{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:P6JXQAXTVGSEVBINKZIAIBIML7","short_pith_number":"pith:P6JXQAXT","schema_version":"1.0","canonical_sha256":"7f937802f3a9a44a850d565004050c5fe2645ab85d7e1d0818cd6d43eca6ff40","source":{"kind":"arxiv","id":"2605.21442","version":1},"attestation_state":"computed","paper":{"title":"torchtune: PyTorch native post-training library","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ariel Kwiatkowski, Evan Smothers, Felipe Mello, Joseph Cummings, Mark Obozov, Maxime Griot, Mircea Mironenco, Nathan Azrak, Philip John Bontrager, Rafi Ayub, Salman Mohammadi","submitted_at":"2026-05-20T17:32:08Z","abstract_excerpt":"Modern LLMs typically require multistage training pipelines to achieve strong downstream performance, with post-training serving as the main interface for adapting open-weight models. We introduce torchtune, a PyTorch-native library designed to streamline the post-training lifecycle of LLMs, enabling efficient fine-tuning, experimentation, and deployment-oriented workflows. Unlike many existing fine-tuning frameworks, which often optimize for ease of use, specialized recipes, or hardware efficiency at the cost of transparency and extensibility, torchtune emphasizes modularity, hackability, and"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.21442","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-20T17:32:08Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a1e92be939b93d93ba368db52bede5fe8e9b4ba143048714667f32f4f16163b7","abstract_canon_sha256":"609ea0bcce9767acd564b8e4c792bd31863d78b524ca3b7778b05349c01f2c6e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T02:05:37.354083Z","signature_b64":"E1wlfUVTQq87AHf8BCVnhxbfWmBHsM0X3HzVZVkbGtUsmcP/S3amt8wmM44sR5yPaUbxS/yI7zu8T6X4kRKuCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f937802f3a9a44a850d565004050c5fe2645ab85d7e1d0818cd6d43eca6ff40","last_reissued_at":"2026-05-21T02:05:37.353405Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T02:05:37.353405Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"torchtune: PyTorch native post-training library","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ariel Kwiatkowski, Evan Smothers, Felipe Mello, Joseph Cummings, Mark Obozov, Maxime Griot, Mircea Mironenco, Nathan Azrak, Philip John Bontrager, Rafi Ayub, Salman Mohammadi","submitted_at":"2026-05-20T17:32:08Z","abstract_excerpt":"Modern LLMs typically require multistage training pipelines to achieve strong downstream performance, with post-training serving as the main interface for adapting open-weight models. We introduce torchtune, a PyTorch-native library designed to streamline the post-training lifecycle of LLMs, enabling efficient fine-tuning, experimentation, and deployment-oriented workflows. Unlike many existing fine-tuning frameworks, which often optimize for ease of use, specialized recipes, or hardware efficiency at the cost of transparency and extensibility, torchtune emphasizes modularity, hackability, and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21442","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.21442/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.21442","created_at":"2026-05-21T02:05:37.353515+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.21442v1","created_at":"2026-05-21T02:05:37.353515+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21442","created_at":"2026-05-21T02:05:37.353515+00:00"},{"alias_kind":"pith_short_12","alias_value":"P6JXQAXTVGSE","created_at":"2026-05-21T02:05:37.353515+00:00"},{"alias_kind":"pith_short_16","alias_value":"P6JXQAXTVGSEVBIN","created_at":"2026-05-21T02:05:37.353515+00:00"},{"alias_kind":"pith_short_8","alias_value":"P6JXQAXT","created_at":"2026-05-21T02:05:37.353515+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P6JXQAXTVGSEVBINKZIAIBIML7","json":"https://pith.science/pith/P6JXQAXTVGSEVBINKZIAIBIML7.json","graph_json":"https://pith.science/api/pith-number/P6JXQAXTVGSEVBINKZIAIBIML7/graph.json","events_json":"https://pith.science/api/pith-number/P6JXQAXTVGSEVBINKZIAIBIML7/events.json","paper":"https://pith.science/paper/P6JXQAXT"},"agent_actions":{"view_html":"https://pith.science/pith/P6JXQAXTVGSEVBINKZIAIBIML7","download_json":"https://pith.science/pith/P6JXQAXTVGSEVBINKZIAIBIML7.json","view_paper":"https://pith.science/paper/P6JXQAXT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.21442&json=true","fetch_graph":"https://pith.science/api/pith-number/P6JXQAXTVGSEVBINKZIAIBIML7/graph.json","fetch_events":"https://pith.science/api/pith-number/P6JXQAXTVGSEVBINKZIAIBIML7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P6JXQAXTVGSEVBINKZIAIBIML7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P6JXQAXTVGSEVBINKZIAIBIML7/action/storage_attestation","attest_author":"https://pith.science/pith/P6JXQAXTVGSEVBINKZIAIBIML7/action/author_attestation","sign_citation":"https://pith.science/pith/P6JXQAXTVGSEVBINKZIAIBIML7/action/citation_signature","submit_replication":"https://pith.science/pith/P6JXQAXTVGSEVBINKZIAIBIML7/action/replication_record"}},"created_at":"2026-05-21T02:05:37.353515+00:00","updated_at":"2026-05-21T02:05:37.353515+00:00"}