Instructions to use keras/flan_large_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/flan_large_multi with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/flan_large_multi") - Keras
How to use keras/flan_large_multi with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/flan_large_multi") - Notebooks
- Google Colab
- Kaggle
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README.md
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library_name: keras-hub
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---
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## Model Overview
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⚠️ T5 is currently only available via the `keras-hub-nightly` package. Use `pip install keras-hub-nightly` to try this model.
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layer normalization layers in the Transformer layers.
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- __tie_embedding_weights__: boolean. If `True`, the weights of the token
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embedding and the weights projecting language model outputs from
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`hidden_dim`
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library_name: keras-hub
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license: apache-2.0
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tags:
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- text-classification
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- keras
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---
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## Model Overview
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⚠️ T5 is currently only available via the `keras-hub-nightly` package. Use `pip install keras-hub-nightly` to try this model.
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layer normalization layers in the Transformer layers.
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- __tie_embedding_weights__: boolean. If `True`, the weights of the token
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embedding and the weights projecting language model outputs from
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`hidden_dim`
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