🚀 CodeMate-Qwen3.5-2B
A lightweight coding assistant specialized for debugging, code generation, code explanation, and software engineering workflows.
Overview
CodeMate-Qwen3.5-2B is a LoRA fine-tuned version of Qwen3.5-2B focused on helping developers write, understand and debug code.
Unlike general-purpose assistants, CodeMate has been optimized for practical programming tasks including:
- Python debugging
- JavaScript & TypeScript
- React
- Next.js
- API development
- Backend engineering
- Error diagnosis
- Code explanation
- Refactoring
- Best practices
The objective of this project is to create a fast and efficient coding model that runs comfortably on consumer hardware while maintaining strong software engineering capabilities.
Base Model
Qwen/Qwen3.5-2B
Highlights of the base model include:
- 2 Billion Parameters
- Native 262K context length
- Apache 2.0 License
- Hybrid Delta Attention Architecture
- Strong multilingual support
- Optimized for instruction following and coding tasks :contentReference[oaicite:0]{index=0}
Fine-tuning Objectives
The model was optimized to improve performance on:
- Bug fixing
- Stack trace interpretation
- Code reasoning
- Production debugging
- Code review
- Refactoring
- Software engineering conversations
- Practical programming assistance
Training
Base Model:
Qwen/Qwen3.5-2B
Method:
- PEFT
- LoRA
Frameworks:
- Transformers
- PEFT
- Accelerate
- PyTorch
Output:
Merged HuggingFace model
GGUF quantizations generated using:
- llama.cpp
Quantizations
| File | Recommended |
|---|---|
| BF16 | Research / Highest Quality |
| Q8_0 | ⭐⭐⭐⭐⭐ |
| Q6_K | ⭐⭐⭐⭐☆ |
| Q5_K_M | ⭐⭐⭐⭐☆ |
| Q4_K_M | ⭐⭐⭐⭐⭐ Recommended |
| Q3_K_M | Low-memory |
| Q2_K | Smallest |
Example
def reverse(text):
return text[::-1]
Prompt:
Optimize this function and explain its time complexity.
Intended Use
✅ Code Generation
✅ Debugging
✅ Learning Programming
✅ Code Review
✅ Refactoring
✅ API Development
✅ Backend Development
Evaluation
Formal benchmark evaluations are currently in progress.
Planned evaluations include:
- HumanEval
- HumanEval+
- MBPP
- MultiPL-E
- LiveCodeBench
- SWE-Bench Lite
- Aider Bench
Benchmark results will be published in future releases.
Roadmap
- Improved reasoning
- Better long-context coding
- Larger instruction dataset
- Agentic coding support
- Better tool use
- Higher benchmark performance
- Production evaluation suite
Acknowledgements
- Alibaba Qwen Team
- Hugging Face
- llama.cpp
- PEFT
- Transformers
License
Apache 2.0 (inherits from the base model license.)
Made with ❤️ by Michael Moses (Micymike)