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Cosmos-Predict2-2B-Video2World/README.md

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+---
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license
+library_name: cosmos
+tags:
+- nvidia
+- cosmos
+- diffusers
+extra_gated_prompt: >-
+  # NVIDIA Open Model License Agreement
+
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+
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+extra_gated_fields:
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+extra_gated_description: >-
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+  Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy/).
+extra_gated_button_content: Submit
+pipeline_tag: image-to-video
+---
+# **Cosmos-Predict2: A Suite of Diffusion-based World Foundation Models Available in 2B, and 14B**
+
+[**Cosmos**](https://huggingface.co/collections/nvidia/cosmos-predict2-68028efc052239369a0f2959) | [**Code**](https://github.com/nvidia-cosmos/cosmos-predict2) | [**Website**](https://research.nvidia.com/labs/dir/cosmos-predict2/)
+
+
+# Model Overview
+
+## Description
+**Cosmos-Predict2**: A family of highly performant pre-trained world foundation models purpose-built for generating physics-aware images, videos and world states for physical AI development.
+
+Cosmos-Predict2 diffusion models are a collection of diffusion based world foundation models that generate dynamic, high quality images and videos from text, image, or video inputs. It can serve as the building block for various applications or research that are related to world generation. The models are ready for commercial use under NVIDIA Open Model license agreement.
+
+**Model Developer**: NVIDIA
+
+## Model Versions
+
+The Cosmos-Predict2 diffusion-based model family includes the following models:
+- [Cosmos-Predict2-2B-Text2Image](https://huggingface.co/nvidia/Cosmos-Predict2-2B-Text2Image)
+  - Given a text description, predict an output image.
+- [Cosmos-Predict2-14B-Text2Image](https://huggingface.co/nvidia/Cosmos-Predict2-14B-Text2Image)
+  - Given a text description, predict an output image.
+- [Cosmos-Predict2-2B-Video2World](https://huggingface.co/nvidia/Cosmos-Predict2-2B-Video2World)
+  - Given a text description and an image as the first frame, predict the future frames.
+  - We have six variants for this model to support different use cases:
+     - variant that produces 720P video with 16FPS (this is our default model)
+     - variant that produces 720P video with 10FPS
+     - variant that produces 480P video with 16FPS
+     - variant that produces 480P video with 10FPS
+     - variant that produces 720P video with 16FPS + NATTEN (Sparse Attention variant)
+     - variant that produces 720P video with 10FPS + NATTEN (Sparse Attention variant)
+- [Cosmos-Predict2-14B-Video2World](https://huggingface.co/nvidia/Cosmos-Predict2-14B-Video2World)
+  - Given a text description and an image as the first frame, predict the future frames.
+  - We have six variants for this model to support different use cases:
+     - variant that produces 720P video with 16FPS (this is our default model)
+     - variant that produces 720P video with 10FPS
+     - variant that produces 480P video with 16FPS
+     - variant that produces 480P video with 10FPS
+     - variant that produces 720P video with 16FPS + NATTEN (Sparse Attention variant)
+     - variant that produces 720P video with 10FPS + NATTEN (Sparse Attention variant)
+- [Cosmos-Predict2-14B-Video2World-Sample-GR00T-Dreams-GR1](https://huggingface.co/nvidia/Cosmos-Predict2-14B-Video2World-Sample-GR00T-Dreams-GR1):
+  - Video + Text based future visual world generation, post trained on GR00T GR1 data
+- [Cosmos-Predict2-14B-Video2World-Sample-GR00T-Dreams-DROID](https://huggingface.co/nvidia/Cosmos-Predict2-14B-Video2World-Sample-GR00T-Dreams-DROID):
+  - Video + Text based future visual world generation, post trained on GR00T DROID data
+- [Cosmos-Predict2-2B-Action-Conditioned-Sample](https://huggingface.co/nvidia/Cosmos-Predict2-2B-Sample-Action-Conditioned)
+  - Given an image as the first frame and next 12 actions, predict the future 12 frames.
+
+### License
+This model is released under the  [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). For a custom license, please contact [cosmos-license@nvidia.com](mailto:cosmos-license@nvidia.com).
+
+Under the NVIDIA Open Model License, NVIDIA confirms:
+
+* Models are commercially usable.
+* You are free to create and distribute Derivative Models.
+* NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.
+
+**Important Note**: If you bypass, disable, reduce the efficacy of, or circumvent any technical limitation, **safety guardrail** or
+associated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism contained
+in the Model, your rights under [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license) will automatically terminate.
+
+### Deployment Geography
+Global
+
+## Model Architecture
+Cosmos-Predict2-2B-Video2World is a diffusion transformer model designed for video denoising in the latent space. The network is composed of interleaved self-attention, cross-attention and feedforward layers as its building blocks. The cross-attention layers allow the model to condition on input text throughout the denoising process. Before each layer, adaptive layer normalization is applied to embed the time information for denoising. When image or video is provided as input, their latent frames are concatenated with the generated frames along the temporal dimension. Augment noise is added to conditional latent frames to bridge the training and inference gap.
+
+## Input/Output Specifications
+
+* **Input**
+
+  * **Input Type(s)**: Text+Image, Text+Video
+  * **Input Format(s)**:
+    * Text: String
+    * Image: jpg, png, jpeg, webp
+    * Video: mp4
+  * **Input Parameters**:
+    * Text: One-dimensional (1D)
+    * Image: Two-dimensional (2D)
+    * Video: Three-dimensional (3D)
+  * **Other Properties Related to Input**:
+    * The input string should contain fewer than 300 words and should provide descriptive content for world generation, such as a scene description, key objects or characters, background, and any specific actions or motions to be depicted within the 5-second duration.
+    * For the 720P model, the input image should be 1280×704; for the 480P model, use 832×480.
+    * The input video should consist of 5 frames, each with a resolution of 1280×704 for the 720P model, or 832×480 for the 480P model.
+
+* **Output**
+  * **Output Type(s)**: Video
