Showing 441-460 of 657 projects
An ongoing research project for training transformer language models at scale, including BERT and GPT-2.
A library for self-supervised learning on graphs, providing contrastive, generative, and predictive pretext tasks.
Unified-Modal Speech-Text Pre-Training for Spoken Language Processing
A PyTorch library for easy access to pre-trained models and model components for developers.
Unified Training of Universal Time Series Forecasting Transformers for accurate time series forecasting.
Web and mobile application security training platform focused on secure coding practices.
A mentorship platform connecting developers who want to learn with experienced mentors to help them grow.
DataComp for Language Models is a library for training, evaluating, and deploying large language models.
A research framework for easy and efficient training of Generative Adversarial Networks (GANs) based on PyTorch.
A Go-based Nvidia GPU exporter for Prometheus monitoring, providing detailed GPU metrics.
A Python library and tools for generating and inspecting data for pre-training large language models (LLMs).
A unified UI and API for processing and training images for facial recognition across various AI tools.
A Python implementation of training for GPT2, supporting TPUs for AI-focused developers.
A Python package for chatting with an AI model and executing the InstructLab workflow to train a model using custom taxonomy data.
This repository contains Haskell 101 and 102 slides and codelabs for developers interested in learning the Haskell programming language.
Fast, modular reference implementation of Semantic Segmentation algorithms in PyTorch for vibe coders.
Softlearning is a powerful reinforcement learning framework for training maximum entropy policies in continuous domains.
A framework for training and deploying latent diffusion models for image reconstruction and generation.
Korean BERT pre-trained model for natural language processing tasks in the Korean language.
A collective communications library with various primitives for multi-machine training.
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