{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:EULB6QSWAZ7S5U7DRGY6NRS75T","short_pith_number":"pith:EULB6QSW","canonical_record":{"source":{"id":"2402.10462","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-16T05:42:17Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"b1c5ec1a55141ed5388eefb377034fd43d1973b1103ea0ec9b01966829eff9e7","abstract_canon_sha256":"8f8ce064873d3fbbcfa71aee70bb202ead79f3e395f76ab3355fafcfb3ac1d87"},"schema_version":"1.0"},"canonical_sha256":"25161f4256067f2ed3e389b1e6c65fece9c170f36ad6f5d17436e8b9554a66d2","source":{"kind":"arxiv","id":"2402.10462","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.10462","created_at":"2026-07-05T11:50:25Z"},{"alias_kind":"arxiv_version","alias_value":"2402.10462v1","created_at":"2026-07-05T11:50:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.10462","created_at":"2026-07-05T11:50:25Z"},{"alias_kind":"pith_short_12","alias_value":"EULB6QSWAZ7S","created_at":"2026-07-05T11:50:25Z"},{"alias_kind":"pith_short_16","alias_value":"EULB6QSWAZ7S5U7D","created_at":"2026-07-05T11:50:25Z"},{"alias_kind":"pith_short_8","alias_value":"EULB6QSW","created_at":"2026-07-05T11:50:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:EULB6QSWAZ7S5U7DRGY6NRS75T","target":"record","payload":{"canonical_record":{"source":{"id":"2402.10462","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-16T05:42:17Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"b1c5ec1a55141ed5388eefb377034fd43d1973b1103ea0ec9b01966829eff9e7","abstract_canon_sha256":"8f8ce064873d3fbbcfa71aee70bb202ead79f3e395f76ab3355fafcfb3ac1d87"},"schema_version":"1.0"},"canonical_sha256":"25161f4256067f2ed3e389b1e6c65fece9c170f36ad6f5d17436e8b9554a66d2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:50:25.717285Z","signature_b64":"L10cpFbkC4ZMKujaoQiVk9YAA2Z26BTUkIND5F2y6SKs21gAS0R3bkFyOVYCL8f1WwN7TmtfJgfSw5VIl1GBCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25161f4256067f2ed3e389b1e6c65fece9c170f36ad6f5d17436e8b9554a66d2","last_reissued_at":"2026-07-05T11:50:25.716823Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:50:25.716823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.10462","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:50:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7BsBC+Y/u5u9RIhWLeLVSwrK/izm42oeWhGQ1MRau4pOePWDy4ZHn2BFk6LiLJM5Oo8xGLGR9Pgcl5StVi0iCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:29:47.508320Z"},"content_sha256":"e99db5378b0ef48699da59ec2b8680aab8808d12c82996a30af49676104fa4f0","schema_version":"1.0","event_id":"sha256:e99db5378b0ef48699da59ec2b8680aab8808d12c82996a30af49676104fa4f0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:EULB6QSWAZ7S5U7DRGY6NRS75T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Ali Ghodsi, Boxing Chen, Hossein Rajabzadeh, Hyock Ju Kwon, Marzieh Tahaei, Mehdi Rezagholizadeh, Mojtaba Valipour, Tianshu Zhu","submitted_at":"2024-02-16T05:42:17Z","abstract_excerpt":"Finetuning large language models requires huge GPU memory, restricting the choice to acquire Larger models. While the quantized version of the Low-Rank Adaptation technique, named QLoRA, significantly alleviates this issue, finding the efficient LoRA rank is still challenging. Moreover, QLoRA is trained on a pre-defined rank and, therefore, cannot be reconfigured for its lower ranks without requiring further fine-tuning steps. This paper proposes QDyLoRA -Quantized Dynamic Low-Rank Adaptation-, as an efficient quantization approach for dynamic low-rank adaptation. Motivated by Dynamic LoRA, QD"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.10462","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/2402.10462/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:50:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a6sHHL3krK1HjJG4NplmjGH8xR5bzxTCsFzNiZYxLHv8kbegAzuKnh0BhGeFs1SLUYUsRm/3uCZsWm+UjD1DBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:29:47.508822Z"},"content_sha256":"11033a98ecccaeabfdf114128b5247568dbcd17cc596e7c887b2bf862f62f6f9","schema_version":"1.0","event_id":"sha256:11033a98ecccaeabfdf114128b5247568dbcd17cc596e7c887b2bf862f62f6f9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EULB6QSWAZ7S5U7DRGY6NRS75T/bundle.json","state_url":"https://pith.science/pith/EULB6QSWAZ7S5U7DRGY6NRS75T