| I reviewed the Build Small Hackathon page and extracted only the information that is useful for an AI coding agent building a submission. I excluded registration, dates, logistics, prizes, and participation process. The content below is ready to save as `build-small-hackathon.md`. ([Hugging Face][1]) | |
| # Build Small Hackathon – Builder Reference | |
| This document summarizes the requirements, constraints, judging criteria, and bonus opportunities for the Hugging Face Build Small Hackathon. | |
| ## Core Philosophy | |
| Build useful, focused applications powered by **small AI models**. | |
| Projects should demonstrate that capable AI experiences can be created without relying on massive frontier models. The emphasis is on practicality, creativity, and thoughtful use of smaller models. | |
| --- | |
| # Mandatory Requirements | |
| ## 1. Small Models Only | |
| **Total model parameters must be ≤ 32B.** | |
| Requirements: | |
| * All AI functionality must use models whose combined parameter count does not exceed 32 billion parameters. | |
| * Solutions should be designed around models that can realistically run on consumer hardware. | |
| * The model choice should be a natural fit for the problem being solved. | |
| Checklist: | |
| * [ ] Total model size ≤ 32B parameters | |
| * [ ] Model choice justified by the use case | |
| * [ ] No dependency on larger models for core functionality | |
| --- | |
| ## 2. Must Be a Gradio Application | |
| Requirements: | |
| * The submission must be implemented as a **Gradio app**. | |
| * The app must be hosted as a **Hugging Face Space**. | |
| * Gradio is the required application framework. | |
| Checklist: | |
| * [ ] Built with Gradio | |
| * [ ] Deployable as a Hugging Face Space | |
| * [ ] End-user interaction occurs through the Gradio app | |
| --- | |
| ## 3. Demonstrable Working Product | |
| Requirements: | |
| * The application must clearly show its value through actual usage. | |
| * Judges expect a working, polished experience rather than a concept or prototype description. | |
| Checklist: | |
| * [ ] Functional end-to-end workflow | |
| * [ ] Usable by non-technical users | |
| * [ ] Clear demonstration of AI functionality | |
| * [ ] Reasonably polished UX | |
| --- | |
| # Competition Tracks | |
| ## Track 1: Backyard AI | |
| ### Goal | |
| Solve a real problem for a real person. | |
| Examples: | |
| * Family member | |
| * Friend | |
| * Neighbor | |
| * Small business owner | |
| * Community member | |
| The application should provide measurable value to a specific person rather than targeting a vague audience. | |
| ### Judging Criteria | |
| #### Problem is Specific and Real | |
| Strong submissions: | |
| * Address a concrete user problem. | |
| * Have a clearly identified beneficiary. | |
| * Avoid generic "AI assistant for everyone" concepts. | |
| #### Real User Usage | |
| Strong submissions: | |
| * Were actually used by the intended person. | |
| * Demonstrate evidence of usefulness. | |
| * Solve a recurring task or pain point. | |
| #### Honest Fit for Small Models | |
| Strong submissions: | |
| * Use small models because they are appropriate. | |
| * Do not force large-model-style tasks into small-model constraints. | |
| * Design around strengths of compact models. | |
| #### Gradio App Polish | |
| Strong submissions: | |
| * Clear workflow. | |
| * Good UX. | |
| * Easy to understand. | |
| * Reliable behavior. | |
| ### Recommended Design Principles | |
| * Build for one real user first. | |
| * Optimize for usefulness over novelty. | |
| * Minimize complexity. | |
| * Focus on task completion. | |
| --- | |
| ## Track 2: An Adventure in Thousand Token Wood | |
| ### Goal | |
| Build something delightful that would not exist without AI. | |
| Possible categories: | |
| * Interactive stories | |
| * Games | |
| * Creative tools | |
| * Artistic experiences | |
| * Experimental interfaces | |
| * AI-native toys | |
| ### Judging Criteria | |
| #### Delight | |
| Strong submissions: | |
| * Are genuinely fun. | |
| * Create memorable experiences. | |
