Search Results for "smartgpu2.h"

DocWire SDK, a standout C++20AI driven data processing tool, has received award from SourceForge and strong backing from Microsoft. It handles nearly 100 file types, empowering efficient text extraction, web data extraction, and document analysis. For businesses, the shift to DocWire SDK signifies a leap forward. It promises comprehensive document format support and the ability to extract valuable insights from email boxes, databases, and websites using cutting-edge AI. DocWire SDK aims to...

ToMe (Token Merging) is a PyTorch-based optimization framework designed to significantly accelerate Vision Transformer (ViT) architectures without retraining. Developed by researchers at Facebook (Meta AI), ToMe introduces an efficient technique that merges similar tokens within transformer layers, reducing redundant computation while preserving model accuracy. This approach differs from token pruning, which removes background tokens entirely; instead, ToMe merges tokens based on feature...

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...Computer Vision and Image Understanding, 93(2), 206–220. doi:10.1016/j.cviu.2003.09.002 The ImageJ *Shape Filter Plugin* (see downloads) uses this library for flitering the blobs by its shape. If you are using IJBlob in a scientific publication, please cite: Wagner, T and Lipinski, H 2013. IJBlob: An ImageJ Library for Connected Component Analysis and Shape Analysis. Journal of Open Research Software 1(1):e6, DOI: http://dx.doi.org/10.5334/jors.ae

...MTL consists of pars B - Basic Functions, Matrices, Images, Hypermodels (3d Models and up) N - Numeric Functions ranging from linear regression over nonlinear optimization to singular-value computation I - Image filters and Image enhancement H - Hardware related (optional part), does require additional libraries and is only useful on certain hosts. G - Hyper-Model functions such as ray-plane intersections etc.

...Compressed from Nemotron 3.5 Super VL using NVIDIA's Star Elastic method, it contains 74B total parameters with 9B active per token. Its hybrid Mamba-2–Transformer LatentMoE architecture integrates a C-RADIO v4-H vision encoder for processing text, images, and video. The model supports a 256K-token context window, structured tool calling, JSON output, and configurable reasoning modes. It is optimized for coding, browser automation, terminal operations, and multi-step agent workflows. BF16 weights preserve flexibility for supervised fine-tuning, reinforcement learning, distillation, and creating quantized variants. ...

...Derived from Nemotron 3.5 Super VL through NVIDIA's Star Elastic compression method, it features 74B total parameters with approximately 9B activated per token. Its hybrid Mamba-2–Transformer LatentMoE architecture incorporates a C-RADIO v4-H vision encoder for processing text, images, and video. The model supports a 256K-token context window and uses IQ2 quantization with quantization-aware distillation to reduce memory requirements while preserving reasoning capabilities. Designed for local coding assistants, browser automation, terminal operations, and tool calling, it supports configurable reasoning modes for balancing speed and accuracy. ...

...Compressed from Nemotron 3.5 Super VL using NVIDIA's Star Elastic method, it contains 74B total parameters with 9B active per token. Its hybrid Mamba-2–Transformer LatentMoE architecture integrates a C-RADIO v4-H vision encoder for understanding text, images, and video. The model supports a 256K-token context window and uses native IQ2 quantization with quantization-aware distillation to preserve accuracy. It targets everyday agentic workflows involving coding, web browsing, terminal operations, and tool calling. Configurable thinking modes balance direct responses and deeper reasoning, while LiLiCorr speculative decoding improves generation speed. ...