# Kermt Finetune

Finetune a pretrained KERMT encoder on a labeled CSV.

- Repository: https://github.com/NVIDIA/skills (3812 stars)
- Category: Writing
- License: Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add NVIDIA/skills --skill kermt-finetune -g -a claude-code -y`
Codex: `npx skills add NVIDIA/skills --skill kermt-finetune -g -a codex -y`
Cursor: `npx skills add NVIDIA/skills --skill kermt-finetune -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA/skills --skill kermt-finetune -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NVIDIA/skills`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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](references/released-models.md) 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.

The rest: https://github.com/NVIDIA/skills/blob/HEAD/skills/bionemo-kermt-finetune/SKILL.md