| # ZeroGPU activation |
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| The full Gradio implementation is already in `app.py`. Once ZeroGPU is attached to this Space: |
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| 1. Change the README frontmatter from `sdk: static` to `sdk: gradio`. |
| 2. Set `sdk_version: 6.26.0`, `app_file: app.py`, `python_version: "3.12"`, and `startup_duration_timeout: 1h`. |
| 3. Keep the hardware flavor set to `zero-a10g`. The handler requests `size="xlarge"` because the BF16 model is well over 56 GB. |
| 4. Upload the changed README and inspect build/runtime logs before calling the API. |
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| Suggested community grant discussion title: |
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| `Apply for a GPU community grant: Personal project` |
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| Suggested description: |
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| > This public open-source research demo lets visitors explore the tool-use and stopping behavior of vcruz305/Muse-Glimmer-30B-Hermes-Agentic. It safely displays proposed tool calls without executing commands, file operations, web requests, or destructive actions, and exposes a documented Gradio API/MCP endpoint for reproducible evaluation. |
| > |
| > The 30B BF16 checkpoint needs more than 56 GB of device memory, so the app requires a ZeroGPU xlarge allocation. The demo is educational and non-commercial, and I cannot currently cover dedicated GPU hosting costs. I am happy to provide more context if helpful. |
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