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name: kermt-finetune 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. license: Apache-2.0 compatibility: Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron. metadata: owner: evax@nvidia.com classification: workflow-skill risk_tier: skill

Line/token budget: targets ~250 lines / ~3000 tokens — within the

500-line / 5000-token cap for skill files.

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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.

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

• --ckpt <path> — input pretrain checkpoint (grover_base / cmim / hybrid). The validator refuses already-finetuned ckpts with a redirect to kermt-infer. If omitted, the skill offers to download the released pretrained hybrid model nvidia/NV-KERMT-70M-v2 and finetune from it — see "Resolve & validate the checkpoint" (workflow step 3).
• --pretrained-release — explicit opt-in to use the released model without the interactive prompt (for non-interactive / agent runs). Mutually exclusive with --ckpt.
• --model-dir <dir> — where to save the downloaded bundle (default $KERMT_REPO/models/NV-KERMT-70M-v2/). An already-complete bundle there is reused, not re-downloaded.

Optional:

• --dataset-type {regression | classification | multiclass} — default regression (from defaults_finetune.json). Drives loss, metric defaults, and head initialization. For classification tasks pass --dataset-type classification.

• --targets COL [COL ...] — explicit target column names. If omitted, the validator auto-detects numeric non-smiles columns and the skill confirms with the user before proceeding.

• --val-csv <path> and --test-csv <path> — user-provided val + test splits. Either pass both or pass neither (the skill auto-splits using the configured --split-type).

• --split-type {random | scaffold_balanced | index_predetermined} — default scaffold_balanced from defaults_finetune.json.

  • random and scaffold_balanced: build the val/test split internally from the train CSV. No --val-csv / --test-csv needed.
  • index_predetermined: requires pre-split CSVs passed via --val-csv + --test-csv (and, separately, per-fold index files — see kermt/util/utils.split_data). Use this when the dataset ships its own canonical split (e.g. tests/data/Biogen_for_grover/scaffold/ balance/<endpoint>/{train,val,test}.csv).

• --metric NAME — mae (regression default), auc (classification default), or any name kermt.util.metrics.get_metric_func accepts.

• --epochs N / --batch-size N / --init-lr F / --max-lr F / --final-lr F / --warmup-epochs F / --weight-decay F / --dropout F / --bond-drop-rate F / --dist-coff F / --early-stop-epoch N / --seed N — training-hyperparameter overrides. Anything not given is filled from config/defaults_finetune.json.

• --ffn-hidden-size N / --ffn-num-layers N — shared FFN trunk dims.

• --ffn-num-task-specific-layers N / --ffn-task-specific-hidden-size H — per-target FFN heads (default 0 = off; useful for heterogeneous multi-target finetunes). Both must be set together when N > 0.

• --ensemble-size N / --num-folds N — multi-model / k-fold CV. Default 1 each.

• --gpus 0 — single GPU id for single-process finetune (default 0). Ignored when --num-gpus > 1.

• --num-gpus N — number of GPUs for data-parallel DDP finetune. Default 1 (single-process, unchanged). N>1 runs main.py finetune with WORLD_SIZE=N (one process per GPU); --batch-size is per-GPU.

• --from-prepare <dir> — skip the prepare step and reuse an existing prepare_data.json in <dir>. Useful when iterating on hyperparameters.

Workflow

Let $KERMT_REPO be the path to your kermt repo checkout, and assume kermt-setup has built kermt:latest. All paths below are on the host; the helper bind-mounts them at known container paths.

1. Pre-flight: ensure container + system probe.

   "$SKILL_DIR/scripts/kermt_container.sh" check_system | python -c "
   import json, sys; d = json.load(sys.stdin)
   if not d['ok']:
       print('System check failed:', d['gaps']); sys.exit(1)
   print(f'OK: {len(d[\"gpus\"])} GPU(s); CUDA via container toolkit')
   "
   

   Refuse to proceed if ok: false.

