Cortex Training models¶

Cortex Training supports a curated set of third-party, open models. Use the canonical model ID (for example, Qwen/Qwen3-8B) as the model_name when you create a job. Copy a model ID directly from any card below.

Each card shows whether inference and training are supported. For supported training models, the card summarizes the maximum context length for LoRA (low-rank adaptation) and full-parameter training.

Select View recommended configurations on a card for a copyable Python starting configuration. Select supervised fine-tuning (SFT) or reinforcement learning (RL), then use the Use LoRA checkbox to switch between the complete recommended LoRA and full-parameter profiles. For models with a long-context profile, use the Enable long sequence checkbox to switch from the standard sequence length with sequence parallelism set to 1 to the maximum sequence length with sequence parallelism set to 8. The profiles can differ in job topology and distributed training settings, not only in the PEFT configuration. Tune batch sizes and optimization parameters for your workload.

Available for training¶

These open weights models support fine-tuning. They also support inference.

Inference only¶

These models support inference (sampling) but not fine-tuning.