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arXiv:2503.19786 · v1

Gemma 3 Technical Report

Gemma TeamAishwarya KamathJohan FerretShreya PathakNino VieillardRamona MerhejSarah PerrinTatiana MatejovicovaAlexandre RaméMorgane RivièreLouis RouillardThomas MesnardGeoffrey CideronJean-bastien GrillSabela RamosEdouard YvinecMichelle CasbonEtienne PotIvo PenchevGaël LiuFrancesco VisinKathleen KenealyLucas BeyerXiaohai ZhaiAnton TsitsulinRobert Busa-FeketeAlex FengNoveen SachdevaBenjamin ColemanYi GaoBasil MustafaIain BarrEmilio ParisottoDavid TianMatan EyalColin CherryJan-Thorsten PeterDanila SinopalnikovSurya BhupatirajuRishabh AgarwalMehran KazemiDan MalkinRavin KumarDavid VilarIdan BrusilovskyJiaming LuoAndreas SteinerAbe FriesenAbhanshu SharmaAbheesht SharmaAdi Mayrav GiladyAdrian GoedeckemeyerAlaa SaadeAlex FengAlexander KolesnikovAlexei BendeburyAlvin AbdagicAmit VadiAndrás GyörgyAndré Susano PintoAnil DasAnkur BapnaAntoine MiechAntoine YangAntonia PatersonAshish ShenoyAyan ChakrabartiBilal PiotBo WuBobak ShahriariBryce PetriniCharlie ChenCharline Le LanChristopher A. Choquette-ChooCJ CareyCormac BrickDaniel DeutschDanielle EisenbudDee CattleDerek ChengDimitris PaparasDivyashree Shivakumar SreepathihalliDoug ReidDustin TranDustin ZelleEric NolandErwin HuizengaEugene KharitonovFrederick LiuGagik AmirkhanyanGlenn CameronHadi HashemiHanna Klimczak-PlucińskaHarman SinghHarsh MehtaHarshal Tushar LehriHussein HazimehIan BallantyneIdan SzpektorIvan NardiniJean Pouget-AbadieJetha ChanJoe StantonJohn WietingJonathan LaiJordi OrbayJoseph FernandezJosh NewlanJu-yeong JiJyotinder SinghKat BlackKathy YuKevin HuiKiran VodrahalliKlaus GreffLinhai QiuMarcella ValentineMarina CoelhoMarvin RitterMatt HoffmanMatthew WatsonMayank ChaturvediMichael MoynihanMin MaNabila BabarNatasha NoyNathan ByrdNick RoyNikola MomchevNilay ChauhanNoveen SachdevaOskar BunyanPankil BotardaPaul CaronPaul Kishan RubensteinPhil CullitonPhilipp SchmidPier Giuseppe SessaPingmei XuPiotr StanczykPouya TaftiRakesh ShivannaRenjie WuRenke PanReza RokniRob WilloughbyRohith ValluRyan MullinsSammy JeromeSara SmootSertan GirginShariq IqbalShashir ReddyShruti ShethSiim PõderSijal BhatnagarSindhu Raghuram PanyamSivan EigerSusan ZhangTianqi LiuTrevor YacovoneTyler LiechtyUday KalraUtku EvciVedant MisraVincent RoseberryVlad FeinbergVlad KolesnikovWoohyun HanWoosuk KwonXi ChenYinlam ChowYuvein ZhuZichuan WeiZoltan EgyedVictor CotrutaMinh GiangPhoebe KirkAnand RaoKat BlackNabila BabarJessica LoErica MoreiraLuiz Gustavo MartinsOmar SansevieroLucas GonzalezZach GleicherTris WarkentinVahab MirrokniEvan SenterEli CollinsJoelle BarralZoubin GhahramaniRaia HadsellYossi MatiasD. SculleySlav PetrovNoah FiedelNoam ShazeerOriol VinyalsJeff DeanDemis HassabisKoray KavukcuogluClement FarabetElena BuchatskayaJean-Baptiste AlayracRohan AnilDmitryLepikhinSebastian BorgeaudOlivier BachemArmand JoulinAlek AndreevCassidy HardinRobert DadashiLéonard Hussenot
首次提交 2025-03-25 23:52
版本更新 2025-03-25 23:52
雷达观测 2026-10-29 21:16
arXiv ↗PDF ↗

摘要

We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achieved by increasing the ratio of local to global attention layers, and keeping the span on local attention short. The Gemma 3 models are trained with distillation and achieve superior performance to Gemma 2 for both pre-trained and instruction finetuned versions. In particular, our novel post-training recipe significantly improves the math, chat, instruction-following and multilingual abilities, making Gemma3-4B-IT competitive with Gemma2-27B-IT and Gemma3-27B-IT comparable to Gemini-1.5-Pro across benchmarks. We release all our models to the community.

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基于摘要 · 非全文审阅

Gemma 3 是 Gemma 轻量级开放模型家族的多模态新版本,参数规模为 1B 至 27B,新增视觉理解、更广的语言覆盖和至少 128K 的长上下文,并通过调整局部与全局注意力层比例来降低 KV-cache 内存。

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v1 · 2025-03-25 23:52 · 最新观测版本

Gemma 3 Technical Report

We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision understanding abilities, a wider coverage of languages and longer context - at least 128K tokens. We also change the architecture of the model to reduce the KV-cache memory that tends to explode with long context. This is achieved by increasing the ratio of local to global attention layers, and keeping the span on local attention short. The Gemma 3 models are trained with distillation and achieve superior performance to Gemma 2 for both pre-trained and instruction finetuned versions. In particular, our novel post-training recipe significantly improves the math, chat, instruction-following and multilingual abilities, making Gemma3-4B-IT competitive with Gemma2-27B-IT and Gemma3-27B-IT comparable to Gemini-1.5-Pro across benchmarks. We release all our models to the community.

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