Refresh root to latest checkpoint (~epoch 18, batch624988)

README.md

@@ -18,12 +18,12 @@ datasets:
 A [DynamicVLA](https://github.com/hzxie/DynamicVLA) policy trained on the **DOM** dataset
 ([hzxie/DOM](https://huggingface.co/datasets/hzxie/DOM)) for dynamic-object manipulation.
 
-> ⚠️ **Mid-training checkpoint** (~epoch 16, train loss ≈ 0.0007–0.002). Self-contained and
+> ⚠️ **Mid-training checkpoint** (~epoch 18, train loss ≈ 0.0007–0.003). Self-contained and
 > eval-ready (includes normalization buffers), but optimizer/scheduler state is **not** included
 > (cannot resume optimizer momentum from this file).
 
 ## Files in this repo
-- `model.safetensors` + `config.json` (root) — **latest** checkpoint (~epoch 16, a mid-epoch step
+- `model.safetensors` + `config.json` (root) — **latest** checkpoint (~epoch 18, a mid-epoch step
   snapshot, refreshed as training proceeds).
 - `epoch0005/`, `epoch0010/` — clean **epoch-milestone** checkpoints (saved at the end of those
   epochs; load with `subfolder="epoch0005"` etc.). Note the folder name uses the internal
@@ -51,7 +51,7 @@ A [DynamicVLA](https://github.com/hzxie/DynamicVLA) policy trained on the **DOM*
 
 ```python
 from policies.dynamicvla.modeling_dynamicvla import DynamicVLAPolicy
-policy = DynamicVLAPolicy.from_pretrained("mickeykang/dynamic-vla-DOM")               # latest (~epoch 16)
+policy = DynamicVLAPolicy.from_pretrained("mickeykang/dynamic-vla-DOM")               # latest (~epoch 18)
 # policy = DynamicVLAPolicy.from_pretrained("mickeykang/dynamic-vla-DOM", subfolder="epoch0010")
 policy.eval().cuda()
 ```

config.json

@@ -101,5 +101,5 @@
             0
         ]
     },
-    "epoch": 16
+    "epoch": 18
 }

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:30703059d646d6e1892bb2a8a3586b469e7d5d4032de6afd04d9dfb8e3f014f6
 size 1118678424