Instructions to use keeeeesz/gemma-civil-code-pl_20250416 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use keeeeesz/gemma-civil-code-pl_20250416 with Transformers:

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

pipe = pipeline("text-generation", model="keeeeesz/gemma-civil-code-pl_20250416")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("keeeeesz/gemma-civil-code-pl_20250416")
model = AutoModelForMultimodalLM.from_pretrained("keeeeesz/gemma-civil-code-pl_20250416", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use keeeeesz/gemma-civil-code-pl_20250416 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "keeeeesz/gemma-civil-code-pl_20250416"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keeeeesz/gemma-civil-code-pl_20250416",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/keeeeesz/gemma-civil-code-pl_20250416

SGLang

How to use keeeeesz/gemma-civil-code-pl_20250416 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 "keeeeesz/gemma-civil-code-pl_20250416" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keeeeesz/gemma-civil-code-pl_20250416",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "keeeeesz/gemma-civil-code-pl_20250416" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "keeeeesz/gemma-civil-code-pl_20250416",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use keeeeesz/gemma-civil-code-pl_20250416 with Docker Model Runner:
```
docker model run hf.co/keeeeesz/gemma-civil-code-pl_20250416
```

gemma-civil-code-pl

Model Gemma 3 (4B) dostrojony do wiedzy z Kodeksu Cywilnego poprzez Continued Pre-training.

Opis modelu

Model bazuje na Gemma 3 (4B) i został dostrojony do odpowiadania na pytania dotyczące Kodeksu Cywilnego. Wykorzystano dane z artykułów Kodeksu Cywilnego wraz z interpretacjami i przykładami zastosowania.

Przykładowe użycie

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Załaduj model i tokenizer
model_name = "TWOJA_NAZWA_UŻYTKOWNIKA/gemma-civil-code-pl"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
)

# Przykładowe pytanie
prompt = "Artykuł 583 Kodeksu cywilnego stanowi, że"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
)

# Wyodrębnij odpowiedź
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

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Safetensors

Model size

4B params

Tensor type

F16

Model tree for keeeeesz/gemma-civil-code-pl_20250416

Base model

google/gemma-3-4b-pt

Finetuned

(328)

this model