micymike/codemate-qwen3.5-2b-gguf

🚀 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

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)