Update README.md

README.md

@@ -17,13 +17,88 @@ Abstract: *This work explores expanding the capabilities of large language model
 
 <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/634e15aec1ce28f1de91c470/CwSCmyJizQderIYC8CaJ4.mp4"></video>
 
-
 ## Method
 
 Overview of our method. LLaMA-Mesh unifies text and 3D mesh in a uniform format by representing the numerical values of vertex coordinates and face definitions of a 3D mesh as plain text. Our model is trained using text and 3D interleaved data in an end-to-end manner. Therefore, our model can generate both text and 3D meshes in a unified model.
 
 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/634e15aec1ce28f1de91c470/0DzHXhoxonG5ZMeTysA6s.jpeg)
 
+### Model Developer: Base model weight is from Meta. Finetuned by Nvidia
+
+## Third-Party Community Consideration:
+
+This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md). 
+
+### License/Terms of Use:
+This model is ready for non-commercial use. 
+This model is distributed under the NSCLv1 license and was built using Llama 3.1. 
+Attribution: "Built with Llama" as per the Llama 3.1 Community License Agreement. Please refer to Llama 3.1 licensing terms for further details.
+
+## Reference(s):
+Llama 3.1 [Github](https://github.com/meta-llama/llama-models/tree/main/models/llama3_1) 
+
+## Model Architecture:
+**Architecture Type:** Transformer
+*Network Architecture:* Llama 3.1
+
+## Input: 
+**Input Type(s):** Text
+
+**Input Format(s):** String 
+
+**Input Parameters:** 1D
+
+**Other Properties Related to Input:** Max token length 8k
+
+## Output: 
+**Output Type(s):** Text
+
+**Output Format:** String
+
+**Output Parameters:** 1D
+
+**Other Properties Related to Output:** Max token length 8k
+
+**Supported Hardware Microarchitecture Compatibility:**
+* NVIDIA Ada
+
+**Supported Operating System(s):**
+* Linux
+
+## Model Version(s):
+Llama 3.1 8B mesh
+
+# Training Dataset: 
+Please refer to [Llama 3.1  Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md) for information on Training, Testing, and Evaluation Datasets).
+
+The data is curated through converting Objaverse mesh data into text string (in the format as vertex index, face index as string). The model is finetuned on the curated dataset with 32 GPU. 
+
+[**Objaverse**](https://objaverse.allenai.org/explore/) 
+
+**Data Collection Method by dataset**: Unknown
+
+**Labeling Method by dataset**: Unknown 
+
+**Properties:** We use 30k mesh data, which is a subset from the Objaverse. We filter the Objaverse dataset by the number of faces, and only keep the shape with the number of faces less than 500. They are saved as obj file format. 
+
+**Dataset License(s):** The use of the dataset as a whole is licensed under the ODC-By v1.0 license. 
+
+[**UltraChat**](https://github.com/thunlp/UltraChat)
+
+**Data Collection Method by dataset**: Unknown
+
+**Labeling Method by dataset**: Unknown 
+
+## Inference:
+**Engine**: Pytorch
+
+**Test Hardware**: A100
+
+## 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.  
+
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
 ## BibTeX
 
 ```bibtex