This repository contains the Hugging Face checkpoint used by Xiaomi-Robotics-1 for VLABench evaluation. It includes the model weights, custom Transformers model and processor code, tokenizer files, and VLABench action normalization statistics. The custom model and processor must be loaded with trustremotecode=True. For the VLABench simulation environment, follow the companion source repository at evalvlabench/README.md. The evaluation client uses a separate vlabench conda environment with Python 3.10 and the pinned MuJoCo / dmcontrol dependencies described there. For reproducible multi-GPU evaluation, use the standard multi-server launcher in the companion Xiaomi-Robotics-1 source…
Open weights
apache-2.0
5.1B parameters
This repository contains the Hugging Face checkpoint used by Xiaomi-Robotics-1 for RoboCasa365 evaluation. It includes the model weights, custom Transformers model and processor code, tokenizer files, and RoboCasa365 action normalization statistics. The custom model and processor must be loaded with trustremotecode=True. For reproducible multi-GPU evaluation, use the standard eight-server launcher in the companion source repository. Use the companion Xiaomi-Robotics-1 source repository and follow evalrobocasa365/README.md. The server launcher accepts either this Hugging Face repository ID after downloading it locally or a local snapshot path. Apache License 2.0. See LICENSE.
Open weights
apache-2.0
5.1B parameters
transformers
This repository contains the Hugging Face checkpoint used by Xiaomi-Robotics-1 for RoboCasa evaluation. It includes the model weights, custom Transformers model and processor code, tokenizer files, and RoboCasa action normalization statistics. The custom model and processor must be loaded with trustremotecode=True. For reproducible multi-GPU evaluation, use the standard eight-server launcher in the companion source repository. Use the companion Xiaomi-Robotics-1 source repository and follow evalrobocasa/README.md. The server launcher accepts either this Hugging Face repository ID after downloading it locally or a local snapshot path. Apache License 2.0. See LICENSE.
Open weights
apache-2.0
5.1B parameters
transformers
MolmoAct2-Pretrain adapts the Molmo2-ER vision-language backbone into a discrete autoregressive robot policy while keeping the Molmo2 token interface. Robot state is represented with discrete state tokens, and future one-second actions are represented with OpenFAST action tokens. This checkpoint is the pre-trained VLA backbone before the continuous flow-matching action expert is attached. It is intended for further post-training or fine-tuning, not direct continuous-control inference. Use this checkpoint for further MolmoAct2 training stages. It was converted with addactionexpert=false, so predictaction(...) is intentionally unavailable. Standard Transformers generation can still be used…
Open weights
4.9B parameters
16,384 tokens
transformers
GALA inference checkpoint checkpointrobocasagr1 for RoboCasa GR1 tabletop evaluation. See the GitHub repository for environment setup and evaluation instructions.
Open weights
4.8B parameters
MolmoAct2 fine-tuned with LeRobot (lerobot commit c13d79e6e350c589babc5b7c5cf5ef05cb0fa58e), exported from /home/sakakibara/bachelorthesis/outputs/train/molmoact2yamspellceateceasyftcontinuousgpu220261008232437/checkpoints/008000/pretrainedmodel (trainer step 8000). LoRA adapters are merged; weights are float32. normstats.json holds the normalization statistics of the fine-tuning run under yamdualmolmoact2. lerobotcheckpoint/ holds the LeRobot policy config, training config, and processor files (no weights); exportmanifest.json records the source checkpoint hash and the export verification. Loading with dtype=torch.bfloat16 rounds the merged weights and loses much of the fine-tuning update.
Open weights
5.4B parameters
16,384 tokens
transformers
