Ohio family law specialist — LoRA adapter for Qwen3.5-9B, fine-tuned on 13,346 curated legal Q&A pairs covering Ohio custody statutes, appellate decisions, and parental rights case law.

Part of the Legal Agent Council

This model is one component of the OhioCustodyBERT Legal Agent Council — a multi-agent system for Ohio family law analysis. See the hub repo for the full architecture.

Model	Role
OhioCustodyBERT	Retriever + Verifier (ModernBERT)
This model	Generative Agents (Statute, Case Law, Adversary)
Training Corpus	13,346 SFT pairs from 19 sources

Status

🔄 Training in progress — LoRA fine-tuning on M4 Pro 48GB with MLX. Adapter weights will be uploaded when training completes.

Training Config

Parameter	Value
Base model	Qwen3.5-9B (8-bit, MLX)
Method	LoRA, 16 layers
Data	v24: 12,679 train + 667 valid
Iterations	5,000 (~3 passes)
Optimizer	AdamW, lr=1e-4
Effective batch	8 (1 × grad_accum 8)
Max sequence	2,048 tokens

Quick Start (after release)

With Ollama

bash

ollama run ohio-custody
>>> What are the best interest factors under R.C. 3109.04?

With Transformers + PEFT

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base, "Roderick3rd/OhioCustodyBERT-Qwen3.5-9B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")

Citation

bibtex

@misc{ohiocustodybert2026,
  title={OhioCustodyBERT: Domain-Specific Legal AI System for Ohio Family Law},
  author={Roderick Mullins},
  year={2026},
  howpublished={HuggingFace: Roderick3rd/OhioCustodyBERT}
}

⚠️ Not legal advice. This is a research tool. Always consult a licensed attorney.