Text Generation
KerasHub
English
text-generation-inference
text-classification
text-conversation
text-to-text-generation
Instructions to use keras/gemma_instruct_7b_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/gemma_instruct_7b_en with KerasHub:
import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/gemma_instruct_7b_en") - Keras
How to use keras/gemma_instruct_7b_en 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/gemma_instruct_7b_en") - Notebooks
- Google Colab
- Kaggle
File size: 401 Bytes
b316368 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | {
"module": "keras_nlp.src.models.gemma.gemma_tokenizer",
"class_name": "GemmaTokenizer",
"config": {
"name": "gemma_tokenizer",
"trainable": true,
"dtype": "int32",
"proto": null,
"sequence_length": null
},
"registered_name": "keras_nlp>GemmaTokenizer",
"assets": [
"assets/tokenizer/vocabulary.spm"
],
"weights": null
} |