Thinking Machines Launches Tinker: A Low-Level Training API that Abstracts Distributed LLM Fine-Tuning without Hiding the Knobs

Understanding the Target Audience

The primary audience for Tinker includes AI researchers, machine learning engineers, and data scientists who are engaged in developing and fine-tuning large language models (LLMs). These professionals often work in academic institutions, research labs, or tech companies focused on AI applications. Their pain points include:

Need for granular control over model training processes.
Challenges with distributed computing and resource management.
Desire for efficient fine-tuning methods that do not compromise on performance.
Concerns regarding data governance and reproducibility in experiments.

Their goals are to:

Enhance model performance through effective fine-tuning.
Streamline the training process while maintaining control over key parameters.
Utilize shared resources efficiently to reduce costs and time.

Interests include advancements in AI methodologies, practical applications of machine learning, and tools that facilitate experimentation. Communication preferences lean towards technical documentation, peer-reviewed articles, and community discussions in forums and newsletters.

What is Tinker?

Tinker is a Python API developed by Thinking Machines that enables researchers and engineers to write training loops locally while executing them on managed distributed GPU clusters. The platform allows users to maintain full control over data, objectives, and optimization steps while offloading scheduling, fault tolerance, and multi-node orchestration.

Key Features

Open-weights model coverage: Supports fine-tuning families such as Llama and Qwen, including large mixture-of-experts variants.
LoRA-based post-training: Implements Low-Rank Adaptation (LoRA) instead of full fine-tuning, which can match full fine-tuning for many practical workloads.
Portable artifacts: Allows users to download trained adapter weights for use outside of Tinker.

Operational Scope

Tinker is positioned as a managed post-training platform for open-weights models, supporting both small LLMs and large mixture-of-experts systems. The API facilitates easy model switching by simply changing a string identifier and rerunning the process. The underlying architecture leverages Thinking Machines’ internal clusters, enabling efficient resource utilization.

Tinker Cookbook

The Tinker Cookbook is an essential resource that provides reference training loops and post-training recipes. It includes:

Ready-to-use reference loops for supervised learning and reinforcement learning.
Worked examples for RLHF (three-stage SFT → reward modeling → policy RL).
Utilities for LoRA hyperparameter calculation and evaluation integration.

Current User Base

Early adopters of Tinker include research teams from prestigious institutions such as Princeton, Stanford, UC Berkeley, and Redwood Research, focusing on various applications of reinforcement learning and model control tasks.

Conclusion

Tinker offers an open and flexible API that enables users to customize open-weight LLMs through explicit training-loop primitives while managing distributed execution. This approach preserves algorithmic control and lowers barriers for experimentation, making it an attractive option for AI practitioners.

For more technical details and to sign up for the waitlist, visit Thinking Machines Tinker. Organizations seeking wide-scale access can contact tinker@thinkingmachines.ai.

Explore the Tinker GitHub Page for tutorials, codes, and notebooks. Follow us on Twitter and join our community on ML SubReddit. You can also connect with us on Telegram.