uber/ludwig

Features

• Declarative Training Frameworks - Implements a declarative machine learning framework that uses configuration files to build and train neural networks.
• Declarative Pipeline Specifications - Allows building and training neural networks using declarative YAML or JSON configurations instead of manual Python code.
• Declarative Machine Learning Frameworks - Provides a framework for building and training neural networks using declarative configuration files instead of manual coding.
• Encoder-Combiner Architectures - Utilizes an architecture that separates diverse input processing into specialized encoders and merges them via a central combiner.
• Multimodal Model Trainers - Ships a system for creating and training models that process mixed modalities including text, image, and audio.
• LLM Fine-Tuning Engines - Provides a low-code engine for the efficient supervised fine-tuning of large language models.
• Large Language Model Fine-Tuning Frameworks - Adapts pre-trained large language models for specific tasks using supervised fine-tuning and parameter-efficient adapters.
• Multimodal Fine-Tuning - Trains vision-language models using gated cross-attention to simultaneously process image and text inputs.
• Model Training - Allows the construction of neural networks and LLMs via declarative configurations instead of manual training loops.
• Multimodal AI Systems - Provides a framework for building AI systems that process and combine text, images, audio, and tabular data.
• Multimodal Analytical Pipelines - Provides a unified system to process text, images, and tabular data and map them into a common latent space.
• Multimodal Models - Builds architectures that process and align diverse data types like text, images, and audio in a shared space.
• Multimodal Processing - Integrates multiple data modalities including text, images, audio, and time series using a single unified configuration.
• Parameter-Efficient Adaptation - Implements parameter-efficient adaptation using trainable adapter layers to customize large models while keeping base weights frozen.
• Supervised Fine-Tuning - Supports adapting pre-trained models using labeled instruction datasets and quantization techniques.
• Distributed Training Orchestration - Manages parallelization and synchronization of model workloads across multiple GPU clusters to accelerate training.
• Distributed Training Scaling Utilities - Scales training workloads across clusters using mixed precision and automatic batch selection to increase speed.
• Experiment Tracking - Logs and versions every training run and configuration to ensure reproducibility and result visualization.
• Multi-Task Learning Models - Enables the training of a single model to predict multiple output features simultaneously.
• Game-Theoretic Loss Balancing - Optimizes multi-task loss functions using game-theoretic methods to prevent any single task from dominating the gradient.
• Model Deployment Pipelines - Offers a standardized pipeline for exporting trained weights and serving models as REST APIs.
• Hyperparameter Tuning - Utilizes sampling algorithms and persistent storage to iteratively optimize model configuration parameters.
• Automated Architecture Search - Automatically searches for the best combination of encoders and combiners based on data and time budgets.
• Model Deployment Pipelines - Provides a deployment pipeline to transition trained models into production via REST APIs and scalable clusters.
• Hyperparameter Optimization - Provides automated methods for searching and selecting the best configuration parameters to optimize model performance.
• Time Series Forecasting - Supports the development of models designed for predicting future values in temporal data sequences.
• Game-Theoretic Loss Balancing - Dynamically adjusts weights of multiple task-specific loss functions using game-theoretic methods to prevent any single task from dominating.
• Model Alignment and Feedback - Provides capabilities for aligning language model outputs using policy optimization and human feedback.
• LLM-Driven Configuration Generation - Automatically generates declarative configuration files using large language models based on provided task descriptions.
• REST APIs - Exposes trained models as web services with REST endpoints to handle programmatic inference requests.
• Containerized Model Serving - Packages trained model weights and inference logic into standardized containers for production deployment.
• Model Inference Deployment - Transitions trained models into production environments to perform real-world inference on live data.
• ML Model Hosting - Hosts models in distributed environments using container orchestration for scalable and reliable serving.
• Automated Machine Learning - Toolbox for training deep learning models without writing code.
• Deep Learning - Toolbox for training deep learning models without coding.
• Simplification Tools - Trains and tests deep learning models without writing code.

Star history