| * Feel worth sharing. | |
| #### AI Is Essential | |
| Strong submissions: | |
| * Require AI to function. | |
| * Use AI as a core mechanic. | |
| * Do not merely bolt AI onto a conventional app. | |
| #### Originality | |
| Strong submissions: | |
| * Explore unusual ideas. | |
| * Present novel interactions. | |
| * Avoid common chatbot wrappers. | |
| #### Gradio App Polish | |
| Strong submissions: | |
| * Feel complete. | |
| * Have thoughtful UX. | |
| * Present a cohesive experience. | |
| ### Recommended Design Principles | |
| * Prioritize surprise and enjoyment. | |
| * Make AI central to the experience. | |
| * Experiment with interaction design. | |
| * Create something users want to show others. | |
| --- | |
| # Bonus Merit Badges | |
| These are optional but can provide additional scoring benefits. | |
| --- | |
| ## Off the Grid | |
| ### Theme | |
| Local-first AI. | |
| ### Requirements | |
| * No cloud AI APIs. | |
| * The application runs entirely using local models. | |
| Examples: | |
| * Local inference | |
| * On-device inference | |
| * Self-contained deployment | |
| --- | |
| ## Well-Tuned | |
| ### Theme | |
| Fine-tuned models. | |
| ### Requirements | |
| * Use a fine-tuned model. | |
| * Publish the fine-tuned model on Hugging Face. | |
| Examples: | |
| * Task-specific fine tuning | |
| * Domain adaptation | |
| * Personalized model variants | |
| --- | |
| ## Off-Brand | |
| ### Theme | |
| Custom interface design. | |
| ### Requirements | |
| * Go beyond default Gradio styling. | |
| * Create a distinctive frontend experience. | |
| Examples: | |
| * Custom layouts | |
| * Advanced theming | |
| * Branded UI systems | |
| * Rich interaction patterns | |
| --- | |
| ## Llama Champion | |
| ### Theme | |
| llama.cpp deployment. | |
| ### Requirements | |
| * Run the model using llama.cpp. | |
| Examples: | |
| * GGUF models | |
| * Local CPU inference | |
| * Edge deployment | |
| --- | |
| ## Sharing Is Caring | |
| ### Theme | |
| Transparency and learning. | |
| ### Requirements | |
| * Publish and share agent traces. | |
| Examples: | |
| * Reasoning traces | |
| * Agent execution logs | |
| * Workflow documentation | |
| --- | |
| ## Field Notes | |
| ### Theme | |
| Documentation and learnings. | |
| ### Requirements | |
| * Document discoveries, experiments, and lessons learned during development. | |
| Examples: | |
| * Build logs | |
| * Technical writeups | |
| * Design decisions | |
| * Model evaluations | |
| --- | |
| # Submission Design Checklist | |
| ## Model Constraints | |
| * [ ] Total parameters ≤ 32B | |
| * [ ] Small model is central to solution | |
| * [ ] Architecture aligns with small-model strengths | |
| ## App Requirements | |
| * [ ] Built with Gradio | |
| * [ ] Deployable as Hugging Face Space | |
| * [ ] End-to-end functionality works | |
| ## Product Quality | |
| * [ ] Clear user value | |
| * [ ] Reliable interaction flow | |
| * [ ] Understandable UX | |
| * [ ] Demonstrable AI contribution | |
| ## Track Alignment | |
| ### Backyard AI | |
| * [ ] Real user identified | |
| * [ ] Real problem solved | |
| * [ ] User actually benefits | |
| ### Thousand Token Wood | |
| * [ ] Delightful experience | |
| * [ ] AI is essential | |
| * [ ] Original concept | |
| ## Bonus Opportunities | |
| * [ ] Local-only inference | |
| * [ ] Fine-tuned model | |
| * [ ] Custom UI | |
| * [ ] llama.cpp runtime | |
| * [ ] Shared traces | |
| * [ ] Development notes | |
| --- | |
| # What Judges Appear to Value | |
| 1. Strong alignment with small-model constraints. | |
| 2. A complete, usable application rather than a demo. | |
| 3. Clear evidence that AI meaningfully improves the experience. | |
| 4. Good UX and Gradio polish. | |
| 5. Focused scope. | |
| 6. Creativity or real-world usefulness. | |
| 7. Thoughtful engineering choices rather than model size. | |
| Source: Build Small Hackathon official page and associated announcement materials. ([Hugging Face][1]) | |
| [1]: https://huggingface.co/build-small-hackathon?utm_source=chatgpt.com "Build Small Hackathon" | |