soufyane/gemma_data_science

This is a Gemma model 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:

#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))