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espejelomar 
posted an update 4 months ago
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4765
Sharing WorldForge with @abdelstark

It's an open-source Python project for evaluating and replaying robotics and world-model workflows.

The useful part is not only calling a model. WorldForge records the run, validates action shapes, translates outputs into actions, and keeps replay artifacts you can inspect later.

The current demo uses LeRobot + LeWorldModel on PushT through the official loader:

stable_worldmodel.policy.AutoCostModel("pusht/lewm")

The harness also has replay-only paths for Cosmos-Policy and GR00T-style outputs, so you can inspect the provider contract from saved artifacts without keeping a GPU server online.

Try it:

pip install worldforge-ai
uv run --extra harness worldforge-harness --flow robotics-compare

Repo: https://github.com/AbdelStark/worldforge
Docs: https://abdelstark.github.io/worldforge/

Pre-1.0, MIT, and actively looking for contributors. Good areas:
- robotics provider adapters
- replay artifacts
- eval flows
- docs & first-run demos

Good first issues: https://github.com/AbdelStark/worldforge/contribute

If you're building robot policy evals or model adapters, would love a PR — or an issue describing what's missing.
emre 
posted an update over 1 year ago
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3961
having trouble with auto train
hello there this is the first time i am testing auto train with a 1.8k SFT dataset. Howevery i am not quite sure the training is going smooth. Logs seem quite confusing, token did not match can not auth, generates confusing train splits, do you know how i can check my running job properly?
what is being used for training as data?
any ideas?
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