name: kermt-continue-pretrain description: Continue KERMT pretraining on a custom SMILES corpus with a grover_base, cmim, or hybrid checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized training and write model bundles, prepared data, logs, and checkpoints to user-selected host directories. license: Apache-2.0 compatibility: Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron. metadata: owner: [email protected] classification: workflow-skill risk_tier: skill
Line/token budget: this file is targeted at ~250 lines / ~3000 tokens —
well within the 500-line / 5000-token cap. Long examples live in
/skill/scripts/run_pretrain_local.py's docstring.
kermt-continue-pretrain
Continue pretraining from a user-supplied KERMT checkpoint (grover_base / cmim / hybrid). The skill is the workflow orchestrator: it validates inputs, prepares the corpus, launches the runner, and returns a run directory.
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 (read-only). Commands inside
the container use ; defaults are bundled in .
See for checkpoint bundle requirements.