+  * **Output Format(s)**: mp4
+  * **Output Parameters**: Three-dimensional (3D)
+  * **Other Properties Related to Output**: The generated video is a 5-second clip, with resolution and frame rate determined by the model variant used. For example, the 720P 16FPS model produces a video with a resolution of 1280×704 and a frame rate of 16 FPS.
+
+The video content visualizes the input text description as a short animated scene, capturing key elements within the specified time constraints.
+
+Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
+
+## Software Integration
+**Runtime Engine(s):**
+* [Cosmos-Predict2](https://github.com/nvidia-cosmos/cosmos-predict2)
+* [Diffusers](https://github.com/huggingface/diffusers)
+
+```python
+import torch
+from diffusers import Cosmos2VideoToWorldPipeline
+from diffusers.utils import export_to_video, load_image
+
+# Available checkpoints: nvidia/Cosmos-Predict2-2B-Video2World, nvidia/Cosmos-Predict2-14B-Video2World
+model_id = "nvidia/Cosmos-Predict2-2B-Video2World"
+pipe = Cosmos2VideoToWorldPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16)
+pipe.to("cuda")
+
+prompt = "A close-up shot captures a vibrant yellow scrubber vigorously working on a grimy plate, its bristles moving in circular motions to lift stubborn grease and food residue. The dish, once covered in remnants of a hearty meal, gradually reveals its original glossy surface. Suds form and bubble around the scrubber, creating a satisfying visual of cleanliness in progress. The sound of scrubbing fills the air, accompanied by the gentle clinking of the dish against the sink. As the scrubber continues its task, the dish transforms, gleaming under the bright kitchen lights, symbolizing the triumph of cleanliness over mess."
+negative_prompt = "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. Overall, the video is of poor quality."
+image = load_image(
+    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yellow-scrubber.png"
+)
+
+video = pipe(
+    image=image, prompt=prompt, negative_prompt=negative_prompt, generator=torch.Generator().manual_seed(1)
+).frames[0]
+export_to_video(video, "output.mp4", fps=16)
+```
+
+**Supported Hardware Microarchitecture Compatibility:**
+* NVIDIA Ampere
+* NVIDIA Blackwell
+* NVIDIA Hopper
+
+**Note**: Only BF16 precision is tested. Other precisions like FP16 or FP32 are not officially supported.
+
+## Inference
+**Acceleration Engine**: [PyTorch](https://pytorch.org/), [Transformer Engine](https://github.com/NVIDIA/TransformerEngine)
+
+**Operating System(s):**
+* Linux (We have not tested on other operating systems.)
+
+**System Requirements and Performance:**
+This model requires 32.54 GB of GPU VRAM.
+The following table shows inference time for a single generation across different NVIDIA GPU hardware:
+
+| GPU Hardware           | 480p, 10 FPS | 480p, 16 FPS | 720p, 10 FPS | 720p, 16 FPS | 720p, 10 FPS + NATTEN | 720p, 16 FPS + NATTEN |
+|------------------------|--------------|--------------|--------------|--------------|-----------------------|-----------------------|
+| H100 SXM               | 25.3  s      | 45.5  s      | 115.1  s     | 228.8  s     | 56   s                | 94.2  s               |
+| H200 SXM               | 24    s      | 43.7  s      | 111.1  s     | 221.7  s     | 52.9 s                | 89.4  s               |
+| B200                   | 14.3  s      | 25.5  s      | 62.4   s     | 123.9  s     | 32.6 s                | 54    s               |
+| H100 NVL               | 34.1  s      | 66.4  s      | 175.5  s     | 355.7  s     | 79   s                | 138.7 s               |
+| H100 PCIe              | 39.5  s      | 73.7  s      | 187.9  s     | 378.5  s     | 87.4 s                | 149.6 s               |
+| H200 NVL               | 27.2  s      | 51.4  s      | 133.1  s     | 267.2  s     | 60.7 s                | 104.3 s               |
+| L40S                   | 256.2 s      | 480.9 s      | 1281.1 s     | 2567.1 s     | -                     | -                     |
+| RTX PRO 6000 Blackwell | 44.6  s      | 84.7  s      | 223.1  s     | 452.2  s     | -                     | -                     |
+
+
+**Quality Benchmarks:**
+For comparative evaluation, we present benchmark scores using the [PBench](https://research.nvidia.com/labs/dir/pbench/).
+
+| **Model**                       | **PBench Overall Score**  | **PBench Domain Score** | **PBench Quality Score** |
+|---------------------------------|---------------------------|-------------------------|--------------------------|
+| LTX-Video                       | 74.0                      | 77.2                    | **70.8**                 |
+| HunyuanVideo-I2V                | 74.0                      | 77.4                    | 70.6                     |
+| CogVideoX-5B-I2V                | 74.2                      | 79.5                    | 69.0                     |
+| Wan2.1-I2V-14B-720P             | 75.8                      | 81.9                    | 69.7                     |
+| Cosmos-Predict1-7B-Video2World  | 73.2                      | 77.4                    | 69.0                     |
+| Cosmos-Predict1-14B-Video2World | 73.3                      | 77.6                    | 69.0                     |
+| Cosmos-Predict2-2B-Video2World  | 77.2                      | 84.8                    | 69.6                     |
+| Cosmos-Predict2-14B-Video2World | **77.4**                  | **84.9**                | 69.9                     |
+
+NOTE: Cosmos-Predict2 numbers in the above table are both our default variants (720p, 16FPS, no sparsity).
+
+Please refer to the table below for PBench scores corresponding to all the Predict2 variants:
+
+| **Model**                                  | **PBench Overall Score** | **PBench Domain Score** | **PBench Quality Score** |
+|--------------------------------------------|--------------------------|-------------------------|--------------------------|
+| Cosmos-Predict2-2B, 480p, 10 fps           | 76.8                     | 84.3                    | 69.2                     |
+| Cosmos-Predict2-2B, 480p, 16 fps           | 76.5                     | 83.6                    | 69.5                     |
+| Cosmos-Predict2-2B, 720p, 10 fps           | 76.5                     | 84.1                    | 68.9                     |
+| Cosmos-Predict2-2B, 720p, 16 fps           | 77.2                     | 84.8                    | 69.6                     |
+| Cosmos-Predict2-2B, 720p, 10 fps + NATTEN  | 76.3                     | 83.6                    | 69.0                     |
