Bootstrap backbones

DA3-Giant backbone weight used by DA3GiantEncoder.__init__ to instantiate the Stage 1 model before our finetuned student_da3 state_dict is loaded on top. This file is the same one referenced as stage_1.ckpt_path in every training config in this lineage.

Files

track4world_da3.pth (~5.2 GB) — DA3-Giant multi-view backbone weights. Load with torch.load(map_location='cpu'). Used only at model instantiation; the finetuned student_da3 weights inside any franka_multitask_v1/*/0XXXXXX.pt checkpoint override these on load_state_dict.

Other dependencies (NOT in this repo — fetch from public HF)

google-t5/t5-base (~900 MB): language encoder used by the shallow12 AR predictor (predictor.language_encoder_type: t5).
openai/clip-vit-large-patch14 (~1.7 GB): only referenced in the config; the multi-task finetune actually routes through T5, so CLIP weights are loaded but unused at inference. Safe to skip on bandwidth-constrained deploy hosts.

Both download automatically on first transformers/huggingface_hub call; configure HF_HOME if the deploy host needs an offline mirror.

Deploy load order

# 1. Instantiate DA3GiantEncoder with this backbone bootstrap.
encoder = DA3GiantEncoder(
    ckpt_path="/local/track4world_da3.pth",
    ...,
)
# 2. Strict-load the finetuned student weights on top.
finetune = torch.load("/local/franka_multitask_0010000.pt", map_location="cpu")
encoder.load_state_dict(finetune["student_da3"], strict=True)

See docs/realrobot-franka-deploy-handoff.md in ONground-Korea/3DA for the full deploy spec.