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mlboydaisuke
/
Timestral-209M-LiteRT

Time Series Forecasting
LiteRT
LiteRT
android
on-device
gpu
npu
time-series
forecasting
timestral
Model card Files Files and versions
xet
Community

Instructions to use mlboydaisuke/Timestral-209M-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • LiteRT

    How to use mlboydaisuke/Timestral-209M-LiteRT with LiteRT:

    # 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
Timestral-209M-LiteRT / assets
1.57 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
mlboydaisuke's picture
mlboydaisuke
Demo re-taken with the sample's chart fixed (the forecast starts at the last observed value): graph call 18.0 ms, host 7.1 ms, first NPU compile 23.6 s in that run
ac11baa verified 11 days ago
  • demo.gif
    906 kB
    xet
    Demo re-taken with the sample's chart fixed (the forecast starts at the last observed value): graph call 18.0 ms, host 7.1 ms, first NPU compile 23.6 s in that run 11 days ago
  • demo.mp4
    493 kB
    xet
    Demo re-taken with the sample's chart fixed (the forecast starts at the last observed value): graph call 18.0 ms, host 7.1 ms, first NPU compile 23.6 s in that run 11 days ago
  • hero.png
    174 kB
    xet
    Timestral-209M on LiteRT: float16 graphs for the GPU and the NPU, int8 graphs for the CPU, host code in Python and Kotlin 13 days ago