This project is a collection of implementation guides, recipes, and developer resources for building applications with Llama models. It serves as a comprehensive kit for developing autonomous agents, establishing retrieval-augmented generation systems, and executing model fine-tuning. The resource provides specific patterns for multimodal workflows that process text, images, and audio. It includes specialized guidance on adapting pre-trained model weights for targeted tasks and implementing tool-calling orchestration to connect models with external APIs and functions. The codebase covers a b
Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet, Inception, SqueezeNet)
PyTorch implementation of Deformable Convolution
The main features of viking-sudo-rm/stacknn are: Model Implementations.
Open-source alternatives to viking-sudo-rm/stacknn include: meta-llama/llama-cookbook — This project is a collection of implementation guides, recipes, and developer resources for building applications with… aaron-xichen/pytorch-playground — Base pretrained models and datasets in pytorch (MNIST, SVHN, CIFAR10, CIFAR100, STL10, AlexNet, VGG16, VGG19, ResNet,… adambielski/capsnet-pytorch. adap/flower — Flower is a federated learning framework and distributed machine learning orchestrator designed to train models across… agermanidis/thingscoop — Search and filter videos based on objects that appear in them using convolutional neural networks. 1zb/deformable-convolution-pytorch — PyTorch implementation of Deformable Convolution.