Agent skill · nvidia
kermt-finetune
Finetune a pretrained KERMT encoder on a labeled CSV. Validate the checkpoint and data, prepare features, and run containerized training. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Write model bundles, prepared data, logs, and trained models to user-selected host directories.
What it needs
About 8k tokens when loaded.
What this skill does
kermt-finetune Finetune a pretrained KERMT encoder on a user-supplied labeled CSV. The skill is the workflow orchestrator: validate ckpt, validate data, prepare data, launch the runner detached, return a run directory + container name. Skill and runtime paths Set SKILLDIR to the absolute path of this installed skill directory. Export KERMTREPO 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/releasedmodel.json for the Hugging Face repository, pinned revision, and filenames. The bundled scripts/fetchreleasedmodel.py downloads the model bundle over HTTPS into the host directory the user selects. Public models work without credentials; if HFTOKEN is set, the container helper forwards it for Hugging Face authentication. Prepared data, logs, and workflow results go into the chosen run directory. Hardware requirements GPUs: 1 by default (single-GPU); pass --gpus 0 (or whichever id) to select one. For faster training on a multi-GPU host, pass --num-gpus N (N>1) to run data-parallel DDP across N GPUs — --batch-size is then per-GPU (effective global batch = batchsize × N). VRAM: ≥ 8 GB for the default batchsize 32 configuration. Lower VRAM works at smaller batch sizes — pass --batch-size N to override. Disk: a few GB per run (checkpoint + features + logs). Driver / CUDA: any host supporting CUDA 12.6 (the kermt image base). kermt-setup validates this up-front. Inputs Required: --csv <path> — labeled CSV. First column is smiles; every other column is a target. Checkpoint (optional — defaults to the released model if omitted): --ckpt <path> — input pretrain checkpoint (groverbase / cmim / hybrid). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/bionemo-kermt-finetune