Instructions to use Karamdargham/cloud-pre with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- TimesFM
How to use Karamdargham/cloud-pre with TimesFM:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
🌩️ Cloud-Pre (500M)
Cloud-Pre is a specialized time-series foundation model designed for predicting cloud environment resource usage (CPU, RAM, Network).
It is fine-tuned based on Google's highly advanced google/timesfm-2.0-500m-pytorch.
Model Details
- Architecture: Decoder-Only Transformer (TimesFM 2.0 Base)
- Parameters: 500 Million
- Fine-tuning Objective: Cloud CPU/Resource peak and anomaly forecasting to aid with Predictive Auto-scaling.
- Developer: Assem Sabry
How to find the training code?
The complete open-source training pipeline and data engineering scripts can be found on my GitHub Repository.
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