This is new updated version of Moirai-1.0-R (https://huggingface.co/Salesforce/moirai-1.0-R-base).
The Moirai-1.1-R model achieved significant improvements (~20%) for low-frequency cases like Yearly and Quarterly data in Normalised Mean Absolute Error (NMAE) for 40 datasets on the Monash repository.
Ethical Considerations
This release is for research purposes only in support of an academic paper.
Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes.
We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model.
We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting
use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety.
For further guidance on use cases, refer to our AUP and AI AUP.
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Salesforce/moirai-1.1-R-small", dtype="auto")