{"id":"https://openalex.org/W4398795285","doi":"https://doi.org/10.48550/arxiv.2405.13386","title":"360Zhinao Technical Report","display_name":"360Zhinao Technical Report","publication_year":2024,"publication_date":"2024-05-22","ids":{"openalex":"https://openalex.org/W4398795285","doi":"https://doi.org/10.48550/arxiv.2405.13386"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2405.13386","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.13386","pdf_url":"https://arxiv.org/pdf/2405.13386","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2405.13386","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5098821111","display_name":"Zhinao Team","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"360Zhinao Team","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5098821111"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T10028","display_name":"Topic Modeling","score":0.9214000105857849,"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/T10028","display_name":"Topic Modeling","score":0.9214000105857849,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.04839999973773956,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.009499999694526196,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/large-language-models","display_name":"large language models","score":0.7699999809265137},{"id":"https://openalex.org/keywords/pretraining","display_name":"pre-training","score":0.6510000228881836},{"id":"https://openalex.org/keywords/data-cleaning","display_name":"data cleaning","score":0.7039999961853027},{"id":"https://openalex.org/keywords/data-filtering","display_name":"data filtering","score":0.5360000133514404},{"id":"https://openalex.org/keywords/reinforcement-learning-from-human-feedback","display_name":"reinforcement learning from human feedback","score":0.7480000257492065},{"id":"https://openalex.org/keywords/reward-modeling","display_name":"reward modeling","score":0.8679999709129333},{"id":"https://openalex.org/keywords/supervised-fine-tuning","display_name":"supervised fine-tuning","score":0.8259999752044678}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3114287257194519}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2405.13386","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.13386","pdf_url":"https://arxiv.org/pdf/2405.13386","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2405.13386","doi":"https://doi.org/10.48550/arxiv.2405.13386","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2405.13386","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2405.13386","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2405.13386","pdf_url":"https://arxiv.org/pdf/2405.13386","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4398795285.pdf","grobid_xml":"https://content.openalex.org/works/W4398795285.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4405783338","https://openalex.org/W7111203734","https://openalex.org/W7133427939","https://openalex.org/W7127631375","https://openalex.org/W4389519531","https://openalex.org/W4404310829","https://openalex.org/W4405875865","https://openalex.org/W4386977302","https://openalex.org/W4391244976","https://openalex.org/W6891798149"],"abstract_inverted_index":{"We":[0,60],"present":[1],"360Zhinao":[2],"models":[3,115],"with":[4,39,75],"7B":[5],"parameter":[6],"size":[7],"and":[8,14,29,35,51,73,77,90,93,99],"context":[9,83],"lengths":[10],"spanning":[11],"4K,":[12],"32K":[13,89],"360K,":[15],"all":[16],"available":[17],"at":[18],"https://github.com/Qihoo360/360zhinao.":[19],"For":[20],"rapid":[21],"development":[22],"in":[23],"pretraining,":[24],"we":[25,46,68],"establish":[26],"a":[27],"stable":[28],"sensitive":[30],"ablation":[31],"environment":[32],"to":[33,54,70,88,102,110],"evaluate":[34],"compare":[36],"experiment":[37],"runs":[38],"minimal":[40],"model":[41],"size.":[42,118],"Under":[43],"such":[44],"guidance,":[45],"perfect":[47],"our":[48],"data":[49,64],"cleaning":[50],"composition":[52],"strategies":[53],"pretrain":[55],"$\\texttt{360Zhinao-7B-Base}$":[56],"on":[57],"3.4T":[58],"tokens.":[59],"also":[61],"mainly":[62],"emphasize":[63],"during":[65],"alignment,":[66],"where":[67],"strive":[69],"balance":[71],"quantity":[72],"quality":[74],"filtering":[76],"reformatting.":[78],"With":[79],"tailored":[80],"data,":[81],"360Zhinao-7B's":[82,111],"window":[84],"is":[85],"easily":[86],"extended":[87],"360K.":[91],"RMs":[92],"RLHF":[94],"are":[95],"trained":[96],"following":[97],"SFT":[98],"credibly":[100],"applied":[101],"specific":[103],"tasks.":[104],"All":[105],"together":[106],"these":[107],"contributions":[108],"lead":[109],"competitive":[112],"performance":[113],"among":[114],"of":[116],"similar":[117]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2024-05-25T00:00:00"}