Search Results for "model-builder"

...Megatron-LM is widely used in research and industry for pretraining GPT-, BERT-, T5-, and multimodal-style models, with tooling for checkpoint conversion and interoperability with Hugging Face. Overall, it is a production-grade system for organizations pushing the limits of large-scale language model training.

...It distills the GPT architecture into a few hundred lines of Python code, making it far easier to understand than large, production-scale implementations. The repo is organized with a training pipeline (dataset preprocessing, model definition, optimizer, training loop) and inference script so you can train a small GPT on text datasets like Shakespeare or custom corpora. It emphasizes readability and clarity: the training loop is cleanly written, and the code avoids heavy abstractions, letting students follow the architecture step by step. While simple, it can still train non-trivial models on modern GPUs and generate coherent text. ...

...Archivematica is a set of free software tools that allow the user to process digital objects from the moment they are entered into the system until their publication according to the ISO-OAIS functional model. The user can monitor and control the ingestion and preservation of micro-services through the control panel. Archivematica uses standards such as METS, PREMIS, Dublin Core, and the BagIt specification.

...The module reco_utils contains functions to simplify common tasks used when developing and evaluating recommender systems. Several utilities are provided in reco_utils to support common tasks such as loading datasets in the format expected by different algorithms, evaluating model outputs, and splitting training/test data. Implementations of several state-of-the-art algorithms are included for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. Independent or incubating algorithms and utilities are candidates for the contrib folder. ...

AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional...

The Brain Tokyo Workshop repository hosts a collection of research materials and experimental code developed by the Google Brain team based in Tokyo. It showcases a variety of cutting-edge projects in artificial intelligence, particularly in the areas of neuroevolution, reinforcement learning, and model interpretability. Each project explores innovative approaches to learning, prediction, and creativity in neural networks, often through unconventional or biologically inspired methods. The repository includes implementations, experimental data, and supporting research papers that accompany published studies. Notable works such as Weight Agnostic Neural Networks and Neuroevolution of Self-Interpretable Agents highlight the team’s exploration of how AI can learn more efficiently and transparently. ...

...It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make something totally new. Catalyst is compatible with Python 3.6+. PyTorch 1.1+, and has been tested on Ubuntu 16.04/18.04/20.04, macOS 10.15, Windows 10 and Windows Subsystem for Linux. ...

The main goal of this project was developing phase-field simulations of lithium dendrite growth with FEniCS programmed in Python. The problem was based in the grand potential-based model of Zijian Hong and Venkatasubramanian Viswanathan (https://doi.org/10.1021/acsenergylett.8b01009) . Some simpler examples were developed before for a first approach with FEniCS: heat equation and combustion model.

...It is built to support both researchers and developers who want to train, evaluate, and extend GANs efficiently across diverse datasets such as CelebA-HQ, FashionGen, DTD, and CIFAR-10. In addition to core GAN training, the repository includes tools for model evaluation, such as Inception Score and SWD metrics, as well as advanced features like GDPP for diverse generation and AC-GAN conditioning for class-specific synthesis. The framework also supports “inspirational generation,” enabling style or content transfer from reference images through pre-trained models.

BibteXML is a bibliography schema for XML that expresses the content model of BibTeX – the bibliographic system for use with LaTeX. Stylesheets and conversion tools are provided.

Yabman is a tool for managing bibliographic references. Its key features are a quality user interface, a carefully designed data model, and sophisticated three-state hierarchical reference labeling. It is currently usable but in a pre-alpha stage.

This is a implementation of the 'enhanced Topic-based Vector Space Model' (eTVSM) using the python language. A Java-Version and maybe other java-code contributions are planned.