Super-squash branch 'main' using huggingface_hub
Browse filesCo-authored-by: youliangt <youliangt@users.noreply.huggingface.co>
Co-authored-by: rchand <rchand@users.noreply.huggingface.co>
- .gitattributes +35 -0
- EXPLAINABILITY.md +14 -0
- LICENSE +61 -0
- PRIVACY.md +11 -0
- README.md +48 -0
- SAFETY_and_SECURITY.md +8 -0
- config.json +70 -0
- embodiment_id.json +9 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +0 -0
- processor_config.json +494 -0
- statistics.json +0 -0
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EXPLAINABILITY.md
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# **Explainability**
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|Field:|Response:|
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|:---:|:---:|
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|Intended Domain:| Open foundation model for generalized humanoid robot reasoning and skills.|
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|Model Type: |Robot VLA model|
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|Intended Users:|This model is intended for developers and community that build and finetune robot foundation models.|
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|Output:|The model outputs are actions, and the units are floating points. This is referred to as "robot action policy." Actions consist of continuous-value vectors that correspond to different motor controls on a robot.|
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|Describe how the model works:|Accepts vision, language and proprioception, outputs robot action policy.|
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|Technical Limitations & Mitigation:| This model is not tested or intended for use in mission critical applications that require functional safety. The use of the model in those applications is at the user's own risk and sole responsibility, including taking the necessary steps to add needed guardrails or safety mechanisms prior to deployment.<br><br>Risk: Model underperformance in highly dynamic environments with varying robot surroundings (e.g. furniture, objects, etc) and lighting conditions.<br>Mitigation: Enhance dataset with dynamic obstacle scenarios and fine-tune models accordingly.<br><br>Risk: Integration challenges in specific customer environments with varying robot surroundings (e.g. furniture, objects, etc) and lighting conditions.<br>Mitigation: Provide detailed integration guides and support, leveraging NVIDIA's ecosystem.<br><br>Risk: Limited initial support for certain robot embodiments.<br>Mitigation: Expand testing and validation across a wider range of robot platforms.|
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|Verified to have met prescribed quality standards?|Yes|
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|Performance Metrics:|Success rate, as well as the following:<br>1) if the trajectory is smooth and does not jitter<br>2) if the robot does not hit any other objects<br>3) if the trajectory is natural|
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|Potential Known Risks:|This model is not tested or intended for use in mission critical applications that require functional safety. The use of the model in those applications is at the user's own risk and sole responsibility, including taking the necessary steps to add needed guardrails or safety mechanisms prior to deployment.|
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|End User License Agreement:| Your use of this model is governed by the [NSCL V1 License](https://developer.download.nvidia.com/licenses/NVIDIA-OneWay-Noncommercial-License-22Mar2022.pdf?t=eyJscyI6ImdzZW8iLCJsc2QiOiJodHRwczovL3d3dy5nb29nbGUuY29tLyIsIm5jaWQiOiJzby15b3V0LTg3MTcwMS12dDQ4In0=).|
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LICENSE
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NVIDIA License
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SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
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RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK (INCLUDING BUT
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EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
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PRIVACY.md
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# **Privacy**
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|Field:|Response:|
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|:---:|:---:|
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|Generatable or reverse engineerable personal data?|None|
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| 6 |
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|Personal data used to create this model?|No|
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| 7 |
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|How often is dataset reviewed?|Before Release|
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| 8 |
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|Is there provenance for all datasets used in training?|Yes|
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| 9 |
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|Does data labeling (annotation, metadata) comply with privacy laws?|Yes|
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| 10 |
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|Is data compliant with data subject requests for data correction or removal, if such a request was made?|Yes|
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|Applicable NVIDIA Privacy Policy|https://www.nvidia.com/en-us/about-nvidia/privacy-policy/|
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README.md
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---
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datasets:
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- nvidia/PhysicalAI-Robotics-GR00T-X-Embodiment-Sim
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tags:
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- robotics
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base_model:
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- nvidia/GR00T-N1.6-3B
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---
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<div align="center">
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<a href="https://github.com/NVIDIA/Isaac-GR00T">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/67b8da81d01134f89899b4a7/8bFQa2ZIGCsOQQ2ho2N_U.png">
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</a>
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<div align="center">
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<a href="https://github.com/NVIDIA/Isaac-GR00T">
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<img src="https://img.shields.io/badge/GitHub-grey?logo=GitHub" alt="GitHub Badge">
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</a>
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<a href="https://developer.nvidia.com/isaac/gr00t">
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<img src="https://img.shields.io/badge/Website-green" alt="Website Badge">
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</a>
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</div>
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</div>
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# GR00T-N1.6-bridge
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This is a finetuned model on the bridge dataset, based on the `GR00T-N1.6-3B` model.
