kermt-continue-pretrain SKILL.md skillA

kermt-continue-pretrain from nvidia-bionemo/bionemo-agent-toolkit is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned
mdr add nvidia-bionemo/bionemo-agent-toolkit/kermt-continue-pretrain@git:20260825.0e67a61

Pins git:20260825.0e67a61 in mdr.lock. Pin the fingerprint instead.

Audit grade
A, 17 of 17 checks passed
Kind
SKILL.md skill
Repository
nvidia-bionemo/bionemo-agent-toolkit
Path
skills/bionemo-agent-toolkit/skills/kermt-continue-pretrain/SKILL.md
Versions recorded
1
Latest version
git:20260825.0e67a61, committed Aug 25, 2026
Size
15,425 bytes
Repository stars
503
License
a license file GitHub does not identify

Continue pretraining from an existing KERMT checkpoint. The skill validates the user's checkpoint and pretrain CSV, prepares the data into shard/vocab/features form, then launches pretrain_ddp.py inside the kermt container (detached for long runs). Auto-dispatches `--pretrain_mode` based on the checkpoint type (grover_base vocab-only, cmim, or hybrid).

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# kermt-continue-pretrain

Continue pretraining from a user-supplied KERMT checkpoint (grover_base /
cmim / hybrid). The skill is the workflow orchestrator: it validates inputs,
prepares the corpus, launches the runner, and returns a run directory.

## Hardware requirements

- **GPUs**: 1–N CUDA-capable NVIDIA GPUs. The runner auto-detects via
  `torch.cuda.device_count()`; `--gpus 0,2` overrides. On a single GPU the
  runner falls back to `--batch_size 32 --save_interval 500`; on multi-GPU
  it uses the `defaults_pretrain.json` values (currently `batch_size 256`).
  Note: `--gpus N` uses **torch.cuda** indexing, which can differ from
  `nvidia-smi`'s display order on multi-GPU hosts (PCI bus vs. CUDA
  enumeration). To target a specific physical GPU, set `CUDA_VISIBLE_DEVICES`
  before invoking, or run
  `python -c "import torch; print([torch.cuda.get_device_name(i) for i in range(torch.cuda.device_count())])"`
  to confirm which device you're picking.
- **VRAM**: the default `--batch-size 256` is sized for A100-class hardware
  (80 GB VRAM). On smaller GPUs, downscale to avoid OOM:

  | GPU class                | VRAM       | Suggested `--batch-size` |
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint
mdr add nvidia-bionemo/bionemo-agent-toolkit/kermt-continue-pretrain@sha256:0498d70f6cdd3464

The line at the top of this page pins the label git:20260825.0e67a61, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_5bwj3aonxfvtcgsn.svg)](https://markdownregistry.com/a/art_5bwj3aonxfvtcgsn)

2 badge image fetches in 30 days. What the badge shows.

Versions

The registry has recorded 1 version of this file, committed on Aug 25, 2026.

versioncommittedcommitsizeaudit
git:20260825.0e67a61 latest2026-08-25 0e67a61 15,425 BA view

Audit of the latest version

A  17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (15425 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

nvidia-bionemo/bionemo-agent-toolkit · 503 stars · license a license file GitHub does not identify · pushed 2026-11-05 · branch main

API

GET https://markdownregistry.com/api/v1/artifacts/art_5bwj3aonxfvtcgsn
GET https://markdownregistry.com/api/v1/resolve?ref=nvidia-bionemo/bionemo-agent-toolkit/kermt-continue-pretrain
GET https://markdownregistry.com/api/v1/blob/0498d70f6cdd3464f1c56cabe2618b15abcf4eed638a06a305b272276e197bfc

Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.

Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.

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Every file in nvidia-bionemo/bionemo-agent-toolkit

Other files named kermt-continue-pretrain

kermt-continue-pretrain SKILL.md skill
nvidia/skills · skills/bionemo-kermt-continue-pretrain/SKILL.md · Continue KERMT pretraining on a custom SMILES corpus with a grover_base, cmim, or hybrid checkpoint. Use a local…
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kermt-continue-pretrain SKILL.md skill
nvidia-bionemo/bionemo-agent-toolkit · open-models-skills/kermt/kermt-continue-pretrain/SKILL.md · Continue pretraining from an existing KERMT checkpoint. The skill validates the user's checkpoint and pretrain CSV…
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