DQN-Labs/dqnCode-v0.2-1.5B

🧠 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.

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