#python #artificial_intelligence #attention_mechanism #machine_learning #pytorch #transformers

https://github.com/lucidrains/reformer-pytorch

#python #artificial_intelligence #attention #attention_mechanism #computer_vision #deep_learning

https://github.com/lucidrains/lambda-networks

#python #attention #attention_is_all_you_need #attention_mechanism #deep_learning #deeplearning #jupyter #original_transformer #pytorch #pytorch_transformer #pytorch_transformers #transformer #transformer_tutorial #transformers

https://github.com/gordicaleksa/pytorch-original-transformer

#python #artificial_intelligence #attention_mechanism #computer_vision #image_classification #transformers

https://github.com/lucidrains/vit-pytorch

#python #artificial_intelligence #attention_mechanism #deep_learning #multi_modal #text_to_image #transformers

https://github.com/lucidrains/DALLE-pytorch

#python #deep_learning #transformers #artificial_intelligence #attention_mechanism

https://github.com/lucidrains/x-transformers

#other #attention_mechanism #attention_mechanisms #awesome_list #computer_vision #deep_learning #detr #papers #self_attention #transformer #transformer_architecture #transformer_awesome #transformer_cv #transformer_models #transformer_with_cv #transformers #vision_transformer #visual_transformer #vit

https://github.com/cmhungsteve/Awesome-Transformer-Attention

#python #attention_mechanism #deep_learning #gpt #gpt_2 #gpt_3 #language_model #linear_attention #lstm #pytorch #rnn #rwkv #transformer #transformers

https://github.com/BlinkDL/RWKV-LM

#python #artificial_intelligence #attention_mechanism #deep_learning #transformers

The `x-transformers` library offers a versatile and feature-rich implementation of transformer models, allowing users to easily build and customize various types of transformers. Here are the key benefits You can create full encoder/decoder models, decoder-only (GPT-like) models, encoder-only (BERT-like) models, and even image classification and image-to-caption models.
- **Experimental Features** You can customize layers with various normalization techniques (e.g., RMSNorm, ScaleNorm), attention variants (e.g., Talking-Heads, One Write-Head), and other enhancements like residual attention and gated feedforward networks.
- **Efficiency** The library provides simple wrappers for autoregressive models, continuous embeddings, and other specialized tasks, making it easier to set up and train complex models.

Overall, `x-transformers` simplifies the process of building advanced transformer models while offering a wide range of customization options to improve performance and efficiency.

https://github.com/lucidrains/x-transformers

#python #artificial_intelligence #attention_mechanism #computer_vision #image_classification #transformers

This text describes a comprehensive implementation of Vision Transformers (ViT) in PyTorch, offering various models and techniques for image classification. Here’s the key information and benefits**
- The repository provides multiple ViT variants, including the original ViT, Simple ViT, NaViT, Deep ViT, CaiT, Token-to-Token ViT, CCT, Cross ViT, PiT, LeViT, CvT, Twins SVT, RegionViT, CrossFormer, ScalableViT, SepViT, MaxViT, NesT, MobileViT, XCiT, and others.
- Each variant introduces different architectural improvements such as efficient attention mechanisms, multi-scale processing, and innovative embedding techniques.
- The implementation includes pre-trained models and supports various tasks like masked image modeling, distillation, and self-supervised learning.

**Benefits** Users can choose from a wide range of ViT models tailored for different needs, such as efficiency, performance, or specific tasks.
- **Performance** Some models, like NaViT and ScalableViT, are designed to be more efficient in terms of computational resources and training time.
- **Ease of Use** The inclusion of various research ideas and techniques allows users to explore new approaches in vision transformer research.

Overall, this repository offers a powerful toolkit for anyone working with vision transformers, providing both practical solutions and cutting-edge research opportunities.

https://github.com/lucidrains/vit-pytorch