| # CogWAM — RoboDojo, 25-step horizon, 50k steps |
|
|
| Released weights for the CogWAM recipe |
| `cogwam_robodojo_causal_dino_mot_h25_eventmem_dino_multilayer_50k_v1`, |
| taken at optimizer step 50,000. |
|
|
| ## Contents |
|
|
| | File | Size | | |
| |---|---|---| |
| | `model.safetensors` | 7.80 GiB | 2272 BF16 tensors: the framework state dict | |
| | `checkpoint_keys.json` | 215 KiB | `key -> [dtype, shape]`, for validating without loading weights | |
| | `dataset_statistics.json` | 5 KiB | action/state normalisation — **the policy server cannot un-normalise actions without it** | |
| | `inference_config.yaml` | | the resolved configuration this checkpoint was trained with | |
| | `artifact_manifest.json` | | sha256 of each file, model geometry, backbone requirements | |
|
|
| ## What is NOT here |
|
|
| The two backbones are referenced, not bundled, because one of them may not be |
| redistributed: |
|
|
| | Backbone | Where to get it | Licence | |
| |---|---|---| |
| | RynnBrain1.1-2B | `Alibaba-DAMO-Academy` on Hugging Face | Apache-2.0 | |
| | DINOv3 ViT-B/16 | `facebook/dinov3-vitb16-pretrain-lvd1689m` | **Meta `dinov3-license`, gated** — accept Meta's terms yourself | |
|
|
| The DINO teacher is also genuinely absent from the state dict: it is attached |
| outside the module tree and its normalisation buffers are non-persistent, so it |
| never enters `state_dict()`. |
|
|
| ## Loading |
|
|
| ```bash |
| export COGWAM_BASE_VLM=/models/rynnbrain1.1-2B |
| export COGWAM_DINO_MODEL=/models/dinov3-vitb16 |
| |
| python tools/verify_checkpoint.py --artifact <this directory> --build |
| ``` |
|
|
| `--build` constructs the framework from `inference_config.yaml` and loads with |
| `strict=True`. No key remapping is needed or performed: checkpoint key prefixes |
| come from `nn.Module` attribute names, not from the framework class name. |
|
|
| To serve it: |
|
|
| ```bash |
| COGWAM_ARTIFACT_DIR=<this directory> NUM_SERVERS=8 bash scripts/serve_policy.sh |
| ``` |
|
|
| ## Geometry |
|
|
| | | | |
| |---|---| |
| | Tensors / dtype | 2272 / BF16 | |
| | Prefixes | `action_model.` 1652, `qwen_vl_interface.` 618, `world_plan_queries.embedding`, `action_plan_queries.embedding` | |
| | Action horizon / dim | 25 / 14 | |
| | World queries | 16 | |
| | Planner hidden | 2048 | |
| | Vocabulary | 248081 (includes `<WORLD_PLAN>`, `<ACTION_PLAN>`, `<KEEP>`, `<UPDATE>`) | |
| | MoT | 30 layers; world 512, action 1024 | |
| | DINO embedding | 768 | |
|
|
| Two digests are recorded in the manifest and they answer different questions: |
| `model.key_inventory_sha256` covers names/shapes/dtypes, so two runs of the same |
| recipe share it; only `files["model.safetensors"].sha256` identifies these |
| particular weights. |
|
|
| ## Evaluation |
|
|
| Run against RoboDojo 0.2.0 at commit |
| `9b4cc885e8f530ed3ab14a30a312ae70242771c4`. Protocol, chunk/replan semantics and |
| the event-memory state machine are documented in the repository under |
| `docs/evaluation.md`. Published results are not included with this release. |
|
|
| ## Licence |
|
|
| MIT for the code; these weights are released under the same terms. The two |
| backbone models carry their own licences, listed above. |
|
|
