Instructions to use uaebn/leesplank-municipal with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use uaebn/leesplank-municipal with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("uaebn/leesplank-municipal")
model = AutoModelForCausalLM.from_pretrained("uaebn/leesplank-municipal")
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

vLLM

How to use uaebn/leesplank-municipal with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/uaebn/leesplank-municipal

SGLang

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

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

Leesplank Municipal Finetune

A specialized adaptation of UWV/leesplank-noot-eurollm-1.7b for simplifying Dutch municipal and legal texts to B1 reading level.

Model Description

This model is fine-tuned specifically for the municipal domain, building upon the UWV Leesplank Noot base model. The fine-tuning focused on adapting the model's behavior to handle the specific vocabulary, structures, and simplification patterns common in Dutch municipal communications.

Intended Use

Simplifying Dutch municipal documents and legal texts
Making government communications more accessible to B1 readers
Supporting clear language initiatives in public administration

Performance

The model has been evaluated on a human-verified municipal text dataset:

Metric	Score	Description
SARI	56.18	Measures simplification quality (higher is typically better*)
BERTScore	0.95	Semantic meaning preservation
LiNT-II	48.76 - 2.69	Dutch readability score (B1 target)
Flesch-Douma	52.68	Reading ease score

*Depends on simplification strategy.

Evaluation dataset: temp

Intended use

Use the ChatML format, and prefix your input text with "Vereenvoudig:" only.

message = [
    {
        "role": "user", 
        "content": f"Vereenvoudig: {input_text}"
    }
]

Training Details

Base Model: UWV/Leesplank-Noot-1.7B

Method: QLoRA fine-tuning with targeted parameter updates to specialize for municipal domain

Configuration:

LoRA Rank: 64
LoRA Alpha: 128
Target Modules: "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"

Limitations

Optimized specifically for Dutch municipal and legal texts
Performance may vary on other text types
Inherits base model limitations

Acknowledgments

This model builds upon the work by UWV on the Leesplank Noot base model.

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for uaebn/leesplank-municipal

Base model

utter-project/EuroLLM-1.7B

Finetuned

UWV/leesplank-noot-eurollm-1.7b

Finetuned

(2)

this model

Quantizations

1 model