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 models.json from jbilcke/timestral-mlx: direct link, hf CLI and curl.
- Browser
- Download file 1.62 kB
-
https://huggingface.co/jbilcke/timestral-mlx/resolve/main/models.json
- Command line
-
hf download hf://jbilcke/timestral-mlx/models.json
-
curl -L -o models.json https://huggingface.co/jbilcke/timestral-mlx/resolve/main/models.json
1.62 kB
| { | |
| "format": "timestral-mlx-collection-v1", | |
| "models": [ | |
| { | |
| "variant": "5M", | |
| "path": "5M", | |
| "source": "mistralai/Timestral-5M", | |
| "source_revision": "bdd20cfc15a3fea0e4cb5ccef37f8d3ca7aa335f", | |
| "parameters": 5208960, | |
| "weight_bytes": 20844010, | |
| "manifest_sha256": "f4bdcc4e0a7255191f1bcf565f05e667fa9c4106351fba6695488be2ca0f9f1e", | |
| "validation": "reports/5M.json", | |
| "passed": true | |
| }, | |
| { | |
| "variant": "28M", | |
| "path": "28M", | |
| "source": "mistralai/Timestral-28M", | |
| "source_revision": "49b26409ac87b5935539a5d8ffe7f397b720db47", | |
| "parameters": 27821184, | |
| "weight_bytes": 111296533, | |
| "manifest_sha256": "126c69da69e45dc0216b8c932afbd024429c784dcdd259b4d67f62fe2d31aa9b", | |
| "validation": "reports/28M.json", | |
| "passed": true | |
| }, | |
| { | |
| "variant": "123M", | |
| "path": "123M", | |
| "source": "mistralai/Timestral-123M", | |
| "source_revision": "5a147287addda85f6acd8da3fb8858fe9af7e8f7", | |
| "parameters": 122606720, | |
| "weight_bytes": 490445982, | |
| "manifest_sha256": "4faec738809456302a480b42b2c270b63fc5e95c7b1b1ac31f178e7729d579d5", | |
| "validation": "reports/123M.json", | |
| "passed": true | |
| }, | |
| { | |
| "variant": "209M", | |
| "path": "209M", | |
| "source": "mistralai/Timestral-209M", | |
| "source_revision": "9874d252ab0aae16f7f7c999f98f98d6ad7d8365", | |
| "parameters": 209312256, | |
| "weight_bytes": 837271692, | |
| "manifest_sha256": "8439041e17342011f5960ccfbb25d9d65b3e0db200e078a37d3eacb78aa5bdcc", | |
| "validation": "reports/209M.json", | |
| "passed": true | |
| } | |
| ] | |
| } | |