Push model using huggingface_hub.

README.md

@@ -1,74 +1,10 @@
 ---
-base_model:
-- declare-lab/nora-long
-datasets:
-- TomNickson/OpenX-Embodiment
-- jxu124/OpenX-Embodiment
-language:
-- en
-license: mit
-pipeline_tag: robotics
+tags:
+- model_hub_mixin
+- pytorch_model_hub_mixin
 ---
 
-# NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards
-
-[![Project Website](https://img.shields.io/badge/Project-Website-blue.svg)](https://declare-lab.github.io/nora-1.5)
-[![Model](https://img.shields.io/badge/Model-NORA--1.5-brightgreen)](https://huggingface.co/declare-lab/nora-1.5)
-[![arXiv](https://img.shields.io/badge/arXiv-2511.14659-b31b1b.svg)](https://arxiv.org/abs/2511.14659)
-[![Code](https://img.shields.io/badge/GitHub-Code-black.svg?logo=github)](https://github.com/declare-lab/nora-1.5)
-![Status](https://img.shields.io/badge/Status-Active-orange)
-
-🔥 Project NORA is supported by Gemini and Lambda Labs! We are thankful to them.
-
-NORA-1.5 is a **Vision-Language-Action (VLA)** model that improves generalization and real-world decision making through **post-training with world-model-based and action-based preference rewards**.  
-The model builds upon the NORA foundation to achieve stronger **instruction following**, **closed-loop control**, and **real-robot success**, demonstrating reliability across **LIBERO** and **SimplerEnv** environments.
-
-<p align="center">
-  <img src="https://declare-lab.github.io/assets/images/nora-1.5-arxiv-teaser.png" width="100%">
-</p>
-
-
----
-
-## 🌐 Project Website
-
-🔗 **https://declare-lab.github.io/nora-1.5**
- 
----
-
-## 🚀 Key Features
-
-- **Vision-Language-Action architecture** with enhanced **task completion rate** and **distraction rate**
-- **Action-based preference optimization** using expert preference rewards  
-- **World-model-based preference learning** for improved planning and consistency  
-- Strong **closed-loop control**, enabling deployment in real robot settings  
-- Supports **multi-task**, **long-horizon**, and **few-shot generalization**  
-- Compatible with **LeRobot**, **LIBERO**, **SimplerEnv**, and custom environments  
-
----
-
-## 📆 TODO <a name="todos"></a>  ~ 1 week
-- [ ] Release the inference code of Nora-1.5
-- [ ] Release all relevant model checkpoints(Pretrained, libero, SimplerEnv etc)
-- [ ] Release the training/fine-tuning code of Nora-1.5 with LeRobot Dataset
-- [ ] Release SimplerEnv evaluation code 
-
-## Minimal Inference Sample (Will update)
-```python
-from inference.modelling_expert import VLAWithExpert
-
-model = VLAWithExpert() 
-model.to('cuda')
-outputs = model.sample_actions(PIL IMAGE,instruction,num_steps=10) ## Outputs 7 Dof action of normalized and unnormalized action
-```
-
-## Citation
-
-```bibtex
-@article{hung2025nora15,
-  title={NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-Based Preference Rewards},
-  author={Hung, Chia-Yu and Majumder, Navonil and Deng, Haoyuan, Liu Renhang, Yankang Ang, Amir Zadeh, Chuan Li, Dorien Herremans, Ziwei Wang, and Soujanya Poria},
-  journal={arXiv preprint},\
-  year={2025}
-}
-```
+This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
+- Code: [More Information Needed]
+- Paper: [More Information Needed]
+- Docs: [More Information Needed]

config.json

@@ -0,0 +1,9 @@
+{
+  "action_chunk_length": 5,
+  "action_dim": 7,
+  "fast_tokenizer_id": "physical-intelligence/fast",
+  "lm_expert_num_attention_head": 6,
+  "lm_expert_width_multiplier": 0.375,
+  "processor_id": "declare-lab/nora",
+  "vlm_model_id": "declare-lab/nora-long"
+}

model.safetensors

@@ -0,0 +1,3 @@
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