开发者狂喜：Thinking Machines发布首款产品Tinker，后训练麻烦全给包了

Core Insights - Tinker, the first product launched by Thinking Machines, is an API designed to simplify the fine-tuning of language models for developers and researchers, allowing them to focus on training data and algorithms while Tinker manages infrastructure-related tasks [2][4][16]. Product Features - Tinker supports various advanced models, including Qwen-235B-A22B, and allows users to switch from small to large models with ease, akin to changing a string in Python code [6][8]. - The API provides low-level primitives such as forward_backward and sample, which are essential for most common post-training methods. An open-source library, Tinker Cookbook, is also available to offer modern implementations of post-training methods [9][11]. Use Cases and Adoption - Teams from prestigious institutions like Princeton, Stanford, and UC Berkeley are already utilizing Tinker, demonstrating its versatility in supporting both supervised fine-tuning and experimental reinforcement learning pipelines [13]. - The Goedel team at Princeton achieved comparable performance to full-parameter models using only 20% of the data, while Stanford's chemistry group improved accuracy from 15% to 50% in a specific task using Tinker [14]. Market Position and Future Outlook - Tinker aims to democratize access to fine-tuning capabilities, potentially leading to more diverse product innovations in the AI space [16]. - The initial phase of Tinker will be free, with a usage-based pricing model to be introduced in the coming weeks [15].