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

Libraries

How to use DQN-Labs-Community/dqnCode-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/dqnCode-v1-GGUF",
	filename="DQN-Code-v1.IQ4_XS.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/dqnCode-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/dqnCode-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DQN-Labs-Community/dqnCode-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/dqnCode-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DQN-Labs-Community/dqnCode-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/dqnCode-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DQN-Labs-Community/dqnCode-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/dqnCode-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DQN-Labs-Community/dqnCode-v1-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

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

Use Docker

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

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

Unsloth Studio new

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

Pi new

How to use DQN-Labs-Community/dqnCode-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/dqnCode-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/dqnCode-v1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DQN-Labs-Community/dqnCode-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/dqnCode-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/dqnCode-v1-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.dqnCode-v1-GGUF-Q4_K_M

List all available models

lemonade list

dqnCode-v1

dqnCode-v1 is a 4B-parameter language model designed for fast, clear, and practical coding assistance.

It focuses on writing, fixing, and explaining code efficiently, with minimal verbosity and strong real-world usefulness. It is optimized for everyday programming tasks with low latency and concise outputs.

Benchmark

dqnCode-v1 is positioned as a high-performance compact coding model, with strong results on standard code generation benchmarks. It is trained with simple prompts in mind, so you don't need to be a developer to use it!

HumanEval

pass@1: 63.4%

This score places dqnCode-v1 among the strongest models in the 4B parameter class for coding tasks (only beaten by one other model in the 4B or below models class!)

Model	Provider	HumanEval (pass@1)
GPT-3.5 Turbo	OpenAI	68%
GPT-4	OpenAI	67%
dqnCode v1 (4B)	DQN Labs	63.4%
Phi-3.5-mini-instruct	Microsoft	62.8%
DeepSeek Coder 33B	DeepSeek	52.4%
Gemma 2 27B	Google	51.8%
Nous Hermes 3 405B	Nous Research	51.4%

Benchmark Context

Evaluated on HumanEval (Python code generation benchmark)
Focused on functional correctness of generated code
Designed to reflect real-world coding performance in a compact model

Positioning

dqnCode-v1 is designed to deliver coding performance comparable to significantly larger models while maintaining a small footprint.

Approaches GPT-3.5 / GPT-4-level performance on many practical coding tasks
Achieves strong performance with significantly fewer parameters
Optimized for fast local inference and real-world development workflows
Excellent at all languages, especially HTML-CSS-JS, Python, Java, and more!

Model Description

Model type: Causal Language Model
Parameters: 4B
Primary use: Coding and programming assistance
Style: Direct code generation with minimal explanation

dqnCode-v1 4B is optimized for practical coding workflows rather than long-form development or benchmark performance.

Intended Uses

Direct Use

Writing code snippets
Debugging errors
Explaining code logic
Converting code between languages
Assisting with small to medium programming tasks
Competitive programming support

Key Characteristics

Produces clean and functional code
Prioritizes correctness and simplicity
Minimizes unnecessary explanation
Strong at debugging and fixing issues
Designed for consistent outputs across common coding tasks
Excellent instruction following and stability.
Trained with simple prompts in mind, so you don't need to be a developer to use it!

Example

Input

Write a Python function to check if a number is prime

Output

def is_prime(n):
    if n < 2:
        return False
    for i in range(2, int(n**0.5) + 1):
        if n % i == 0:
            return False
    return True

Input

Fix this code:
for i in range(5)
    print(i)

Output

for i in range(5):
    print(i)

Usage

This model is available on many platforms and is compatible with many formats.

The GGUF format is compatible with llama.cpp and LM Studio.
Other formats include MLX (LM Studio, optimized for Apple devices), and HF (universal compatibility).

Training Details

dqnCode-v1 is fine-tuned for practical coding tasks and efficient problem solving.

The training process emphasizes:

Functional correctness
Minimal and clean outputs
Real-world coding scenarios
Debugging and code repair

Limitations

Limited performance on very large or complex codebases
Not optimized for long-form software architecture design
May simplify explanations rather than provide deep theoretical detail

Efficiency

dqnCode-v1 is designed to run efficiently on consumer hardware, with support for quantized formats.

License

Apache 2.0

Author

Developed by DQN Labs.
Huge thanks to the team at mradermacher for quantizing this model! This model card was generated with the help of dqnGPT v0.2!

Downloads last month: 347

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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

dqnCode-v1

Collection

dqnCode-v1 is a bespoke 4B coding focused model. With a score of 63% on HumanEval, dqnCode has top-tier coding performance for local AI deployment. • 2 items • Updated Apr 7