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
| { | |
| "module": "keras_nlp.models.gemma.gemma_backbone", | |
| "class_name": "GemmaBackbone", | |
| "config": { | |
| "name": "gemma_backbone", | |
| "trainable": true, | |
| "vocabulary_size": 256000, | |
| "num_layers": 28, | |
| "num_query_heads": 16, | |
| "num_key_value_heads": 16, | |
| "hidden_dim": 3072, | |
| "intermediate_dim": 49152, | |
| "head_dim": 256, | |
| "layer_norm_epsilon": 1e-06, | |
| "dropout": 0 | |
| }, | |
| "registered_name": "keras_nlp>GemmaBackbone", | |
| "assets": [], | |
| "weights": "model.weights.h5" | |
| } |