Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
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Updated
Jan 6, 2026 - Python
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Declarative and readable tensor operations in Julia
Clean, reproducible, boilerplate-free deep learning project template.
C++17 implementation of einops for libtorch - clear and reliable tensor manipulations with einstein-like notation
A collection of components for transformers 🧩
Layer normalization with einops semantics.
Modern Eager TensorFlow implementation of Attention Is All You Need
Demonstration for NVIDIA's Nemotron-Parse-v1.1 model, designed for advanced document parsing and OCR. Upload images of documents (e.g., papers, forms) to extract structured content: text, tables (LaTeX), figures, and titles. Outputs annotated images with colored bounding boxes and processed markdown/LaTeX text for easy integration.
Demonstration for the Lightricks LTX-2 Distilled model, enhanced with specialized LoRA adapters for cinematic camera movements (dolly left/right/in/out, jib up/down, static). Generates animated videos from text prompts or input images, with optional prompt enhancement using Gemma-3-12b.
대조 학습을 추천 시스템에 적용하여 개인화 추천의 다양성을 향상하는 프로젝트입니다.
📄 Enhance document processing with NVIDIA's Nemotron-Parse-OCR, extracting structured content and providing clear visual annotations for easier integration.
Code implementation of computer vision models for practice based on pytorch and einops.
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