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What is Tinker?

Tinker is a groundbreaking training API designed specifically for researchers and developers, granting them extensive control over model fine-tuning while alleviating the intricacies associated with infrastructure management. It provides fundamental building blocks that enable users to construct custom training loops, implement various supervision methods, and develop reinforcement learning workflows. At present, Tinker supports LoRA fine-tuning on open-weight models from the LLama and Qwen families, catering to a spectrum of model sizes that range from compact versions to large mixture-of-experts setups. Users have the flexibility to craft Python scripts for data handling, loss function management, and algorithmic execution, while Tinker efficiently manages scheduling, resource allocation, distributed training, and failure recovery independently. The platform empowers users to download model weights at different checkpoints, freeing them from the responsibility of overseeing the computational environment. Offered as a managed service, Tinker runs training jobs on Thinking Machines’ proprietary GPU infrastructure, relieving users of the burdens associated with cluster orchestration and allowing them to concentrate on refining and enhancing their models. This harmonious combination of features positions Tinker as an indispensable resource for propelling advancements in machine learning research and development, ultimately fostering greater innovation within the field.

What is Core ML?

Core ML makes use of a machine learning algorithm tailored to a specific dataset to create a predictive model. This model facilitates predictions based on new incoming data, offering solutions for tasks that would be difficult or unfeasible to program by hand. For example, you could create a model that classifies images or detects specific objects within those images by analyzing their pixel data directly. After the model is developed, it is crucial to integrate it into your application and ensure it can be deployed on users' devices. Your application takes advantage of Core ML APIs and user data to enable predictions while also allowing for the model to be refined or retrained as needed. You can build and train your model using the Create ML application included with Xcode, which formats the models for Core ML, thus facilitating smooth integration into your app. Alternatively, other machine learning libraries can be utilized, and Core ML Tools can be employed to convert these models into the appropriate format for Core ML. Once the model is successfully deployed on a user's device, Core ML supports on-device retraining or fine-tuning, which improves its accuracy and overall performance. This capability not only enhances the model based on real-world feedback but also ensures that it remains relevant and effective in various applications over time. Continuous updates and adjustments can lead to significant advancements in the model's functionality.

Media

Media

Integrations Supported

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

Integrations Supported

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

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
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

Thinking Machines Lab

Company Location

United States

Company Website

thinkingmachines.ai/tinker/

Company Facts

Organization Name

Apple

Company Location

United States

Company Website

developer.apple.com/documentation/coreml

Categories and Features

Machine Learning

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

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