{"schema_version":"onlylabs.public_signal.v1","title":"Microsoft Model: microsoft/GridSFM_Open","description":"Microsoft model signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/cd8eabc5-5eaa-45d6-b730-5985a927230f","json_url":"https://onlylabs.fyi/signals/cd8eabc5-5eaa-45d6-b730-5985a927230f/signal.json","generated_at":"2026-06-11T02:40:52.288561+00:00","org":{"slug":"microsoft","name":"Microsoft","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/microsoft","dossier_json_url":"https://onlylabs.fyi/labs/microsoft/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/cd8eabc5-5eaa-45d6-b730-5985a927230f","signal_json":"https://onlylabs.fyi/signals/cd8eabc5-5eaa-45d6-b730-5985a927230f/signal.json","source":"https://huggingface.co/microsoft/GridSFM_Open","lab_dossier":"https://onlylabs.fyi/labs/microsoft","lab_dossier_json":"https://onlylabs.fyi/labs/microsoft/dossier.json","analysis":"https://onlylabs.fyi/analysis/microsoft","analysis_json":"https://onlylabs.fyi/analysis/microsoft/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/microsoft/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":"https://onlylabs.fyi/topics/releases","topic_signals_json":"https://onlylabs.fyi/topics/releases/signals.json","topic_feed":"https://onlylabs.fyi/topics/releases/feed.xml","data_business":null},"answer_pack":{"answer":"Microsoft published microsoft/GridSFM_Open. 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The model leverages simulated data to learn relationships between topology, load distribution, and system behavior, enabling generalization across diverse grid configurations. Training involves large-scale synthetic scenario generation combined with supervised learning on simulation outputs.* | | Model architecture | *Heterogeneous graph attention network with Hodge positional encoding, residual blocks, and dual heads for AC-OPF operating-point prediction and feasibility classification* | | Parameters |..."},"evidence_pages":[{"url":"https://huggingface.co/microsoft/GridSFM_Open/raw/main/README.md","final_url":"https://huggingface.co/microsoft/GridSFM_Open/raw/main/README.md","title":"microsoft/GridSFM_Open model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:40:52.288561+00:00","bytes":18644,"raw_path":"a303d0ebfb6d6450c8491122692a5db2a3338ac974b6d098c9589c256d88eb23.md","content_hash":"f27a56b082d667cad416d578e0086b1a7abe0ba4bb2110a30dc0bd5d7c66900c","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: mit library_name: pytorch tags: - graph-neural-network - power-systems - power-grid - ac-opf - optimal-power-flow - graph-attention-network - scientific-ml - research datasets: - microsoft/GridSFM_US_power_grid pretty_name: Grid Small Foundation Model (GridSFM) language: - en pipeline_tag: graph-ml --- Grid Small Foundation Model (GridSFM) Model summary | Field | Value | | --- | --- | | Developer | Microsoft Corporation<br><br>Authorized representative: Microsoft Ireland Operations Limited 70 Sir John Rogerson’s Quay, Dublin 2, D02 R296, Ireland | | Description | *GridSFM is a graph neural-network-based model trained on structured representations of power grid systems. 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