Upload folder using huggingface_hub

.gitattributes

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checkpoints/050000/pretrained_model_migrated/README.md

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+---
+datasets: unknown
+library_name: lerobot
+license: apache-2.0
+model_name: groot
+pipeline_tag: robotics
+tags:
+- robotics
+- lerobot
+- groot
+---
+
+# Model Card for groot
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+_Model type not recognized — please update this template._
+
+
+This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
+See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
+
+---
+
+## How to Get Started with the Model
+
+For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
+Below is the short version on how to train and run inference/eval:
+
+### Train from scratch
+
+```bash
+lerobot-train \
+  --dataset.repo_id=${HF_USER}/<dataset> \
+  --policy.type=act \
+  --output_dir=outputs/train/<desired_policy_repo_id> \
+  --job_name=lerobot_training \
+  --policy.device=cuda \
+  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
+  --wandb.enable=true
+```
+
+_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
+
+### Evaluate the policy/run inference
+
+```bash
+lerobot-record \
+  --robot.type=so100_follower \
+  --dataset.repo_id=<hf_user>/eval_<dataset> \
+  --policy.path=<hf_user>/<desired_policy_repo_id> \
+  --episodes=10
+```
+
+Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
+
+---
+
+## Model Details
+
+- **License:** apache-2.0

checkpoints/050000/pretrained_model_migrated/config.json

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+    "tokenizer_assets_repo": "lerobot/eagle2hg-processor-groot-n1p5",
+    "embodiment_tag": "new_embodiment",
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+    "lora_target": "llm",
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+    "output_dir": "./tmp/gr00t",
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+    "max_steps": 10000,
+    "batch_size": 32,
+    "dataloader_num_workers": 8,
+    "report_to": "wandb",
+    "resume": false
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+2026-04-23 13:09:04,837 INFO    MainThread:2446823 [wandb_setup.py:_flush():81] Configure stats pid to 2446823
+2026-04-23 13:09:04,837 INFO    MainThread:2446823 [wandb_setup.py:_flush():81] Loading settings from environment variables
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+2026-04-23 13:09:04,838 INFO    MainThread:2446823 [wandb_init.py:init():849] wandb.init called with sweep_config: {}
+config: {'repo_id': 'paragon7060/INSIGHTfixposV3', 'base_model_path': 'nvidia/GR00T-N1.5-3B', 'steps': 100000, 'lr': 0.0001, 'batch_size': 4, 'num_processes': 1, 'effective_batch_size': 4, 'tune_llm': False, 'tune_visual': True, 'tune_projector': True, 'tune_diffusion_model': True, 'lora_rank': 0, 'lora_alpha': 16, 'lora_dropout': 0.1, 'lora_target': 'llm', 'gradient_checkpointing': False, 'seed': 42, 'valid_episodes': 3788, 'num_cameras': 4, 'cameras': ['guide', 'keyframe', 'right_shoulder', 'wrist'], 'task_prompt_mode': 'non_guide', 'action_dim': 8, 'state_dim': 16, '_wandb': {}}
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wandb/run-20260423_130904-ec0hb0kz/files/output.log

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+[GROOT] Flash Attention version: 2.8.3
+Loading pretrained dual brain from nvidia/GR00T-N1.5-3B
+Tune backbone vision tower: True
+Tune backbone LLM: False
+Tune action head projector: True
+Tune action head DiT: True
+Fetching 13 files: 100%|██████████| 13/13 [00:00<00:00, 1053.80it/s]
+`torch_dtype` is deprecated! Use `dtype` instead!
+[GROOT] Copying vendor Eagle files to cache: /home/seonho/clvla/lerobot_cl/src/lerobot/policies/groot/eagle2_hg_model -> /home/seonho/.cache/huggingface/lerobot/lerobot/eagle2hg-processor-groot-n1p5
+[GROOT] Assets repo: lerobot/eagle2hg-processor-groot-n1p5
+ Cache dir: /home/seonho/.cache/huggingface/lerobot/lerobot/eagle2hg-processor-groot-n1p5
+Tune backbone llm: False
+Tune backbone visual: True
+Total number of DiT parameters:  550386688
+Total number of SelfAttentionTransformer parameters:  201433088
+Tune action head projector: True
+Tune action head diffusion model: True
+Tune backbone llm: False
+Tune backbone visual: True
+Tune action head projector: True
+Tune action head diffusion model: True
+`use_fast` is set to `True` but the image processor class does not have a fast version.  Falling back to the slow version.
+The tokenizer you are loading from '/home/seonho/.cache/huggingface/lerobot/lerobot/eagle2hg-processor-groot-n1p5' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue.
+The tokenizer you are loading from '/home/seonho/.cache/huggingface/lerobot/lerobot/eagle2hg-processor-groot-n1p5' with an incorrect regex pattern: https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503/discussions/84#69121093e8b480e709447d5e. This will lead to incorrect tokenization. You should set the `fix_mistral_regex=True` flag when loading this tokenizer to fix this issue.
+wandb: Adding directory to artifact (/home/seonho/clvla/memory_module/outputs/insight_groot_keyframe_fix/checkpoints/025000)... Done. 46.7s
+wandb: Adding directory to artifact (/home/seonho/clvla/memory_module/outputs/insight_groot_keyframe_fix/checkpoints/050000)... Traceback (most recent call last):
+  File "/home/seonho/clvla/lerobot_cl/scripts/train_groot_keyframe.py", line 678, in <module>
+    main()
+  File "/home/seonho/clvla/lerobot_cl/src/lerobot/configs/parser.py", line 233, in wrapper_inner
+    response = fn(cfg, *args, **kwargs)
+               ^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/seonho/clvla/lerobot_cl/scripts/train_groot_keyframe.py", line 661, in main
+    artifact.add_dir(str(checkpoint_dir))
+  File "/home/seonho/miniconda3/envs/lerobot050_groot/lib/python3.12/site-packages/wandb/sdk/artifacts/_validators.py", line 271, in wrapper
+    return method(self, *args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/seonho/miniconda3/envs/lerobot050_groot/lib/python3.12/site-packages/wandb/sdk/artifacts/artifact.py", line 1563, in add_dir
+    pool.starmap(add_manifest_file, paths)
+  File "/home/seonho/miniconda3/envs/lerobot050_groot/lib/python3.12/multiprocessing/pool.py", line 375, in starmap
+    return self._map_async(func, iterable, starmapstar, chunksize).get()
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/seonho/miniconda3/envs/lerobot050_groot/lib/python3.12/multiprocessing/pool.py", line 774, in get
+    raise self._value
+  File "/home/seonho/miniconda3/envs/lerobot050_groot/lib/python3.12/multiprocessing/pool.py", line 125, in worker
+    result = (True, func(*args, **kwds))
+                    ^^^^^^^^^^^^^^^^^^^
+  File "/home/seonho/miniconda3/envs/lerobot050_groot/lib/python3.12/multiprocessing/pool.py", line 51, in starmapstar
+    return list(itertools.starmap(args[0], args[1]))
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/home/seonho/miniconda3/envs/lerobot050_groot/lib/python3.12/site-packages/wandb/sdk/artifacts/artifact.py", line 1553, in add_manifest_file
+    self._add_local_file(

wandb/run-20260423_130904-ec0hb0kz/files/requirements.txt

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