| --- |
| language: |
| - en |
| library_name: bagel-mot |
| license: other |
| tags: |
| - geoug |
| - geometry |
| - supervised-fine-tuning |
| pipeline_tag: image-text-to-text |
| --- |
| |
| # GeoUG-ColdStart |
|
|
| [Project page](https://yangr116.github.io/geoug-project-page/) · |
| [Paper](https://yangr116.github.io/geoug-project-page/assets/geoug-paper.pdf) · |
| [Hugging Face paper](https://huggingface.co/papers/2610.36022) |
|
|
| **The supervised cold-start checkpoint for GeoUG spatial-policy RL.** |
|
|
| GeoUG-ColdStart is the supervised initialization checkpoint for spatial-policy |
| reinforcement learning. It provides the starting point for training GeoUG-Spatial. |
|
|
| | Property | Value | |
| |---|---| |
| | Stage | Interleaved reasoning supervised fine-tuning | |
| | Weights | EMA cold-start checkpoint | |
| | Precision | Original BF16 weights | |
| | Downstream model | GeoUG-Spatial after spatial-policy RL | |
| | Bundled transform | Matches reasoning SFT and the spatial RL recipe | |
|
|
| ## Download |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| |
| snapshot_download( |
| repo_id="rayruiyang/GeoUG-ColdStart", |
| local_dir="ckpt/geoug-coldstart", |
| ) |
| ``` |
|
|
| These are custom BAGEL/MoT weights. Use the GeoUG runtime rather than a generic |
| `AutoModel.from_pretrained` call. The weight repository contains the checkpoint |
| assets; inference and training code live in the GeoUG code checkout. |
|
|
| Verify the download: |
|
|
| ```bash |
| (cd ckpt/geoug-coldstart && sha256sum -c checksums.sha256) |
| ``` |
|
|
| ## Initialize RL training |
|
|
| Use the GeoUG checkout's separate RL environment, described in |
| `docs/RL_TRAINING.md`. After downloading this model and |
| [train_geoug_rl.parquet](https://huggingface.co/datasets/rayruiyang/GeoUG_data/blob/main/train_geoug_rl.parquet), |
| restore the public training layout: |
|
|
| ```bash |
| python scripts/pack_data_parquet.py unpack-rl \ |
| --input /absolute/path/to/train_geoug_rl.parquet \ |
| --output /absolute/path/to/rl_train |
| |
| export MODEL_PATH="$PWD/ckpt/geoug-coldstart" |
| export RL_TRANSFORM_CONFIG="$MODEL_PATH/transform_config.yaml" |
| export RL_DATASET=/absolute/path/to/rl_train |
| export DEPTH_CACHE_DIR=/absolute/path/to/rl_train/depth-cache |
| ``` |
|
|
| The parquet includes training images, camera metadata, and generated-depth |
| caches. Prepare your own validation/evaluation inputs as required by the RL |
| recipe; the internal RL evaluation subset is not distributed. |
|
|
| The transform uses multiscale disparity normalization with |
| `c: [2.0, 8.0, 32.0]`. The RL loader uses `ema.safetensors`, matching the |
| initialization used by the released recipe. |
|
|
| ## Scope and interpretation |
|
|
| This repository provides the initialization checkpoint, not the final adaptive |
| policy. Use GeoUG-Spatial for the final model. Its reported RL benchmark scores |
| must not be attributed to GeoUG-ColdStart. |
|
|
| The cold start **model** is released here. The cold start **training data** is a |
| separate, pending release; it is not included in this repository or in the |
| 800-sample RL training parquet. |
|
|
| ## Model family |
|
|
| | Checkpoint | Intended use | |
| |---|---| |
| | [GeoUG-Spatial](https://huggingface.co/rayruiyang/GeoUG-Spatial) | Final adaptive spatial reasoning policy | |
| | [GeoUG-Depth](https://huggingface.co/rayruiyang/GeoUG-Depth) | Standalone metric-depth generation | |
| | [GeoUG-ColdStart](https://huggingface.co/rayruiyang/GeoUG-ColdStart) | SFT initialization for spatial-policy RL | |
|
|
| ## Package and integrity |
|
|
| | File | Contents | |
| |---|---| |
| | `ema.safetensors` | BF16 EMA model weights, approximately 27.21 GiB | |
| | `ae.safetensors` | VAE weights | |
| | `llm_config.json`, `vit_config.json`, `config.json`, `generation_config.json` | Model and generation configuration | |
| | `tokenizer.json`, `tokenizer_config.json`, `vocab.json`, `merges.txt` | Tokenizer assets | |
| | `transform_config.yaml` | Image/depth preprocessing and normalization | |
| | `checksums.sha256` | SHA-256 checksums for the package files | |
|
|
| Download the complete repository. The release uses EMA weights and does not |
| require a separate non-EMA checkpoint or a shard index. |
|
|
| ## Checkpoint provenance |
|
|
| This is the EMA checkpoint at step 1500 of the v6 interleaved reasoning SFT |
| run. The original SFT stage starts from the v5 step-1000 initialization. These |
| training identifiers are retained for experiment traceability. |
|
|
| ## License and attribution |
|
|
| GeoUG builds on the BAGEL/MoT architecture and its pretrained components. |
| This checkpoint accompanies the mixed-license GeoUG code release. Consult |
| `LICENSE` and `THIRD_PARTY_NOTICES.md` in the GeoUG code checkout for the |
| applicable component terms, including the CC BY-NC restrictions on the listed |
| BAGEL/DiT-derived code files. This card does not assign a new blanket license |
| to upstream components. The data repositories document their own source and |
| usage terms separately. |
|
|
| ## Contact |
|
|
| For questions about this release, contact |
| [rayyang0116@connect.hku.hk](mailto:rayyang0116@connect.hku.hk). |
