Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
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Updated
Aug 26, 2026 - Python
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
High-performance C++ tensor library with NumPy/PyTorch-like API
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
DeepSeek-OCR-2-Unlimited-OCR is an advanced, experimental visual document processing and open-ended text localization dashboard. This application establishes a unified interface that allows users to swap between two premier vision-language document models: deepseek-ai/DeepSeek-OCR-2 and baidu/Unlimited-OCR.
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.
A collection of components for transformers 🧩
SAM3-Plus-Qwen3.5 is an advanced, experimental computer vision suite that seamlessly integrates Facebook's Segment Anything Model 3 (SAM3) with the Qwen3.5 multimodal reasoning engine.
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.
Cheers-HF-Demo is an advanced, highly optimized full-stack web application built on the Gradio framework, engineered to interface seamlessly with the ai9stars/Cheers multimodal
Modern Eager TensorFlow implementation of Attention Is All You Need
Layer normalization with einops semantics.
A transformer language model built from scratch, from byte-level BPE tokenization through pretraining and QA fine-tuning.
Typed tensor-shaping, masking, padding, device-routing, and checkpoint utilities for PyTorch and `einops`. A superset of `lucidrains/torch-einops-utils` with similar utilities from other lucidrains packages. Adds strict typing, extensive tests, and comprehensive docstrings.
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