Instructions to use DQN-Labs/dqnCode-v0.2-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use DQN-Labs/dqnCode-v0.2-1.5B 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 DQN-Labs/dqnCode-v0.2-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DQN-Labs/dqnCode-v0.2-1.5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DQN-Labs/dqnCode-v0.2-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DQN-Labs/dqnCode-v0.2-1.5B: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/dqnCode-v0.2-1.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DQN-Labs/dqnCode-v0.2-1.5B: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/dqnCode-v0.2-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DQN-Labs/dqnCode-v0.2-1.5B:Q4_K_M

Use Docker

docker model run hf.co/DQN-Labs/dqnCode-v0.2-1.5B:Q4_K_M

LM Studio
Jan
Ollama
How to use DQN-Labs/dqnCode-v0.2-1.5B with Ollama:
```
ollama run hf.co/DQN-Labs/dqnCode-v0.2-1.5B:Q4_K_M
```

Unsloth Studio

How to use DQN-Labs/dqnCode-v0.2-1.5B 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/dqnCode-v0.2-1.5B 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/dqnCode-v0.2-1.5B 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/dqnCode-v0.2-1.5B to start chatting

How to use DQN-Labs/dqnCode-v0.2-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DQN-Labs/dqnCode-v0.2-1.5B: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/dqnCode-v0.2-1.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use DQN-Labs/dqnCode-v0.2-1.5B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DQN-Labs/dqnCode-v0.2-1.5B: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 "DQN-Labs/dqnCode-v0.2-1.5B: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"

Docker Model Runner
How to use DQN-Labs/dqnCode-v0.2-1.5B with Docker Model Runner:
```
docker model run hf.co/DQN-Labs/dqnCode-v0.2-1.5B:Q4_K_M
```

Lemonade

How to use DQN-Labs/dqnCode-v0.2-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DQN-Labs/dqnCode-v0.2-1.5B:Q4_K_M

Run and chat with the model

lemonade run user.dqnCode-v0.2-1.5B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use DQN-Labs/dqnCode-v0.2-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DQN-Labs/dqnCode-v0.2-1.5B: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/dqnCode-v0.2-1.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat

🧠 DQN Code v0.2

A 1.5B Python Specialist

DQN Code v0.2 is a lightweight coding-focused model built on Qwen2.5-1.5B-Instruct and fine-tuned specifically for high-quality Python generation.

This release focuses on algorithmic correctness, structured implementation, and clean function completion.

🚀 Highlights

🔹 1.5B parameters
🔹 LoRA fine-tuned
🔹 Python-specialized
🔹 Optimized for deterministic completion
🔹 Designed to run locally on 8GB systems

📊 Benchmark Performance

HumanEval (0-shot, temperature=0.0)
Evaluated using mlx_lm.

Model	Parameters	HumanEval pass@1
Qwen2.5-1.5B-Instruct (official)	1.5B	~37.8%
MistralAI Mistral 7B	7B	~30.5%
Google Gemma 2 9B (as on llmstats.com)	9B	~40.2%
DQN Code v0.2	1.5B	49.39%

Evaluation settings:

0-shot

Temperature = 0.0

No few-shot prompting

Full 164 HumanEval tasks

This represents a +11.6% absolute improvement over the base Instruct model.

🎯 Design Philosophy

Instead of scaling parameters, DQN Code focuses on:

High-quality distilled supervision
Python-heavy training distribution
Clean function-style completions
Reduced conversational overhead
Local inference efficiency

Small models benefit heavily from specialization.
This release demonstrates how targeted fine-tuning can significantly improve coding performance without increasing model size.

🔧 Training Details

Base: Qwen2.5-1.5B-Instruct
Fine-tune type: LoRA
Effective batch size: 8
Max sequence length: 512
Optimizer: AdamW
Learning rate: 5e-6
Dataset size: ~1.8k curated Python-focused samples
Training hardware: 8GB RAM system (MLX)

Training focused on:

Function completion
Algorithmic correctness
Clean Python structure
Reduced hallucinated commentary

💻 Intended Use

Local coding assistant (with decent performance in other languages too!)
Python function completion
Algorithm practice
Educational use
Lightweight code generation

⚠ Limitations

Primarily optimized for Python (but performs well on other languages too.)
Not benchmarked on multi-language coding
Limited evaluation on mathematical reasoning
Not trained for tool use or multi-step planning

🌍 Philosophy

Powerful coding models do not require massive infrastructure. You don't need a datacenter at home!

Focused training + efficient inference can deliver strong results on modest hardware.

❓ Queries

If you have any questions regarding the model, want to know how it was trained and our pipleine process, how you can make a better version of the model, or you just want to chat about AI, feel free to contact me on Discord at @dqnlabs.

Enjoy this model, for this is the best so far. There's more coming.

-- DQN Labs

This model card was made with the assistance of dqnGPT-v0.1-7B :D