thinking-machines-lab/tinker-cookbook

Health v2 · maintenance only

Adoption not part of the score

4058 stars · 522 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Tinker Cookbook is a Python library of realistic examples and abstractions for post-training (fine-tuning) language models via the Tinker API from Thinking Machines Lab. It provides recipes for supervised fine-tuning, RL, and multimodal training while the Tinker service handles distributed training infrastructure.

Use cases

• fine-tune a language model on custom data
• run supervised fine-tuning recipes on an LLM
• train a model with reinforcement learning post-training
• customize a language model without managing GPU infrastructure
• fine-tune a multimodal model with image and audio inputs
• experiment with post-training algorithms via an API

When to choose

• you want to fine-tune LLMs through a managed API instead of running your own distributed training
• you need ready-made, tested recipes for SFT and RLHF-style post-training
• you want to work with Thinking Machines' Inkling models or Tinker-supported models

When to avoid

• you need fully local, self-hosted training on your own GPUs
• you don't want to depend on a third-party API service with an account and API key
• you need frameworks like Hugging Face TRL or Axolotl for direct control over training hardware

Facets

library · maturity active

llm-training machine-learning sdk agent-framework large-language-models machine-learning deep-learning developer-tools python cloud fine-tuning post-training reinforcement-learning supervised-fine-tuning multimodal api-based-training

Member repositories

For agents

markdown · JSON · MCP: product_card(name="thinking-machines-lab/tinker-cookbook")