Time Series Forecasting
MLX
Safetensors
apple-silicon
timestral
time-series
forecasting
quantile-regression
Instructions to use jbilcke/timestral-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use jbilcke/timestral-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download jbilcke/timestral-mlx --local-dir timestral-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download reports/tests.json from jbilcke/timestral-mlx: direct link, hf CLI and curl.
- Browser
- Download file 344 Bytes
-
https://huggingface.co/jbilcke/timestral-mlx/resolve/main/reports/tests.json
- Command line
-
hf download hf://jbilcke/timestral-mlx/reports/tests.json
-
curl -L -o tests.json https://huggingface.co/jbilcke/timestral-mlx/resolve/main/reports/tests.json
344 Bytes
| { | |
| "passed": true, | |
| "tests_passed": 15, | |
| "summary": "15 passed in 1.25s", | |
| "command": "python -m pytest -q", | |
| "coverage": [ | |
| "upstream parity", | |
| "covariates", | |
| "block feedback", | |
| "batch isolation", | |
| "input validation", | |
| "lossless wheel inventory", | |
| "corrupt checkpoint rejection", | |
| "Apple metadata exclusion" | |
| ] | |
| } | |