sop-cr-finetuning

To fine-tune a vision-language model for SOP monitoring, launch Cosmos-Reason2 training on augmented video question-answer datasets via the local API, tracking progress and GPU anomalies until completion. The workflow handles job execution and produces deployable artifacts.

GPU pre-flight validation for VLM fine-tuning checks service health, GPU availability, and conflicting training jobs before starting. This ensures the environment is ready and prevents resource contention during the Cosmos-Reason2 training process.

Yes, Docker Compose services are required to run Cosmos-Reason2 training for SOP monitoring. The workflow leverages the local fine-tuning API and Docker Compose to orchestrate training jobs and monitor GPU usage effectively.

To merge LoRA adapters with a base model after VLM fine-tuning, the checkpoint preparation process detects adapters, normalizes configuration, and merges them with the base model. It verifies inference-ready artifacts while preserving the original adapter separately.

During vision-language model training monitoring, the system detects out-of-memory events, NaN losses, and abnormal GPU usage. It tracks status, progress, and loss metrics to ensure the SOP monitoring fine-tuning job runs without silent failures or hardware issues.

To generate a training report for a VLM fine-tuning job, the workflow collects final metrics and anomaly summaries into a documented report. This report is produced for completed, failed, or cancelled jobs to provide fully traceable training results.