basetenlabs/tinker-cookbook

basetenlabs/tinker-cookbook

Description: Post-training with Tinker

License: Apache-2.0

Stars: 1

Forks: 0

Open issues: 1

Created: 2026-06-03T01:38:21Z

Pushed: 2026-06-09T23:23:35Z

Default branch: main

Fork: yes

Parent repository: thinking-machines-lab/tinker-cookbook

Archived: no

README: 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

Here we introduce a few Tinker primitives — the basic components to fine-tune LLMs (see the quickstart guide for more details):

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

Tutorials

New to Tinker? The [tutorials/](tutorials/) directory contains 20+ progressive marimo notebooks that walk through core concepts — rendering, loss functions, completers, weight management — and advanced topics such as custom RL environments, DPO, RLHF, and weight export. Run any tutorial with marimo edit tutorials/101_hello_tinker.py. See the [tutorials README](tutorials/README.md) for the full list, or browse rendered versions on the Tinker docs site.

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](tinker_cookbook/recipes/sl_basic.py) and [tinker_cookbook/recipes/rl_basic.py](tinker_cookbook/recipes/rl_basic.py) contain minimal examples to configure supervised learning and reinforcement learning.

We also include more complete examples in the [tinker_cookbook/recipes/](tinker_cookbook/recipes/) folder:

• [Chat SFT](tinker_cookbook/recipes/chat_sl/): supervised fine-tuning on conversational datasets (e.g., Tulu3).
• [Math RL](tinker_cookbook/recipes/math_rl/): reinforcement learning for mathematical reasoning with verifiable rewards.
• [Code RL](tinker_cookbook/recipes/code_rl/): RL on competitive programming with sandboxed code execution (DeepCoder replication).
• [Preference learning](tinker_cookbook/recipes/preference/): DPO and a three-stage RLHF pipeline (SFT, reward model, RL).
• [Distillation](tinker_cookbook/recipes/distillation/): on-policy and off-policy knowledge distillation with single- and multi-teacher configurations.
• [Tool use](tinker_cookbook/recipes/search_tool/): RL for retrieval-augmented generation (Search-R1 replication).
• [Multi-agent](tinker_cookbook/recipes/multiplayer_rl/): multi-agent RL with self-play and cross-play.

The [recipes README](tinker_cookbook/recipes/README.md) covers all available recipes, including Harbor RL, rubric-based grading, VLM classification, and SDFT. Each recipe includes a README.md with implementation details, launch commands, and expected results.

Evaluation (experimental)

Tinker Cookbook includes a [benchmark framework](tinker_cookbook/eval/) for evaluating trained models:

from tinker_cookbook.eval.benchmarks import run_benchmarks, BenchmarkConfig

results = await run_benchmarks(
["gsm8k", "mmlu_pro", "ifeval"],
sampling_client, renderer,
BenchmarkConfig(save_dir="evals/step500"),
)

The framework currently supports 12 benchmarks (GSM8K, MATH-500, MMLU-Pro, MMLU-Redux, GPQA, IFEval, MBPP, C-Eval, SuperGPQA, IFBench, AIME 2025, AIME 2026) with verified scores against published results, plus experimental benchmarks such as LiveCodeBench, Terminal Bench, and SWE-bench. Benchmarks can also serve as inline training evaluators via BenchmarkEvaluator.

Note: Benchmark scores are sensitive to evaluation configuration — system prompts, max_tokens, temperature, and timeout settings can shift results significantly. We document our exact settings alongside all reported scores. This framework is under active development; feedback and contributions are welcome. See the [eval README](tinker_cookbook/eval/README.md) for verified scores, configuration details, and instructions for adding new benchmarks.

Documentation

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

Utilities

Tinker Cookbook also provides reusable building blocks:

• [renderers](tinker_cookbook/renderers/) — bidirectional conversion between token sequences and structured chat messages
• [hyperparam_utils](tinker_cookbook/hyperparam_utils.py) — learning rate and hyperparameter scaling for LoRA training
• [eval](tinker_cookbook/eval/) — benchmark framework and inline training evaluators (see [Evaluation](#evaluation-experimental) above)

Claude Code Skills

Tinker…