{"id":"https://openalex.org/W7168246803","doi":"https://doi.org/10.48550/arxiv.2607.09885","title":"Index SLM Technical Report","display_name":"Index SLM Technical Report","publication_year":2026,"publication_date":"2026-07-10","ids":{"openalex":"https://openalex.org/W7168246803","doi":"https://doi.org/10.48550/arxiv.2607.09885"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.09885","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09885","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.09885","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140708891","display_name":"Lusheng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Lusheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140652337","display_name":"Shien He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Shien","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140668194","display_name":"Tianxing Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Tianxing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140720401","display_name":"Mengran Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Mengran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140670307","display_name":"Ziang Cui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cui, Ziang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140634829","display_name":"Kai Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Kai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5140716466","display_name":"Xiaojing Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xiaojing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5140726616","display_name":"Tianjiao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Tianjiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.34299999475479126,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.34299999475479126,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.27489998936653137,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14347","display_name":"Big Data and Digital Economy","score":0.04230000078678131,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/suite","display_name":"Suite","score":0.6690000295639038},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.656499981880188},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5212000012397766},{"id":"https://openalex.org/keywords/schedule","display_name":"Schedule","score":0.5098999738693237},{"id":"https://openalex.org/keywords/technical-report","display_name":"Technical report","score":0.5034999847412109},{"id":"https://openalex.org/keywords/index","display_name":"Index (typography)","score":0.48579999804496765},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4771000146865845},{"id":"https://openalex.org/keywords/preference","display_name":"Preference","score":0.4300000071525574}],"concepts":[{"id":"https://openalex.org/C79581498","wikidata":"https://www.wikidata.org/wiki/Q1367530","display_name":"Suite","level":2,"score":0.6690000295639038},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.656499981880188},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5317000150680542},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5212000012397766},{"id":"https://openalex.org/C68387754","wikidata":"https://www.wikidata.org/wiki/Q7271585","display_name":"Schedule","level":2,"score":0.5098999738693237},{"id":"https://openalex.org/C182449105","wikidata":"https://www.wikidata.org/wiki/Q3099732","display_name":"Technical report","level":2,"score":0.5034999847412109},{"id":"https://openalex.org/C2777382242","wikidata":"https://www.wikidata.org/wiki/Q6017816","display_name":"Index (typography)","level":2,"score":0.48579999804496765},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4771000146865845},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.4431000053882599},{"id":"https://openalex.org/C2781249084","wikidata":"https://www.wikidata.org/wiki/Q908656","display_name":"Preference","level":2,"score":0.4300000071525574},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4162999987602234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41339999437332153},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3140000104904175},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2948000133037567},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2840999960899353},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2824999988079071},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.2687000036239624},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.26260000467300415},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.2614000141620636},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.25270000100135803}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.09885","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09885","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.09885","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.09885","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.7828084230422974,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,145],"present":[1],"Index-1.9B,":[2],"a":[3,19,37,84,104,116],"series":[4,14],"of":[5,92,118,132,140,168],"open":[6,138],"small":[7],"language":[8],"models":[9,139],"developed":[10],"at":[11,196],"Bilibili.":[12],"The":[13],"comprises":[15],"four":[16],"models:":[17],"Index-1.9B-Base,":[18],"foundation":[20],"model":[21,60,74,151],"with":[22,41,46,61,75,103,135,191],"1.9":[23],"billion":[24],"non-embedding":[25],"parameters":[26],"pre-trained":[27],"on":[28,150],"2.8":[29],"trillion":[30],"predominantly":[31],"Chinese":[32],"and":[33,64,68,125,155,162,165,174],"English":[34],"tokens;":[35],"Index-1.9B-Pure,":[36],"control":[38],"variant":[39],"trained":[40],"an":[42,129,177],"identical":[43],"recipe":[44],"but":[45],"all":[47],"instruction-like":[48],"data":[49,94,163,171],"strictly":[50],"filtered":[51],"from":[52,57],"the":[53,58,72,90,99,157,166,185],"corpus;":[54],"Index-1.9B-Chat,":[55],"aligned":[56],"base":[59],"supervised":[62],"fine-tuning":[63],"direct":[65],"preference":[66],"optimization;":[67],"Index-1.9B-Character,":[69],"which":[70,89],"augments":[71],"chat":[73],"retrieval-augmented":[76],"generation":[77],"for":[78],"few-shot":[79],"role-playing":[80],"customization.":[81],"Pre-training":[82],"employs":[83],"Warmup-Stable-Decay":[85],"learning-rate":[86,153,160],"schedule":[87],"in":[88,180],"concentration":[91],"curated":[93],"is":[95],"raised":[96],"substantially":[97],"during":[98,172],"decay":[100,161],"phase,":[101],"together":[102,190],"Norm-Head":[105],"output":[106],"layer":[107],"that":[108],"stabilizes":[109],"training":[110],"under":[111],"large":[112],"learning":[113],"rates.":[114],"On":[115],"suite":[117],"standard":[119],"benchmarks":[120],"covering":[121],"examination,":[122],"reasoning,":[123],"mathematics,":[124],"code,":[126,193],"Index-1.9B-Base":[127],"attains":[128],"average":[130],"score":[131],"64.92,":[133],"competitive":[134],"or":[136],"exceeding":[137],"several":[141],"times":[142],"its":[143],"size.":[144],"further":[146],"report":[147],"controlled":[148],"studies":[149],"depth,":[152],"magnitude":[154],"scheduling,":[156],"interaction":[158],"between":[159],"quality,":[164],"effect":[167],"including":[169],"instruction":[170],"pre-training,":[173],"we":[175],"document":[176],"unexplained":[178],"surge":[179],"benchmark":[181],"performance":[182],"midway":[183],"through":[184],"constant-learning-rate":[186],"phase.":[187],"All":[188],"models,":[189],"evaluation":[192],"are":[194],"released":[195],"https://github.com/bilibili/Index-1.9B.":[197]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-15T00:00:00"}