This video shows how to install and manage Gemma LLM with Keras. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models.
Code:
!pip install keras --upgrade
!pip install kaggle
from google.colab import files
uploaded = files.upload()
for fn in uploaded.keys():
print('User uploaded file "{name}" with length {length} bytes'.format(
name=fn, length=len(uploaded[fn])))
# Then move kaggle.json into the folder where the API expects to find it.
!mkdir -p ~/.kaggle/ && mv kaggle.json ~/.kaggle/ && chmod 600 ~/.kaggle/kaggle.json
!pip install keras_nlp --upgrade
!pip install keras --upgrade
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras_nlp
import keras
import tensorflow as tf
import time
keras.mixed_precision.set_global_policy("mixed_float16")
preprocessor = keras_nlp.models.GemmaPreprocessor.from_preset(
"gemma_2b_en"
)
gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_2b_en")
gemma_lm.generate("which one came first, egg or chicken?", max_length=130)