# Kermt Embed

Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint.

- Repository: https://github.com/NVIDIA/skills (3812 stars)
- Category: AI and agents
- 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-embed -g -a claude-code -y`
Codex: `npx skills add NVIDIA/skills --skill kermt-embed -g -a codex -y`
Cursor: `npx skills add NVIDIA/skills --skill kermt-embed -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA/skills --skill kermt-embed -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NVIDIA/skills`

## Description

Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized embedding extraction and write model bundles, per-readout .npy embeddings, canonical SMILES, and validity arrays to user-selected host directories.

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

# kermt-embed

Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint.
The skill is the workflow orchestrator: validate ckpt, validate CSV, clean
SMILES, launch the runner blocking, return the per-readout `.npy` files.

## 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 (single-GPU).
- **VRAM**: ≥ 4 GB for the default `batch_size 64`.
- **Disk**: depends on output size, roughly a few MB per 1k molecules at
  `hidden 800` per readout, so ~10, 20 MB per 1k molecules across the 4
  readouts. Plus a small `canonical_smiles.npy` + `validity.npy` per run.
- **Driver / CUDA**: any host supporting CUDA 12.6.

## Inputs

Required:

- `--csv <path>`, SMILES CSV. First column is `smiles`; other columns
  are ignored (no targets needed).

Checkpoint (optional, defaults to the released model if omitted):

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