+| Cosmos-Predict2-2B, 720p, 16 fps + NATTEN  | 77.0                     | 84.5                    | 69.5                     |
+| Cosmos-Predict2-14B, 480p, 10 fps          | 77.0                     | 84.2                    | 69.7                     |
+| Cosmos-Predict2-14B, 480p, 16 fps          | 77.0                     | 84.0                    | **70.0**                 |
+| Cosmos-Predict2-14B, 720p, 10 fps          | 77.3                     | **85.0**                | 69.6                     |
+| Cosmos-Predict2-14B, 720p, 16 fps          | **77.4**                 | 84.9                    | 69.9                     |
+| Cosmos-Predict2-14B, 720p, 10 fps + NATTEN | 76.7                     | 84.1                    | 69.4                     |
+| Cosmos-Predict2-14B, 720p, 16 fps + NATTEN | 77.0                     | 84.2                    | 69.9                     |
+
+
+# Usage
+
+* See [Cosmos-Predict2](https://github.com/nvidia-cosmos/cosmos-predict2) for details.
+
+## Limitations
+
+Despite various improvements in world generation for Physical AI, Cosmos-Predict2 video2world models still face technical and application limitations for world prediction. In particular, they struggle to generate long, high-resolution videos without artifacts. Common issues include temporal inconsistency, camera and object motion instability, and imprecise interactions. The models may inaccurately represent 3D space, 4D space-time, or physical laws in the generated videos, leading to artifacts such as disappearing or morphing objects, unrealistic interactions, and implausible motions. As a result, applying these models for applications that require simulating physical law-grounded environments or complex multi-agent dynamics remains challenging.
+
+## Ethical Considerations
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+
+Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
+
+For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+### Plus Plus (++) Promise
+
+We value you, the datasets, the diversity they represent, and what we have been entrusted with. This model and its associated data have been:
+* Verified to comply with current applicable disclosure laws, regulations, and industry standards.
+* Verified to comply with applicable privacy labeling requirements.
+* Annotated to describe the collector/source (NVIDIA or a third-party).
+* Characterized for technical limitations.
+* Reviewed to ensure proper disclosure is accessible to, maintained for, and in compliance with NVIDIA data subjects and their requests.
+* Reviewed before release.
+* Tagged for known restrictions and potential safety implications.
+
+### Bias
+
+Field                                                                                               |  Response
+:---------------------------------------------------------------------------------------------------|:---------------
+Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing:  |  None
+Measures taken to mitigate against unwanted bias:                                                   |  None
+
+
+### Explainability
+
+Field                                                                                                  |  Response
+:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------
+Intended Application & Domain:                                                                   |  World Generation
+Model Type:                                                                                            |  Transformer
+Intended Users:                                                                                        |  Physical AI developers
+Output:                                                                                                |  Videos
+Describe how the model works:                                                                          |  Generates videos based on video inputs
+Technical Limitations:                                                                                 |  The model may not follow the video input accurately.
+Verified to have met prescribed NVIDIA quality standards:  |  Yes
+Performance Metrics:                                                                                   |  Quantitative and Qualitative Evaluation
+Potential Known Risks:                                                                                 |  The model's output can generate all forms of videos, including what may be considered toxic, offensive, or indecent.
+Licensing:                                                                                             |  [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)
+
+
+### Privacy
+Field                                                                                                                              |  Response
+:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------
+Generatable or reverse engineerable personal information?                                                     |  None Known
+Protected class data used to create this model?                                                                                       |  None Known
+Was consent obtained for any personal data used?                                                                                             |  None Known
+How often is dataset reviewed?                                                                                                     |  Before Release
+Is there provenance for all datasets used in training?                                                                                |  Yes
+Does data labeling (annotation, metadata) comply with privacy laws?                                                                |  Yes
+Is data compliant with data subject requests for data correction or removal, if such a request was made?                           |  Not Applicable
+Applicable Privacy Poicy        | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/
+
+### Safety
+
+Field                                               |  Response
+:---------------------------------------------------|:----------------------------------
+Model Application(s):                               |  World Generation
+Describe the life critical impact (if present).   |  None Known
+Use Case Restrictions:                              |  [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)
+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. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog.

