Text Generation
KerasFormers
Keras
PyTorch
JAX
TensorFlow
English
gemma2
gemma
gemma-2-9b
arxiv:2408.00118
Instructions to use zeromodels/gemma-2-9b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasFormers
How to use zeromodels/gemma-2-9b with KerasFormers:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Keras
How to use zeromodels/gemma-2-9b with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://zeromodels/gemma-2-9b") - Notebooks
- Google Colab
- Kaggle
metadata
pipeline_tag: text-generation
license: gemma
base_model: google/gemma-2-9b
library_name: kerasformers
extra_gated_heading: Access Gemma on Hugging Face
language:
- en
tags:
- keras
- kerasformers
- gemma2
- gemma
- gemma-2-9b
- text-generation
- arxiv:2408.00118
- pytorch
- jax
- tf
See our collection for all Gemma 2 sizes and variants.
Run Gemma 2 with Keras 3: JAX, PyTorch, or TensorFlow
kerasformers/gemma-2-9b
Pure-Keras 3 conversion of google/gemma-2-9b for
kerasformers. One implementation runs unmodified on
TensorFlow / Torch / JAX. This is a base (pretrained) checkpoint, served here as text -> text via Gemma2TextGenerate; weights are
stored in bfloat16.
For model details, license, and usage terms, see Google's model card.
✨ Quick start
import os
os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
from kerasformers.models.gemma2 import Gemma2TextGenerate, Gemma2Tokenizer
model = Gemma2TextGenerate.from_weights("kerasformers/gemma-2-9b")
tokenizer = Gemma2Tokenizer.from_weights("kerasformers/gemma-2-9b")
inputs = tokenizer([{"role": "user", "content": "Hello, who are you?"}])
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0]))
Load any Gemma 2 variant the same way with from_weights("kerasformers/<variant>"):
| Variant | Hub |
|---|---|
gemma-2-27b |
kerasformers/gemma-2-27b |
gemma-2-27b-it |
kerasformers/gemma-2-27b-it |
gemma-2-2b |
kerasformers/gemma-2-2b |
gemma-2-2b-it |
kerasformers/gemma-2-2b-it |
gemma-2-9b |
kerasformers/gemma-2-9b |
gemma-2-9b-it |
kerasformers/gemma-2-9b-it |
Tips
- Set
KERAS_BACKENDbefore importing Keras / kerasformers. - Loads in bfloat16 by default. Pass
load_dtype="float32"for full precision, orquantization="int8"to shrink further. - See the Gemma 2 docs.
- Community / upstream weights still work via the
hf:prefix:Gemma2TextGenerate.from_weights("hf:google/gemma-2-9b").
Special Thanks
A huge thank you to the Google Gemma authors for creating and releasing these models.
License: Gemma (gated). Accept the license on the upstream Hub card before downloading.