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Add sharded WorldVLN backbone weights
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metadata
license: other
library_name: pytorch
tags:
  - custom-code
  - visual-navigation
  - worldvln
  - safetensors

WorldVLN Backbone

This repository was exported from a WorldVLN training checkpoint into a Hugging Face friendly layout. It is meant for direct folder upload: upload this whole directory as the root of a Hugging Face model repo.

Included Weights

  • gpt/: standard sharded safetensors export of trainer.gpt_fsdp
  • vae/: standard sharded safetensors export of trainer.vae_local
  • load_weights.py: helper utilities for loading the two subfolders directly
  • export_manifest.json: export provenance and metadata

Source Checkpoint

  • Original checkpoint: /manifold-obs/vln-uav/rluavflowcheckpoint_partialfreeze_stageb_only/train_run_pf_stageb_clipmix_gatemean_tok20480_vb1_ac4_iter1200_20260408_084520/ckpts/WorldVLN_backbone.pth
  • Architecture: infinity_qwen8b
  • Epoch: 0
  • Iter: 1200
  • Global step: 1200

File Layout

  • gpt/model.safetensors.index.json
  • gpt/model-00001-of-xxxxx.safetensors
  • vae/model.safetensors.index.json
  • vae/model-00001-of-xxxxx.safetensors

GPT shard count: 4

VAE shard count: 1

Direct Loading

This export is intentionally split into two model folders instead of one mixed training checkpoint. Instantiate your GPT model and VAE model with this project's code, then load them separately.

from load_weights import load_worldvln_models

load_worldvln_models(
    repo_dir=".",
    gpt_model=infinity_model,
    vae_model=vae_model,
    strict=False,
    device="cpu",
)

Or load raw state dicts only:

from load_weights import load_worldvln_state_dicts

bundle = load_worldvln_state_dicts(".", device="cpu")
gpt_state_dict = bundle["gpt"]
vae_state_dict = bundle["vae"]

Notes

  • This is a custom-code model export, not a generic transformers.AutoModel.from_pretrained(...) repo.
  • The weights are in standard sharded safetensors format and do not require manual file concatenation.
  • For inference in this codebase, point the GPT loader to gpt/ and the VAE loader to vae/.