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jonprompter/
german-legal-qa-3b-lora

mlx-community/Llama-3.2-3B-Instruct-4bit

adapter

mit

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German Legal QA 3B: Domain-Adapted LLM for German Law

A LoRA-adapted Llama 3.2 3B model fine-tuned on German legal question-answering data. Designed for use with a RAG (Retrieval-Augmented Generation) pipeline that grounds answers in actual statutory text.

This model answers questions about German civil, criminal, and procedural law with paragraph citations (§-references).

Note: This model is for educational and research purposes only. It does not constitute legal advice.

Supported Legal Domains

BGB (Civil Code) · StGB (Criminal Code) · ZPO (Civil Procedure) · GG (Basic Law) · HGB (Commercial Code) · ArbGG (Labor Courts)

Model Details

The adapter was trained using Apple MLX on an M1 Pro MacBook, demonstrating efficient legal domain adaptation on consumer hardware.

Component	Configuration
Base Model	Llama-3.2-3B-Instruct (4-bit quantized)
Adapter	LoRA, rank 16, 16 layers
Training Data	10,000 German legal Q&A pairs
Training Framework	MLX (Apple Silicon native)
Training Steps	500
Final Train Loss	1.874
Final Val Loss	1.908
Peak Memory	4.3 GB
Adapter Size	46 MB

Training Data

Trained on GerLayQA (EACL 2024) — real legal questions from German citizens paired with expert lawyer answers.

Source	Examples	Coverage
GerLayQA (BGB)	8,000+	Civil law, contracts, liability
GerLayQA (StGB)	1,000+	Criminal law, sentencing
GerLayQA (ZPO)	500+	Procedural law

RAG Pipeline (Recommended)

This adapter works best combined with retrieval over German federal statutes. The full system indexes 103,000+ legal text chunks from 6,111 German federal laws.

Query → Embed → FAISS Search → Retrieve § Text → Generate Answer

With RAG, the model produces factually grounded answers citing the correct statutory provisions. Without RAG, the model may hallucinate legal content.

Usage

With MLX (Apple Silicon)

python

from mlx_lm import load, generate

model, tokenizer = load(
    "mlx-community/Llama-3.2-3B-Instruct-4bit",
    adapter_path="jonprompter/german-legal-qa-3b-lora"
)

prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Du bist ein deutschsprachiger Rechtsassistent. Beantworte rechtliche Fragen präzise und verständlich. Zitiere relevante Paragraphen wenn möglich. Dies ist keine Rechtsberatung.<|eot_id|><|start_header_id|>user<|end_header_id|>

Was besagt §823 BGB?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

"""

response = generate(model, tokenizer, prompt=prompt, max_tokens=256)
print(response)

With Transformers (PyTorch)

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "jonprompter/german-legal-qa-3b-lora")
tokenizer = AutoTokenizer.from_pretrained("unsloth/Llama-3.2-3B-Instruct")

Evaluation

Evaluated on held-out GerLayQA test set with RAG retrieval (50 samples):

Metric	Score	Description
Token F1	0.218	Word overlap with reference
ROUGE-L	0.101	Longest common subsequence
BERTScore F1	0.652	Semantic similarity
Citation Accuracy	0.059	Correct §-reference matching

Scores reflect comparison against verbose forum-style lawyer answers. The system provides concise statutory answers — different style, same meaning (shown by BERTScore).

Example Output

Q: Was besagt §823 BGB?

A: § 823 BGB besagt, dass wer vorsätzlich oder fahrlässig das Leben, den Körper, die Gesundheit, die Freiheit, das Eigentum oder ein sonstiges Recht eines anderen widerrechtlich verletzt, dem anderen zum Ersatz des daraus entstehenden Schadens verpflichtet ist.

Limitations

Requires RAG retrieval for factually accurate answers
Trained on German law only — not applicable to other jurisdictions
Should not be used as a substitute for professional legal advice
May produce hallucinations without retrieval context
Early-stage model (500 training steps) — answer quality improves with further training

Full System

The complete RAG pipeline (data acquisition, indexing, training, evaluation, demo) is available at:

github.com/myaxyo/german-legal-qa

Features:

Data pipeline: 6,111 German federal statutes indexed
FAISS vector search: 103K chunks, sub-second retrieval
Hybrid retrieval: keyword + semantic search
Gradio demo: Interactive chatbot with source citations
MLX training: Fine-tune on Apple Silicon in 30 minutes

Citation

bibtex

@software{german_legal_qa_2026,
  title={German Legal QA Assistant},
  author={jonprompter},
  year={2026},
  url={https://github.com/myaxyo/german-legal-qa}
}