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VideoElevator: Elevating Video Generation Quality with Versatile Text-to-Image Diffusion Models
Paper • 2403.05438 • Published • 15 -
Chronos: Learning the Language of Time Series
Paper • 2403.07815 • Published • 43 -
Adding NVMe SSDs to Enable and Accelerate 100B Model Fine-tuning on a Single GPU
Paper • 2403.06504 • Published • 52 -
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
Paper • 2211.07600 • Published
Collections
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Collections including paper arxiv:2403.06504
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Adding NVMe SSDs to Enable and Accelerate 100B Model Fine-tuning on a Single GPU
Paper • 2403.06504 • Published • 52 -
Token-Level Adaptation of LoRA Adapters for Downstream Task Generalization
Paper • 2311.10847 • Published • 2 -
PERL: Parameter Efficient Reinforcement Learning from Human Feedback
Paper • 2403.10704 • Published • 55
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Measuring the Effects of Data Parallelism on Neural Network Training
Paper • 1811.03600 • Published • 2 -
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Paper • 1804.04235 • Published • 2 -
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Paper • 1905.11946 • Published • 3 -
Yi: Open Foundation Models by 01.AI
Paper • 2403.04652 • Published • 59
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Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 50 -
Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 45 -
StarCoder 2 and The Stack v2: The Next Generation
Paper • 2402.19173 • Published • 126 -
Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 17
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 567 -
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
Paper • 2402.14905 • Published • 81 -
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
Paper • 2402.13616 • Published • 44 -
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
Paper • 2402.19479 • Published • 30
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MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Paper • 2402.15627 • Published • 32 -
Beyond Language Models: Byte Models are Digital World Simulators
Paper • 2402.19155 • Published • 45 -
VisionLLaMA: A Unified LLaMA Interface for Vision Tasks
Paper • 2403.00522 • Published • 40 -
Stealing Part of a Production Language Model
Paper • 2403.06634 • Published • 86
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Divide-or-Conquer? Which Part Should You Distill Your LLM?
Paper • 2402.15000 • Published • 22 -
Adding NVMe SSDs to Enable and Accelerate 100B Model Fine-tuning on a Single GPU
Paper • 2403.06504 • Published • 52 -
MoAI: Mixture of All Intelligence for Large Language and Vision Models
Paper • 2403.07508 • Published • 72 -
AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks
Paper • 2403.14468 • Published • 18
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LoRA+: Efficient Low Rank Adaptation of Large Models
Paper • 2402.12354 • Published • 5 -
The FinBen: An Holistic Financial Benchmark for Large Language Models
Paper • 2402.12659 • Published • 13 -
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization
Paper • 2402.13249 • Published • 10 -
TrustLLM: Trustworthiness in Large Language Models
Paper • 2401.05561 • Published • 62
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Rethinking Optimization and Architecture for Tiny Language Models
Paper • 2402.02791 • Published • 12 -
More Agents Is All You Need
Paper • 2402.05120 • Published • 46 -
Scaling Laws for Forgetting When Fine-Tuning Large Language Models
Paper • 2401.05605 • Published -
Aligning Large Language Models with Counterfactual DPO
Paper • 2401.09566 • Published • 2