Instructions to use keras/esm2_t30_150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/esm2_t30_150M with KerasHub:
import keras_hub # Load TextClassifier model text_classifier = keras_hub.models.TextClassifier.from_preset( "hf://keras/esm2_t30_150M", num_classes=2, ) # Fine-tune text_classifier.fit(x=["Thilling adventure!", "Total snoozefest."], y=[1, 0]) # Classify text text_classifier.predict(["Not my cup of tea."])import keras_hub # Create a MaskedLM model task = keras_hub.models.MaskedLM.from_preset("hf://keras/esm2_t30_150M")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/esm2_t30_150M") - Keras
How to use keras/esm2_t30_150M 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://keras/esm2_t30_150M") - Notebooks
- Google Colab
- Kaggle
Download model.weights.h5 from keras/esm2_t30_150M: direct link, hf CLI and curl.
- Browser
- Download file 592 MB
-
https://huggingface.co/keras/esm2_t30_150M/resolve/main/model.weights.h5
- Command line
-
hf download hf://keras/esm2_t30_150M/model.weights.h5
-
curl -L -o model.weights.h5 https://huggingface.co/keras/esm2_t30_150M/resolve/main/model.weights.h5
592 MB
- Xet hash:
- 613684c8b45baffeadf688551130fecc5dc4d56d3cdec9b9584bb91c419642bc
- Size of remote file:
- 592 MB
- SHA256:
- 11442ab0bab42121db55efd27ed858a381e9061800fed4d66e4d4371085c26cf
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