{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:X6NLZEALRQ4RKVBUJAU7GFZEZZ","short_pith_number":"pith:X6NLZEAL","schema_version":"1.0","canonical_sha256":"bf9abc900b8c391554344829f31724ce455f1820a81c6b9d099e95d8fe13d87a","source":{"kind":"arxiv","id":"2606.21257","version":1},"attestation_state":"computed","paper":{"title":"An Empirical Study of OpenPangu Quantization on Ascend NPUs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Aishan Liu, Hui Xie, Jiacheng Wang, Jinyang Guo, Tong Shi, Xianglong Liu, Ying Li","submitted_at":"2026-06-19T09:33:11Z","abstract_excerpt":"OpenPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs has not been systematically characterized. This paper conducts a controlled empirical study of OpenPangu 1B and 7B models on Huawei Ascend 910B1 NPUs. We evaluate representative weight-only and weight-activation post-training quantization methods, including RTN, GPTQ, AWQ, SmoothQuant, GPTAQ, BiLLM, and SliM-LLM, under a unified calibration and evaluation protocol. Across 18 evaluation tasks, we find that 8-bit weight-"},"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":"2606.21257","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-19T09:33:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ec829a0c27f9780cd26ee39017269e5ddcb8837d816165101d54f04ae29431d5","abstract_canon_sha256":"3a7eef65b6827f0a7c4ffb7a98ab0b3b7697964c850aa9e311dfbc3eb1e57e24"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T01:12:34.988469Z","signature_b64":"/EAHtNNERvptzbK+cKqY77mEhCf//uJq7vPAkYZqmc/B9KeAzXa2PbEx4ebv3gugfxoQC3+CM24vBNaXru3DDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bf9abc900b8c391554344829f31724ce455f1820a81c6b9d099e95d8fe13d87a","last_reissued_at":"2026-06-23T01:12:34.988043Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T01:12:34.988043Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Empirical Study of OpenPangu Quantization on Ascend NPUs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Aishan Liu, Hui Xie, Jiacheng Wang, Jinyang Guo, Tong Shi, Xianglong Liu, Ying Li","submitted_at":"2026-06-19T09:33:11Z","abstract_excerpt":"OpenPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training quantization on Ascend NPUs has not been systematically characterized. This paper conducts a controlled empirical study of OpenPangu 1B and 7B models on Huawei Ascend 910B1 NPUs. We evaluate representative weight-only and weight-activation post-training quantization methods, including RTN, GPTQ, AWQ, SmoothQuant, GPTAQ, BiLLM, and SliM-LLM, under a unified calibration and evaluation protocol. Across 18 evaluation tasks, we find that 8-bit weight-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21257","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/2606.21257/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":"2606.21257","created_at":"2026-06-23T01:12:34.988116+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.21257v1","created_at":"2026-06-23T01:12:34.988116+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21257","created_at":"2026-06-23T01:12:34.988116+00:00"},{"alias_kind":"pith_short_12","alias_value":"X6NLZEALRQ4R","created_at":"2026-06-23T01:12:34.988116+00:00"},{"alias_kind":"pith_short_16","alias_value":"X6NLZEALRQ4RKVBU","created_at":"2026-06-23T01:12:34.988116+00:00"},{"alias_kind":"pith_short_8","alias_value":"X6NLZEAL","created_at":"2026-06-23T01:12:34.988116+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/X6NLZEALRQ4RKVBUJAU7GFZEZZ","json":"https://pith.science/pith/X6NLZEALRQ4RKVBUJAU7GFZEZZ.json","graph_json":"https://pith.science/api/pith-number/X6NLZEALRQ4RKVBUJAU7GFZEZZ/graph.json","events_json":"https://pith.science/api/pith-number/X6NLZEALRQ4RKVBUJAU7GFZEZZ/events.json","paper":"https://pith.science/paper/X6NLZEAL"},"agent_actions":{"view_html":"https://pith.science/pith/X6NLZEALRQ4RKVBUJAU7GFZEZZ","download_json":"https://pith.science/pith/X6NLZEALRQ4RKVBUJAU7GFZEZZ.json","view_paper":"https://pith.science/paper/X6NLZEAL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.21257&json=true","fetch_graph":"https://pith.science/api/pith-number/X6NLZEALRQ4RKVBUJAU7GFZEZZ/graph.json","fetch_events":"https://pith.science/api/pith-number/X6NLZEALRQ4RKVBUJAU7GFZEZZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X6NLZEALRQ4RKVBUJAU7GFZEZZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X6NLZEALRQ4RKVBUJAU7GFZEZZ/action/storage_attestation","attest_author":"https://pith.science/pith/X6NLZEALRQ4RKVBUJAU7GFZEZZ/action/author_attestation","sign_citation":"https://pith.science/pith/X6NLZEALRQ4RKVBUJAU7GFZEZZ/action/citation_signature","submit_replication":"https://pith.science/pith/X6NLZEALRQ4RKVBUJAU7GFZEZZ/action/replication_record"}},"created_at":"2026-06-23T01:12:34.988116+00:00","updated_at":"2026-06-23T01:12:34.988116+00:00"}