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EULB6QSWAZ7S5U7DRGY6NRS75T/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T05:29:47Z","links":{"resolver":"https://pith.science/pith/EULB6QSWAZ7S5U7DRGY6NRS75T","bundle":"https://pith.science/pith/EULB6QSWAZ7S5U7DRGY6NRS75T/bundle.json","state":"https://pith.science/pith/EULB6QSWAZ7S5U7DRGY6NRS75T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EULB6QSWAZ7S5U7DRGY6NRS75T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:EULB6QSWAZ7S5U7DRGY6NRS75T","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8f8ce064873d3fbbcfa71aee70bb202ead79f3e395f76ab3355fafcfb3ac1d87","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-16T05:42:17Z","title_canon_sha256":"b1c5ec1a55141ed5388eefb377034fd43d1973b1103ea0ec9b01966829eff9e7"},"schema_version":"1.0","source":{"id":"2402.10462","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.10462","created_at":"2026-07-05T11:50:25Z"},{"alias_kind":"arxiv_version","alias_value":"2402.10462v1","created_at":"2026-07-05T11:50:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.10462","created_at":"2026-07-05T11:50:25Z"},{"alias_kind":"pith_short_12","alias_value":"EULB6QSWAZ7S","created_at":"2026-07-05T11:50:25Z"},{"alias_kind":"pith_short_16","alias_value":"EULB6QSWAZ7S5U7D","created_at":"2026-07-05T11:50:25Z"},{"alias_kind":"pith_short_8","alias_value":"EULB6QSW","created_at":"2026-07-05T11:50:25Z"}],"graph_snapshots":[{"event_id":"sha256:11033a98ecccaeabfdf114128b5247568dbcd17cc596e7c887b2bf862f62f6f9","target":"graph","created_at":"2026-07-05T11:50:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2402.10462/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Finetuning large language models requires huge GPU memory, restricting the choice to acquire Larger models. While the quantized version of the Low-Rank Adaptation technique, named QLoRA, significantly alleviates this issue, finding the efficient LoRA rank is still challenging. Moreover, QLoRA is trained on a pre-defined rank and, therefore, cannot be reconfigured for its lower ranks without requiring further fine-tuning steps. This paper proposes QDyLoRA -Quantized Dynamic Low-Rank Adaptation-, as an efficient quantization approach for dynamic low-rank adaptation. Motivated by Dynamic LoRA, QD","authors_text":"Ali Ghodsi, Boxing Chen, Hossein Rajabzadeh, Hyock Ju Kwon, Marzieh Tahaei, Mehdi Rezagholizadeh, Mojtaba Valipour, Tianshu Zhu","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-16T05:42:17Z","title":"QDyLoRA: Quantized Dynamic Low-Rank Adaptation for Efficient Large Language Model Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.10462","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e99db5378b0ef48699da59ec2b8680aab8808d12c82996a30af49676104fa4f0","target":"record","created_at":"2026-07-05T11:50:25Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8f8ce064873d3fbbcfa71aee70bb202ead79f3e395f76ab3355fafcfb3ac1d87","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-02-16T05:42:17Z","title_canon_sha256":"b1c5ec1a55141ed5388eefb377034fd43d1973b1103ea0ec9b01966829eff9e7"},"schema_version":"1.0","source":{"id":"2402.10462","kind":"arxiv","version":1}},"canonical_sha256":"25161f4256067f2ed3e389b1e6c65fece9c170f36ad6f5d17436e8b9554a66d2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"25161f4256067f2ed3e389b1e6c65fece9c170f36ad6f5d17436e8b9554a66d2","first_computed_at":"2026-07-05T11:50:25.716823Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:50:25.716823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L10cpFbkC4ZMKujaoQiVk9YAA2Z26BTUkIND5F2y6SKs21gAS0R3bkFyOVYCL8f1WwN7TmtfJgfSw5VIl1GBCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:50:25.717285Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.10462","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e99db5378b0ef48699da59ec2b8680aab8808d12c82996a30af49676104fa4f0","sha256:11033a98ecccaeabfdf114128b5247568dbcd17cc596e7c887b2bf862f62f6f9"],"state_sha256":"7191c81f1fc1cc95f0abcce3ec4dcfeeff80f342fc28ad6d39fc983c9b2012a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ryNhUOegLgH1PwXI7n0+m9Jm8iKmf1nZAFdNy1l5e6YazgZJVNQ+IcPHyH9WRGunDMMuG6Q67CTl8bLDg9uMCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T05:29:47.512368Z","bundle_sha256":"7340595fdc6beef44f6c5b40b21f5428c631bf28c9b2a4d6fc5556b4f89ceebe"}}