An AI project by Thomas Galloway, intended to produce an LLM trained specifically on sports law.In the legal profession, the ability to quickly access and analyze domain-specific information is critical. General AI models provide broad answers but often lack the deep, nuanced understanding required for specialized fields like sports law.This project, the "Gridiron Counsel," is a working proof-of-concept for a specialized AI research assistant. I created this tool by fine-tuning an open-source large language model (LLM) to understand and respond to complex questions specifically about the intersection of football and law.It is designed to provide detailed, context-aware answers on topics ranging from collective bargaining agreements and antitrust litigation to specific player-related cases. Detail of my workflow, and the LLM chat window, are below.Note: This demo runs on T4 GPU hardware and is set to auto-sleep to conserve resources. The app may take 60-90 seconds to "wake up" upon your first query.
Project Methodology: From Idea to Application
This project was completed in three distinct phases, moving from legal research to model training and finally to a usable web application.Step 1: Data Curation & Legal "Textbook" Creation
A language model's expertise comes from its training data. The first and most critical step was to build a custom "legal textbook" from scratch. This involved sourcing, verifying, and curating a high-quality dataset of specialized legal texts, including:Key articles from the NFL Collective Bargaining Agreement (CBA), focusing on commissioner discipline (Article 46), salary cap, and free agency.Landmark case summaries and judicial opinions (e.g., Brady v. NFL, NCAA v. Alston, American Needle v. NFL).Law review articles and scholarly analysis on sports-related antitrust, labor, and intellectual property law.Detailed legal summaries of major events like "Deflategate," the NFL Concussion Settlement, and the rise of NIL collectives.This curated dataset formed the "mind" of my specialist model.Step 2: Efficient Model Fine-Tuning
I selected a powerful, open-source model (Mistral-7B-Instruct) as my foundation. I then used a modern, highly efficient fine-Tuning technique (LoRA, or Low-Rank Adaptation) to train this model on our custom legal dataset.This process is like "precision surgery" on the model. Instead of retraining the entire 7-billion-parameter model (which is slow and cost-prohibitive), I trained a small, new "adapter" that layers specialized sports law knowledge on top of the model's existing reasoning capabilities. This entire process was completed efficiently on a single cloud-based GPU.Step 3: Deployment & Public Web ApplicationA model is only useful if it can be accessed. I packaged our newly-trained model and deployed it to a Hugging Face Space, a platform for hosting live AI applications.This interactive web interface was built with the Gradio library, providing the conversational front-end you see above. The final step was configuring the application to run on dedicated GPU hardware, ensuring the model can deliver fast, real-time responses for this demo.