
Tinker
Tinker is a flexible API for fine-tuning language models that empowers researchers and developers to control algorithms and data while automating complex distributed training infrastructure management.
https://thinkingmachines.ai/tinker?ref=producthunt

Product Information
Updated:Oct 11, 2025
What is Tinker
Tinker is the first product launched by Thinking Machines Lab, an AI startup founded by former OpenAI CTO Mira Murati. It's designed as a managed service that provides a Python-based API for fine-tuning large language models (LLMs). The platform bridges the gap between advanced AI capabilities and practical implementation by making model customization more accessible to researchers, businesses, and developers without requiring them to manage complex infrastructure.
Key Features of Tinker
Tinker is a flexible API developed by Thinking Machines Lab that enables researchers and developers to fine-tune large language models efficiently. It handles complex infrastructure management, distributed training, and resource allocation while giving users full control over algorithms and data. The service uses LoRA technology for efficient fine-tuning and provides simple Python-based interfaces for training, optimization, and model sampling.
Infrastructure Management: Automatically handles scheduling, resource allocation, and failure recovery on distributed GPU clusters, allowing users to focus on their core work
LoRA-based Fine-tuning: Uses LoRA technology to train small adaptors instead of modifying all model weights, providing efficient fine-tuning while maintaining performance
Simple API Interface: Offers four core functions (forward_backward, optim_step, sample, save_state) for controlling model training and fine-tuning through clean Python code
Model Flexibility: Supports various open-source models from compact ones like Llama-3.2-1B to large mixture-of-experts models like Qwen3-235B-A22B
Use Cases of Tinker
Academic Research: Enables university researchers to conduct experiments and training without dealing with infrastructure complexities
Custom Model Development: Allows businesses to create specialized AI models tailored to their specific industry needs
Reinforcement Learning: Supports implementation of RL-based fine-tuning for improving model behavior through feedback
Model Experimentation: Enables developers and hobbyists to experiment with different training approaches and datasets
Pros
Eliminates need for infrastructure management
Provides full control over training process
Efficient resource utilization through LoRA
Simple and clean API abstraction
Cons
Currently in private beta with limited access
Pricing structure not yet fully established
Limited to supported open-source models
How to Use Tinker
Sign up for access: Join the Tinker waitlist through their website to get access to the private beta
Get API key: Once approved, create an API key from the Tinker console and export it as environment variable TINKER_API_KEY
Initialize ServiceInterface: Create a ServiceInterface object to access available base models that can be fine-tuned
Create TrainingClient: Initialize the main TrainingClient object which corresponds to the model you want to fine-tune
Prepare training data: Prepare your supervised learning dataset or reinforcement learning environments
Write training loop: Use the four main API functions: forward_backward (for gradients), optim_step (weight updates), sample (generate outputs), and save_state (save progress)
Run training: Execute your training code - Tinker will automatically handle the distributed training on their GPU infrastructure
Download weights: Download the fine-tuned model weights during or after training to use with your preferred inference provider
Tinker FAQs
Tinker is a flexible API for fine-tuning language models, designed for researchers and developers who want control over their data and algorithms without managing infrastructure. It's a managed service that runs on internal clusters and handles training infrastructure complexities.
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