Real-world fine-tuning and inference code

The complete implementation is now included in JAM-realrobot, on both main and inference-gello-client. Use the current inference branch when incorporating later changes, or the pinned version above to keep an experiment fixed.

git clone --branch inference-gello-client https://github.com/lixuan27/JAM-realrobot.git
cd JAM-realrobot/baselines/fastwam

For an existing checkout on that branch, use git pull --ff-only. Record the Git revision and bundle revision together when deploying.

Complete fine-tuning workflow: pinned raw demonstrations, conversion, task-local splits and target checks, normalization, native visual/text preparation, training, validation, full-state resume and export.
Inference setup: environment, original base assets, published or newly exported task bundles, GPU serving and the robot-side client.
HTTP interface and client integration: exact request fields, pose10 conventions, model-specific observation requirements and denormalized outputs.

The code copied into GitHub matches the published source identified by its SOURCE_ORIGIN.json. Checkpoints remain in this Hugging Face repository. Keep each adapter, normalization, frozen prompt and identity together; use the loader for the selected model. The same source remains available under this repository's finetune/ and inference/ directories.

Fast-WAM consumes the current image and measured state. LingBot-VA requires continuous real observed image history and its companion history client. Both return absolute flange pose10 targets; do not denormalize their HTTP output again. Follow the selected model's guide before adapting a custom client.