{"schema_version":"onlylabs.public_signal.v1","title":"Fireworks AI Repo: fw-ai/flash-attention","description":"Fireworks AI repo signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/17427f79-b441-4c86-945f-ad7786981d19","json_url":"https://onlylabs.fyi/signals/17427f79-b441-4c86-945f-ad7786981d19/signal.json","generated_at":"2026-06-11T04:10:33.180753+00:00","org":{"slug":"fireworks-ai","name":"Fireworks AI","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/fireworks-ai","dossier_json_url":"https://onlylabs.fyi/labs/fireworks-ai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/17427f79-b441-4c86-945f-ad7786981d19","signal_json":"https://onlylabs.fyi/signals/17427f79-b441-4c86-945f-ad7786981d19/signal.json","source":"https://github.com/fw-ai/flash-attention","lab_dossier":"https://onlylabs.fyi/labs/fireworks-ai","lab_dossier_json":"https://onlylabs.fyi/labs/fireworks-ai/dossier.json","analysis":"https://onlylabs.fyi/analysis/fireworks-ai","analysis_json":"https://onlylabs.fyi/analysis/fireworks-ai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/fireworks-ai/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Fireworks AI published fw-ai/flash-attention (Python). 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This repository signal exposes tooling, eval, infrastructure, or model-adjacent work before it may appear in a launch post. 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Fu, Stefano Ermon, Atri Rudra, Christopher Ré Paper: https://arxiv.org/abs/2205.14135 IEEE Spectrum [article](https://spectrum.ieee.org/mlperf-rankings-2022) about our submission to the MLPerf 2.0 benchmark using FlashAttention. ![FlashAttention](assets/flashattn_banner.jpg) **FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning** Tri Dao Paper: https://tridao.me/publications/flash2/flash2.pdf ![FlashAttention-2](assets/flashattention_logo.png) Usage We've been very happy to see FlashAttention being widely adopted in such a short time after its release. This [page](https://github.com/Dao-AILab/flash-attention/blob/main/usage.md) contains a partial list of..."},"evidence_pages":[{"url":"https://github.com/fw-ai/flash-attention","final_url":"https://github.com/fw-ai/flash-attention","title":"fw-ai/flash-attention repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T04:10:33.180753+00:00","bytes":25740,"raw_path":"1ee0650292100d887ca0809366c32c3e9b2b814def9c0917b0189fae8f456f6e.json","content_hash":"1f97a5a2ad13822a13eea4a9128b4ccd29954ecbee4224f3acd2ae481a0dd276","excerpt_chars":1200,"truncated":true,"excerpt":"fw-ai/flash-attention Description: Clone of https://github.com/HazyResearch/flash-attention/ Language: Python License: BSD-3-Clause Stars: 0 Forks: 0 Open issues: 1 Created: 2023-06-28T20:17:47Z Pushed: 2026-04-04T03:39:06Z Default branch: main Fork: no Archived: no README: FlashAttention This repository provides the official implementation of FlashAttention and FlashAttention-2 from the following papers. **FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness** Tri Dao, Daniel Y. 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