Gemma 3 Technical Report
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Bridging Offline and Online Reinforcement Learning for LLMs
Multi-lingual Functional Evaluation for Large Language Models
MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching
Personalized Exercise Recommendation with Semantically-Grounded Knowledge Tracing
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil Engineering
Reasoning or Memorization? Unreliable Results of Reinforcement Learning Due to Data Contamination
토론
이 논문과 연결된 포럼 글이 아직 없습니다.