Spaces:
Running on Zero
Running on Zero
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Download README.md from Beexly/mimo-brain-engine: direct link, hf CLI and curl.
- Browser
- Download file 1.72 kB
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https://huggingface.co/spaces/Beexly/mimo-brain-engine/resolve/main/README.md
- Command line
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hf download hf://spaces/Beexly/mimo-brain-engine/README.md
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curl -L -o README.md https://huggingface.co/spaces/Beexly/mimo-brain-engine/resolve/main/README.md
1.72 kB
A newer version of the Gradio SDK is available: 6.30.0
metadata
title: GSE Brain Engine (MiMo-V2.6, ZeroGPU)
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
license: mit
GSE Brain Engine — MiMo-V2.6 on ZeroGPU
The GSE NFL intelligence engine's LLM specialist layer: Xiaomi MiMo-V2.6-Distill-Qwen-9B (MIT license) in 4-bit, served on Hugging Face ZeroGPU — the free tier.
Billing policy (from HF official docs, verified 2026-10-02)
- This Space runs on ZeroGPU hardware only (
spaces.GPU, defaultlargesize). ZeroGPU Spaces are free to host and use — free accounts host up to 2, PRO up to 10. - Daily GPU quotas: unauthenticated 2 min · free 5 min · PRO 40 min (highest queue priority). Resets 24h after first GPU use.
- Overage draws from the pre-paid credit balance only. No card is charged unless credits were explicitly purchased or auto-recharge is enabled. With no credits, over-quota requests fail/queue — they do not bill.
- Paid Space hardware (t4-small, a10g, …) is what bills usage-based. This Space is pinned to ZeroGPU; a drift monitor alerts loudly if the hardware ever changes. Never upgrade this Space's hardware without Garrett's explicit approval.
Sources: https://huggingface.co/docs/hub/spaces-zerogpu · https://huggingface.co/docs/hub/billing
The /chat contract
Send VERIFIED DATA plus a grounding prompt. The model reasons (L3 causal chains, L4 adversarial review, L5 synthesis) under hard rules: cite provided numbers, label inference as INFERENCE with a breaking condition, output the requested JSON schema. The deterministic GSE contract (adversary_review, correlated_theses, checklist) still disposes — the model proposes.