Instructions to use DJF-on-arm/Iamam1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DJF-on-arm/Iamam1 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://DJF-on-arm/Iamam1") - Notebooks
- Google Colab
- Kaggle
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README.md
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@@ -61,7 +61,6 @@ These models may produce incoherent or incorrect outputs because:
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# Why does it say some (probally all) models .keras files are 'unsafe'?
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Because i'm using callbacks that are custom python definitions, theres .keras files *should not be* unsafe.
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# However, if you do not trust me or don't want to take any risk what-so-ever or this just feels off to you YOU DON'T *HAVE* TO BELIVE **ME** (Please don't go around trusting random people online or random models that don't have the code used to train them public and that you can verify that was the code in the model!)
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# Well how can i know it is safe?
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If you want to audit the custom logic before running it, you can load the model's metadata using:
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# Why does it say some (probally all) models .keras files are 'unsafe'?
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Because i'm using callbacks that are custom python definitions, theres .keras files *should not be* unsafe.
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# Well how can i know it is safe?
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If you want to audit the custom logic before running it, you can load the model's metadata using:
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