{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/51b53e0a7200458a97923b21a0cc2d69\" frameborder=\"0\" width=\"1670\" height=\"1252\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1252,"width":1670,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1252,"thumbnail_width":1670,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/51b53e0a7200458a97923b21a0cc2d69-1d6fc43292fc06ab.gif","duration":1676.251,"title":"namenotfound.ai - Complete Setup Guide: ImproveLLMStructure - Local Text Formatting with Small Language Models - 9 June 2025","description":"# How to Use Small Local Language Models for Text Formatting & Restructuring\n\nhttps://github.com/namenotfound-ai/ImproveLLMStructure\nhttps://github.com/namenotfound-ai/a_agent\n\nLearn 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.).\n\n## What You'll Learn:\n- **Two-step approach**: Separate reasoning from formatting using smaller, specialized models instead of relying on large cloud-based LLMs\n- **Local setup**: Run everything on your own hardware without cloud dependencies\n- **Complete installation**: Step-by-step environment setup using the A-Agent project requirements\n- **Model management**: Download and configure local models (Phi 8-bit) from HuggingFace\n- **API integration**: Set up local endpoints for model communication\n- **Real examples**: See live demonstrations of text-to-JSON, HTML table generation, and Python function creation\n\n## Key Benefits:\n✅ **100% Local** - No API costs or cloud dependencies  \n✅ **Scalable** - Use smaller models with iterative validation for reliable outputs  \n✅ **Confidence-based** - System validates responses until 95% confidence is reached  \n✅ **Format flexibility** - Convert between multiple output formats seamlessly  \n\n## Technical Setup Covered:\n- Conda environment configuration\n- Git LFS for large model files\n- Local API endpoints (ports 5015 & 5025)\n- Debugging with VS Code/Cursor\n- Model validation and error correction workflows\n\n**Perfect for developers** who want to reduce reliance on cloud LLM services while maintaining high-quality text processing capabilities using local hardware.\n\n## Projects Referenced:\n- [Improve LLM Structure](link-to-repo)\n- [A-Agent Project](link-to-repo)\n\n*Resources needed: ~15GB storage for models, sufficient RAM (demo uses 36GB peak)*"}