Instructions to use DQN-Labs-Community/DQN-GPT-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DQN-Labs-Community/DQN-GPT-v0.2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DQN-Labs-Community/DQN-GPT-v0.2",
	filename="DQN-GPT-v0.2-3B.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DQN-Labs-Community/DQN-GPT-v0.2 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/DQN-GPT-v0.2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DQN-Labs-Community/DQN-GPT-v0.2: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/DQN-GPT-v0.2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DQN-Labs-Community/DQN-GPT-v0.2: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/DQN-GPT-v0.2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DQN-Labs-Community/DQN-GPT-v0.2: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/DQN-GPT-v0.2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DQN-Labs-Community/DQN-GPT-v0.2:Q4_K_M

Use Docker

docker model run hf.co/DQN-Labs-Community/DQN-GPT-v0.2:Q4_K_M

LM Studio
Jan
Ollama
How to use DQN-Labs-Community/DQN-GPT-v0.2 with Ollama:
```
ollama run hf.co/DQN-Labs-Community/DQN-GPT-v0.2:Q4_K_M
```

Unsloth Studio

How to use DQN-Labs-Community/DQN-GPT-v0.2 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/DQN-GPT-v0.2 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/DQN-GPT-v0.2 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/DQN-GPT-v0.2 to start chatting

How to use DQN-Labs-Community/DQN-GPT-v0.2 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/DQN-GPT-v0.2: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/DQN-GPT-v0.2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DQN-Labs-Community/DQN-GPT-v0.2 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/DQN-GPT-v0.2: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/DQN-GPT-v0.2:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use DQN-Labs-Community/DQN-GPT-v0.2 with Docker Model Runner:
```
docker model run hf.co/DQN-Labs-Community/DQN-GPT-v0.2:Q4_K_M
```

Lemonade

How to use DQN-Labs-Community/DQN-GPT-v0.2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DQN-Labs-Community/DQN-GPT-v0.2:Q4_K_M

Run and chat with the model

lemonade run user.DQN-GPT-v0.2-Q4_K_M

List all available models

lemonade list

DQN-GPT v0.2

DQN-GPT v0.2 is a 3.4B parameter instruction-following language model designed with a single guiding philosophy:

AI with obedience in mind.

The model prioritizes precise instruction adherence, reliable formatting, and predictable behavior for developers building local AI systems.

⸻

Model Overview

•	Model Name: DQN-GPT v0.2
•	Parameters: 3.4B
•	Base Architecture: Extracted text backbone from Ministral 3 3B
•	Training Type: Instruction fine-tuning
•	Developer: DQN Labs
•	License: Apache 2.0

DQN-GPT v0.2 was created by extracting the pure text model component from the Ministral 3 3B architecture. This custom extraction removed non-essential components and retained only the core language modeling capability.

The resulting base model was then instruction tuned on a carefully curated dataset of ~20,000 samples, focusing specifically on improving: • Instruction obedience • Response clarity • Task completion reliability • Structured outputs

This design philosophy emphasizes predictable and controllable AI behavior rather than pure conversational creativity.

⸻

Training Details

Base Model Extraction

The base model used for DQN-GPT v0.2 originates from Ministral 3 3B.

However, instead of using the full architecture, the text-only language model component was extracted and repurposed as a standalone foundation model. This approach allowed tighter control over the training pipeline and model behavior.

Instruction Fine-Tuning

The model was fine-tuned on a curated mix of approximately 20,000 instruction examples, combining several instruct-style datasets designed to improve: • task completion accuracy • formatting compliance • structured responses • deterministic instruction following

The dataset mixture was selected and filtered specifically to reinforce obedience and clarity rather than casual chat behavior.

⸻

Design Philosophy

Most instruction models aim for helpfulness and personality.

DQN-GPT v0.2 instead focuses on something different:

Obedience.

The goal is to create a model that: • does exactly what the user asks • respects formatting instructions • follows structured prompts reliably • avoids unnecessary verbosity

This makes the model particularly useful for: • AI agents • automation workflows • coding assistants • structured generation tasks • tool-using systems

⸻

Intended Use

DQN-GPT v0.2 is designed primarily for: • Local AI assistants • Developer tools • Coding help • Instruction-following tasks • Structured text generation • Agent pipelines

The model performs best when prompts are clear and structured.

⸻

Limitations

•	The model is relatively small compared to frontier models.
•	Knowledge is limited by the base model’s training data.
•	Performance on complex reasoning tasks may vary.
•	Not supported for multimodal tasks.

⸻

Motto

DQN Labs — Local AI for Everyone.

Downloads last month: 26

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

16-bit

Inference Providers NEW

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Model tree for DQN-Labs-Community/DQN-GPT-v0.2

Base model

mistralai/Ministral-3-3B-Base-2512

Quantized

mistralai/Ministral-3-3B-Instruct-2512

Quantized

DQN-Labs/ministral-3-3B-it2512-mlx-quantized

Quantized

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