| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - legal |
| - ohio |
| - custody |
| - family-law |
| - qwen |
| - lora |
| - peft |
| - legal-agent-council |
| base_model: Qwen/Qwen3.5-9B |
| datasets: |
| - Roderick3rd/OhioCustodyBERT-corpus |
| pipeline_tag: text-generation |
| --- |
| |
| # OhioCustodyBERT — Qwen3.5-9B LoRA Adapter |
|
|
| **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](https://huggingface.co/Roderick3rd/OhioCustodyBERT) — a multi-agent system for Ohio family law analysis. See the hub repo for the full architecture. |
|
|
| | Model | Role | |
| |-------|------| |
| | [OhioCustodyBERT](https://huggingface.co/Roderick3rd/OhioCustodyBERT) | Retriever + Verifier (ModernBERT) | |
| | **This model** | Generative Agents (Statute, Case Law, Adversary) | |
| | [Training Corpus](https://huggingface.co/datasets/Roderick3rd/OhioCustodyBERT-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. |
| |
