# Tinker: a training API for researchers and developers
Tinker lets you focus on what matters in LLM post-training — 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 on** | **You write** | **We handle** |
| --- | --- | --- |
| **Datasets and RL environments**
Your custom training data | **Simple Python script**
Runs on your CPU | **Efficient distributed training of large models**
Qwen3.5-397B, Kimi K2.6 |
| **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 ranging from 1B to 1T+ parameters, including both dense and mixture-of-experts architectures. See [Models & Pricing](https://tinker-docs.thinkingmachines.ai/tinker/models/) for the full list.
- Tinker supports vision-language models (VLMs) like Qwen3.6-35B-A3B for image understanding tasks. See the [Rendering tutorial](https://tinker-docs.thinkingmachines.ai/tutorials/core-concepts/rendering/) for details.
- 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](/content/blog/lora/index.html)).
- 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
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