{"schema_version":"onlylabs.public_signal.v1","title":"Together AI Repo: togethercomputer/stripedhyena","description":"Together AI repo signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/752e1d78-66b6-47d9-b2a8-af24ac46df02","json_url":"https://onlylabs.fyi/signals/752e1d78-66b6-47d9-b2a8-af24ac46df02/signal.json","generated_at":"2026-06-11T04:10:05.558852+00:00","org":{"slug":"together-ai","name":"Together AI","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/together-ai","dossier_json_url":"https://onlylabs.fyi/labs/together-ai/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/752e1d78-66b6-47d9-b2a8-af24ac46df02","signal_json":"https://onlylabs.fyi/signals/752e1d78-66b6-47d9-b2a8-af24ac46df02/signal.json","source":"https://github.com/togethercomputer/stripedhyena","lab_dossier":"https://onlylabs.fyi/labs/together-ai","lab_dossier_json":"https://onlylabs.fyi/labs/together-ai/dossier.json","analysis":"https://onlylabs.fyi/analysis/together-ai","analysis_json":"https://onlylabs.fyi/analysis/together-ai/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/together-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":"Together AI published togethercomputer/stripedhyena (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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StripedHyena is a deep signal processing, hybrid architecture composed of rotary (grouped) attention and gated convolutions arranged in [Hyena](https://arxiv.org/abs/2302.10866) blocks, with improved scaling over decoder-only Transformers. StripedHyena is designed to leverage the specialization of each of its layer classes, with Hyena layers implementing the bulk of the computation required for sequence processing and attention layers supplementing the ability to perform targeted pattern recall. -..."},"evidence_pages":[{"url":"https://github.com/togethercomputer/stripedhyena","final_url":"https://github.com/togethercomputer/stripedhyena","title":"togethercomputer/stripedhyena repository metadata","http_status":200,"content_type":"application/json","capture_method":"plain","fetched_at":"2026-06-11T04:10:05.558852+00:00","bytes":14934,"raw_path":"251c82421449ad1d32cf088e49ef14dbab787c9dd2d320858ad169a74dc37aeb.json","content_hash":"45f6313aee55813b7b209ca6e40fe4b73179ae99ff711c8237a29da64e86f4d2","excerpt_chars":1200,"truncated":true,"excerpt":"togethercomputer/stripedhyena Description: Repository for StripedHyena, a state-of-the-art beyond Transformer architecture Language: Python License: Apache-2.0 Stars: 432 Forks: 24 Open issues: 10 Created: 2023-11-21T15:56:04Z Pushed: 2024-03-07T17:01:22Z Default branch: main Fork: no Archived: no README: StripedHyena Minimal implementation of a StripedHyena model. <p align=\"center\"> <img src=\"https://github.com/togethercomputer/stripedhyena/assets/34561392/ce11c785-c8c5-43b2-a34a-41ee9cba3958\" width=\"60%\" /> </p> About StripedHyena is the **first alternative model architecture competitive with the best open-source Transformers** of similar sizes in short and long-context evaluations. 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