video1.0<iframe src="https://www.loom.com/embed/51b53e0a7200458a97923b21a0cc2d69" frameborder="0" width="1670" height="1252" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>12521670Loomhttps://www.loom.com12521670https://cdn.loom.com/sessions/thumbnails/51b53e0a7200458a97923b21a0cc2d69-1d6fc43292fc06ab.gif1676.251# How to Use Small Local Language Models for Text Formatting & Restructuring https://github.com/namenotfound-ai/ImproveLLMStructure https://github.com/namenotfound-ai/a_agent Learn how to set up and use the **Improve LLM Structure** project from NaveNotFound - a powerful local solution that leverages small, specialized language models to transform text between different formats (plain text → JavaScript, JSON, Python, HTML, etc.). ## What You'll Learn: - **Two-step approach**: Separate reasoning from formatting using smaller, specialized models instead of relying on large cloud-based LLMs - **Local setup**: Run everything on your own hardware without cloud dependencies - **Complete installation**: Step-by-step environment setup using the A-Agent project requirements - **Model management**: Download and configure local models (Phi 8-bit) from HuggingFace - **API integration**: Set up local endpoints for model communication - **Real examples**: See live demonstrations of text-to-JSON, HTML table generation, and Python function creation ## Key Benefits: ✅ **100% Local** - No API costs or cloud dependencies ✅ **Scalable** - Use smaller models with iterative validation for reliable outputs ✅ **Confidence-based** - System validates responses until 95% confidence is reached ✅ **Format flexibility** - Convert between multiple output formats seamlessly ## Technical Setup Covered: - Conda environment configuration - Git LFS for large model files - Local API endpoints (ports 5015 & 5025) - Debugging with VS Code/Cursor - Model validation and error correction workflows **Perfect for developers** who want to reduce reliance on cloud LLM services while maintaining high-quality text processing capabilities using local hardware. ## Projects Referenced: - [Improve LLM Structure](link-to-repo) - [A-Agent Project](link-to-repo) *Resources needed: ~15GB storage for models, sufficient RAM (demo uses 36GB peak)*