Agent skill · nvidia
kermt-embed
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.
What it needs
About 4k tokens when loaded.
What this skill does
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 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 (single-GPU). VRAM: ≥ 4 GB for the default batchsize 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 canonicalsmiles.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): --ckpt <path> — any encoder-bearing checkpoint. Groverbase, cmim, hybrid, and finetuned ckpts are all accepted. The validator only refuses ckpts with no encoder. If omitted, the skill offers to download the released pretrained hybrid model nvidia/NV-KERMT-70M-v2 and embed with it — see "Resolve & validate the checkpoint" (workflow step 3). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/bionemo-kermt-embed