kermt-continue-pretrain

To resume pretraining a biomolecular model from an interrupted checkpoint, this skill automates exact-state resumption by validating the checkpoint file, preparing the corpus, and orchestrating detached distributed training execution.

Yes, continuing pretraining deep learning chemistry models requires a CUDA-capable NVIDIA GPU environment and Docker containerization to ensure hardware-aware optimization and consistent distributed training execution.

Yes, you can continue pretraining a KERMT model on a new chemical dataset using a fresh-schedule continuation, which validates the existing checkpoint, prepares the new training data CSV, and launches the training job.

Automated workflow orchestration handles biomolecular pretraining by automatically scaling batch sizes and training parameters based on detected GPU VRAM and hardware capabilities during the distributed training execution.

Limitations include strict dependencies on specific model architecture compatibility and the requirement for Docker containerization, meaning biomolecular distributed training cannot execute without CUDA-capable NVIDIA GPU environments.