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Refer to [Isaac-GR00T](https://github.com/NVIDIA/Isaac-GR00T) examples for more details.
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# Usage
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| 31 |
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```bash
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| 32 |
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.venv/bin/python gr00t/eval/run_gr00t_server.py \
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--model-path nvidia/GR00T-N1.6-bridge \
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--embodiment_tag OXE_WIDOWX \
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--use_sim_policy_wrapper
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```
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# Citation
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| 39 |
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```bibtex
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| 40 |
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@misc{nvidia2025gr00tn1openfoundation,
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| 41 |
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title={GR00T N1: An Open Foundation Model for Generalist Humanoid Robots},
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| 42 |
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author={NVIDIA and Johan Bjorck and Fernando Castañeda and Nikita Cherniadev and Xingye Da and Runyu Ding and Linxi "Jim" Fan and Yu Fang and Dieter Fox and Fengyuan Hu and Spencer Huang and Joel Jang and Zhenyu Jiang and Jan Kautz and Kaushil Kundalia and Lawrence Lao and Zhiqi Li and Zongyu Lin and Kevin Lin and Guilin Liu and Edith Llontop and Loic Magne and Ajay Mandlekar and Avnish Narayan and Soroush Nasiriany and Scott Reed and You Liang Tan and Guanzhi Wang and Zu Wang and Jing Wang and Qi Wang and Jiannan Xiang and Yuqi Xie and Yinzhen Xu and Zhenjia Xu and Seonghyeon Ye and Zhiding Yu and Ao Zhang and Hao Zhang and Yizhou Zhao and Ruijie Zheng and Yuke Zhu},
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year={2025},
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eprint={2503.14734},
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archivePrefix={arXiv},
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primaryClass={cs.RO},
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}
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```
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SAFETY_and_SECURITY.md
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# **Safety & Security**
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|Field:|Response:|
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|:---:|:---:|
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|Model Application(s):|Machinery and Robotics<br>Robot VLA - single-arm manipulation, bimanual grippers, bi-manual dex hands manipulation and humanoid dexterous manipulation|
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|Describe life critical application (if present):|This model is not tested or intended for use in mission critical applications that require functional safety. The use of the model in those applications is at the user's own risk and sole responsibility, including taking the necessary steps to add needed guardrails or safety mechanisms prior to deployment.|
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|Use Case Restrictions:|Abide by the [NSCL V1 License](https://developer.download.nvidia.com/licenses/NVIDIA-OneWay-Noncommercial-License-22Mar2022.pdf?t=eyJscyI6ImdzZW8iLCJsc2QiOiJodHRwczovL3d3dy5nb29nbGUuY29tLyIsIm5jaWQiOiJzby15b3V0LTg3MTcwMS12dDQ4In0=)|
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|Model and Dataset Restrictions:|The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to.|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
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{
|
| 2 |
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"action_horizon": 50,
|
| 3 |
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"add_pos_embed": true,
|
| 4 |
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"apply_sincos_state_encoding": true,
|
| 5 |
+
"architectures": [
|
| 6 |
+
"Gr00tN1d6"
|
| 7 |
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],
|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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"contrast": 0.1,
|
| 17 |
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"hue": 0.1,
|
| 18 |
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|
| 19 |
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},
|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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"dropout": 0.2,
|
| 24 |
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"final_dropout": true,
|
| 25 |
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|
| 26 |
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"norm_type": "ada_norm",
|
| 27 |
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"num_attention_heads": 32,
|
| 28 |
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"num_layers": 32,
|
| 29 |
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"output_dim": 1024,
|
| 30 |
