Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
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Updated
Apr 9, 2026 - Python
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
Clean, reproducible, boilerplate-free deep learning project template.
A collection of components for transformers 🧩
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.
This application allows users to perform various OCR tasks such as converting documents to markdown, extracting text, locating specific text within images, and parsing figures, all through a user-friendly interface. This demo leverages the deepseek-ai/DeepSeek-OCR-2
Layer normalization with einops semantics.
Modern Eager TensorFlow implementation of Attention Is All You Need
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.
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.
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
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.
🖥️ Utilize DeepSeek-OCR-2 to effortlessly execute advanced OCR tasks, converting documents to markdown and extracting text through an intuitive web app.
Code implementation of computer vision models for practice based on pytorch and einops.
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