{"schema_version":"onlylabs.public_signal.v1","title":"Amazon (Nova) Repo: amazon-science/attune","description":"Amazon (Nova) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/5334795d-9810-4d77-88eb-661c7c0a71cc","json_url":"https://onlylabs.fyi/signals/5334795d-9810-4d77-88eb-661c7c0a71cc/signal.json","generated_at":"2026-10-24T20:20:52.101Z","evidence_latest_fetched_at":"2026-10-14T07:03:59.808085+00:00","signal_first_seen_at":"2026-10-14T07:00:37.608755+00:00","org":{"slug":"amazon","name":"Amazon (Nova)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/amazon","dossier_json_url":"https://onlylabs.fyi/labs/amazon/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/5334795d-9810-4d77-88eb-661c7c0a71cc","signal_json":"https://onlylabs.fyi/signals/5334795d-9810-4d77-88eb-661c7c0a71cc/signal.json","source":"https://github.com/amazon-science/attune","lab_dossier":"https://onlylabs.fyi/labs/amazon","lab_dossier_json":"https://onlylabs.fyi/labs/amazon/dossier.json","analysis":"https://onlylabs.fyi/analysis/amazon","analysis_json":"https://onlylabs.fyi/analysis/amazon/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/amazon/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":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Amazon (Nova) published amazon-science/attune (Python). 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Given a working training recipe and a GPU-hour budget, the agent improves the recipe's tokens/s/GPU for your model, cluster and data, unattended: it runs short benchmark jobs, profiles them, finds the bottleneck, reads the framework source to learn what a flag does, and changes one setting at a time. A correctness gate checks that a faster recipe still trains the same model before it ships. This repository contains the skill (`skill/`): the tuning loop, measurement rules, lever catalog, helper scripts and the correctness gate; and a harness (`harness/`) that keeps a campaign running unattended. The scripts and harness target Megatron-LM + slime recipes run as RayJobs on Kubernetes and driven by Claude Code; other stacks and agent harnesses work with adapted launch, metrics and hook scripts. 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