Developers working with large language models (LLMs) frequently encounter significant challenges in fine-tuning these models. Traditional approaches require substantial computing resources, meticulous scheduling, and robust handling of training failures.
Fine-tuning large models often involves distributed training across multiple GPUs or clusters, which adds operational complexity. Researchers must monitor training progress, handle potential interruptions, and carefully manage data pipelines. These demands can slow research progress, particularly when experimenting with different datasets or algorithms.
Fine-tuning tools aim to simplify this process by providing managed infrastructure while giving developers programmatic control over the model’s behavior. Tools of this kind allow researchers to test hypotheses, optimize datasets, and adjust algorithms without manually configuring compute clusters or handling failures.
Thinking Machines Lab, an artificial intelligence startup founded by former OpenAI CTO Mira Murati, has introduced Tinker, its first product designed specifically for model fine-tuning. The company recently raised $2 billion in seed funding, providing it with substantial resources to develop and expand its AI tools.
Tinker Launch: How the Platform Works
Tinker is an API-based platform that supports fine-tuning of multiple open-weight models, including large mixture-of-experts models. Supported models include Alibaba’s Qwen-235B-A22B and the Meta Llama family. The platform is currently in private beta, with early access granted to researchers at Princeton, Stanford, Berkeley, and Redwood Research.
The platform allows developers to write training loops in Python on local machines while offloading computations to Thinking Machines’ distributed GPU infrastructure. The platform manages resource allocation, scheduling, and failure recovery.
John Schulman, co-founder of OpenAI and currently at Thinking Machines, said, “Tinker provides an abstraction layer that is the right one for post-training R&D.”
Tinker incorporates the Low-Rank Adaptation (LoRA) method. LoRA reduces computational demands by updating small add-on weights rather than the entire model, making fine-tuning more efficient. The platform provides low-level API primitives such as forward_backward and sample, enabling developers to experiment with post-training techniques.
The Tinker Cookbook is an open-source library that complements the API. It implements a range of modern fine-tuning methods, allowing researchers to adapt models efficiently without building every method from scratch. By combining the API and Cookbook, Tinker offers a structured and reproducible framework for fine-tuning that works with different model architectures and sizes.
Early adopters of Tinker have applied the platform to diverse research areas. Princeton and Stanford researchers have used the tool to fine-tune models for mathematical theorem proving. Berkeley teams have applied it to chemistry-related reasoning tasks.
Redwood Research has utilized Tinker for multi-agent experiments. These applications demonstrate the platform’s flexibility and relevance across academic and experimental research domains.
Implementation and Current Use
Tinker allows users to retain control over algorithms and datasets while delegating intensive computations to Thinking Machines’ infrastructure. Andrej Karpathy, founder of Eureka Labs, wrote in a public X (formerly Twitter) post,”
Mira Murati, CEO of Thinking Machines, emphasized, “Our goal with Tinker is to give researchers and developers the ability to experiment without worrying about the infrastructure. They can explore model behaviors, test hypotheses, and refine data efficiently.”
By supporting multiple model architectures and providing access to distributed GPU infrastructure, Tinker enables researchers to run large-scale fine-tuning experiments under controlled conditions.
