codemate-qwen3.5-2b-gguf

🚀 CodeMate-Qwen3.5-2B

A lightweight coding assistant specialized for debugging, code generation, code explanation, and software engineering workflows.

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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.

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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}

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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

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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

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Quantizations

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Example

def reverse(text):
    return text[::-1]

Prompt:

Optimize this function and explain its time complexity.

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Intended Use

✅ Code Generation

✅ Debugging

✅ Learning Programming

✅ Code Review

✅ Refactoring

✅ API Development

✅ Backend Development

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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.

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Roadmap

• Improved reasoning
• Better long-context coding
• Larger instruction dataset
• Agentic coding support
• Better tool use
• Higher benchmark performance
• Production evaluation suite

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Acknowledgements

• Alibaba Qwen Team
• Hugging Face
• llama.cpp
• PEFT
• Transformers

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License

Apache 2.0 (inherits from the base model license.)

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Made with ❤️ by Michael Moses (Micymike)