Model Overview
Description:
USD Code (usdcode-llama3-70b-instruct) is an OpenUSD Python code generation and knowledge answering model that helps developers to write OpenUSD code and answer OpenUSD knowledge questions.
This model is available for preview, demonstration, and non-production usage on the NVIDIA API Catalog.
References:
- Llama3 - https://ai.meta.com/blog/meta-llama-3/
- OpenUSD - https://www.openusd.org/
Model Architecture:
Architecture Type: Transformer-Based Architecture
Network Architecture: Llama-3
Input
Input Type(s): Text
Input Format(s): String
Other Properties Related to Input: Max context length of 8k tokens
Output
Output Type(s): Text (Code, Python)
Output Format: String
Other Properties Related to Output: Max output length of 8k tokens
Software Integration:
Runtime Engine(s):
- NIM 1.0.0
Supported Hardware Microarchitecture Compatibility:
- NVIDIA Hopper
[Preferred/Supported] Operating System(s):
- Linux
Model Version(s):
- usdcode-llama3-70b-instruct-tuned-0703
Training, Testing, and Evaluation Datasets:
Training Dataset:
Data Collection Method by dataset
- Hybrid: Automated, Synthetic
Labeling Method by dataset
- Unknown
Properties (Quantity, Dataset Descriptions, Sensor(s)):
- 59,729 question/answer pairs (text)
Testing Dataset:
Data Collection Method by dataset
- Not Applicable
Labeling Method by dataset
- Not Applicable
Properties (Quantity, Dataset Descriptions, Sensor(s)):
- Not Applicable
Evaluation Dataset:
Data Collection Method by dataset
- Hybrid: Automated, Synthetic
Labeling Method by dataset
- Not Applicable
Properties (Quantity, Dataset Descriptions, Sensor(s)):
- 100 question/answer pairs (text)
Inference:
Engine:
- TensorRT
Test Hardware:
- H100
Ethical Considerations (For NVIDIA Models Only):
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. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.