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Average Ratings 0 Ratings

Total
ease
features
design
support

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Write a Review

Description

Microsoft Frontier Tuning enables businesses to tailor one or multiple of Microsoft’s leading MAI models to fit their specific operational requirements, allowing for training in a secure setting rather than depending on a standard AI model. The customization process begins by outlining the objectives and criteria for success, followed by integrating data, workflows, and insights gathered from Microsoft 365 and other sources. Continuous improvement is achieved through ongoing training and iterative refinement, with the model being deployed in platforms like Microsoft Foundry or Copilot, where it can enhance itself based on actual usage patterns. This innovative approach ensures that the models are well-versed in the organization’s terminology, context, processes, and expertise while maintaining strict privacy and security for all data within the client’s ecosystem. Additionally, Microsoft Frontier Tuning empowers teams with greater control over their models, minimizes the risks of vendor lock-in, and maximizes the return on investment by providing cutting-edge performance paired with exceptional token efficiency. As a result, organizations can expect to see enhanced operational effectiveness and a stronger alignment with their unique business strategies.

Description

Unsloth is an innovative open-source platform specifically crafted to enhance and expedite the fine-tuning and training process of Large Language Models (LLMs). This platform empowers users to develop customized models, such as ChatGPT, in just a single day, a remarkable reduction from the usual training time of 30 days, achieving speeds that can be up to 30 times faster than Flash Attention 2 (FA2) while significantly utilizing 90% less memory. It supports advanced fine-tuning methods like LoRA and QLoRA, facilitating effective customization for models including Mistral, Gemma, and Llama across its various versions. The impressive efficiency of Unsloth arises from the meticulous derivation of computationally demanding mathematical processes and the hand-coding of GPU kernels, which leads to substantial performance enhancements without necessitating any hardware upgrades. On a single GPU, Unsloth provides a tenfold increase in processing speed and can achieve up to 32 times improvement on multi-GPU setups compared to FA2, with its functionality extending to a range of NVIDIA GPUs from Tesla T4 to H100, while also being portable to AMD and Intel graphics cards. This versatility ensures that a wide array of users can take full advantage of Unsloth's capabilities, making it a compelling choice for those looking to push the boundaries of model training efficiency.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

ChatGPT
Devstral
Gemma
Google Colab
Kaggle
Llama
Llama 2
Llama 3
Microsoft Azure
Microsoft Copilot
Microsoft Foundry
Mistral AI
NVIDIA DRIVE
Thunder Compute

Integrations

ChatGPT
Devstral
Gemma
Google Colab
Kaggle
Llama
Llama 2
Llama 3
Microsoft Azure
Microsoft Copilot
Microsoft Foundry
Mistral AI
NVIDIA DRIVE
Thunder Compute

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

Free
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

Microsoft AI

Founded

2024

Country

United States

Website

microsoft.ai/models/microsoft-frontier-tuning/

Vendor Details

Company Name

Unsloth

Founded

2023

Country

United States

Website

unsloth.ai/

Product Features

Product Features

Alternatives

No Alternatives

Alternatives

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