Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-1.5B-Instruct
+tags:
+- dqnlabs
+- coding
+- python
+- humaneval
+- lora
+metrics:
+- name: HumanEval (zero-shot)
+  type: pass@1
+  value: 49.39%
+---
+
+# 🧠 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% |
+| Qwen2.5-Coder-1.5B (reported) | 1.5B | ~41.6% |
+| Qwen2.5-Coder-1.5B (refined) | 1.5B | ~46.8% |
+| **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**