kermt-pretrain-scratch

Pretrain a brand-new KERMT model from scratch on a user-provided corpus. Useful when you want to retrain a model on a custom chemistry domain rather than extending one of the released checkpoints. Significantly more expensive than kermt-continue-pretrain, no warm start, so the loss curves need to descend from scratch over many epochs.

Skill and runtime paths

Set SKILL_DIR to the absolute path of this installed skill directory. Export KERMT_REPO as the absolute path to the KERMT checkout used for model execution. The bundled container helper mounts that checkout at /workspace and this skill at /skill (read-only). Commands inside the container use /skill/scripts/; defaults are bundled in config/.

Hardware requirements

Same as kermt-continue-pretrain:

• GPUs: 1, N CUDA-capable. The runner auto-detects via torch.cuda.device_count(); --gpus 0,2 overrides. Single-GPU fallback: --batch_size 32 --save_interval 500. Multi-GPU keeps defaults (--batch_size 256 etc.). Note: --gpus N uses torch.cuda indexing, which can differ from nvidia-smi's display order on multi-GPU hosts (PCI bus vs. CUDA enumeration). To target a specific physical GPU, set CUDA_VISIBLE_DEVICES before invoking, or run python -c "import torch; print([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])" to confirm which device you're picking.

• VRAM: the default --batch-size 256 is sized for A100-class hardware (80 GB VRAM). On smaller GPUs, downscale to avoid OOM:

  GPU class	VRAM	Suggested --batch-size
  L4, T4, V100 16 GB	16, 24 GB	32, 64
  A100 40 GB, L40, A40	40, 48 GB	128
  A100 80 GB, H100, H200	80 GB	256 (default)