Cosmos-Predict2-2B-Video2World/config.json

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+  ],
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+  ]
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Cosmos-Predict2-2B-Video2World/tokenizer/LICENSE.txt

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Cosmos-Predict2-2B-Video2World/tokenizer/config.json

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+{
+  "_class_name": "WanModel",
+  "_diffusers_version": "0.30.0",
+  "dim": 1536,
+  "eps": 1e-06,
+  "ffn_dim": 8960,
+  "freq_dim": 256,
+  "in_dim": 16,
+  "model_type": "t2v",
+  "num_heads": 12,
+  "num_layers": 30,
+  "out_dim": 16,
+  "text_len": 512
+}

Cosmos-Predict2-2B-Video2World/tokenizer/special_tokens_map.json

@@ -0,0 +1,107 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
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+    "<extra_id_65>",
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+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
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+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

Cosmos-Predict2-2B-Video2World/tokenizer/tokenizer_config.json

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+}

Cosmos-Predict2-2B-Video2World/transformer/config.json

@@ -0,0 +1,29 @@
+{
+  "_class_name": "CosmosTransformer3DModel",
+  "_diffusers_version": "0.34.0.dev0",
+  "adaln_lora_dim": 256,
+  "attention_head_dim": 128,
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+  "max_size": [
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+  "num_attention_heads": 16,
+  "num_layers": 28,
+  "out_channels": 16,
+  "patch_size": [
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+  "rope_scale": [
+    1.0,
+    3.0,
+    3.0
+  ],
+  "text_embed_dim": 1024
+}

Cosmos-Predict2-2B-Video2World/vae/config.json

@@ -0,0 +1,57 @@
+{
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+  "_diffusers_version": "0.34.0.dev0",
+  "_name_or_path": "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
+  "attn_scales": [],
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+    0.5517,
+    -0.3632,
+    -0.1922,
+    -0.9497,
+    0.2503,
+    -0.2921
+  ],
+  "latents_std": [
+    2.8184,
+    1.4541,
+    2.3275,
+    2.6558,
+    1.2196,
+    1.7708,
+    2.6052,
+    2.0743,
+    3.2687,
+    2.1526,
+    2.8652,
+    1.5579,
+    1.6382,
+    1.1253,
+    2.8251,
+    1.916
+  ],
+  "num_res_blocks": 2,
+  "temperal_downsample": [
+    false,
+    true,
+    true
+  ],
+  "z_dim": 16
+}

