📖 LLM Compatibility Advisor — User Growth & Documentation Plan
📖 Overview
This document outlines the strategy for expanding the user base of the LLM Compatibility Advisor, continuously improving its functionality, and maintaining transparent project documentation for contributors, students, and AI communities. It will also serve as a living changelog and roadmap.
📅 Weekly/Monthly Actionables
Week 1-3:
Literature review, finalize objectives, build initial app prototype, deploy to Hugging Face Spaces.Week 4-5:
Collect feedback from early users, conduct AI readiness surveys, integrate performance tier adjustments.Week 6:
Finalize project documentation, references, publish contribution guide and deployment tutorial.Every Month (Post-release):
- Review compatibility tiers against new model releases (Gemma, Mistral, TinyLLaMA variants).
- Integrate new quantized model recommendations.
- Archive older device datasets and refresh with latest submissions.
- Track community feedback and feature requests.
📢 Outreach Channels
- Hugging Face Community Hub
- AI Saturdays India and Global Chapters
- Student AI club Discord servers
- LinkedIn AI student groups
- Swecha, PyCon India, and AI open-source events
- Open-source AI tool directories (awesome-LLM-tools, AI Community repos)
📑 Contribution Guide Link
How to contribute:
👉 https://huggingface.co/spaces/Suryansh14124/LLM-Advisor-ICFAI/wiki/Contributing
Includes:
- Forking instructions
- Branching conventions
- Code style guide
- Issue reporting protocol
- How to add new LLM entries to the compatibility matrix
🔄 Update Cycle Plan
LLM Compatibility Matrix:
Review and update every 3 months with latest open-source quantized models and GGUF variants.Documentation Updates:
Revise tutorials, contribution guides, and changelogs with each major release.Regional Survey Campaigns:
Conduct annual or biannual hardware capability surveys via AI clubs and educational networks.
📌 This document will be updated quarterly alongside model updates and major community onboarding milestones.