Open Model Zoo OpenVINO का उपयोग करके उच्च-प्रदर्शन इन्फरेंस के लिए डिज़ाइन किए गए प्री-ट्रेंड और अनुकूलित डीप लर्निंग मॉडल का एक क्यूरेटेड संग्रह है। यह एक मॉडल रिपॉजिटरी और तैनाती फ्रेमवर्क के रूप में कार्य करता है जो प्रोडक्शन वातावरण में न्यूरल नेटवर्क के एकीकरण को सुव्यवस्थित करता है।
openvinotoolkit/open_model_zoo की मुख्य विशेषताएं हैं: Deep Learning Models, Pre-trained Model Zoos, Model Deployment, Model Registries, Model Asset Managers, Model Deployment Frameworks, Model Weight Management, Model Downloaders।
openvinotoolkit/open_model_zoo के ओपन-सोर्स विकल्पों में शामिल हैं: tingsongyu/pytorch_tutorial — This project is a comprehensive collection of educational examples and reference implementations for building vision… meta-llama/llama-models — This project provides a foundational framework and reference implementation for executing causal language modeling and… facebookresearch/parlai — ParlAI is a conversational AI research framework designed for training, evaluating, and sharing dialogue models using… facebookresearch/fairseq — Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic… jaymody/picogpt — picoGPT is a lightweight, low-level runtime environment and inference engine designed to load pre-trained checkpoints… paddlepaddle/paddlenlp — PaddleNLP is a development library and toolkit for training, fine-tuning, and deploying large and small language…
This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene
This project provides a foundational framework and reference implementation for executing causal language modeling and multimodal reasoning on local systems. It includes a set of core components for managing model assets, a fine-tuning framework, and structural definitions required to instantiate transformer-based architectures. The system is distinguished by its ability to process combined text and image inputs through multimodal transformer models for visual reasoning and document analysis. It also supports the deployment of quantized models, reducing memory footprints through low-precision
ParlAI is a conversational AI research framework designed for training, evaluating, and sharing dialogue models using a unified interface for datasets and agents. It functions as a PyTorch-based training platform and a dialogue data collection system, providing a centralized model zoo for the distribution of versioned pretrained agents. The project distinguishes itself through a knowledge-grounded retrieval system that combines dense and sparse indexing to ground responses in external information. It also provides a comprehensive infrastructure for gathering human-AI interaction data via inte
Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specializ