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Qualitatively characterizing neural network optimization problems
Paper • 1412.6544 • Published • 4 -
Convergent Learning: Do different neural networks learn the same representations?
Paper • 1511.07543 • Published • 2 -
Mixout: Effective Regularization to Finetune Large-scale Pretrained Language Models
Paper • 1909.11299 • Published • 1 -
Model Fusion via Optimal Transport
Paper • 1910.05653 • Published • 1
Collections
Discover the best community collections!
Collections including paper arxiv:2403.13187
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LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
Paper • 2309.12307 • Published • 82 -
NEFTune: Noisy Embeddings Improve Instruction Finetuning
Paper • 2310.05914 • Published • 13 -
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
Paper • 2312.15166 • Published • 55 -
Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon
Paper • 2401.03462 • Published • 25
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Evolutionary Optimization of Model Merging Recipes
Paper • 2403.13187 • Published • 44 -
Gemma: Open Models Based on Gemini Research and Technology
Paper • 2403.08295 • Published • 41 -
ViTAR: Vision Transformer with Any Resolution
Paper • 2403.18361 • Published • 48 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 98
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Evolutionary Optimization of Model Merging Recipes
Paper • 2403.13187 • Published • 44 -
Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 9 -
Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models
Paper • 2405.01535 • Published • 79