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 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.

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 = batch_size × N).
• VRAM: ≥ 8 GB for the default batch_size 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.