Legal-Document-Simplifier-Llama4

Legal Document Simplifier (LoRA)

Model Overview

This repository contains a LoRA adapter fine-tuned on Meta Llama-4 Scout-17B-16E-Instruct for legal document simplification.

The model converts complex legal clauses into clear, plain-English explanations while preserving the original legal meaning.

Base Model

• Base Model: Meta Llama-4 Scout-17B-16E-Instruct
• Fine-tuning Method: Supervised Fine-Tuning (SFT)
• Fine-tuning Technique: Parameter-Efficient Fine-Tuning (PEFT) using LoRA

Task

Input: Complex legal clause

Output: Plain-English legal explanation suitable for non-lawyers while preserving the original legal intent.

Training Configuration

Dataset

The training dataset consists of English legal clauses paired with simplified plain-English explanations.

Legal Domains

• Privacy Policies
• Terms of Service
• Employment Agreements
• Non-Disclosure Agreements (NDAs)
• Lease Agreements
• Insurance Policies
• Consumer Agreements

The dataset was enhanced using Adaption AutoScientist before fine-tuning.

Evaluation Results

These results demonstrate improved performance on legal simplification tasks compared with the evaluated base model.

Intended Use

This model is intended to:

• Simplify legal clauses
• Improve readability of legal documents
• Support education and research
• Help users understand complex legal language

Note: This model is intended as an educational and productivity tool. It should not replace professional legal advice.

Limitations

• Outputs should be reviewed before use in high-stakes legal situations.
• Performance may vary on legal systems, jurisdictions, or document types that were not represented in the training dataset.

License

This project is released under the MIT License.