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name: kermt-add-cmim-pretrain description: Convert a grover_base checkpoint (encoder-only or encoder + vocab heads) into a hybrid checkpoint by adding a randomly-initialized cMIM decoder + latent_dist, then continue pretraining on the user's corpus as hybrid (vocab + contrast). Effectively kermt-continue-pretrain with a one-time ckpt-conversion step prepended. 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: ~165 lines, ~1900 tokens — well within the

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

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kermt-add-cmim-pretrain

Convert a grover_base checkpoint (legacy original-GROVER grover.encoders.* or modern kermt.encoders.*, with or without vocab heads) into a fully-formed hybrid (cMIM + vocab) checkpoint, then continue pretraining on the user's corpus as hybrid.

This is a thin wrapper: upgrade_to_hybrid.py produces a new ckpt that classifies as model_type: hybrid via check_checkpoint.py, and the rest of the workflow is identical to kermt-continue-pretrain.

Status: experimental. This workflow is functional end-to-end but has not been benchmarked against the manuscript's from-scratch hybrid training (which produces the released checkpoint). Use as an experimental alternative to kermt-pretrain-scratch when you want to extend an existing grover_base checkpoint rather than restart from random init. Validate downstream performance on your own benchmark before relying on the upgraded ckpt for production work.

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/.

Hardware requirements

Same as kermt-continue-pretrain (the cMIM decoder adds parameters but not substantially; VRAM headroom should be fine). The upgrade step itself is fast (~5 s) and CPU-only — only the subsequent continue-pretrain consumes GPU.

When to invoke

• User has a grover_base checkpoint (encoder-only or with vocab heads) and wants to extend it into a hybrid (vocab + cMIM contrastive) pretrain.
• Useful for adding the SMILES-reconstruction contrastive objective to a pretrained encoder without restarting pretraining from scratch (which kermt-pretrain-scratch would do at days-scale).

For continuing an existing hybrid or cmim ckpt: use kermt-continue-pretrain directly. For training a fresh model on a custom corpus: use kermt-pretrain-scratch.

Inputs

Required:

• --ckpt <path> — grover_base ckpt to upgrade. Validated via check_checkpoint.py --mode upgrade_to_hybrid; rejected if the ckpt already has a contrast head or task FFN.
• --csv <path> — pretrain corpus CSV. Same shape as kermt-continue-pretrain's --csv input.

Optional (same as kermt-continue-pretrain):

• --val-csv <path> — separate validation CSV. Without it, prepare_data auto-splits by --val-frac 0.1.
• Training-hyperparameter overrides (--epochs N, --batch-size N, lr triple, --warmup-epochs F, etc.).
• --vocab-loss-weight F / --latent-dim N / --contrastive-temperature F.
• --wandb-project NAME / --wandb-run-name NAME — optional Weights & Biases logging (run name honored only alongside a project). Off by default.
• --gpus 0,2.

Workflow

Let $KERMT_REPO be the path to your kermt repo checkout.

1. Pre-flight: check_system (same as kermt-continue-pretrain step 1).

2. Compute run directory:

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

3. Validate the input ckpt with check_checkpoint --mode upgrade_to_hybrid. Abort on ok: false. The validator rejects ckpts that already have contrast head (suggest kermt-continue-pretrain) or task FFN heads (the ckpt has been finetuned; suggest using the original pretrain checkpoint).

4. Validate the corpus via check_data --mode pretrain. Abort on ok: false.

5. Prepare the data with --mode pretrain — without --vocab-dir. The upgrade builds fresh vocab heads sized to the corpus's vocab, so we want prepare_data to produce a new vocab from the corpus rather than passing through the ckpt's old vocab (which may not even exist for encoder-only legacy grover_base ckpts):

   "$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> --run-dir $RUN_DIR -- \
       "python /skill/scripts/prepare_data.py --mode pretrain \\
            --csv /data/<basename> --out /runs/data \\
            [--val-csv /data/<val-basename>] [--val-frac 0.1] [--seed 0]"
   

   The output manifest has vocab_source: "built_fresh" and includes a smiles_vocab (built from the corpus, needed for the new decoder).

6. Upgrade the ckpt.

   "$SKILL_DIR/scripts/kermt_container.sh" run --ckpt <user-ckpt> --run-dir $RUN_DIR -- \
       "python /skill/scripts/upgrade_to_hybrid.py \\
            --ckpt /ckpt \\
            --prepare-manifest /runs/data/prepare_data.json \\
            --out /runs/upgraded.pt"
   

   Surface the JSON summary to the user — especially warnings[], which includes any encoder-arch drift notes (e.g. legacy GROVER had two extra act_func_* keys that modern KERMTEmbedding doesn't) and the pretrain_ddp.py --backbone argparse-restriction note if the upgraded ckpt's backbone is anything other than gtrans.

7. Estimate runtime + confirm with the user. Same heuristic as kermt-continue-pretrain (corpus size × epochs × GPU count → wall time).

8. Launch the runner detached.

   "$SKILL_DIR/scripts/kermt_container.sh" run_detached \\
       --name kermt-add-cmim-pretrain-<ts> \\
       --run-dir $RUN_DIR -- \\
       "python /skill/scripts/run_pretrain_local.py \\
            --ckpt /runs/upgraded.pt \\
            --prepare-manifest /runs/data/prepare_data.json \\
            --out /runs \\
            [--epochs N --batch-size N ...]"
   

   The runner sees the upgraded ckpt as model_type: hybrid, so it auto-dispatches --pretrain_mode hybrid --vocab_loss_weight 1.0 with smiles_vocab plumbed through.

9. Report to the user with the upgraded ckpt path + the same run.json pointer / log path / tensorboard URL pattern as kermt-continue-pretrain.

Hard rules

• Never modify the user's input ckpt. The upgrade writes a new file at <run_dir>/upgraded.pt; the source ckpt stays untouched.
• Vocab heads are always fresh. Even if the input grover_base has vocab heads, they're discarded and rebuilt sized to the new corpus's vocab. Continue-pretraining the upgraded ckpt will train those new heads alongside the decoder.
• Don't auto-relax --backbone choices. If the upgrade warning fires because the input ckpt's backbone isn't gtrans (e.g. legacy dualtrans), surface the warning and ask the user. Do NOT silently modify parsing.py to add the legacy backbone to the choices list.

Common errors

• check_checkpoint rejected the ckpt with model_type=hybrid or cmim → user's ckpt already has a contrast head. Redirect to kermt-continue-pretrain.
• check_checkpoint rejected the ckpt with task_ffn=true → the ckpt has been finetuned. The upgrade workflow only supports pretrain checkpoints.
• prepare manifest missing smiles_vocab → prepare_data was invoked with --skip-vocab or some equivalent that omitted the smiles vocab. Re-run prepare without those flags.
• unexpected key(s) in encoder load warning → legacy GROVER architectures saved a couple of act_func_* weights that modern KERMTEmbedding doesn't use. Benign; the rest of the encoder loaded correctly.

What's in run.json after a successful run

Same reproducibility fields as kermt-continue-pretrain, plus the upgrade step's summary.json is captured under the inputs.upgrade_summary path so the provenance of the upgraded ckpt is auditable.

Replayability

Same as kermt-continue-pretrain: cmd_replay rebuilds the run_pretrain_local.py --ckpt <upgraded.pt> ... invocation. To redo the full add-cmim flow end-to-end, the user also needs the input grover_base ckpt and the corpus — both are captured in the prepare_data and upgrade manifests by absolute path.