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
python
1defreverse(text):2return 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.)