2. Compute run directory.

   RUN_DIR=$KERMT_REPO/runs/finetune_$(date -u +%Y-%m-%dT%H-%M-%SZ)
   

3. Resolve & validate the checkpoint.

   Resolve — only if --ckpt was omitted. Default to the released pretrained hybrid model nvidia/NV-KERMT-70M-v2:

   • Consent gate. Unless --pretrained-release was passed, ask the user: "No checkpoint given — download the released model nvidia/NV-KERMT-70M-v2 (NVIDIA Open Model License, https://huggingface.co/nvidia/NV-KERMT-70M-v2) and finetune from it? [y/N]". Never download without an explicit yes (or --pretrained-release). If both --ckpt and --pretrained-release are given, abort — they conflict.
   • Save location. Default $KERMT_REPO/models/NV-KERMT-70M-v2/; honor --model-dir <dir> if given. An already-complete bundle is reused.
   • Download (foreground; ~282 MB on first fetch):

     "$SKILL_DIR/scripts/kermt_container.sh" run --model-dir <save-dir> -- \
         "python /skill/scripts/fetch_released_model.py --out /model"
     

     Parse the JSON; abort on ok: false (surface errors). On success set <user-ckpt> = <save-dir>/kermt_contrastive_v2.0.pt.

   Validate the resolved (or user-provided) ckpt:

   "$SKILL_DIR/scripts/kermt_container.sh" run --ckpt <user-ckpt> -- \
       "python /skill/scripts/check_checkpoint.py --mode finetune_init --ckpt /ckpt"
   

   Parse the JSON. Abort on ok: false. The validator rejects already- finetuned ckpts (has_task_ffn: true) with a redirect to kermt-infer.

4. Validate the data.

   "$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> -- \
       "python /skill/scripts/check_data.py --mode finetune --csv /data/<basename> [--targets COL1 COL2 ...]"
   

   If --targets was not given by the user, surface auto_detected_targets from the JSON and ask the user to confirm before continuing. Abort on ok: false.

5. Prepare the data (skip if --from-prepare given).

   Pre-flight: check for sibling val.csv / test.csv. Before invoking prepare_data, inspect the parent directory of <user-csv>. If a canonical-looking sibling val.csv (or val_*.csv — common variants include val_T.csv, val_clean.csv) AND a matching test.csv / test_*.csv exist next to the train CSV, the dataset ships its own pre-defined split. In that case set --split-type index_predetermined AND pass --val-csv / --test-csv — otherwise the configured split_type (default scaffold_balanced) will re-split the train CSV from scratch and silently discard the user's val/test files. When in doubt — or when the sibling files use non-canonical suffixes (_T, _v2, etc.) — surface the situation to the user and ask which they want.

   Quoting target names. If any of the --targets column names contain shell metacharacters (>, &, |, (, ), $, etc.), single-quote each one when passing on the CLI to keep the shell from eating part of the name. Example: --targets 'Log_Caco2_Papp_A>B' 'logD'. The CSV header itself is read directly by the downstream trainer and is unaffected, but the prepare_data.json manifest's targets[] field captures whatever the shell delivers — unquoted metacharacters get truncated there.

   Mount note: kermt_container.sh --data <host-csv> mounts the parent directory of <host-csv> at /data. --val-csv and --test-csv must therefore reference files in that same parent directory. If val/test live in a separate directory (e.g. a sibling splits/ folder), mount the parent of all three using --data <dir> on a directory rather than a file.

   "$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> --run-dir $RUN_DIR -- \
       "python /skill/scripts/prepare_data.py --mode finetune \\
            --csv /data/<basename> --out /runs/data \\
            --split-type <split_type> \\
            [--val-csv /data/<val-basename> --test-csv /data/<test-basename>] \\
            [--val-frac 0.1 --test-frac 0.1 --seed 0] \\
            --targets <COL1> [COL2 ...]"
   

   Outputs land at $RUN_DIR/data/prepare_data.json. For scaffold_balanced and index_predetermined, prep emits a single clean_full_csv + .npz; the runner passes them through to main.py finetune which calls split_data internally with the user-supplied seed.

6. Estimate runtime + echo applied defaults.

   • Finetune wall time is typically minutes-to-hours on 1 GPU.
   • Surface a summary of every flag that was filled from the defaults vs user-supplied, so the user knows what was assumed. The runner records this in args_applied.
   • Sample message: "Filling from defaults_finetune.json: epochs=30, batch_size=32, split_type=scaffold_balanced. Override any of these with --<flag>."

7. Targets confirmation gate (hard requirement). Before launching the runner, regardless of how the targets list was determined (CLI --targets, auto-detection in step 4, or a user natural-language request like "finetune on Caco2 and HLM"), echo the final targets list to the user with an explicit count: "Will finetune on N target(s): COL1, COL2, ...". If the user's request specified a subset that doesn't match this list (e.g., they asked for 2 tasks via natural language bu