{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:XQBHF5UUUJOVEEARN4VGS2BU4N","short_pith_number":"pith:XQBHF5UU","canonical_record":{"source":{"id":"2305.14152","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-23T15:20:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b8bb3f3eb943c9f7123f6d7e4917cfa9a240753d92d10dcc8fa1641ab19be7f5","abstract_canon_sha256":"ec131d7ea2c2f4adb8ed34768d44445575ef92fba3d346532a09e6455b223b3a"},"schema_version":"1.0"},"canonical_sha256":"bc0272f694a25d5210116f2a696834e34f6c64fa6e95ede3d3620a521074efe0","source":{"kind":"arxiv","id":"2305.14152","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.14152","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"arxiv_version","alias_value":"2305.14152v2","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.14152","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_12","alias_value":"XQBHF5UUUJOV","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_16","alias_value":"XQBHF5UUUJOVEEAR","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_8","alias_value":"XQBHF5UU","created_at":"2026-07-05T07:06:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:XQBHF5UUUJOVEEARN4VGS2BU4N","target":"record","payload":{"canonical_record":{"source":{"id":"2305.14152","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-23T15:20:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b8bb3f3eb943c9f7123f6d7e4917cfa9a240753d92d10dcc8fa1641ab19be7f5","abstract_canon_sha256":"ec131d7ea2c2f4adb8ed34768d44445575ef92fba3d346532a09e6455b223b3a"},"schema_version":"1.0"},"canonical_sha256":"bc0272f694a25d5210116f2a696834e34f6c64fa6e95ede3d3620a521074efe0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:06:27.521665Z","signature_b64":"FdCvW3dGCj++yobEeKGwyfwft4G4Cpg7nGRL5F9MRETXuOytr3N3Ku7D5WeGUrYosKz1aV0vkJYXnAaNOI6xBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bc0272f694a25d5210116f2a696834e34f6c64fa6e95ede3d3620a521074efe0","last_reissued_at":"2026-07-05T07:06:27.521133Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:06:27.521133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.14152","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:06:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MRVTLLTiW7fN9kvz5JROY9MVTeMju8N1/lN4JVWiJjN2nPj97uHhMY2P6OpKT37oD0wLOmrTay1PKF+bjbG+Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T11:46:44.852380Z"},"content_sha256":"0565126f3fa0115888a0eee11583674cedc18b20bcf33233a8caa3561945d8e3","schema_version":"1.0","event_id":"sha256:0565126f3fa0115888a0eee11583674cedc18b20bcf33233a8caa3561945d8e3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:XQBHF5UUUJOVEEARN4VGS2BU4N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dongsoo Lee, Jeonghoon Kim, Joonsuk Park, Jung Hyun Lee, Kang Min Yoo, Se Jung Kwon, Sungdong Kim","submitted_at":"2023-05-23T15:20:01Z","abstract_excerpt":"Large language models (LLMs) face the challenges in fine-tuning and deployment due to their high memory demands and computational costs. While parameter-efficient fine-tuning (PEFT) methods aim to reduce the memory usage of the optimizer state during fine-tuning, the inherent size of pre-trained LLM weights continues to be a pressing concern. Even though quantization techniques are widely proposed to ease memory demands and accelerate LLM inference, most of these techniques are geared towards the deployment phase. To bridge this gap, this paper presents Parameter-Efficient and Quantization-awa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.14152","kind":"arxiv","version":2},"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/2305.14152/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":"2026-07-05T07:06:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dE80kw5LzZFo+CoxBGVj2GNXxb9Nw+i06R1H1It+OxSn6xrYT+NaKKb1z9RMi9DIRtOsN1c1mvsdtviaHe9pAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T11:46:44.852754Z"},"content_sha256":"37f047f4da5ab2a7baf8f83d7d872f4784a8dc7c509f0b577ff9d53a716930fe","schema_version":"1.0","event_id":"sha256:37f047f4da5ab2a7baf8f83d7d872f4784a8dc7c509f0b577ff9d53a716930fe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XQBHF5UUUJOVEEARN4VGS2BU4N/bundle.json","state_url":"https://pith.science/pith/XQBHF5UUUJOVEEARN4VGS2BU4N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XQBHF5UUUJOVEEARN4VGS2BU4N/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":"2026-07-06T11:46:44Z","links":{"resolver":"https://pith.science/pith/XQBHF5UUUJOVEEARN4VGS2BU4N","bundle":"https://pith.science/pith/XQBHF5UUUJOVEEARN4VGS2BU4N/bundle.json","state":"https://pith.science/pith/XQBHF5UUUJOVEEARN4VGS2BU4N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XQBHF5UUUJOVEEARN4VGS2BU4N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:XQBHF5UUUJOVEEARN4VGS2BU4N","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":"ec131d7ea2c2f4adb8ed34768d44445575ef92fba3d346532a09e6455b223b3a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-23T15:20:01Z","title_canon_sha256":"b8bb3f3eb943c9f7123f6d7e4917cfa9a240753d92d10dcc8fa1641ab19be7f5"},"schema_version":"1.0","source":{"id":"2305.14152","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.14152","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"arxiv_version","alias_value":"2305.14152v2","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.14152","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_12","alias_value":"XQBHF5UUUJOV","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_16","alias_value":"XQBHF5UUUJOVEEAR","created_at":"2026-07-05T07:06:27Z"},{"alias_kind":"pith_short_8","alias_value":"XQBHF5UU","created_at":"2026-07-05T07:06:27Z"}],"graph_snapshots":[{"event_id":"sha256:37f047f4da5ab2a7baf8f83d7d872f4784a8dc7c509f0b577ff9d53a716930fe","target":"graph","created_at":"2026-07-05T07:06:27Z","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/2305.14152/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) face the challenges in fine-tuning and deployment due to their high memory demands and computational costs. While parameter-efficient fine-tuning (PEFT) methods aim to reduce the memory usage of the optimizer state during fine-tuning, the inherent size of pre-trained LLM weights continues to be a pressing concern. Even though quantization techniques are widely proposed to ease memory demands and accelerate LLM inference, most of these techniques are geared towards the deployment phase. To bridge this gap, this paper presents Parameter-Efficient and Quantization-awa","authors_text":"Dongsoo Lee, Jeonghoon Kim, Joonsuk Park, Jung Hyun Lee, Kang Min Yoo, Se Jung Kwon, Sungdong Kim","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-23T15:20:01Z","title":"Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.14152","kind":"arxiv","version":2},"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":"sha256:0565126f3fa0115888a0eee11583674cedc18b20bcf33233a8caa3561945d8e3","target":"record","created_at":"2026-07-05T07:06:27Z","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":"ec131d7ea2c2f4adb8ed34768d44445575ef92fba3d346532a09e6455b223b3a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-05-23T15:20:01Z","title_canon_sha256":"b8bb3f3eb943c9f7123f6d7e4917cfa9a240753d92d10dcc8fa1641ab19be7f5"},"schema_version":"1.0","source":{"id":"2305.14152","kind":"arxiv","version":2}},"canonical_sha256":"bc0272f694a25d5210116f2a696834e34f6c64fa6e95ede3d3620a521074efe0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bc0272f694a25d5210116f2a696834e34f6c64fa6e95ede3d3620a521074efe0","first_computed_at":"2026-07-05T07:06:27.521133Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:06:27.521133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FdCvW3dGCj++yobEeKGwyfwft4G4Cpg7nGRL5F9MRETXuOytr3N3Ku7D5WeGUrYosKz1aV0vkJYXnAaNOI6xBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:06:27.521665Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.14152","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0565126f3fa0115888a0eee11583674cedc18b20bcf33233a8caa3561945d8e3","sha256:37f047f4da5ab2a7baf8f83d7d872f4784a8dc7c509f0b577ff9d53a716930fe"],"state_sha256":"29df780cfffbd2ff6dcf3a642e3d993cab05a1e8c196520d32bf92731b46d223"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qYdadQ5ohbPexwRkhrdrbWCMEarLi/sfINGChQjkzDUcKZVyAABpdv3tHadJ2Bs5HJOLrCL4znoJ3KGbIq9RBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T11:46:44.854902Z","bundle_sha256":"ba2e265f8145f272bb7073b34f104a877149d859cd5764b68da9197e9639463e"}}