| # Hackathon Submission Package |
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| This document collects the judge-facing story, demo flow, and submission assets for the Gradio |
| Hugging Face Build Small Hackathon. |
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| ## Track |
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| Recommended track: Backyard AI. |
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| Reason: the app is local-first, small-model focused, and designed to help a solo builder inspect, |
| test, correct, and document OpenBMB model workflows on their own machine before moving to a Space. |
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| ## Project Story |
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| OpenBMB Local AI Workbench is a Gradio app for trying small OpenBMB models locally, capturing |
| human corrections as field notes, and turning those notes into training or evaluation artifacts. |
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| ## Target User |
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| The target user is a hackathon builder or small-team AI tinkerer who wants a practical local |
| workflow before committing to cloud deployment, GPU rentals, or a larger training stack. |
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| ## Measurable Benefit |
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| The app reduces setup uncertainty by keeping model choices, backend availability, field-note |
| exports, traces, and deployment next steps visible in one Gradio surface. |
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| ## Final Model Family |
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| Primary family: OpenBMB MiniCPM. |
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| Current configured models: |
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| | Config ID | Model | Parameters | Role | |
| | --- | --- | ---: | --- | |
| | `minicpm5_1b` | `openbmb/MiniCPM5-1B` | 1B | local text baseline | |
| | `minicpm5_1b_thinking` | `openbmb/MiniCPM5-1B-Thinking` | 1B | reasoning/text variant | |
| | `minicpm41_8b` | `openbmb/MiniCPM4.1-8B` | 8B | long-context text candidate | |
| | `minicpm_v46` | `openbmb/MiniCPM-V-4.6` | 1.3B | vision candidate | |
| | `minicpm_v46_thinking` | `openbmb/MiniCPM-V-4.6-Thinking` | 1.3B | vision reasoning candidate | |
| | `minicpm_o45` | `openbmb/MiniCPM-o-4.5` | 8B | omnimodal stretch candidate | |
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| All configured models are at or below the 32B hackathon limit. |
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| ## Badge Targets |
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| - Local-first: yes, through verified llama.cpp CLI, llama-cpp-python GGUF, LM Studio/OpenAI-compatible, and OpenBMB MiniCPM-V Plant image paths; Ollama and SGLang remain setup paths until generation is verified. |
| - llama.cpp: target badge path; requires local llama.cpp install and GGUF model verification. |
| - Open trace: yes, through local JSONL tracking and trace export. |
| - Field notes/report: yes, through corrected field notes, JSONL export, and local HF Dataset-style export. |
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| ## Demo Flow |
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| 1. Open the Gradio app locally. |
| 2. Show the Status tab and explain model-size compliance plus backend availability. |
| 3. Use Chat with the local `llama-cpp-python` GGUF backend to show a visible real response. |
| 4. Use the Dataset tab to preview a local JSONL/CSV training candidate. |
| 5. Save a correction in Field Notes and export corrected rows to JSONL. |
| 6. Open Traces to show local event history and optional Trackio status. |
| 7. Open Export to show GGUF conversion and quantization planning. |
| 8. Explain the remaining llama.cpp mmproj, Ollama, SGLang, and Space build verification tasks. |
| 9. Show the GitHub repo and, when available, the Hugging Face Space URL. |
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| ## Screenshot Assets |
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| - Workbench home: `assets/e2e/workbench/01-workbench-home.png` |
| - Workbench backend status: `assets/e2e/workbench/05-backend-status.png` |
| - Plant tool home: `assets/e2e/plant/01-plant-home.png` |
| - Plant corrections export: `assets/e2e/plant/03-corrections-export.png` |
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| ## Demo Video Script |
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| 1. "This is OpenBMB Local AI Workbench, a Gradio app for small-model local experimentation." |
| 2. "The model registry keeps every configured model below 32B parameters." |
| 3. "The app starts safely in placeholder mode, so it never downloads model weights on startup." |
| 4. "The Status tab shows which local backends are configured or missing." |
| 5. "A user can try a prompt, capture a correction, and export those corrections as training data." |
| 6. "The Traces tab records local workflow events for reproducibility." |
| 7. "The Export tab prepares explicit GGUF conversion and quantization commands." |
| 8. "The next deployment step is pushing this same Gradio app to a Hugging Face Space." |
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| ## Social Post Draft |
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| Built OpenBMB Local AI Workbench for the Gradio Hugging Face Build Small Hackathon: a local-first |
| Gradio app for testing MiniCPM models, collecting field-note corrections, exporting training data, |
| planning GGUF/llama.cpp workflows, and keeping traceable evidence of small-model experiments. |
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| GitHub: https://github.com/Ckal/codex |
| Workbench Space: https://huggingface.co/spaces/build-small-hackathon/workbench |
| Plant Space: https://huggingface.co/spaces/build-small-hackathon/plant_identification_tool |
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| ## Submission Checklist |
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| - GitHub URL: https://github.com/Ckal/codex |
| - Workbench Space URL: https://huggingface.co/spaces/build-small-hackathon/workbench |
| - Plant Identification Tool Space URL: https://huggingface.co/spaces/build-small-hackathon/plant_identification_tool |
| - Space build verification: blocked until `hf auth login --force` is run with a fresh token |
| - Demo video URL: pending |
| - Social post URL: pending |
| - Field notes/report URL: pending |
| - Final track: Backyard AI |
| - App name: OpenBMB Local AI Workbench |
| - Deadline: June 15, 2026 |
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