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Cosmos-Reason1-7B/README.md

@@ -0,0 +1,375 @@
+---
+license: other
+license_name: nvidia-open-model-license
+license_link: >-
+  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license
+datasets:
+- nvidia/Cosmos-Reason1-SFT-Dataset
+- nvidia/Cosmos-Reason1-RL-Dataset
+- nvidia/Cosmos-Reason1-Benchmark
+library_name: transformers
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+tags:
+- nvidia
+- cosmos
+---
+
+# **Cosmos-Reason1: Physical AI Common Sense and Embodied Reasoning Models**
+
+[**Cosmos**](https://huggingface.co/collections/nvidia/cosmos-reason1-67c9e926206426008f1da1b7) | [**Code**](https://github.com/nvidia-cosmos/cosmos-reason1) | [**Paper**](https://arxiv.org/abs/2503.15558) | [**Paper Website**](https://research.nvidia.com/labs/dir/cosmos-reason1)
+
+# Model Overview
+
+## Description:
+
+**Cosmos-Reason1 Models**: Physical AI models understand physical common sense and generate appropriate embodied decisions in natural language through long chain-of-thought reasoning processes.
+
+The Cosmos-Reason1 models are post-trained with physical common sense and embodied reasoning data with supervised fine-tuning and reinforcement learning. These are Physical AI models that can understand space, time, and fundamental physics, and can serve as planning models to reason about the next steps of an embodied agent.
+
+The models are ready for commercial use.
+
+**Model Developer**: NVIDIA
+
+## Model Versions
+
+The Cosmos-Reason1 includes the following model:
+
+- [Cosmos-Reason1-7B](https://huggingface.co/nvidia/Cosmos-Reason1-7B): Given a text prompt and an input video, think and generate the answer with respect to the input text prompt and video.
+
+### License:
+
+This model is released under the  [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license). For a custom license, please contact [cosmos-license@nvidia.com](mailto:cosmos-license@nvidia.com).
+
+Under the NVIDIA Open Model License, NVIDIA confirms:
+
+* Models are commercially usable.
+* You are free to create and distribute Derivative Models.
+* NVIDIA does not claim ownership to any outputs generated using the Models or Derivative Models.
+
+**Important Note**: If You bypass, disable, reduce the efficacy of, or circumvent any technical limitation, safety guardrail or associated safety guardrail hyperparameter, encryption, security, digital rights management, or authentication mechanism (collectively “Guardrail”) contained in the Model without a substantially similar Guardrail appropriate for your use case, your rights under this Agreement [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license) will automatically terminate.
+
+### Deployment Geography:
+
+Global
+
+### Use Case:
+
+Physical AI: Space, time, fundamental physics understanding and embodied reasoning, encompassing robotics, and autonomous vehicles (AV).
+
+### Release Date:
+
+* Github: [05/17/2025](https://github.com/nvidia-cosmos/cosmos-reason1)
+* Huggingface:
+  * [08/01/2025](https://huggingface.co/nvidia/Cosmos-Reason1-7B/commit/0caf724f837efea5e25bf6d5818dcdeec0a36604). Shipped a few improvements which include captions with temporal timestamp, Set of Mark prompting.
+  * [06/10/2025](https://huggingface.co/nvidia/Cosmos-Reason1-7B/commit/2464fff43c5c0bfb1916ac8c009feda4aed81be9). Enhanced critic capability for physical plausibility.
+  * [05/17/2025](https://huggingface.co/nvidia/Cosmos-Reason1-7B/commit/098a5bb62a1f4fc05e5c4ac89aae8005e301aa18). Initial release.
+
+## Model Architecture:
+
+Architecture Type: A Multi-modal LLM consists of a Vision Transformer (ViT) for vision encoder and a Dense Transformer model for LLM.
+Network Architecture: Qwen2.5-VL-7B-Instruct.
+
+Cosmos-Reason-7B is post-trained based on [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) and follows the same model architecture.
+
+
+**Number of model parameters:**
+
+Cosmos-Reason1-7B:<br>
+  * Vision Transformer (ViT): 675.76M (675,759,104)
+  * Language Model (LLM): 7.07B (7,070,619,136)
+  * Other components (output projection layer): 545.00M (544,997,376)
+
+
+## Computational Load:
+
+  * Cumulative Compute: 3.2603016e+21 FLOPS
+  * Estimated Energy and Emissions for Model Training:
+    * Total kWh = 16658432
+    * Total Emissions (tCO2e) = 5380.674
+
+## Input
+
+  **Input Type(s)**: Text+Video/Image
+
+   **Input Format(s)**:
+   * Text: String
+   * Video: mp4
+   * Image: jpg
+
+  **Input Parameters**:
+  * Text: One-dimensional (1D)
+  * Video: Three-dimensional (3D)
+  * Image: Two-dimensional (2D)
+  
+  **Other Properties Related to Input**:
+  * Use `FPS=4` for input video to match the training setup.
+  * Append `Answer the question in the following format: <think>\nyour reasoning\n</think>\n\n<answer>\nyour answer\n</answer>.` in the system prompt to encourage long chain-of-thought reasoning response.
+
+## Output
+
+ **Output Type(s)**: Text
+
+ **Output Format**: String
+
+ **Output Parameters**: Text: One-dimensional (1D)
+
+ **Other Properties Related to Output**:
+ * Recommend using 4096 or more output max tokens to avoid truncation of long chain-of-thought response.
+
+ * Our AI model recognizes timestamps added at the bottom of each frame for accurate temporal localization.
+
+ * Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>
+
+
+## Software Integration
+
+**Runtime Engine(s):**
+
+* [vLLM](https://github.com/vllm-project/vllm)
+
+**Supported Hardware Microarchitecture Compatibility:**
+
+* NVIDIA Blackwell
+* NVIDIA Hopper
+
+**Note**: We have only tested doing inference with BF16 precision.
+
+**Operating System(s):**
+
+* Linux (We have not tested on other operating systems.)
+
+
+# Usage
+
+See [Cosmos-Reason1](https://github.com/nvidia-cosmos/cosmos-reason1) for details.
+* Post Training: [Cosmos-Reason1](https://github.com/nvidia-cosmos/cosmos-reason1) provides examples of supervised fine-tuning and reinforcement learning on embodied reasoning datasets.
+
+## Training and Evaluation Sections:
+### 05/17/2025
+Please see our [technical paper](https://arxiv.org/pdf/2503.15558) for detailed evaluations on physical common sense and embodied reasoning. Part of the evaluation datasets are released under [Cosmos-Reason1-Benchmark](https://huggingface.co/datasets/nvidia/Cosmos-Reason1-Benchmark). The embodied reasoning datasets and benchmarks focus on the following areas: robotics (RoboVQA, BridgeDataV2, Agibot, RobFail), ego-centric human demonstration (HoloAssist), and Autonomous Vehicle (AV) driving video data. The AV dataset is collected and annotated by NVIDIA.
+
+All datasets go through the data annotation process described in the technical paper to prepare training and evaluation data and annotations.
+
+### 08/01/2025
+We enhance the model capability with the augmented training data. PLM-Video-Human and Nexar are used to enable dense temporal captioning. Describe Anything is added to enhance a set of mark (SoM) prompting. We enrich data in intelligent transportation systems (ITS) and warehouse applications. Lastly, Visual Critics dataset contains a collection of AI generated videos from Cosmos-Predict2 and Wan2.1 with human annotations to describe the physical correctness in AI videos.
+
+
+## Training Datasets:
+
+**Data Collection Method**:
+* RoboVQA:  Hybrid:  Automatic/Sensors
+* BridgeDataV2: Automatic/Sensors
+* AgiBot: Automatic/Sensors
