Tinker Cookbook

We provide two libraries for the broader community to customize their language models: tinker and tinker-cookbook.

• tinker is a training SDK for researchers and developers to fine-tune language models. You send API requests to us and we handle the complexities of distributed training.
• tinker-cookbook includes realistic examples of fine-tuning language models. It builds on the Tinker API and provides common abstractions to fine-tune language models.

Installation

1. Sign up for Tinker here.
2. Once you have access, create an API key from the console and export it as environment variable TINKER_API_KEY.
3. Install tinker-cookbook (includes the tinker SDK as a dependency):

   # Latest stable release from PyPI
   uv pip install tinker-cookbook
   
   # Or install the nightly build
   uv pip install 'tinker-cookbook @ git+https://github.com/thinking-machines-lab/tinker-cookbook.git@nightly'

Tinker

Refer to the docs to start from basics. Here we introduce a few Tinker primitives - the basic components to fine-tune LLMs:

import tinker
service_client = tinker.ServiceClient()
training_client = service_client.create_lora_training_client(
  base_model="meta-llama/Llama-3.2-1B", rank=32,
)
training_client.forward_backward(...)
training_client.optim_step(...)
training_client.save_state(...)
training_client.load_state(...)

sampling_client = training_client.save_weights_and_get_sampling_client()
sampling_client.sample(...)

See tinker_cookbook/recipes/sl_loop.py and tinker_cookbook/recipes/rl_loop.py for minimal examples of using these primitives to fine-tune LLMs.

To download the weights of any model:

rest_client = service_client.create_rest_client()
future = rest_client.get_checkpoint_archive_url_from_tinker_path(sampling_client.model_path)
with open(f"model-checkpoint.tar.gz", "wb") as f:
    f.write(future.result())

Tinker Cookbook

Besides these primitives, we also offer Tinker Cookbook (a.k.a. this repo), a library of a wide range of abstractions to help you customize training environments. tinker_cookbook/recipes/sl_basic.py and tinker_cookbook/recipes/rl_basic.py contain minimal examples to configure supervised learning and reinforcement learning.

We also include a wide range of more sophisticated examples in the tinker_cookbook/recipes/ folder:

1. Chat supervised learning: supervised fine-tuning on conversational datasets like Tulu3.
2. Math reasoning: improve LLM reasoning capability by rewarding it for answering math questions correctly.
3. Preference learning: showcase a three-stage RLHF pipeline: 1) supervised fine-tuning, 2) learning a reward model, 3) RL against the reward model.
4. Tool use: train LLMs to better use retrieval tools to answer questions more accurately.
5. Prompt distillation: internalize long and complex instructions into LLMs.
6. Multi-Agent: optimize LLMs to play against another LLM or themselves.

These examples are located in each subfolder, and their README.md files will walk you through the key implementation details, the commands to run them, and the expected performance.

Documentation

For the full Tinker documentation, visit tinker-docs.thinkingmachines.ai.

Import our utilities

Tinker cookbook includes several utilities. Here's a quick overview:

• renderers converts tokens from/to structured chat message objects
• hyperparam_utils helps calculate hyperparameters suitable for LoRAs
• evaluation provides abstractions for evaluating Tinker models and inspect_evaluation shows how to integrate with InspectAI to make evaluating on standard benchmarks easy.

Claude Code Skills

Tinker Cookbook ships with Claude Code skills that teach Claude how to use the Tinker API. Install them so Claude can help you write training code in any project:

/plugin marketplace add thinking-machines-lab/tinker-cookbook

Then install the tinker plugin from the Discover tab (/plugin → Discover). Once installed, the following skills are available:

Command	What it does
/tinker:core	Getting started — installation, models, SDK basics, hyperparameters
/tinker:sft	Supervised fine-tuning, datasets, renderers, distillation
/tinker:rl	Reinforcement learning — GRPO, custom environments, multi-turn
/tinker:preferences	DPO and RLHF pipelines
/tinker:ops	Checkpoints, weight export, logging, evaluation
/tinker:debug	Diagnose slow training, hangs, output mismatches, errors
/tinker:dev	Contributing to this repo — tests, CI, recipes

Skills also trigger automatically based on context — ask Claude to "set up SFT training" and it will load the right skill without a slash command. Skills update automatically when the repo is updated.

Development Setup

uv sync --extra dev
pre-commit install

This installs dev dependencies and registers pre-commit hooks that run ruff formatting and linting on every commit. CI enforces these checks on all pull requests.

Contributing

This project is built in the spirit of open science and collaborative development. We believe that the best tools emerge through community involvement and shared learning.

We welcome PR contributions after our private beta is over. If you have any feedback, please email us at tinker@thinkingmachines.ai.

Citation

If you use Tinker for your research, please cite it as:

Thinking Machines Lab, 2025. Tinker. https://thinkingmachines.ai/tinker/.

Or use this BibTeX citation:

@misc{tml2025tinker,
  author = {Thinking Machines Lab},
  title = {Tinker},
  year = {2025},
  url = {https://thinkingmachines.ai/tinker/},
}