Kermt Continue Pretrain
Continue KERMT pretraining on a custom SMILES corpus with a grover_base, cmim, or...
Install
npx skills add NVIDIA/skills --skill kermt-continue-pretrain -g -a claude-code -y- Repository
- NVIDIA/skills3.8k
- Category
- Writing
- Updated
- 5d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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 /skill (read-only). Commands inside
the container use /skill/scripts/; defaults are bundled in config/.
See Released models for checkpoint bundle requirements.
Downloads and local outputs
The optional released-model branch reads config/released_model.json for the
Hugging Face repository, pinned revision, and filenames. The bundled
scripts/fetch_released_model.py downloads the model bundle over HTTPS into
the host directory the user selects. Public models work without credentials;
if HF_TOKEN is set, the container helper forwards it for Hugging Face
authentication. Prepared data, logs, and workflow results go into the chosen
run directory.
