A training API for researchers and developers.

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January 30, 2026

Thinking Machines Tinker

United States Thinking Machines Lab Model training

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A training API for researchers and developers.

Model Fine-tuning AI Training Infrastructure Management Distributed Training Forward Backward Pass Weight Updatingfrom $0.12

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Overview

Tinker, by Thinking Machines Lab, is a powerful training API designed specifically for researchers and developers working with AI models. It simplifies the complexities of model training and fine-tuning by handling the infrastructure aspects.

Tinker provides users with complete control over their model's training and fine-tuning processes. This is achieved through four primary functions: forward_backward for a forward and backward pass, optim_step for updating weights based on the accumulated gradient, sample for generating tokens for interaction, evaluation, or RL actions, and save_state for saving training progress.

Tinker further supports a wide range of open-source models, accommodating various needs and requirements of its users. To enhance efficiency, it utilizes LoRA, a method focused on training a small add-on instead of modifying all the original weights.

This streamlined approach matches the learning performance of full fine-tuning while offering more flexibility and requiring less compute. Tinker manages scheduling, tuning, resource management, and infrastructure reliability, which allows users to concentrate solely on their data and algorithms.

This also includes distributed training on powerful GPU clusters for effective utilization. No need for users to worry about hardware or infrastructure management.

Notably, Tinker maintains a strict privacy policy to ensure user data is exclusively used for fine-tuning their own models.

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Initial release

October 1, 2025

Initial release of Thinking Machines Tinker.

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Organization Thinking Machines Lab 🇺🇸 United States

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United States Thinking Machines Lab Model training

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Pros and Cons

Pros

Designed for researchers, developers

Simplifies model training, fine-tuning

Complete control over training

Supports forward and backward pass

Optim_step for weight updates

Generates tokens for interaction

Evaluation and RL actions

Save_state for training progress

Supports various open-source models

Utilizes LoRA method

Less compute requirement

Efficient scheduling and tuning

Handles resource management

Infrastructure reliability

Distributed training on GPU clusters

Strict privacy policy

Concentration on data, algorithms

No hardware, infrastructure management

Fine-tunes small add-ons

More learning flexibility

Time and compute efficiency

Algorithm focusing

Efficient model fine-tuning

Training state saving

Open-Source Model Support

Efficient utilization of resources

Strict Privacy Policy

No infrastructure management needed

Efficiency in model training

Supports a wide range of models

No need for hardware handling

Concrete privacy for user data

Effective resource management

Detailed model training process

Supported Models List

The tool handles infrastructure complexities

Allows user control

Reduces engineering overhead

Supports distributed training

Uses GPU clusters for efficiency

simplified gradient computation

Efficiently handles weights updating

Supports token generation

Optimized for less computational load

User data strictly for fine-tuning

Chunked computations

Handles infrastructure reliability

Concentrates on datasets and algorithms

Quick iteration of models

No hardware fears

Security for user data

View 46 more pros

Cons

Limited to open-source models

Doesn't modify original weights

Strict privacy may limit functionality

No infrastructure management flexibility

Dependent on LoRA method

No native GUI

Pricing ties to compute usage

Limited model selection

No explicit multi-platform support

No information on offline capabilities

View 5 more cons

Q&A

What is Tinker by Thinking Machines Lab?

Tinker is a robust training Application Programming Interface (API) developed by Thinking Machines Lab, dedicated to helping researchers and developers work with Artificial Intelligence (AI) models. It handles complex aspects of model training and fine-tuning, thus enabling users to focus on their datasets and algorithms.

What are the four primary functions of Tinker?

Tinker encompasses four primary functions which are 'forward_backward', 'optim_step', 'sample', and 'save_state'. 'forward_backward' performs a forward and backward pass, accumulating the gradient. 'optim_step' updates model weights based on the accumulated gradient. 'sample' is used to generate tokens for interaction, evaluation, or RL actions. Lastly, 'save_state' saves the training progress for resumption.

What range of open-source models does Tinker support?

Tinker offers support for a diverse array of open-source models. Some of the models include QWEN, Dense, MoE, LLAMA, GPT-OSS, DEEPSEEK, and MOONSHOT among others. This support ranges from compact models like Llama-3.2-1B to large models like Qwen3-235B-A22B-Instruct.

