Model card: n0_twam package rename

README.md

@@ -1,6 +1,6 @@
 ---
 license: cc-by-nc-sa-4.0
-library_name: wan_twam
+library_name: n0_twam
 pipeline_tag: robotics
 tags:
   - robotics
@@ -64,12 +64,12 @@ bundle = snapshot_download("NeoteAI/n0-twam-base")
 
 ```python
 import torch
-from wan_twam.models.utils import load_mot_checkpoint
+from n0_twam.models.utils import load_mot_checkpoint
 
 model = load_mot_checkpoint(f"{bundle}/transformer",
                             torch_dtype=torch.bfloat16, torch_device="cuda")
-print(type(model).__name__, sum(p.numel() for p in model.parameters()) / 1e9, "B params")
-# -> FSDPWanMoTTransformer3DModel  7.16 B params
+print(f"{sum(p.numel() for p in model.parameters()) / 1e9:.2f} B params")
+# -> 7.16 B params
 ```
 
 ### 3b. Or serve it (observation → action)
@@ -80,21 +80,21 @@ every component the server needs, including the training-time action norm stats
 websocket server:
 
 ```python
-# in wan_twam/configs/twam_server_cfg.py
+# in n0_twam/configs/twam_server_cfg.py
 twam_server_cfg.wan22_pretrained_model_name_or_path = "<path to the downloaded bundle>"
 ```
 
 ```bash
-export PYTHONPATH=$PWD:$PWD/wan_twam CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PWD:$PWD/n0_twam CUDA_VISIBLE_DEVICES=0
 export RANK=0 LOCAL_RANK=0 WORLD_SIZE=1 MASTER_ADDR=127.0.0.1 MASTER_PORT=29988
-python -m wan_twam.wan_twam_server --config-name twam_server --port 29601
+python -m n0_twam.n0_twam_server --config-name twam_server --port 29601
 ```
 
 Query it from a client (a reset with a language prompt, then per-step observations):
 
 ```python
 import numpy as np
-from wan_twam.utils.Simple_Remote_Infer.deploy.websocket_client_policy import WebsocketClientPolicy
+from n0_twam.utils.Simple_Remote_Infer.deploy.websocket_client_policy import WebsocketClientPolicy
 
 client = WebsocketClientPolicy("127.0.0.1", 29601)
 client.infer({"reset": True, "prompt": "pick up the object"})