+* RoboFail: Automatic/Sensors
+* HoloAssist: Human
+* AV: Automatic/Sensors
+* PLM-Video-Human: Human
+* Nexar: Automatic/Sensors
+* Describe Anything: Human
+* ITS / Warehouse: Human, Automatic
+* Visual Critics: Automatic
+
+**Labeling Method**:
+* RoboVQA:  Hybrid:  Human,Automated
+* BridgeDataV2: Hybrid:  Human,Automated
+* AgiBot: Hybrid:  Human,Automated
+* RoboFail: Hybrid:  Human,Automated
+* HoloAssist: Hybrid:  Human,Automated
+* AV: Hybrid:  Human,Automated
+* PLM-Video-Human: Human,Automated
+* Nexar: Human
+* Describe Anything: Human,Automated
+* ITS / Warehouse: Human, Automated
+* Visual Critics: Human,Automated
+
+
+# Evaluation Datasets:
+
+**Data Collection Method**:
+* RoboVQA:  Hybrid:  Automatic/Sensors
+* BridgeDataV2: Automatic/Sensors
+* AgiBot: Automatic/Sensors
+* RoboFail: Automatic/Sensors
+* HoloAssist: Human
+* AV: Automatic/Sensors
+
+
+**Labeling Method**:
+* RoboVQA:  Hybrid:  Human,Automated
+* BridgeDataV2: Hybrid:  Human,Automated
+* AgiBot: Hybrid:  Human,Automated
+* RoboFail: Hybrid:  Human,Automated
+* HoloAssist: Hybrid:  Human,Automated
+* AV: Hybrid:  Human,Automated
+
+
+**Metrics**:
+We report the model accuracy on the embodied reasoning benchmark introduced in [Cosmos-Reason1](https://arxiv.org/abs/2503.15558). The results differ from those presented in Table 9 due to additional training aimed at supporting a broader range of Physical AI tasks beyond the benchmark.
+|                   | [RoboVQA](https://robovqa.github.io/)        | AV       | [BridgeDataV2](https://rail-berkeley.github.io/bridgedata/)| [Agibot](https://github.com/OpenDriveLab/AgiBot-World)| [HoloAssist](https://holoassist.github.io/)       | [RoboFail](https://robot-reflect.github.io/)                               | Average |
+|--------------------|---------------------------------------------|----------|------------------------------------------------------|------------------------------------------------|------------------------------------------------|------------------------------------------------|------------------------------------------------|
+| **Accuracy**        | 87.3                                        | 70.8    | 63.7                                                 | 48.9                                          | 62.7                                           | 57.2                                            |  65.1                                          |
+
+## Dataset Format
+Modality: Video (mp4) and Text
+
+## Dataset Quantification
+### 05/17/2025
+We release the embodied reasoning data and benchmarks. Each data sample is a pair of video and text. The text annotations include understanding and reasoning annotations described in the Cosmos-Reason1 paper. Each video may have multiple text annotations. The quantity of the video and text pairs is described in the table below.
+**The AV data is currently unavailable and will be uploaded soon!**
+
+|                   | [RoboVQA](https://robovqa.github.io/)        | AV       | [BridgeDataV2](https://rail-berkeley.github.io/bridgedata/)| [Agibot](https://github.com/OpenDriveLab/AgiBot-World)| [HoloAssist](https://holoassist.github.io/)       | [RoboFail](https://robot-reflect.github.io/)                               | Total Storage Size |
+|--------------------|---------------------------------------------|----------|------------------------------------------------------|------------------------------------------------|------------------------------------------------|------------------------------------------------|--------------------|
+| **SFT Data**        | 1.14m                                        | 24.7k    | 258k                                                 | 38.9k                                          | 273k                                           | N/A                                            | **300.6GB**         |
+| **RL Data**         | 252                                          | 200      | 240                                                  | 200                                            | 200                                            | N/A                                            | **2.6GB**           |
+| **Benchmark Data**  | 110                                          | 100      | 100                                                  | 100                                            | 100                                            | 100                                            | **1.5GB**           |
+
+We release text annotations for all embodied reasoning datasets and videos for RoboVQA and AV datasets. For other datasets, users may download the source videos from the original data source and find corresponding video sources via the video names. The held-out RoboFail benchmark is released for measuring the generalization capability.
+
+### 08/01/2025
+|                   | [PLM-Video-Human](https://huggingface.co/datasets/facebook/PLM-Video-Human) | Nexar       | [Describe Anything](https://huggingface.co/datasets/nvidia/describe-anything-dataset)| [ITS / Warehouse]       | Visual Critics                             | Total Storage Size |
+|------------------ |-----------------------------------------------------------------------------|-------------|--------------------------------------------------------------------------------------|-------------------------|--------------------------------------------|--------------------|
+| **SFT Data**      | 39k                                                                         | 240k        | 178k                                                                                 | 24k                     | 24k                                        | **2.6TB**          |
+
+
+
+## Inference:
+**Test Hardware:** H100, A100, GB200 <br>
+> [!NOTE]
+> We suggest using `fps=4` for the input video and `max_tokens=4096` to avoid truncated response.
+```python
+from transformers import AutoProcessor
+from vllm import LLM, SamplingParams
+from qwen_vl_utils import process_vision_info
+
+# You can also replace the MODEL_PATH by a safetensors folder path mentioned above
+MODEL_PATH = "nvidia/Cosmos-Reason1-7B"
+
+llm = LLM(
+    model=MODEL_PATH,
+    limit_mm_per_prompt={"image": 10, "video": 10},
+)
+
+sampling_params = SamplingParams(
+    temperature=0.6,
+    top_p=0.95,
+    repetition_penalty=1.05,
+    max_tokens=4096,
+)
+
+video_messages = [
+    {"role": "system", "content": "You are a helpful assistant. Answer the question in the following format: <think>\nyour reasoning\n</think>\n\n<answer>\nyour answer\n</answer>."},
+    {"role": "user", "content": [
+            {"type": "text", "text": (
+                    "Is it safe to turn right?"
+                )
+            },
+            {
+                "type": "video", 
+                "video": "file:///path/to/your/video.mp4",
+                "fps": 4,
+            }
+        ]
+    },
+]
+
+# Here we use video messages as a demonstration
+messages = video_messages
+
+processor = AutoProcessor.from_pretrained(MODEL_PATH)
+prompt = processor.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
+
+mm_data = {}
+if image_inputs is not None:
+    mm_data["image"] = image_inputs
+if video_inputs is not None:
+    mm_data["video"] = video_inputs
+
+llm_inputs = {
+    "prompt": prompt,
+    "multi_modal_data": mm_data,
+
