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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 25 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 12 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 39 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 20
Collections
Discover the best community collections!
Collections including paper arxiv:2410.12381
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Self-Rewarding Language Models
Paper • 2401.10020 • Published • 145 -
Orion-14B: Open-source Multilingual Large Language Models
Paper • 2401.12246 • Published • 12 -
MambaByte: Token-free Selective State Space Model
Paper • 2401.13660 • Published • 52 -
MM-LLMs: Recent Advances in MultiModal Large Language Models
Paper • 2401.13601 • Published • 45
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glaiveai/glaive-coder-7b
Text Generation • Updated • 822 • 54 -
glaiveai/glaive-code-assistant-v3
Viewer • Updated • 950k • 426 • 46 -
ibm-granite/granite-3b-code-base-128k
Text Generation • Updated • 950 • 4 -
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Paper • 2405.04324 • Published • 22
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LLM Pruning and Distillation in Practice: The Minitron Approach
Paper • 2408.11796 • Published • 57 -
TableBench: A Comprehensive and Complex Benchmark for Table Question Answering
Paper • 2408.09174 • Published • 51 -
To Code, or Not To Code? Exploring Impact of Code in Pre-training
Paper • 2408.10914 • Published • 41 -
Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
Paper • 2408.11878 • Published • 52
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Multimodal Self-Instruct: Synthetic Abstract Image and Visual Reasoning Instruction Using Language Model
Paper • 2407.07053 • Published • 42 -
LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models
Paper • 2407.12772 • Published • 33 -
VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Models
Paper • 2407.11691 • Published • 13 -
MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models
Paper • 2408.02718 • Published • 60