What is Tinker?

Tinker: a training API for researchers and developers

Tinker lets you focus on what matters in LLM fine-tuning – your data and algorithms – while we handle the heavy lifting of distributed training.

You write a simple loop that runs on your CPU-only machine, including the data or environment and the loss function. We figure out how to make the training work on a bunch of GPUs, doing the exact computation you specified, efficiently. To change the model you're working with, you only need to change a single string in your code.

Tinker gives you full control over the training loop and all the algorithmic details. It's not a magic black box that makes fine-tuning "easy". It's a clean abstraction that shields you from the complexity of distributed training while preserving your control.

Here's how the division of responsibilities works in practice:

You focus onYou writeWe handle
📊 Datasets and RL environments
Your custom training data		💻 Simple Python script
Runs on your CPU		⚡ Efficient distributed training of large models
Llama 70B, Qwen 235B
🎯 Training logic
Your loss functions, training loop, and evals		🔧 API calls
forward_backward()
optim_step()
sample()
save_state()		🛡️ Reliability
Hardware failures handled transparently

Features

What the Tinker service currently supports:

  • Tinker lets you fine-tune open-weight models like the Qwen and Llama series, including large mixture-of-experts models like Qwen3-235B-A22B.
  • Tinker implements low-rank adaptation (LoRA) fine-tuning, not full fine-tuning. However, we believe that LoRA gives the same performance as full fine-tuning for many important use cases, especially in RL (see LoRA Without Regret (opens in a new tab)).
  • You can download the weights of your trained model to use outside of Tinker, for example with your inference provider of choice.

A quick look at functionality

Tinker's main functionality is contained in a few key functions:

  • forward_backward: feed in your data and loss function, and we'll compute and accumulate the gradients for you.
  • optim_step: update your model using the accumulated gradients
  • sample: Generate outputs from your trained model
  • other functions for saving and loading weights and optimizer state

What's next?

Some features we expect to support in the future:

  • Image input for applicable models
  • Full fine-tuning
Installation