Thinking Machines首款产品重大更新：K2 Thinking、Qwen3-VL都可以微调了

Core Insights - The article discusses the launch of Tinker, an API developed by Thinking Machines Lab, aimed at simplifying the fine-tuning of language models for developers and researchers [1][4] - Tinker has removed its candidate list, allowing all users to access the platform directly, which marks a significant shift in accessibility for AI model training [1][4] - The article highlights three major updates to Tinker: enhanced reasoning capabilities, a new inference interface compatible with OpenAI API, and the introduction of visual input support through new models [1][4] Group 1: Tinker Overview - Tinker allows developers to focus solely on training data and algorithms, while it manages infrastructure aspects like scheduling and resource management, significantly lowering the barrier to entry for model training [4] - The platform now supports fine-tuning of the Kimi K2 model, which has a trillion parameters, previously accessible only to top-tier labs [4] - Tinker’s visual input capabilities enable users to handle images and visual content in various applications, further broadening its usability [1][4] Group 2: Model Performance and Comparisons - Tinker has been tested with the Qwen3-VL-235B-A22B model on several image classification benchmarks, including Caltech-101, Stanford Cars, Oxford Flowers, and Oxford Pets [4][5] - The performance of Qwen3-VL-235B-A22B was compared to DINOv2, a self-supervised visual transformer, showing superior results in small sample scenarios due to its larger model size and integrated language knowledge [7] - The ability of Qwen3-VL to combine language and visual understanding allows for easier adaptation to various visual tasks beyond image classification [7]