LoRA fine-tuning supports rank up to 128, default stays at 64

You can now train low-rank adapters (LoRA) with a rank of up to 128 for the majority of models, up from 64. The default rank for these models stays at 64, so set lora_r to use a higher one.

The model limits response has a new lora_training.default_rank field next to lora_training.max_rank. Run tg fine-tuning model-limits <model> to see both values for a model.

Version 2.36.0 of the Together CLI and Python SDK uses the default rank when you don't set lora_r. Earlier versions, including the 1.x SDK, use the model's maximum rank instead, which is now 128 on these models. Upgrade to 2.36.0 or set lora_r yourself, especially if you continue training from a rank-64 adapter, where the rank has to match.

In the console, the rank field now starts at the model's default rank (64 on most models) instead of 8.

See Supported models for each model's default and maximum rank.