MolmoAct
SkillDev toolsUse when working on MolmoAct VLA fine-tuning/serving/evaluation workbench stages, MolmoAct policy configs, or the npa workbench molmoact CLI.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the MolmoAct skill
What this skill tells your AI
The instructions your AI receives, as published by nebius/nebius-physical-ai in skills/tools/molmoact/SKILL.md and read by ahel’s review.
MolmoAct is the vision-language-action tool for fine-tuning, serving, and evaluating MolmoAct VLA policies in npa.
The three core stages (finetune, serve, eval) are currently stubs:
they validate their arguments and return a plan-only manifest — actual
trainer/server/evaluator execution is not wired up yet (tracking issue
nebius/nebius-physical-ai#502). The base model (default
allenai/MolmoAct-7B-O-0812) is resolved at runtime through the HF Hub
cache via npa.workbench.model_access — weights are never bundled in the
repo or image.
Interfaces
- CLI:
npa workbench molmoact <finetune|serve|eval> --help - Python SDK:
npa.sdk.workbench.molmoact(finetune,serve,eval) - Workflow module:
npa.workflows.byof.molmoact_pipeline(argument validation, stub-manifest plumbing, argparse entrypoint)
Conventions
- The CLI lives at
npa.cli.workbench.molmoact(multi-command package form for new tools); the SDK surface lives atnpa.sdk.workbench.molmoact. - The
finetunestage is the headlinemolmoact/finetunethree-tier contract (CLI <-> SDK <->workflows/testing/molmoact-finetune.yaml);serveandevalremain public-reusable sibling toolRefs.
Signals
- GitHub stars
- 30
- Forks
- 16
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
molmoact- Source
- github.com/nebius/nebius-physical-ai