+    # FPS will be returned in video_kwargs
+    "mm_processor_kwargs": video_kwargs,
+}
+
+outputs = llm.generate([llm_inputs], sampling_params=sampling_params)
+generated_text = outputs[0].outputs[0].text
+
+print(generated_text)
+```
+
+
+## Ethical Considerations
+
+NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
+
+Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
+
+For more detailed information on ethical considerations for this model, please see the subcards of Explainability, Bias, Safety & Security, and Privacy below. 
+
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+### Plus Plus (++) Promise
+
+We value you, the datasets, the diversity they represent, and what we have been entrusted with. This model and its associated data have been:
+
+* Verified to comply with current applicable disclosure laws, regulations, and industry standards.
+* Verified to comply with applicable privacy labeling requirements.
+* Annotated to describe the collector/source (NVIDIA or a third-party).
+* Characterized for technical limitations.
+* Reviewed to ensure proper disclosure is accessible to, maintained for, and in compliance with NVIDIA data subjects and their requests.
+* Reviewed before release.
+* Tagged for known restrictions and potential safety implications.
+
+### Bias
+
+| Field                                                                                                                                                            | Response |
+| :--------------------------------------------------------------------------------------------------------------------------------------------------------------- | :------- |
+| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing: | None     |
+| Measures taken to mitigate against unwanted bias:                                                                                                                | The training video sources contain multiple physical embodiments and environments including human, car, single arm robot, bimanual robot in indoor and outdoor environments. By training on numerous and various physical interactions and curated datasets, we strive to provide a model that does not possess biases towards certain embodiments or environments.   |
+
+### Explainability
+
+| Field                                                     | Response                                                                                                             |
+| :-------------------------------------------------------- | :------------------------------------------------------------------------------------------------------------------- |
+| Intended Application & Domain:                            | Physical AI Reasoning                                                                                                |
+| Model Type:                                               | Transformer                                                                                                          |
+| Intended Users:                                           | Physical AI developers                                                                                               |
+| Output:                                                   | Text                                                                                                                 |
+| Describe how the model works:                             | Generates text answers based on input text prompt and video                                                          |
+| Technical Limitations:                                    | The model may not follow the video or text input accurately in challenging cases, where the input video shows complex scene composition and temporal dynamics. Examples of challenging scenes include: fast camera movements, overlapping human-object interactions, low lighting with high motion blur, and multiple people performing different actions simultaneously.  |
+| Verified to have met prescribed NVIDIA quality standards: | Yes                                                                                                                  |
+| Performance Metrics:                                      | Quantitative and Qualitative Evaluation. Cosmos-Reason1 proposes the embodied reasoning benchmark and physical common sense benchmark to evaluate accuracy with visual question answering.                                                                              |
+| Potential Known Risks:                                    | The model's output can generate all forms of texts, including what may be considered toxic, offensive, or indecent.  |
+| Licensing:                                                | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)   |
+
+### Privacy
+
+| Field                                                               | Response       |
+| :------------------------------------------------------------------ | :------------- |
+| Generatable or reverse engineerable personal information?           | None Known     |
+| Protected class data used to create this model?                     | None Known     |
+| Was consent obtained for any personal data used?                    | None Known     |
+| How often is dataset reviewed?                                      | Before Release |
+| Is there provenance for all datasets used in training?              | Yes            |
+| Does data labeling (annotation, metadata) comply with privacy laws? | Yes            |
+| Applicable Privacy Policy | [NVIDIA Privacy Policy](https://www.nvidia.com/en-us/about-nvidia/privacy-policy)            |
+
+
+### Safety
+
+| Field                                           | Response                                                                                                                                                                                                                                                                                                                             |
+| :---------------------------------------------- | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| Model Application(s):                           | Physical AI common sense understanding and embodied reasoning                                                                                                                                                                                                                                                                                                                     |
+| Describe the life critical impact (if present). | None Known                                                                                                                                                                                                                                                                                                                           |
+| Use Case Restrictions:                          | [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license)                                                                                                                                                                                                                   |
+| 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. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog. |
+

