---
library_name: keras-hub
pipeline_tag: text-generation
language:
- en
tags:
- gemma2b
- gemma
- google
- gemini
- gemma data science
- gemma 2b data science
- data science model
datasets:
- soufyane/DATA_SCIENCE_QA
---
This is a [`Gemma` model](https://keras.io/api/keras_nlp/models/gemma) uploaded using the KerasNLP library and can be used with JAX, TensorFlow, and PyTorch backends.
This model is related to a `CausalLM` task.

Model config:
* **name:** gemma_backbone
* **trainable:** True
* **vocabulary_size:** 256000
* **num_layers:** 18
* **num_query_heads:** 8
* **num_key_value_heads:** 1
* **hidden_dim:** 2048
* **intermediate_dim:** 32768
* **head_dim:** 256
* **layer_norm_epsilon:** 1e-06
* **dropout:** 0

* **Model Details:**
* **Architecture:** Gemma 2b is based on a deep neural network architecture, utilizing state-of-the-art techniques in natural language processing and machine learning.
* **Fine-tuning Framework:** Gemma 2b was fine-tuned using the Keras NLP framework, which provides powerful tools for building and training neural network models specifically tailored for natural language processing tasks.
* **Training Data:** Gemma 2b was fine-tuned on a diverse set of data science datasets. https://huggingface.co/datasets/soufyane/DATA_SCIENCE_QA
* **Preprocessing:** The model incorporates standard preprocessing techniques including tokenization, normalization, and feature scaling to handle input data effectively.

**use it on kaggle:**
I recommend to use the model on kaggle(free GPU use P100) for fast responses here's the link to my notebook:
https://www.kaggle.com/code/sufyen/gemma-2b-data-science-from-hugging-face

**how to use:**
```python
#install the necessery PKGs
!pip install -q -U keras-nlp
!pip install -q -U keras>=3
import keras_nlp
from keras_nlp.models import GemmaCausalLM
import warnings
warnings.filterwarnings('ignore')
import os

#set the envirenment
os.environ["KERAS_BACKEND"] = "jax"  # Or "torch" or "tensorflow".
os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"]="1.00"

#load the model from HF
model = keras_nlp.models.CausalLM.from_preset(f"hf://soufyane/gemma_data_science")

while True:
  x = input("enter your question: ")
  print(model.generate(f"question: {x}", max_length=256))

```