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.

pocketmonkey
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