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name: training-and-alignment description: "Fine-tune and align xTuring causal models with supervised training, LoRA/quantized variants, and DPO." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Training and Alignment

Use this sub-skill for xTuring model adaptation work:

• supervised fine-tuning via model.finetune(...)
• preference alignment via model.dpo_finetune(...)
• choosing between full, LoRA, int8, and k-bit training variants
• tuning finetuning config, optimizer, DeepSpeed, and output settings

Route by task

• For standard text or instruction SFT, start with finetuning workflows.
• For preference data and DPO, use DPO workflow.
• For schema errors, quantization limits, DeepSpeed, and optimizer failures, use troubleshooting.
• For a safe local input check that does not launch training, run scripts/check_finetuning_inputs.py.

If the task is only about model loading, registry inspection, or generation settings, route to the inference-focused skill instead.