GRANT_DEPLOY.md

ZeroGPU activation

The full Gradio implementation is already in app.py. Once ZeroGPU is attached to this Space:

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.

Suggested community grant discussion title:

Apply for a GPU community grant: Personal project

Suggested description:

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.