Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Average Ratings 0 Ratings

Total
ease
features
design
support

No User Reviews. Be the first to provide a review:

Write a Review

Description

This repository showcases an implementation of model parallel autoregressive transformers utilizing GPUs, leveraging the capabilities of the DeepSpeed library. It serves as a record of EleutherAI's framework designed for training extensive language models on GPU architecture. Currently, it builds upon NVIDIA's Megatron Language Model, enhanced with advanced techniques from DeepSpeed alongside innovative optimizations. Our goal is to create a centralized hub for aggregating methodologies related to the training of large-scale autoregressive language models, thereby fostering accelerated research and development in the field of large-scale training. We believe that by providing these resources, we can significantly contribute to the progress of language model research.

Description

Ludwig serves as a low-code platform specifically designed for the development of tailored AI models, including large language models (LLMs) and various deep neural networks. With Ludwig, creating custom models becomes a straightforward task; you only need a simple declarative YAML configuration file to train an advanced LLM using your own data. It offers comprehensive support for learning across multiple tasks and modalities. The framework includes thorough configuration validation to identify invalid parameter combinations and avert potential runtime errors. Engineered for scalability and performance, it features automatic batch size determination, distributed training capabilities (including DDP and DeepSpeed), parameter-efficient fine-tuning (PEFT), 4-bit quantization (QLoRA), and the ability to handle larger-than-memory datasets. Users enjoy expert-level control, allowing them to manage every aspect of their models, including activation functions. Additionally, Ludwig facilitates hyperparameter optimization, offers insights into explainability, and provides detailed metric visualizations. Its modular and extensible architecture enables users to experiment with various model designs, tasks, features, and modalities with minimal adjustments in the configuration, making it feel like a set of building blocks for deep learning innovations. Ultimately, Ludwig empowers developers to push the boundaries of AI model creation while maintaining ease of use.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Aim
Alpaca
Comet
Discord
Docker
Forefront
Hugging Face
Kubernetes
Llama 2
MLflow
Python
RAY
TensorBoard
Triton
Weights & Biases
ZBrain

Integrations

Aim
Alpaca
Comet
Discord
Docker
Forefront
Hugging Face
Kubernetes
Llama 2
MLflow
Python
RAY
TensorBoard
Triton
Weights & Biases
ZBrain

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

EleutherAI

Founded

2020

Website

github.com/EleutherAI/gpt-neox

Vendor Details

Company Name

Uber AI

Founded

2016

Country

United States

Website

ludwig.ai/latest/

Product Features

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Alternatives

OPT Reviews

OPT

Meta

Alternatives

GPT-J Reviews

GPT-J

EleutherAI
DeepSpeed Reviews

DeepSpeed

Microsoft
DeepSpeed Reviews

DeepSpeed

Microsoft
Pythia Reviews

Pythia

EleutherAI
MLBox Reviews

MLBox

Axel ARONIO DE ROMBLAY