Instructions to use logasja/instagram-earlybird with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use logasja/instagram-earlybird with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://logasja/instagram-earlybird") - Notebooks
- Google Colab
- Kaggle
Download assets/summary_plot.png from logasja/instagram-earlybird: direct link, hf CLI and curl.
- Browser
- Download file 2.16 MB
-
https://huggingface.co/logasja/instagram-earlybird/resolve/main/assets/summary_plot.png
- Command line
-
hf download hf://logasja/instagram-earlybird/assets/summary_plot.png
-
curl -L -o summary_plot.png https://huggingface.co/logasja/instagram-earlybird/resolve/main/assets/summary_plot.png
2.16 MB

- Xet hash:
- a82500678f2210b99c2007e09ba58e1c40fa1a9556d395f12f89c0b004dd1db1
- Size of remote file:
- 2.16 MB
- SHA256:
- 42e25365509bcf144fc382e54c206c6d215ada9d29f6f4162d06ea940e13e1b7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.