xGen-MM (BLIP-3): A Family of Open Large Multimodal Models
원문 초록
This report introduces xGen-MM (also known as BLIP-3), a framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model architectures, and a resulting suite of LMMs. xGen-MM, short for xGen-MultiModal, expands the Salesforce xGen initiative on foundation AI models. Our models undergo rigorous evaluation across a range of tasks, including both single and multi-image benchmarks. Our pre-trained base model exhibits strong in-context learning capabilities and the instruction-tuned model demonstrates competitive performance among open-source LMMs with similar model sizes. In addition, we introduce a safety-tuned model with DPO, aiming to mitigate harmful behaviors such as hallucinations and improve safety. We open-source our models, curated large-scale datasets, and our fine-tuning codebase to facilitate further advancements in LMM research. Associated resources will be available on our project page above.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Assay2Mol: large language model-based drug design using BioAssay context
Universal pre-training by iterated random computation
Evolving Prompts In-Context: An Open-ended, Self-replicating Perspective
Conquering the Retina: Bringing Visual in-Context Learning to OCT
Evaluating Cell Type Inference in Vision Language Models Under Varying Visual Context
PRO-V: An Efficient Program Generation Multi-Agent System for Automatic RTL Verification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.