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Showing 8 open source projects for "op"
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1
OP Vault
Give ChatGPT long-term memory using the OP Stack
OP Vault is an open-source system designed to give large language models long-term memory by enabling them to interact with a custom knowledge base built from user-provided documents. It combines a backend written in Go with a React frontend, allowing users to upload files such as PDFs, text documents, and books to create a searchable repository of information. -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. The front-end language is Python. ... -
3
AI File Organizer (AFO)
Een slimme AI-app die je documenten automatisch ordent en hernoemt.
...Met deze applicatie hoef je nooit meer handmatig door je bestanden te spitten. AI File Organizer scant, analyseert en organiseert je documenten (PDF, DOCX, TXT, MD) automatisch in logische categorieën en mappen, op basis van de inhoud. Bovendien kan de applicatie automatisch suggesties doen voor duidelijke, beschrijvende bestandsnamen, zodat je altijd overzicht houdt. -
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TensorFlow Addons
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
TensorFlow Addons is a repository of contributions that conform to well-established API patterns but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast-moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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5
YOLOX
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. Prepare your own dataset with images and labels first. For labeling images, you can use tools like Labelme or CVAT. One more thing worth noting is that you should also implement pull_item and load_anno method... -
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PyTorch pretrained BigGAN
PyTorch implementation of BigGAN with pretrained weights
An op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind. This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper Large Scale GAN Training for High Fidelity Natural Image Synthesis. This PyTorch implementation of BigGAN is provided with the pretrained 128x128, 256x256 and 512x512 models by DeepMind. -
7
Darkbot
The IRC's Talking Robot
[ Please read https://sourceforge.net/p/darkbot/news/2014/01/darkbots-revitalization/ ] Darkbot is a portable IRC chat robot written in the C language that can be taught responses to user inquiries, and even have conversations with them. Darkbot was originally created by Jason Hamilton as an aid for help channels on Intenet Relay Chat. -
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Dutch sentiment analysis engine
Een module om de sentiment van een stuk Nederlandse tekst to bepalen
...Momenteel is de module in alpha versie beschikbaar en een beta versie wordt verwacht in november 2012. Als u ons wilt helpen bij het ontwikkelen of testen van deze module, neem dan contact op met Incentro via +31[0]15 76 40 750 - of info {at} incentro.com.
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