#self-attention

The PyTorch implementation of the Transformer model uses self-attention for advanced translation, achieving leading results in the 2014 WMT English-German task. It supports training and translation with the model, with byte pair encoding features under development. The project is suitable for those interested in Transformer architecture without convolutional or recurrent layers. Contributions and suggestions are welcome.

Participate in this free and open-source course exploring transformer architecture, featuring hands-on exercises, paper reviews, and Jupyter notebooks. Ideal for those interested in encoder-decoder models, self-attention mechanisms, and practical implementations like BERT and GPT-2. Engage collaboratively via GitHub and anticipate upcoming educational videos.

Discover a comprehensive guide to implementing Llama3 from scratch using direct tensor and matrix operations. This article explains how to load model weights provided by Meta, use tiktoken for tokenization, and delve into embedding normalization and self-attention mechanics. Gain insights into configuring the transformer model that features 32 layers and multi-head attention, facilitating an understanding of neural network dynamics without heavy reliance on built-in neural modules.

StoryDiffusion leverages consistent self-attention to achieve character-consistent image generation and employs a motion predictor for long-range video production. It integrates with diverse diffusion models and utilizes user-input text prompts to optimize layout arrangements. Focusing on the initial generation of consistent images, the tool facilitates smooth video transitions and supports a two-stage methodology for creating extensive videos. The initiative supports the advancement of AI-generated media by supplying resources for the development of high-quality visual content, while endorsing responsible use in adherence to legal standards.