Instructions to use Alijeff1214/DeutscheLexAI_BGB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alijeff1214/DeutscheLexAI_BGB with Transformers:

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

pipe = pipeline("text-generation", model="Alijeff1214/DeutscheLexAI_BGB")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Alijeff1214/DeutscheLexAI_BGB")
model = AutoModelForCausalLM.from_pretrained("Alijeff1214/DeutscheLexAI_BGB", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Alijeff1214/DeutscheLexAI_BGB with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Alijeff1214/DeutscheLexAI_BGB"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Alijeff1214/DeutscheLexAI_BGB",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Alijeff1214/DeutscheLexAI_BGB

SGLang

How to use Alijeff1214/DeutscheLexAI_BGB 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 "Alijeff1214/DeutscheLexAI_BGB" \
    --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": "Alijeff1214/DeutscheLexAI_BGB",
		"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 "Alijeff1214/DeutscheLexAI_BGB" \
        --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": "Alijeff1214/DeutscheLexAI_BGB",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Alijeff1214/DeutscheLexAI_BGB with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Alijeff1214/DeutscheLexAI_BGB to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Alijeff1214/DeutscheLexAI_BGB to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Alijeff1214/DeutscheLexAI_BGB to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Alijeff1214/DeutscheLexAI_BGB",
    max_seq_length=2048,
)

Docker Model Runner
How to use Alijeff1214/DeutscheLexAI_BGB with Docker Model Runner:
```
docker model run hf.co/Alijeff1214/DeutscheLexAI_BGB
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

Model Details

Model Description

DeutscheLexAI_BGB is a fine-tuned Qwen2.5-3B model specializing in German legal text processing, trained on the Bürgerliches Gesetzbuch (BGB) dataset. It enhances legal text understanding, summarization, and reasoning for German legal documents.

Developed by: [Ali Asghar (jaffry258@gmail.com)]
Funded by [optional]: [still under progress ]
Shared by [optional]: []
Model type: [Large Language Model (LLM)]
Language(s) (NLP): [pytorch,transformers,python]
License: [Appache 2.0]
Finetuned from model [optional]: [Qwen2.5-3B]

Model Sources [optional]

Repository: [https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB/tree/main]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

Legal research: Extract, summarize, and analyze BGB texts.

AI-powered legal assistants: Provide insights into German law.

Academic purposes: Assists in legal document structuring.

[More Information Needed]

Downstream Use [optional]

Chatbots for legal guidance.

AI-based contract analysis.

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

The model may reflect biases in the BGB dataset.

Not suitable for real-time legal decision-making.

Might struggle with non-German legal texts.

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]
trainer = GRPOTrainer( model = model, processing_class = tokenizer, reward_funcs = [ xmlcount_reward_func, soft_format_reward_func, strict_format_reward_func, int_reward_func, correctness_reward_func, ], args = training_args, train_dataset = dataset, ) trainer.train()

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

@article{DeutscheLexAI_BGB, title={DeutscheLexAI_BGB: A Fine-Tuned Qwen2.5-3B Model for German Legal Texts}, author={Your Name or Organization}, journal={Hugging Face Model Hub}, year={2025}, url={https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB} }

BibTeX:

[More Information Needed]

APA:

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Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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Paper for Alijeff1214/DeutscheLexAI_BGB

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 60