Cosmos-Reason1-7B/chat_template.json

@@ -0,0 +1,3 @@
+{
+    "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

Cosmos-Reason1-7B/config.json

@@ -0,0 +1,61 @@
+{
+  "architectures": [
+    "Qwen2_5_VLForConditionalGeneration"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "vision_start_token_id": 151652,
+  "vision_end_token_id": 151653,
+  "vision_token_id": 151654,
+  "image_token_id": 151655,
+  "video_token_id": 151656,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 128000,
+  "max_window_layers": 28,
+  "model_type": "qwen2_5_vl",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vision_config": {
+    "depth": 32,
+    "hidden_act": "silu",
+    "hidden_size": 1280,
+    "intermediate_size": 3420,
+    "num_heads": 16,
+    "in_chans": 3,
+    "out_hidden_size": 3584,
+    "patch_size": 14,
+    "spatial_merge_size": 2,
+    "spatial_patch_size": 14,
+    "window_size": 112,
+    "fullatt_block_indexes": [
+      7,
+      15,
+      23,
+      31
+    ],
+    "tokens_per_second": 2,
+    "temporal_patch_size": 2
+  },
+  "rope_scaling": {
+    "type": "mrope",
+    "mrope_section": [
+      16,
+      24,
+      24
+    ]
+  },
+  "vocab_size": 152064
+}

Cosmos-Reason1-7B/generation_config.json

@@ -0,0 +1,12 @@
+{
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+  "pad_token_id": 151643,
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+}

Cosmos-Reason1-7B/preprocessor_config.json

@@ -0,0 +1,19 @@
+{
+  "min_pixels": 3136,
+  "max_pixels": 12845056,
+  "patch_size": 14,
+  "temporal_patch_size": 2,
+  "merge_size": 2,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "processor_class": "Qwen2_5_VLProcessor"
+}

Cosmos-Reason1-7B/tokenizer_config.json

@@ -0,0 +1,207 @@
+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "add_bos_token": false
+}

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/0_Open the box.txt

@@ -0,0 +1 @@
+Open the box

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/10_Use right hand to strum ukelele.txt

@@ -0,0 +1 @@
+Use right hand to strum ukelele

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/11_Use the left hand to hold mouse and move the mouse around.txt

@@ -0,0 +1 @@
+Use the left hand to hold mouse and move the mouse around

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/12_Use the left hand to pick up shaker and shake it.txt

@@ -0,0 +1 @@
+Use the left hand to pick up shaker and shake it

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/13_Use the left hand to pick up the dustpan, use the right hand to pick up the tape dispenser, then sweep the dust on the table into the dustpan.txt

@@ -0,0 +1 @@
+Use the left hand to pick up the dustpan, use the right hand to pick up the tape dispenser, then sweep the dust on the table into the dustpan

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/15_Use the right hand to close microwave.txt

@@ -0,0 +1 @@
+Use the right hand to close microwave

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/16_Use the right hand to grab the pan handle and toss the pan.txt

@@ -0,0 +1 @@
+Use the right hand to grab the pan handle and toss the pan

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/17_Use the right hand to open macbook.txt

@@ -0,0 +1 @@
+Use the right hand to open macbook

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/18_Use the right hand to pick up blue scoop and scoop powder from container.txt

@@ -0,0 +1 @@
+Use the right hand to pick up blue scoop and scoop powder from container

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/19_Use the right hand to pick up expo eraser and erase whiteboard.txt

@@ -0,0 +1 @@
+Use the right hand to pick up expo eraser and erase whiteboard

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/1_Use both hands to pick up pot.txt

@@ -0,0 +1 @@
+Use both hands to pick up pot

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/20_Use the right hand to pick up glass and bring it close to the camera as if drinking.txt

@@ -0,0 +1 @@
+Use the right hand to pick up glass and bring it close to the camera as if drinking

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/21_Use the right hand to pick up hat and put it on top of mini tripod.txt

@@ -0,0 +1 @@
+Use the right hand to pick up hat and put it on top of mini tripod

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/22_Use the right hand to pick up iron and press the T-shirt.txt

@@ -0,0 +1 @@
+Use the right hand to pick up iron and press the T-shirt

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/23_Use the right hand to pick up long-reach lighter to light the candle.txt

@@ -0,0 +1 @@
+Use the right hand to pick up long-reach lighter to light the candle

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/24_Use the right hand to pick up marker and write on whiteboard.txt

@@ -0,0 +1 @@
+Use the right hand to pick up marker and write on whiteboard

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/25_Use the right hand to pick up pink bottle and pour water on flower.txt

@@ -0,0 +1 @@
+Use the right hand to pick up pink bottle and pour water on flower

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/26_Use the right hand to pick up plate and place it onto the dish rack.txt

@@ -0,0 +1 @@
+Use the right hand to pick up plate and place it onto the dish rack

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/27_Use the right hand to pick up rag and erase whiteboard.txt

@@ -0,0 +1 @@
+Use the right hand to pick up rag and erase whiteboard

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/28_Use the right hand to pick up sauce bottle and shake it.txt

@@ -0,0 +1 @@
+Use the right hand to pick up sauce bottle and shake it

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/29_Use the right hand to pick up scraper and scrape cutting board.txt

@@ -0,0 +1 @@
+Use the right hand to pick up scraper and scrape cutting board

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/2_Use knife to cut the object on the cutting board.txt

@@ -0,0 +1 @@
+Use knife to cut the object on the cutting board

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/30_Use the right hand to pick up shaker and crack it on side of bowl.txt

@@ -0,0 +1 @@
+Use the right hand to pick up shaker and crack it on side of bowl

PhysicalAI-Robotics-GR00T-Eval/gr1_behavior/31_Use the right hand to pick up spatula and spread butter on bread.txt

@@ -0,0 +1 @@
+Use the right hand to pick up spatula and spread butter on bread