fm-dev/pi05-checkpoint-backups

π0.5 checkpoint backups

Complete backup checkpoints for the Franka Button Order, Pick3, and Shuffle experiments.

5,000, 10,000, and 12,500-step milestones remain standalone models at their original addresses in fm-dev. Other saved checkpoints are grouped here. Training continues to save every 1,000 steps, and future intermediate checkpoints are uploaded directly to these folders.

Browse backups

All backups are under checkpoints/:

checkpoints/
  r1/<task>/<method>/step6250/
  r2/<task>/<method>/step<number>/

Field	Values
Task	button_order, pick3, shuffle
Method	baseline, uniform32, status_d
Round 1	Original experiments; final 6,250-step checkpoints are archived here, with 5k models retained separately
Round 2	Execution-only action supervision, 12,500 updates, global batch 4 on 4 GPUs; 5k/10k/12.5k remain separate models

Each folder preserves the complete original checkpoint bundle. Round-two bundles include inference weights, assets, source, dependency versions, and full optimizer/RNG/sampler resume state. The migrated file contents were verified against the original repositories using their existing Git/LFS identities and sizes before the original intermediate model repositories were removed.

migration-index.json maps repositories moved during the initial cleanup to their archive folders. New training uploads appear directly in the directory tree.

Load a milestone

For example, download the Pick3 Status-D 10k model:

hf download fm-dev/pi05-pick3-status-d-lora-gbs4-pgb1-gpu4-r2-step10000 --local-dir ./pick3-10k

Follow its README and dependency instructions, then load from the downloaded checkpoint directory:

from load_model import load
policy = load()

Task-specific history, Writer, and Status inference inputs are documented in each checkpoint's README.

Recover a backup

Download just the required checkpoint folder:

hf download fm-dev/pi05-checkpoint-backups --type dataset \
  --include 'checkpoints/r2/pick3/status_d/step9000/**' \
  --local-dir ./pi05-backups

The complete bundle is then in pi05-backups/checkpoints/r2/pick3/status_d/step9000/. Its loading entry point and saved resume state are preserved.

The original model cards and evaluation limitations remain in the individual folders. A saved checkpoint or successful load does not establish physical robot success rate.