{"schema_version":"onlylabs.public_signal.v1","title":"Microsoft Model: microsoft/Dayhoff-170M-GRS-SS-134000","description":"Microsoft model signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/6889a75a-61a2-4683-b59a-1c49cec51ddc","json_url":"https://onlylabs.fyi/signals/6889a75a-61a2-4683-b59a-1c49cec51ddc/signal.json","generated_at":"2026-06-11T02:41:02.726157+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/6889a75a-61a2-4683-b59a-1c49cec51ddc","signal_json":"https://onlylabs.fyi/signals/6889a75a-61a2-4683-b59a-1c49cec51ddc/signal.json","source":"https://huggingface.co/microsoft/Dayhoff-170M-GRS-SS-134000","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/Dayhoff-170M-GRS-SS-134000. This model signal is evidence of what shipped on model infrastructure and how the release is positioned. High-signal details: license mit · 124 HF downloads · Small model, low downloads. onlylabs links this event to 1 captured evidence page and 6 related model signals.","signal_desk":"releases","source_context":{"source_url":"https://huggingface.co/microsoft/Dayhoff-170M-GRS-SS-134000","source_host":"huggingface.co","occurred_at":"2026-04-03T22:16:56+00:00","first_seen_at":"2026-06-06T01:49:31.285803+00:00","date_source":"source","context":null},"context_markers":[{"label":"Lab","value":"Microsoft","source":"signal"},{"label":"Signal desk","value":"releases","source":"signal"},{"label":"Source 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This model signal is evidence of what shipped on model infrastructure and how the release is positioned. 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These models can natively predict zero-shot mutation effects on fitness, scaffold structural motifs by conditioning on evolutionary or structural context, and perform guided generation of novel proteins within specified families. Learning from metagenomic and structure-based synthetic data from the Dayhoff Atlas increased the cellular expression rates of generated proteins, highlighting the real-world value of expanding the scale, diversity, and novelty of protein sequence data. The Dayhoff architecture is a hybrid of state-space Mamba layers and Transformer self-attention, interleaved with Mixture-of-Experts modules to maximize capacity while preserving..."},"evidence_pages":[{"url":"https://huggingface.co/microsoft/Dayhoff-170M-GRS-SS-134000/raw/main/README.md","final_url":"https://huggingface.co/microsoft/Dayhoff-170M-GRS-SS-134000/raw/main/README.md","title":"microsoft/Dayhoff-170M-GRS-SS-134000 model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:41:02.726157+00:00","bytes":19254,"raw_path":"58a3c3b2510b3d61410e4f15b59b985c252136de2f6624e2a2a2bf4ed77c7049.md","content_hash":"2c00a40332b16f626bacec533f1d2027e37637b08ce04f1555abee7dcf6258c5","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: mit pipeline_tag: text-generation library_name: transformers tags: - protein-generation - jamba datasets: - microsoft/Dayhoff --- Model Card for Dayhoff Dayhoff is an Atlas of both protein sequence data and generative language models — a centralized resource that brings together 3.34 billion protein sequences across 1.7 billion clusters of metagenomic and natural protein sequences (GigaRef), 46 million structure-derived synthetic sequences (BackboneRef), and 16 million multiple sequence alignments (OpenProteinSet). 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