Together AI expands fine-tuning service with more models, live metrics, and finer controls

Together Fine-Tuning has expanded its platform to support a broader range of recent open-weight models, including GLM, Kimi, DeepSeek, Qwen, and Gemma variants, while adding tools intended to improve visibility, control, and cost efficiency throughout fine-tuning workflows. New experiment-tracking features expose live training and validation metrics through the API, CLI, SDK, and dashboard, enabling teams to compare runs and monitor model behavior during training. The release introduces expert-layer LoRA for Mixture-of-Experts models, early stopping based on validation loss, gradient accumulation for larger effective batch sizes, and training-price reductions of 30% to 70% for many models. Data-processing improvements include pre-flight JSONL validation, previews of tokenized and packed training data, per-example sample weights, and configurable sequence packing. Together also highlights Adaption’s use of its infrastructure for automated training and evaluation loops on models up to one trillion parameters, and plans to allow deployment of intermediate LoRA adapters before training completes, initially for GLM-5.3 and later Kimi K3.

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