{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4JOR5STVADWZYNG7LIKM3I63UI","short_pith_number":"pith:4JOR5STV","schema_version":"1.0","canonical_sha256":"e25d1eca7500ed9c34df5a14cda3dba20fa494a7d70f55e7fc3490f64e3e74db","source":{"kind":"arxiv","id":"2410.14766","version":1},"attestation_state":"computed","paper":{"title":"Evaluating Quantized Large Language Models for Code Generation on Low-Resource Language Benchmarks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG","cs.PL"],"primary_cat":"cs.SE","authors_text":"Enkhbold Nyamsuren","submitted_at":"2024-10-18T15:50:59Z","abstract_excerpt":"Democratization of AI is an important topic within the broader topic of the digital divide. This issue is relevant to LLMs, which are becoming popular as AI co-pilots but suffer from a lack of accessibility due to high computational demand. In this study, we evaluate whether quantization is a viable approach toward enabling LLMs on generic consumer devices. The study assesses the performance of five quantized code LLMs in Lua code generation tasks. To evaluate the impact of quantization, the models with 7B parameters were tested on a consumer laptop at 2-, 4-, and 8-bit integer precisions 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":"2410.14766","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2024-10-18T15:50:59Z","cross_cats_sorted":["cs.AI","cs.ET","cs.LG","cs.PL"],"title_canon_sha256":"c9cfd8bc0b511830aa2a131e30c866b70a0966a877694be034997d6cda8e543a","abstract_canon_sha256":"d4068bbb906c0a8513c784e369c6138356367beb171960dc542c39c0c85fbb0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:22:44.702310Z","signature_b64":"sBWzcpY6fk0LjL1JZRK4nFJXfYLYCQIPGmCmWGbyQ/RVWEkJ/3TRHnLDquJBpyWVdukYRxxqVw/qKImF6qE8Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e25d1eca7500ed9c34df5a14cda3dba20fa494a7d70f55e7fc3490f64e3e74db","last_reissued_at":"2026-07-05T09:22:44.701816Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:22:44.701816Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Quantized Large Language Models for Code Generation on Low-Resource Language Benchmarks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.ET","cs.LG","cs.PL"],"primary_cat":"cs.SE","authors_text":"Enkhbold Nyamsuren","submitted_at":"2024-10-18T15:50:59Z","abstract_excerpt":"Democratization of AI is an important topic within the broader topic of the digital divide. This issue is relevant to LLMs, which are becoming popular as AI co-pilots but suffer from a lack of accessibility due to high computational demand. In this study, we evaluate whether quantization is a viable approach toward enabling LLMs on generic consumer devices. The study assesses the performance of five quantized code LLMs in Lua code generation tasks. To evaluate the impact of quantization, the models with 7B parameters were tested on a consumer laptop at 2-, 4-, and 8-bit integer precisions and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14766","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/2410.14766/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":"2410.14766","created_at":"2026-07-05T09:22:44.701893+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.14766v1","created_at":"2026-07-05T09:22:44.701893+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14766","created_at":"2026-07-05T09:22:44.701893+00:00"},{"alias_kind":"pith_short_12","alias_value":"4JOR5STVADWZ","created_at":"2026-07-05T09:22:44.701893+00:00"},{"alias_kind":"pith_short_16","alias_value":"4JOR5STVADWZYNG7","created_at":"2026-07-05T09:22:44.701893+00:00"},{"alias_kind":"pith_short_8","alias_value":"4JOR5STV","created_at":"2026-07-05T09:22:44.701893+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.14200","citing_title":"Capturing the Effects of Quantization on Trojans in Code LLMs","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4JOR5STVADWZYNG7LIKM3I63UI","json":"https://pith.science/pith/4JOR5STVADWZYNG7LIKM3I63UI.json","graph_json":"https://pith.science/api/pith-number/4JOR5STVADWZYNG7LIKM3I63UI/graph.json","events_json":"https://pith.science/api/pith-number/4JOR5STVADWZYNG7LIKM3I63UI/events.json","paper":"https://pith.science/paper/4JOR5STV"},"agent_actions":{"view_html":"https://pith.science/pith/4JOR5STVADWZYNG7LIKM3I63UI","download_json":"https://pith.science/pith/4JOR5STVADWZYNG7LIKM3I63UI.json","view_paper":"https://pith.science/paper/4JOR5STV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.14766&json=true","fetch_graph":"https://pith.science/api/pith-number/4JOR5STVADWZYNG7LIKM3I63UI/graph.json","fetch_events":"https://pith.science/api/pith-number/4JOR5STVADWZYNG7LIKM3I63UI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4JOR5STVADWZYNG7LIKM3I63UI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4JOR5STVADWZYNG7LIKM3I63UI/action/storage_attestation","attest_author":"https://pith.science/pith/4JOR5STVADWZYNG7LIKM3I63UI/action/author_attestation","sign_citation":"https://pith.science/pith/4JOR5STVADWZYNG7LIKM3I63UI/action/citation_signature","submit_replication":"https://pith.science/pith/4JOR5STVADWZYNG7LIKM3I63UI/action/replication_record"}},"created_at":"2026-07-05T09:22:44.701893+00:00","updated_at":"2026-07-05T09:22:44.701893+00:00"}