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.

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.

Compute run directory.

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

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.

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.

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.
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>."
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 but the list still has 4), treat it as a discrepancy and re-prompt with the diff — never silently proceed on the wrong target set. Wait for explicit confirmation before launching unless --yes was given.

Launch the runner detached. (Consistent with the pretrain skills.)

"$SKILL_DIR/scripts/kermt_container.sh" run_detached \\
    --name kermt-finetune-<ts> \\
    --ckpt <user-ckpt> --run-dir $RUN_DIR -- \\
    "python /skill/scripts/run_finetune_local.py \\
         --ckpt /ckpt \\
         --prepare-manifest /runs/data/prepare_data.json \\
         --dataset-type <type> \\
         --out /runs \\
         [--gpus 0] \\
         [--num-gpus N] \\
         [--epochs N --batch-size N --init-lr F ...] \\
         [--ffn-num-task-specific-layers N --ffn-task-specific-hidden-size H]"

Returns the container name + id + log file path.

Report to the user. Output a short summary:
- Container name + id
- $RUN_DIR/run.json (manifest with cmd_replay + image digest)
- Log file: $RUN_DIR/logs/finetune.log
- TensorBoard: $RUN_DIR/logs/tb (open with tensorboard --logdir $RUN_DIR/logs/tb)
- Final checkpoints land at $RUN_DIR/ckpt/fold_0/model_0/model.pt (best-val) and last_checkpoint.pt (sibling, auto-resume target). Held-out test predictions + metrics land at $RUN_DIR/ckpt/fold_0/test_result.csv. Paths vary with --num-folds / --ensemble-size.
- To follow progress: kermt-monitor <RUN_DIR> (one-shot) or docker logs -f <container-name> (streaming).
- To block until the run finishes (useful for short test runs): docker wait <container-name> — prints the exit code on completion.

Hard rules

Never download the released model without consent. When --ckpt is omitted, download nvidia/NV-KERMT-70M-v2 only after an explicit user "yes" or an explicit --pretrained-release flag. --ckpt and --pretrained-release are mutually exclusive.
Never modify the user's input ckpt. The runner passes its path via --checkpoint_path; task/train.py loads it read-only into the model and attaches a new FFN head. The source file stays untouched.
Arch comes from the ckpt, not from CLI/defaults. The runner extracts hidden_size, depth, num_attn_head, activation, embedding_output_type, self_attention (+ attn_hidden / attn_out when applicable) from the ckpt's saved_args. There is no --hidden-size flag on this runner.
Never block on the long-running finetune. The skill launches via run_detached and returns immediately after step 9. Use kermt-monitor.
Echo applied defaults back to the user. The args_applied field of run.json records every flag's value + source (user / default-config). Surface a one-line summary of every filled-from-default flag so the user knows what was assumed.

Common errors

finetune_init requires a pretrain ckpt (grover_base / cmim / hybrid) → the ckpt you passed is already finetuned (has task FFN heads). Pick a pretrain ckpt instead, or use kermt-infer if you want to run predictions with the existing finetuned model. To resume a finetune on the SAME dataset, bypass the skill and call python main.py finetune --checkpoint_path <ckpt> ... directly — the agent skill doesn't support resume because saved-task identity can't be machine-verified against the new training data.
prepare_data manifest reports ok=False → check errors for the failed step (typically clean_smiles or save_features). Fix and re-run.
ffn_num_task_specific_layers=N>0 but ffn_task_specific_hidden_size is unset → MTL heads need an explicit hidden size. Pass --ffn-task-specific-hidden-size H.
finetune is single-GPU (from --gpus 0,1) → --gpus selects one device for single-process finetune. For multi-GPU, use --num-gpus N (DDP) instead.

Replayability

The run.json cmd_replay field is a single-line command that re-runs the finetune with the same inputs, hyperparameters, and arch. To replay inside the kermt container:

$(jq -r .cmd_replay $RUN_DIR/run.json)

If ok_to_replay: false in the manifest (because the kermt repo working tree was dirty at launch time), the replay may not be bit-exact — pin the exact commit via the repo.commit field and git checkout it first.