WilAI/bloom-560m-lora-tagger-test

BLOOM-560M-LoRA Tagger Test

Welcome to the BLOOM-560M-LoRA Tagger Test project! This project is inspired by the HuggingFace Colab notebook and demonstrates how to use the BLOOM-560M model with LoRA (Low-Rank Adaptation) for efficient text tagging tasks. Below you will find detailed descriptions of the project's components, setup instructions, and usage guidelines.

Project Overview

Introduction

This project utilizes the BLOOM-560M model with LoRA to perform text tagging. The combination of BLOOM-560M's large language model capabilities and LoRA's efficient adaptation techniques ensures high-quality tagging with optimized resource usage.

Key Features

• Text Tagging: Perform high-quality, accurate text tagging based on the provided input.
• Efficient Adaptation: Utilize LoRA for efficient fine-tuning and adaptation of the BLOOM-560M model.
• Customizable Tagging Prompts: Define and customize prompts to generate specific types of tags.

Components

BLOOM-560M Model

The core of the system is the BLOOM-560M model, which generates human-like text tags based on the provided input.

• Large Language Model: BLOOM-560M is a powerful transformer-based language model capable of understanding and generating complex text.
• LoRA Integration: LoRA enables efficient fine-tuning and adaptation of the model to specific tasks with reduced computational requirements.

Text Tagging Pipeline

The text tagging pipeline handles the input processing, model inference, and output generation.

• Input Processing: Preprocess and format the input prompts for the model.
• Model Inference: Use the BLOOM-560M model to generate tags based on the input prompts.
• Output Generation: Post-process the generated tags and present them in a readable format.

Setup Instructions

Prerequisites

• Python 3.8 or higher
• Access to HuggingFace Transformers and Datasets libraries

Monitoring and Logs

Monitor the application logs for insights into the text tagging processes.

Acknowledgements

Special thanks to the creators of the BLOOM-560M model and the inspiration from the "HuggingFace Colab notebook".