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features
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Description

Core ML utilizes a machine learning algorithm applied to a specific dataset to generate a predictive model. This model enables predictions based on incoming data, providing solutions for tasks that would be challenging or impossible to code manually. For instance, you could develop a model to classify images or identify particular objects within those images directly from their pixel data. Following the model's creation, it is essential to incorporate it into your application and enable deployment on users' devices. Your application leverages Core ML APIs along with user data to facilitate predictions and to refine or retrain the model as necessary. You can utilize the Create ML application that comes with Xcode to build and train your model. Models generated through Create ML are formatted for Core ML and can be seamlessly integrated into your app. Alternatively, a variety of other machine learning libraries can be employed, and you can use Core ML Tools to convert those models into the Core ML format. Once the model is installed on a user’s device, Core ML allows for on-device retraining or fine-tuning, enhancing its accuracy and performance. This flexibility enables continuous improvement of the model based on real-world usage and feedback.

Description

Tinker is an innovative training API tailored for researchers and developers, providing comprehensive control over model fine-tuning while simplifying the complexities of infrastructure management. It offers essential primitives that empower users to create bespoke training loops, supervision techniques, and reinforcement learning workflows. Currently, it facilitates LoRA fine-tuning on open-weight models from both the LLama and Qwen families, accommodating a range of model sizes from smaller variants to extensive mixture-of-experts configurations. Users can write Python scripts to manage data, loss functions, and algorithmic processes, while Tinker autonomously takes care of scheduling, resource distribution, distributed training, and recovery from failures. The platform allows users to download model weights at various checkpoints without the burden of managing the computational environment. Delivered as a managed service, Tinker executes training jobs on Thinking Machines’ proprietary GPU infrastructure, alleviating users from the challenges of cluster orchestration and enabling them to focus on building and optimizing their models. This seamless integration of capabilities makes Tinker a vital tool for advancing machine learning research and development.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Apple tvOS
Apple watchOS
Llama 3
Llama 3.1
Llama 3.2
Llama 3.3
Python
Qwen
Qwen3
Xcode

Integrations

Apple tvOS
Apple watchOS
Llama 3
Llama 3.1
Llama 3.2
Llama 3.3
Python
Qwen
Qwen3
Xcode

Pricing Details

No price information available.
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

Apple

Country

United States

Website

developer.apple.com/documentation/coreml

Vendor Details

Company Name

Thinking Machines Lab

Country

United States

Website

thinkingmachines.ai/tinker/

Product Features

Machine Learning

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

Alternatives

Alternatives

Create ML Reviews

Create ML

Apple
LLaMA-Factory Reviews

LLaMA-Factory

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