| | --- |
| | 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)) |
| | |
| | ``` |
