Building on HF

Adam Munawar Rahman PRO

msradam

https://adamr.io

msradam

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liked a Space about 4 hours ago

lablab-ai-amd-developer-hackathon/riprap-nyc

posted an update about 5 hours ago

Riprap: citation-grounded NYC flood-exposure briefings 🌊 Any NYC address → a four-section, citation-grounded flood-exposure briefing in about two minutes. Every claim points back to a `[doc_id]` in public-record data. 🎬 Live demo: https://huggingface.co/spaces/lablab-ai-amd-developer-hackathon/riprap-nyc About 25 atomic data probes fan out across NYC datasets, Sentinel-2 imagery, live sensors, and forecasts, organized as the Five Stones: 🪨 Cornerstone : hazard memory 🏛️ Keystone : asset registers 📡 Touchstone : live state 🧭 Lodestone : forecasts ✍️ Capstone : citation-grounded synthesis (Granite 4.1 8B + Mellea rejection sampling, four grounding checks per draft) Three NYC-specialised foundation-model fine-tunes shipped Apache 2.0 alongside, trained on a single AMD MI300X via AMD Developer Cloud: 🛰️ msradam/TerraMind-NYC-Adapters (https://huggingface.co/msradam/TerraMind-NYC-Adapters) : LULC mIoU 0.5866, +6.13 pp over full-FT baseline (plus Buildings + TiM heads). 🌊 msradam/Prithvi-EO-2.0-NYC-Pluvial : (https://huggingface.co/msradam/Prithvi-EO-2.0-NYC-Pluvial) : flood IoU 0.5979 vs 0.10 base, a 6× lift. 📈 msradam/Granite-TTM-r2-Battery-Surge (https://huggingface.co/msradam/Granite-TTM-r2-Battery-Surge) : Battery surge nowcast, MAE 0.1091 m, 41% better than persistence. Repo: https://github.com/msradam/riprap-nyc If this is the kind of agentic AI civic tech should be building toward, drop a like on the Space! The foundation-model teams whose work made this possible: @ibm-granite @ibm-nasa-geospatial @ibm-esa-geospatial @amd #agenticai #civictech #climateai #floodresilience #nyc #foundationmodels #granite #terramind #prithvi #amd #mi300x

updated a Space about 7 hours ago

msradam/riprap-vllm

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liked a Space about 4 hours ago

Riprap Nyc

😻

Generate flood exposure briefings for any NYC location

posted an update about 5 hours ago

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Riprap: citation-grounded NYC flood-exposure briefings 🌊

Any NYC address → a four-section, citation-grounded flood-exposure briefing in about two minutes. Every claim points back to a [doc_id] in public-record data.

🎬 Live demo: lablab-ai-amd-developer-hackathon/riprap-nyc

About 25 atomic data probes fan out across NYC datasets, Sentinel-2 imagery, live sensors, and forecasts, organized as the Five Stones:

🪨 Cornerstone : hazard memory
🏛️ Keystone : asset registers
📡 Touchstone : live state
🧭 Lodestone : forecasts
✍️ Capstone : citation-grounded synthesis (Granite 4.1 8B + Mellea rejection sampling, four grounding checks per draft)

Three NYC-specialised foundation-model fine-tunes shipped Apache 2.0 alongside, trained on a single AMD MI300X via AMD Developer Cloud:

🛰️ msradam/TerraMind-NYC-Adapters ( msradam/TerraMind-NYC-Adapters) : LULC mIoU 0.5866, +6.13 pp over full-FT baseline (plus Buildings + TiM heads).

🌊 msradam/Prithvi-EO-2.0-NYC-Pluvial : ( msradam/Prithvi-EO-2.0-NYC-Pluvial) : flood IoU 0.5979 vs 0.10 base, a 6× lift.

📈 msradam/Granite-TTM-r2-Battery-Surge ( msradam/Granite-TTM-r2-Battery-Surge) : Battery surge nowcast, MAE 0.1091 m, 41% better than persistence.

Repo: https://github.com/msradam/riprap-nyc

If this is the kind of agentic AI civic tech should be building toward, drop a like on the Space!

The foundation-model teams whose work made this possible: @ibm-granite @ibm-nasa-geospatial @ibm-esa-geospatial @amd

#agenticai #civictech #climateai #floodresilience #nyc #foundationmodels #granite #terramind #prithvi #amd #mi300x