The PyTorch-converted π0.5 base used as pytorch_weight_path by every BEHAVIOR-1K skill adapter in this collection (pi05-b1k-nav-move-to-lora, pi05-b1k-pick-up-from-lora, pi05-b1k-place-in-on-lora). Converted 2026-08-01; published 2026-08-25.

A trained step-N/ checkpoint in the adapter repos is self-contained — 1,390 tensors, fp32, base weights and LoRA together — so evaluating a trained policy does not need this file. It is needed to (a) START a training run, since train_pytorch.py loads pytorch_weight_path before wrapping the model with PEFT, and (b) serve as the init (pre-LoRA) arm in attribution work, which is the only way to say what the adapter changed.

It lived only on the two hosts that used it, copied box-to-box, until 2026-08-25.

π0.5 in openpi depends on a transformers_replace patch that injects AdaRMS into gemma. Convert the JAX base inside a venv without that patch and you get a 6.52 GiB file with plain RMSNorm that silently loads and then fails on every checkpoint:

Missing key(s):    ...gemma_expert.model.layers.0.input_layernorm.dense.weight
Unexpected key(s): ...gemma_expert.model.layers.0.input_layernorm.weight

The 0.22 GiB difference between the two builds is exactly those 72 layernorm.dense layers. This file is the patched build: it has all 72.

The JAX checkpoint this was converted FROM is not identified anywhere, and no copy of it survives on the training host. The conversion tool is openpi's examples/convert_jax_model_to_pytorch.py, but the input is unknown. So this file is currently the only artifact of its own provenance — which is the reason it is on the Hub rather than on two disks.

Note that madokalif/pi05-b1k-sft50k-merged is not a substitute despite a similar size and an identical 812-key layout: its vision tower matches byte-for-byte but its action expert does not. That repo is a merged LoRA at step 50,000, a different artifact.