JurisSim-32B-v3

<div align="center"> <img src="https://img.shields.io/badge/AMD-Developer_Hackathon-ED1C24?style=for-the-badge&logo=amd&logoColor=white" alt="AMD Hackathon Badge"/> <img src="https://img.shields.io/badge/Hardware-MI300X_Accelerator-00A3E0?style=for-the-badge" alt="MI300X Badge"/> <img src="https://img.shields.io/badge/Model-Qwen3--32B-blue?style=for-the-badge" alt="Qwen Model Badge"/> <h1>⚖️ JurisSim</h1> <p><b>A Neuro-Symbolic Legal Auditor & Formal Verification Agent</b></p> </div>

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📖 Overview

JurisSim-32B v3.1 is a neuro-symbolic legislative stress-tester designed for the AMD Instinct MI300X. It translates natural language legal clauses into Z3 SMT-LIB formal constraints to identify adversarial loopholes with mathematical certainty.

🚀 Version 3.1: Dual-Agent Swarm

We have moved beyond a single model to a Multi-Agent Feedback Loop:

1. The Auditor (Qwen3-32B): Translates legalese into symbolic logic.
2. The Skeptic (Qwen2.5-7B): A dedicated linter agent that dry-runs the generated Z3 code and performs real-time self-correction on syntax errors.

🛠 Features

• Universal Logic Bridge: Pre-trained on abstract logical archetypes (Thresholds, Temporal Loops, Circularity).
• Architecture v2.0 Sandbox: Automatic injection of physical invariants (Budget sums, Time-forward flow).
• Agentic Linter: Automated repair of Z3 code hallucinations.
• MI300X Optimized: Fully utilizes ROCm 6.2 and 192GB VRAM for high-fidelity reasoning.

📦 Deployment & Portability

To switch to another device (e.g., another MI300X or a high-VRAM workstation):

1. Clone the Repository:

   git clone https://github.com/Mark-Joseph-42/JurisSim.git
   cd JurisSim
   

2. Download the Weights: The merged 32B weights are available at markjoseph2003/JurisSim-32B-v3.

   huggingface-cli download markjoseph2003/JurisSim-32B-v3 --local-dir jurissim-merged-v1
   

3. Run the Swarm:

   python app.py
   

⚖️ Logical Archetypes Covered

• Threshold Splitting: Identifying bypassed numeric limits.
• Temporal Paradoxes: Detecting impossible timelines.
• Jurisdictional Null-Zones: Set-theory conflicts between overlapping authorities.
• Circular Deadlocks: Structural contradictions in statute drafting.

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📚 Technical Documentation & Architecture

1. Hardware & Environment Stack

• Compute: AMD Instinct™ MI300X Accelerator (192GB VRAM)
• Software Stack: ROCm 6.2, PyTorch 2.5.1
• Base Model: Qwen/Qwen3-32B
• Training Method: QLoRA (Quantized Low-Rank Adaptation) using bitsandbytes

2. The Fine-Tuning Pipeline & ROCm Optimizations

Training a massive 32-billion parameter model on a single MI300X GPU required severe optimization to avoid mathematical overflows and Out of Memory (OOM) crashes.

• The PyTorch SDPA ROCm Bug: During initial training runs, PyTorch's default sdpa (Scaled Dot-Product Attention) implementation suffered a math overflow bug on ROCm 6.2 when handling sequences with padding tokens alongside gradient checkpointing, resulting in catastrophic NaN gradients.
• The "Ultra-Stable" Workaround: Because compiling flash_attention_2 for ROCm from source is time-prohibitive, we engineered an ultra-stable configuration that perfectly maximized the 192GB VRAM without hitting the bug:
  1. Attention: Reverted to PyTorch's native eager mathematical attention block to avoid sdpa math corruption.
  2. Memory Compression: Reduced per_device_train_batch_size=1 but massively increased gradient_accumulation_steps=8 to maintain an effective batch size of 8.
  3. Checkpointing: Enabled gradient_checkpointing=True to prevent the eager attention matrices from consuming all 192GB of VRAM during the backward pass.
  4. Evaluation Safety: Enforced per_device_eval_batch_size=1 to ensure the un-checkpointed validation phase did not crash the GPU.

3. Training Results

The model successfully converged after 3 Epochs (1,713 Steps).

• Final Train Loss: 1.756
• Final Validation Loss: 1.676 (Signaling excellent generalization and zero overfitting).
• Token Prediction Accuracy: 62.61% (Extremely high for complex Legal English → Python Z3 logic translation).
• Gradient Stability: Maintained a remarkably stable grad_norm of ~0.3 to ~0.8 throughout the run.

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🚀 Quickstart & Installation

1. Clone the Repository

git clone https://github.com/Mark-Joseph-42/JurisSim.git
cd JurisSim

2. Install Dependencies

Ensure you are running on an AMD machine with ROCm installed.

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# Ensure bitsandbytes is configured for ROCm
pip install https://github.com/bitsandbytes-foundation/bitsandbytes/releases/download/v0.44.1/bitsandbytes-0.44.1-py3-none-manylinux_2_24_x86_64.whl

3. Download the Fine-Tuned Model

The LoRA adapters are publicly available on Hugging Face:

huggingface-cli download markjoseph2003/JurisSim-32B-LoRA --local-dir ./jurissim-lora

4. Run the Agent (Coming Soon!)

(The Agentic Execution loop and Frontend UI are currently being finalized. Instructions for launching the UI will be placed here shortly).

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🛠️ Hugging Face Space

The interactive demo of JurisSim will be deployed as a Hugging Face Space for the hackathon judging. The link will be provided upon completion of the UI.

<div align="center"> <p><i>Built with ❤️ for the AMD Developer Hackathon</i></p> </div>