-facing AI in a modular system
Route complex problems to specialized models when needed

It prioritizes usability, clarity, and personality over raw benchmark performance.

🧩 System Role

dqnGPT-v1 is part of a modular AI system:

dqnCode → programming
dqnMath → mathematics
dqnScience → scientific reasoning

dqnGPT-v1:

Attempts to solve problems independently
Recognizes when deeper expertise is required
Suggests specialized models only when appropriate

This creates a balanced and natural delegation system. dqnGPT will naturally give you suggestions to use one of the other specialized models like dqnCode, dqnMath, or dqnScience when chatting in order to achieve a potential better response.

🎭 Personality & Interaction Style

dqnGPT-v1 is designed to feel like a conversational, human-like assistant.

Key traits:

Natural and engaging tone
Slightly playful but not excessive
Reacts to interesting or complex ideas
Adjusts energy based on context
Avoids overly formal or robotic responses

👁️ Multimodal Capability

dqnGPT-v1 supports multimodal reasoning through its base architecture.

It can:

Interpret images
Explain visual content
Assist with diagrams and structured inputs

Note: Multimodal performance depends on runtime support.

🧠 Model Description

Base model: google/gemma-4-E4B-it
Architecture: Mixture-of-Experts (MoE)
Parameters: ~~8B total (~~4.5B active)
Type: Causal Language Model
Primary role: General assistant / controller

💡 Intended Uses

Direct Use

General AI assistant
Learning and explanations
Creative writing
Brainstorming
Everyday problem solving

System Integration

Front-end assistant for multi-model systems
Routing layer for specialized models
Conversational interface for AI pipelines

⚙️ Key Characteristics

Balanced reasoning and personality
Strong instruction following
Natural conversation flow
Context-aware delegation
Consistent tone across responses
Designed for real-world usability

⚠️ Limitations

Not optimized for highly specialized domains
May defer advanced tasks to specialist models
Multimodal performance depends on runtime support
Not intended for large-scale enterprise workloads

⚡ Efficiency

dqnGPT-v1 is designed for efficient inference:

Supports quantized formats (GGUF, 4-bit, etc.)
Runs on consumer GPUs and local setups
Optimized for responsiveness and usability

📦 Usage

This repository provides:

Custom chat template
System prompt
Behavior configuration

🧠 Training Details

dqnGPT-v1 is not fine-tuned on domain-specific datasets.

Instead, it uses:

Prompt-based personality shaping
Structured chat formatting
System-level behavior design

📜 License

Apache 2.0

👨‍💻 Author

Developed by DQN Labs.

This model represents the central interface of the DQN ecosystem. This model card was generated with the help of dqnGPT v0.2.

Downloads last month: 163

GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Collection including DQN-Labs-Community/dqnGPT-v1-GGUF

dqnGPT-v1

Collection

dqnGPT-v1 is the human-like, general assistant of the DQN suite. Based on the new Gemma 4 architecture, this 8B E4B model is excellent for daily use. • 2 items • Updated Apr 7