Instructions to use DomainLLM/gemma-3-12b-it-law-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DomainLLM/gemma-3-12b-it-law-fine-tuned with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DomainLLM/gemma-3-12b-it-law-fine-tuned")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("DomainLLM/gemma-3-12b-it-law-fine-tuned")
model = AutoModelForMultimodalLM.from_pretrained("DomainLLM/gemma-3-12b-it-law-fine-tuned", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DomainLLM/gemma-3-12b-it-law-fine-tuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DomainLLM/gemma-3-12b-it-law-fine-tuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DomainLLM/gemma-3-12b-it-law-fine-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DomainLLM/gemma-3-12b-it-law-fine-tuned

SGLang

How to use DomainLLM/gemma-3-12b-it-law-fine-tuned with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DomainLLM/gemma-3-12b-it-law-fine-tuned" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DomainLLM/gemma-3-12b-it-law-fine-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DomainLLM/gemma-3-12b-it-law-fine-tuned" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DomainLLM/gemma-3-12b-it-law-fine-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DomainLLM/gemma-3-12b-it-law-fine-tuned with Docker Model Runner:
```
docker model run hf.co/DomainLLM/gemma-3-12b-it-law-fine-tuned
```

Gemma-3-12B-IT Law Fine-Tuned

A domain-adapted version of Google's Gemma 3 12B Instruct model, fine-tuned on German legal data for improved performance on law-related tasks.

Model Details

Property	Value
Base Model	`google/gemma-3-12b-it` (via `unsloth/gemma-3-12b-it`)
Fine-tuning Method	LoRA (Low-Rank Adaptation)
Domain	German Law
Parameters	12B
Precision	bfloat16
License	Gemma License

Training Configuration

Hyperparameter	Value
LoRA Rank (`r`)	16
LoRA Alpha	32
LoRA Dropout	0.05
Learning Rate	1e-05
Batch Size	16
Max Sequence Length	2048
Epochs	7
Training Steps	21
Warmup Steps	3 (linear)
Optimizer	AdamW
Final Training Loss	1.775
Final Eval Loss	1.781

LoRA Target Modules

The adapter was applied to all attention and MLP projection layers: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Training Loss Curve

Epoch	Step	Training Loss	Eval Loss
0.28	1	2.047	—
1.55	5	1.990	—
3.28	10	3.828*	1.892
4.55	14	1.845	—
5.55	17	1.809	—
6.55	20	1.785	1.781
6.83	21	1.775	—

*Higher loss at some steps is due to epoch boundary effects with small datasets.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "DomainLLM/gemma-3-12b-it-law-fine-tuned"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Erkläre den Unterschied zwischen Verbrechen und Vergehen im deutschen Strafrecht."}
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
outputs = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Intended Use

This model is designed for:

🏛️ Legal Question Answering — Answering questions about German law
📜 Legal Text Understanding — Comprehending and summarizing legal documents
⚖️ Legal Reasoning — Assisting with legal analysis and argumentation
🔍 Legal Research — Supporting legal research tasks in the German legal domain

Limitations

This model is fine-tuned for German legal domain tasks and may not generalize well to other domains or languages.
The model should not be used as a substitute for professional legal advice.
Outputs should always be verified by qualified legal professionals.
The model may still produce hallucinations or inaccurate legal information.

Citation

If you use this model in your research, please cite:

@misc{domainllm-gemma3-law,
  title={Gemma-3-12B-IT Law Fine-Tuned},
  author={DomainLLM},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/DomainLLM/gemma-3-12b-it-law-fine-tuned}
}

Acknowledgements

Built on Google's Gemma 3 model family
Fine-tuned using PEFT/LoRA with the Hugging Face Transformers library

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for DomainLLM/gemma-3-12b-it-law-fine-tuned

Base model

google/gemma-3-12b-pt

Finetuned

google/gemma-3-12b-it

Adapter

(438)

this model

Adapters

1 model