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What is Taylor AI?

Creating open source language models requires a significant investment of both time and expertise. Taylor AI empowers your engineering team to focus on delivering true business value rather than getting entangled in complex libraries and the establishment of training frameworks. Partnering with external LLM providers can often lead to the exposure of sensitive organizational data, as many of these providers retain the right to retrain models with your information, introducing potential risks. With Taylor AI, you retain ownership and complete control over your models, avoiding these pitfalls. Move away from the traditional pay-per-token pricing structure; with Taylor AI, you only pay for the training of the model itself, granting you the freedom to deploy and interact with your AI models as often as you wish. New open-source models are introduced monthly, and Taylor AI keeps you informed about the latest releases, relieving you of that responsibility. By opting for Taylor AI, you ensure a competitive edge and access to state-of-the-art models for your training needs. As the owner of your model, you have the flexibility to deploy it in line with your organization's specific compliance and security standards, ensuring all requirements are met. This level of autonomy fosters greater innovation and adaptability within your projects, making it easier to pivot as necessary. Furthermore, it allows your team to focus their creative energies on developing groundbreaking solutions rather than managing operational complexities.

What is DeepSpeed?

DeepSpeed is an innovative open-source library designed to optimize deep learning workflows specifically for PyTorch. Its main objective is to boost efficiency by reducing the demand for computational resources and memory, while also enabling the effective training of large-scale distributed models through enhanced parallel processing on the hardware available. Utilizing state-of-the-art techniques, DeepSpeed delivers both low latency and high throughput during the training phase of models. This powerful tool is adept at managing deep learning architectures that contain over one hundred billion parameters on modern GPU clusters and can train models with up to 13 billion parameters using a single graphics processing unit. Created by Microsoft, DeepSpeed is intentionally engineered to facilitate distributed training for large models and is built on the robust PyTorch framework, which is well-suited for data parallelism. Furthermore, the library is constantly updated to integrate the latest advancements in deep learning, ensuring that it maintains its position as a leader in AI technology. Future updates are expected to enhance its capabilities even further, making it an essential resource for researchers and developers in the field.

Media

Media

Integrations Supported

Axolotl
Cake AI
Comet LLM
Falcon-40B
Falcon-7B
Llama 2
Nurix
PyTorch
Python
StarCoder

Integrations Supported

Axolotl
Cake AI
Comet LLM
Falcon-40B
Falcon-7B
Llama 2
Nurix
PyTorch
Python
StarCoder

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Free
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Taylor AI

Company Website

www.trytaylor.ai/

Company Facts

Organization Name

Microsoft

Date Founded

1975

Company Location

United States

Company Website

www.deepspeed.ai/

Categories and Features

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

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