Gemma Project
Overview
This project involves setting up and running inference using a pre-trained model configured with Low-Rank Adaptation (LoRA). The main components include:
- gemma.ipynb: A Jupyter notebook for configuring and experimenting with the model.
- Inference.py: A Python script for loading the model and tokenizer, and running inference with specified configurations.
Files
gemma.ipynb
This notebook includes:
- Loading Lora Configuration: Setting up the LoRA configuration for the model.
- Loading Model and Tokenizer: Loading the pre-trained model and tokenizer for further tasks.
- Additional cells likely involve experimenting with model fine-tuning and evaluation.
Inference.py
This script includes:
- Importing Libraries: Necessary imports including transformers, torch, and specific configurations.
- Model and Tokenizer Setup: Loading the model and tokenizer from the specified paths.
- Quantization Configuration: Applying quantization for efficient model computation.
- Inference Execution: Running inference on the input data.
Setup
Requirements
- Python 3.x
- Jupyter Notebook
- PyTorch
- Transformers
- Peft
Installation
- Clone the repository:
git clone <repository_url> cd <repository_directory> - Install the required packages:
pip install torch transformers peft jupyter
Usage
Running the Notebook
- Open the Jupyter notebook:
jupyter notebook gemma.ipynb - Follow the instructions in the notebook to configure and experiment with the model.
Running the Inference Script
- Execute the inference script:
python Inference.py - The script will load the model and tokenizer, apply the necessary configurations, and run inference on the provided input.
Notes
- Ensure that you have the necessary permissions and access tokens for the pre-trained models.
- Adjust the configurations in the notebook and script as needed for your specific use case.
License
This project is licensed under the MIT License.