nemotron-3-ultra-text2sql-lora

Text2SQL LoRA fine-tuning on SLURM runs by collecting cluster configurations, submitting multi-node jobs for data preparation, checkpoint conversion, and packed LoRA training, then verifying job completion and output validation.

Packed LoRA fine-tuning for the 550B Mamba-Transformer MoE applies low-rank adaptation using packed sequences, requiring distributed checkpoint conversion into Megatron-Bridge format before launching training jobs on a configured SLURM cluster.

Yes, fine-tuning Nemotron models with LoRA requires a shared filesystem for workspace paths, container access, and a configured Hugging Face token placement to safely launch and verify distributed cluster jobs.

Converting Hugging Face checkpoints to Megatron-Bridge format involves launching multi-node SLURM jobs that transform the checkpoint structure, enabling subsequent packed-sequence LoRA fine-tuning on the target cluster environment.

Distributed Text2SQL fine-tuning jobs require SLURM account details, partitions, QOS settings, GPU layout, workspace paths, container image access, and Hugging Face token placement to safely launch and verify execution.

BIRD Text2SQL dataset preparation builds training data from no-reasoning and reasoning splits by applying the Ultra chat template, generating JSONL files needed for the subsequent packed LoRA fine-tuning workflow.