Gemma 3 4b Document Writer

🚀 GEMMA Document Rewriter for RAG Pipeline

The GEMMA Document Rewriter for RAG Pipeline is a cutting - edge text rewriting model. It's built on the pre - trained Google Gemma 3 4B language model. Fine - tuned with LoRA (Low - Rank Adaptation) technique, it uses adapter weights from ZySec - AI/gemma - 3 - 4b - document - writer - lora. Its main purpose is to rewrite documents smartly, removing needless info, byte spaces, and redundant content. It focuses on significant info for Retrieval - Augmented Generation (RAG) pipelines and outputs a clean, structured Markdown document.

🚀 Quick Start

You can start using the model by referring to the following Colab notebook: Usage Example

✨ Features

• Efficient Document Rewriting: Extracts essential content from long documents, removing extra details and whitespace, making it perfect for RAG systems.
• Markdown Output: Reformats content into Markdown, auto - generating headings and sub - headings for better readability and further processing.
• Cost - Effective and Speed Optimized: Built on a relatively small language model (Gemma 3 4B), it offers a cost - effective solution with fast inference speeds for production pipelines.
• LoRA Fine - Tuning: Uses LoRA adapter layers to fine - tune the base model efficiently, adapting quickly to the document rewriting task without full - scale retraining.
• State - of - the - Art Performance: Integrates smoothly into modern RAG pipelines, highlighting only the most relevant and structured information.

📚 Documentation

Intended Use Cases

This model suits various document processing and natural language understanding tasks:

• Document Summarization & Rewriting: Simplifies and restructures long documents or articles, presenting key info in an organized Markdown style.
• Data Preprocessing for RAG Pipelines: Serves as a pre - processing step in RAG systems, providing clean, condensed documents to improve retrieval quality.
• Content Cleanup & Standardization: Removes noise like extra whitespace, irrelevant bytes, and redundant words, standardizing documents before further processing.
• Cost - Effective Deployment: Ideal for organizations needing document rewriting without the high cost of large, resource - intensive models.

Model Architecture

The model is based on the Google Gemma 3 4B architecture, a transformer - based language model for high - speed inference. LoRA adapter layers are added to specialize it for document rewriting. This adapter mechanism allows the model to learn task - specific changes with minimal parameter updates, making fine - tuning memory and compute - efficient.

How It Works

1. Input Processing: Accepts raw text strings (a whole document or text section). Tokenizes the input and identifies areas with extra content.
2. Information Extraction: Uses fine - tuned attention mechanisms to extract semantically important content for RAG tasks, evaluating context and relevance.
3. Content Rewriting & Formatting: Rewrites the extracted info into a concise format and organizes the output into Markdown with appropriate headings.
4. Output Generation: Produces a clean, structured document for RAG pipelines or other downstream applications.

📄 License

This project is licensed under the Apache 2.0 license.