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"positional_embeddings": null
|
| 31 |
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},
|
| 32 |
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"eagle_collator": true,
|
| 33 |
+
"formalize_language": true,
|
| 34 |
+
"gemma_collator": false,
|
| 35 |
+
"hidden_size": 1024,
|
| 36 |
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|
| 37 |
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"image_target_size": null,
|
| 38 |
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"input_embedding_dim": 1536,
|
| 39 |
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"load_bf16": true,
|
| 40 |
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"max_action_dim": 128,
|
| 41 |
+
"max_num_embodiments": 32,
|
| 42 |
+
"max_seq_len": 1024,
|
| 43 |
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"max_state_dim": 128,
|
| 44 |
+
"model_dtype": "bfloat16",
|
| 45 |
+
"model_name": "nvidia/Eagle-Block2A-2B-v2",
|
| 46 |
+
"model_type": "Gr00tN1d6",
|
| 47 |
+
"noise_beta_alpha": 1.5,
|
| 48 |
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"noise_beta_beta": 1.0,
|
| 49 |
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"noise_s": 0.999,
|
| 50 |
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"num_inference_timesteps": 4,
|
| 51 |
+
"num_timestep_buckets": 1000,
|
| 52 |
+
"random_rotation_angle": null,
|
| 53 |
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"reproject_vision": false,
|
| 54 |
+
"select_layer": 16,
|
| 55 |
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"shortest_image_edge": 256,
|
| 56 |
+
"state_dropout_prob": 0.8,
|
| 57 |
+
"torch_dtype": "bfloat16",
|
| 58 |
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"transformers_version": "4.51.3",
|
| 59 |
+
"tune_diffusion_model": true,
|
| 60 |
+
"tune_llm": false,
|
| 61 |
+
"tune_projector": true,
|
| 62 |
+
"tune_top_llm_layers": 4,
|
| 63 |
+
"tune_visual": false,
|
| 64 |
+
"tune_vlln": true,
|
| 65 |
+
"use_albumentations_transforms": true,
|
| 66 |
+
"use_alternate_vl_dit": true,
|
| 67 |
+
"use_flash_attention": true,
|
| 68 |
+
"use_relative_action": true,
|
| 69 |
+
"use_vlln": true
|
| 70 |
+
}
|
embodiment_id.json
ADDED
|
@@ -0,0 +1,9 @@
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| 1 |
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{
|
| 2 |
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"robocasa_panda_omron": 13,
|
| 3 |
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"gr1": 20,
|
| 4 |
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"behavior_r1_pro": 24,
|
| 5 |
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"unitree_g1": 8,
|
| 6 |
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"oxe_google": 0,
|
| 7 |
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"oxe_widowx": 1,
|
| 8 |
+
"libero_panda": 2
|
| 9 |
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}
|
model-00001-of-00002.safetensors
ADDED
|
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 4991094616
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model-00002-of-00002.safetensors
ADDED
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 1582283096
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model.safetensors.index.json
ADDED
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The diff for this file is too large to render.
See raw diff
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|
|
processor_config.json
ADDED
|
@@ -0,0 +1,494 @@
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| 1 |
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{
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| 2 |
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|
| 3 |
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| 4 |
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| 5 |
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|
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|
| 12 |
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|
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|
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|
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|
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|
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|
| 30 |
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|
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|
| 32 |
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|
| 33 |
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|
| 34 |
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|
| 35 |
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|
| 36 |
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|
| 37 |
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|
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|
| 39 |
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|
| 40 |
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|
| 41 |
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|
| 42 |
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|
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|
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|
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statistics.json
ADDED
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|
|
|