Deblank: Tool to Strip Code Formatting for LLM Token Reduction
What Deblank Does
Deblank is a preprocessing tool that removes code formatting (indentation, whitespace, line breaks) before sending code to LLMs, with a postprocessing step to restore readability. The transformation is bidirectional and AST-safe.
Performance Results
In evaluations across several models (DeepSeek-V3, Claude, Gemini, etc.):
- ~30% token reduction for languages like Java and C++
- ~9% token reduction for Python
- Negligible impact on Pass@1 accuracy for code completion
- Average latency: ~76ms
Supported Languages and Features
- Python, Java, C/C++, C#, JavaScript/TypeScript, and Go
- Handles incomplete snippets reasonably well
- Useful for fill-in-the-middle workflows
Getting Started
The project is open-sourced with these resources:
- GitHub: https://github.com/anpl-code/Deblank
- Paper: https://arxiv.org/abs/2508.13666
- Docker:
docker pull zhangcen456/deblank:latest
This type of token optimization can be particularly useful when working with context-limited LLMs or when processing large codebases, though the impact varies by language due to differences in formatting conventions.
📖 Read the full source: r/LocalLLaMA
👀 See Also
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