How does Tinker utilize LoRA?

Tinker employs LoRA to enhance efficiency during the training process. LoRA stands for Low Rank Adaptation, a unique approach where a small add-on model is trained, rather than modifying the original model weights. It helps to reduce the compute requirements, increase the flexibility of training while matching the learning performance of full fine-tuning.

What aspects of model training does Tinker handle?

Tinker handles facets of model training including scheduling, tuning, resource management, and infrastructure reliability. These elements allow users to focus exclusively on their datasets and algorithms.

What is Tinker's approach to infrastructure management?

Tinker's approach to infrastructure management is to handle the complexities so that the users can focus on the data and algorithms. It manages infrastructure aspects such as scheduling, tuning, resource management, and ensures reliability. It also handles distributed training on potent GPU clusters for efficient resource utilization.

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How does Tinker maintain user data privacy?

Tinker maintains a rigid privacy policy ensuring that user data is solely used for fine-tuning their own models. They do not use client's data to train their models.

How is distributed training done on Tinker?

In Tinker, distributed training is managed behind the scenes. It is carried out on potent GPU clusters for efficient resource utilization, relieving users from the need to worry about hardware or infrastructure management.

How does Tinker facilitate model training and fine-tuning?

Tinker facilitates model training and fine-tuning by streamlining complex aspects and handling infrastructure. It enables full user control over every aspect of model training and fine-tuning, offers four principal functions for forward and backward pass, updating weights based on gradient, generating tokens, and saving training progress. Tinker also uses LoRA for efficient fine-tuning and supports wide-ranging open-source models for different user needs.

What benefits does LoRA provide when used by Tinker?

When used by Tinker, LoRA offers several benefits. It helps streamline the fine-tuning process by training a small add-on instead of modifying all original weights. This approach reduces compute requirements and boosts flexibility. Moreover, it matches the learning performance of full fine-tuning, thus striking a balance between efficiency, performance, and flexibility.

Can users save their training progress using Tinker?

Yes, with the help of the 'save_state' function, Tinker enables users to save their training progress at any point, facilitating smooth continuity of the process.

What does 'forward_backward' function do in Tinker?

The 'forward_backward' function in Tinker is utilized to perform a forward and a backward pass, thereby accumulating the gradient for the machine learning model.

How does Tinker's 'optim_step' function work?

Tinker's 'optim_step' function works by updating the weights of the models based on the accumulated gradient gathered from the 'forward_backward' function.

What is the purpose of 'sample' function in Tinker?

The 'sample' function in Tinker is designed for generating tokens for interaction, evaluation, or Reinforcement Learning (RL) actions.

What open-source models are featured on Tinker's supported models' list?

The supported model list on Tinker includes an assortment of open-source models. Some of the models highlighted are: QWEN, Dense, MoE, LLAMA, GPT-OSS, DEEPSEEK, and MOONSHOT.

What kind of control does Tinker provide to the users regarding model training?

Tinker provides complete control to users over different aspects of model training and fine-tuning. This includes a high level of control over key functions such as forward and backward pass, updating weights based on gradient, generating tokens, and saving training progress.

What is the role of GPU clusters in Tinker?

GPU clusters in Tinker play a pivotal role in enabling efficient resource utilization during the training process. Behind the scenes, Tinker manages distributed training on these powerful GPU clusters.

How does Tinker help in streamlining the process of AI model training?

Tinker assists in streamlining the process of AI model training by handling intricate aspects related to model infrastructure - tuning, scheduling, resource management. It permits end-to-end control over model training, offers tools for forward and backward pass, updating weights, generating tokens, saving training progress, and efficient use of the LoRA method.

How does Tinker reduce the computational resources required by fine-tuning?

Tinker reduces the computational resources needed for fine-tuning by utilizing the LoRA method. It fine-tunes the models by training a small add-on instead of changing all original weights, which leads to less computational burden.

How does Tinker manage to balance performance and flexibility in model training?

Tinker manages to balance performance and flexibility in model training by using the LoRA method. This approach modifies only a small add-on model instead of the entire original model weights, allowing increased flexibility, reduced computational requirements, and matching the learning performance of full fine-tuning.

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