Upload policy with preprocessor, postprocessor, and model card

README.md

@@ -1,5 +1,5 @@
 ---
-datasets: sengi/flatten_towel_pi05_adv
+datasets: ai2-cortex/27112025-yam-01
 library_name: lerobot
 license: apache-2.0
 model_name: pi05

config.json

@@ -46,7 +46,7 @@
             ]
         }
     },
-    "device": "cuda",
+    "device": "cuda:3",
     "use_amp": false,
     "push_to_hub": true,
     "repo_id": "sengi/rabc_pi05",

model.safetensors

@@ -1,3 +1,3 @@
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 size 7473096344

policy_postprocessor_step_0_unnormalizer_processor.safetensors

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 size 9168

policy_preprocessor_step_2_normalizer_processor.safetensors

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+oid sha256:17af18b8ee861a0cff4774729c612aadbaae6077ce6c35e4cc69be8add1b6a85
 size 9168

train_config.json

@@ -1,8 +1,8 @@
 {
     "dataset": {
-        "repo_id": "sengi/flatten_towel_pi05_adv",
+        "repo_id": "ai2-cortex/27112025-yam-01",
         "repo_ids": null,
-        "root": null,
+        "root": "/mount/weka/jiafeid/matcha/downloaded_datasets/ai2-cortex__27112025-yam-01",
         "episodes": null,
         "image_transforms": {
             "enable": true,
@@ -78,7 +78,7 @@
         "revision": null,
         "use_imagenet_stats": true,
         "video_backend": "torchcodec",
-        "force_cache_sync": true,
+        "force_cache_sync": false,
         "use_annotated_tasks": false
     },
     "num_datasets": 100,
@@ -131,7 +131,7 @@
                 ]
             }
         },
-        "device": "cuda",
+        "device": "cuda:0",
         "use_amp": false,
         "compiled": false,
         "push_to_hub": true,
@@ -185,13 +185,13 @@
     "compile": true,
     "strict": true,
     "loss_threshold": 3.0,
-    "output_dir": "outputs/train/2026-02-20/19-39-30_rabc_pi05_finetuning",
-    "job_name": "rabc_pi05_finetuning",
+    "output_dir": "outputs/train/2026-02-22/02-33-46_rabac_pi05_finetuning",
+    "job_name": "rabac_pi05_finetuning",
     "resume": false,
     "resume_scheduler": true,
     "seed": 3407,
     "num_workers": 4,
-    "batch_size": 24,
+    "batch_size": 16,
     "gradient_accumulation_steps": 1,
     "steps": 10000,
     "eval_freq": 20000,