Together AI expands fine-tuning service with tool calling, reasoning, and vision support

What this post added

This post introduces native support for tool call, reasoning, and vision-language model (VLM) fine-tuning within the Together Fine-Tuning Platform. For tool calls, it enables end-to-end fine-tuning and inference using OpenAI-compatible schemas, with validation of tool_calls entries against declared tools. Reasoning fine-tuning allows training directly on thinking traces using a 'reasoning' or 'reasoning_content' field in assistant messages. VLM fine-tuning supports inline vision training data with base64 encoded images and allows for freezing the vision encoder or joint training. The training stack has been upgraded to handle models up to 1T parameters more efficiently, achieving up to 6x higher throughput for models like Kimi-K2, and supports datasets up to 100GB. Custom CUDA kernels for loss computation and elimination of GPU-to-CPU synchronization points have been integrated to accelerate training, particularly for mixture-of-experts architectures. The platform now provides job cost estimations before training and dynamic ETAs during training.