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- - NV-EMBED: IMPROVED TECHNIQUES FOR TRAINING LLMS AS GENERALIST EMBEDDING MODELS
    Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models

Overview
- - Scalable Diffusion Models with Transformers
- - FLOW MATCHING FOR GENERATIVE MODELING
- - Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
- - DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
  - AUXILIARY-LOSS-FREE LOAD BALANCING STRATEGY FOR MIXTURE-OF-EXPERTS
- - AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE
- - Patch n’ Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution

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Architecture

This section contains paper notes related to models architecture.

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